Developing a Hazard Profile of The Kashmir Valley Through Historical Data Analysis For The Period 1900-2020

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A part of the NW Himalayas, the Kashmir Valley is very distinct with respect to its location, topography, climate, socioeconomic structure, and strategic geopolitical nature owing to which it has witnessed a multitude of disasters ranging from local incidents of rockfalls to catastrophic earthquakes, and has often paid heavily in terms of loss of life and property. However, the information on most of the events is either partially reported or exaggerated or sometimes not recorded at all and largely scattered. Availability of organized and reliable record of past hazards and disasters is essential for tackling the risks and mitigating the future disasters. In this context, the present study attempts to address the lack of data availability by focusing on developing a dependable hazard and disaster catalogue of the Kashmir Valley by investigating into the existing literature and the available secondary data sources. A record of natural hazards and disasters most prevalent in the valley viz., earthquakes, floods, landslides and snow avalanches, has been compiled for the time period 1900 to 2020 by making use of various secondary sources, comprising of 1854 events with a range of triggers and impacts reported in the valley, which provide an insight into the spatial and temporal (frequency and distribution) trends of different hazard types for the selected time-period. Developing a catalogue of events reported in the Kashmir Valley can help in building a hazard and disaster scenario which serves as a reliable information source and is of great value from the perspective of regional design, planning and policy responses to promote disaster risk reduction. Atmospheric Sciences Planetary Science Hazard profile Kashmir Valley Earthquakes Landslides Floods Snow avalanches Historical data analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Disasters appear in the news headlines almost every day. Most happen in remote places, with lesser human interaction or limited extent and impact, are forgotten easily, while others have compelling consequences and leave a mark in history. Disasters are an outcome of hazardous events which cross the threshold of human endurance capacity and bring about devastating consequences upon interaction with human existence (vulnerable populations) (UN-ISDR, 2004). Whereas a hazard is defined as a potentially damaging physical event, phenomenon, or human activity that may cause loss of life or injury, property damage, social and economic disruption, or environmental degradation (UNISDR, 2004 ). Statistics show that the world has experienced an increasing impact of disasters in the past decades, the main cause of which is attributed to a higher frequency of extreme hazardous events (especially, hydro-meteorological events, mostly related to climate change) and to an increase in vulnerable population (as a result of enhanced exposure) (Westen, 2012 ; CRED Report, 2018, 2020). To reduce disaster losses more efforts should be put into Disaster Risk Management, primarily based on a comprehensive and detailed Risk Assessment. Hazard Identification and disaster profiling is an essential element and the first step of the entire process of evaluating risks (i.e., risk assessment) and of disaster management (Weston, 2004, 2013). Developing disaster scenarios through historical perspective represents a valid back analysis tool that offers useful insights to understand the occurrence and impacts of natural disasters, establish existing and potential hazards, the conditions within which a given hazard takes place, determine the degree of vulnerability and the capabilities of response of the society subjected to that hazard (Jahn and Wehling 1998 ; Ahmad et al., 2021 ). It is typically contextual to 'forensic investigation of disasters' to indicate the root causes and risk drivers (Burton, 2010 ; IRDR, 2015); or like the 'science of past disasters' (Riede, 2014 ); or similar to 'charting a historical trajectory of disasters' (Bankoff, 2007 ; Ahmad et al., 2021 ). Cataloguing disaster data of different hazards has picked great pace at international, regional, and national scale in order to facilitate various activities of assessment, policy and decision making, mitigation and management of disasters, relief, rehabilitation, risk reduction, development and research (Oliver-Smith and Hoffman, 2002 ; Schenk, 2014 ; Riede, 2017 ; Oliver-Smith et al., 2017 ). National (Indian Statistical Institute, Kolkata; Vulnerability Atlas of India) and international databases (EM-DAT International; Munich RE NATHAN Database; Dartmouth Flood Observatory Database), regional (Asian Disaster Preparedness Centre (ADPC); Asian Disaster Reduction and Response Network (ADRRN)) and intergovernmental (SAARC—Disaster Knowledge Network) and international nongovernmental organizations and programs (UNDP’s Global Risk Identification Program; CRED; UN Office for Coordination of Humanitarian Affairs), and national disaster agencies (NIDM) play a pivotal role in keeping a track of disaster events taking place worldwide (Gupta and Muralikrishna, 2010 ; National Research Council, 2012 ). These Datasets differ in coverage and data quality, have different filters, strengths, and limitations (Beckman, 2009; Gall et al., 2009), and despite some overlap, each offers different information and insights into disasters (Below et al., 2010 ; National Research Council, 2012 ). Inventorying disasters and hazard profiling have been attempted in various research works which add substantially to the disaster database. Compiling and analysing data from existing research publications can be sought as a reliable means to provide insight into different natural disasters to which a specific area is subjected to (Kapur, 2010 ). Chronological archival records can promote an understanding of social and economic consequences of natural disasters to a place (Malamud, 2004; Prakash, 2011; Prakash and Kathait, 2014). From the historical perspective Kashmir Valley has been vulnerable to multiple natural disasters and intermittently subjected to their consequent impacts which can be established from its rich archival data sources (Kelman et al., 2018 ; Ahmad, 2021). Historical events data have been utilized to study the patterns of seismicity and trends of earthquake occurrences in the Kashmir region (Ghaffar and Abbas, 2010 ; Anees and Bhat, 2016 ) and to illustrate hotspots for seismic activity (Sharma, Kumar and Ghangas, 2013 ). Reconstruction of chronology of floods in Kashmir Valley by employing historical hydrology has been attempted to overcome the deficiency of sufficient time-series database for better flood hazard assessment (Bhat et al., 2019 ). Historical natural hazards were profiled by Ahmad (2021) to have better insight into what vulnerable populations were subjected to under severe natural and deprived socio-economic conditions in Nineteenth Century Kashmir. An intersection of vulnerability to environmental hazards and to socio-political conflict to provide an overview of the disaster diplomacy of Jammu and Kashmir throughout history was studied by Kelman (2018) by compiling events of both environmental hazards and socio-political violence. The valley of Kashmir is subjected to several natural hazards for example, earthquakes, floods, landslides, snow avalanches, droughts, wildfires, extreme temperatures, lightning and thunderstorms, snowstorms, hailstorms, etc., (SDMP, 2017; Patel et al., 2020 ). Henceforth, a case study on the recent past emphasizes the need to know how the valley of Kashmir has been impacted by natural hazards and disasters during the entire Twentieth Century and early Twenty-first Century through historical review and to stress its utility in disaster preparedness (Reide, 2014, 2017; Ahmad, 2021). Therefore, the present study attempts to develop a hazard and disaster profile of the Kashmir Valley focusing on the four most prominent natural hazards viz., earthquakes, floods, landslides, and snow avalanches. 2. Study Area Kashmir, a separate geographical entity, is an oval shaped valley, and one of the mesoregions of erstwhile Jammu and Kashmir, located in the North-Western Himalayas, spanning over 15,984 km 2 (Ganjoo, 2014 ). The region is one of the most unfortunate portions across the globe where natural disasters and political unrest have greatly challenged the progressive development (Shah, 2018). Its physiography typically consists of mountain ranges on all three sides– Zanskar (~ 6000m amsl) and Pir Panjal (~5000m amsl) on Northeast and South-Southeast, respectively, contrary to which the valley floor drops to a minimum elevation of ~1570 m amsl (as shown in the Fig. 1 ). The geological past suggests that it was formed when Indian tectonic plate collided with the Eurasian plate during the Eocene epoch, which lead to the development of some intermontane basins and a prehistoric lake, by the uplift of mountains between the present Indian and Pakistan Administered Kashmir which over geological time, silted in and the alluvium from the mountains became the fertile soil of the valley floor, which itself is a peculiar combination of depositional and erosional features (Gansser, 1964 ; Bhat, 1987 ; Alam et al., 2015 ). A characteristic feature of valley floor is the presence of trunk river Jhelum which stretches over almost the entire length of the valley, originating at its southern end, near Verinag and flowing in a north-west direction receiving numerous tributaries before entering Wular Lake (Albinia, 2010 ). In general, the valley is spread over three major physiographic divisions i.e., mountains, karewa uplands (Plio-Pliestocene deposits), and floodplains. The altitude and climate of the region favour plenty of precipitation both in the form of rains and snow and thus, snow bearing peaks and glaciers are a dominant feature of the mountainous stretches (Ahmad et al., 2016 ). Every thousand feet of elevation brings some new phase of topography, climate, and vegetation (Lawrence, 1967 ). As the tectonics is still actively shaping the topography, geology, geomorphology, and climate of the region, the occurrence of hazards like, earthquakes, landslides, floods, snow avalanches, etc. in the area is potentially unavoidable. More than 5.5 million people reside in areas prone to multiple risks and are posed by various geophysical hazards. The presence of active faults, the river drainage network, physiology, topography, lithology, geomorphology, climate, and demography all make the region vulnerable to different types of hazards and pose potential threat to the population of the valley (Shah, 2018). 3. Methods And Materials 3.1 Search strategy and data sources The study has compiled a hazard and disaster events database of the Kashmir Valley to generate a profile focussing on four potentially most prevalent and majorly impacting hazards viz., earthquakes, floods, landslides, and snow avalanches, for the time-period 1900 to 2020, which includes charting information on disaster events in the form of various attributes (Table 1 ) (Lin and Wang, 2018 ; Kelman et al., 2018 ). Identifying and inventorying various hazard events can be performed through some defined means and sources which include analysing historical data, government records/documents, newspaper reports, research literature, primary field surveys and geological study of the region (Westen et al., 2002; Taylor et al., 2015 ; Sultana, 2020 ). For the present research we studied and incorporated information from mixed sources of data, which primarily include secondary data sources like national and international open access databases, portals and websites, government and non-government documents and reports, existing research literature, news reports, private and public online blogs, portals and websites, books, personal and travel accounts, etc. In general, we used a diverse range of data sources in collecting information on natural hazards and disasters to assess and establish their authenticity. Despite the scattered nature of information dispersed across numerous sources of varied types and credibility we have been able to condense 1854 events in time and their trends spanning over a century and more. Table 1 Summary of variables collected for the database and their description Category Description of variable Relevance to each hazard type Date of occurrence Year (for all events), month and date (wherever available) It gives an idea of the distribution and occurrence of events throughout the time period. It may be used to estimate the increase or decrease in hazard/disaster events over time and also, which time (season) of the year is more likely to witness a particular hazard type like floods, snow avalanches and landslides. Location Geographic or spatial information as name of the place (village, block, sector, district, etc) or latitudes and longitudes. It gives an idea of the place of occurrence/onset of any hazard event. For earthquakes epicentres were considered within or near Kashmir valley. While, for landslide and snow avalanches geographic coordinates were assigned based on the place of occurrence, and for floods, names of the area were used. Places affected Includes the places impacted by the event (villages, sectors, blocks, districts, etc) It denotes the extent and spread of hazard/disaster. It includes places where ground shaking or any damage or casualty is witnessed in case of an earthquake or the extent of inundation during a flood or areas impacted by avalanches and landslides. Cause/ triggering mechanism It could either be an environmental or anthropogenic factor acting as trigger or a primary disaster leading to secondary events. It helps determine the causes that make the area prone to any particular hazard. Magnitude It is one of the factors to measure the strength and size of a disaster event. Magnitude recorded on the Richter scale for earthquake hazard type and water level for flood hazard type. Casualties Including fatalities and injuries It reflects loss to human life and injury. It is one of the important determinants of the severity of a hazard or disaster event. Associated impacts Impacts other than casualties, including damage to property, economic losses, missing, trapped, dislocated and evacuated/rescued people, secondary disaster events, etc. Not available for all the events. 3.2. Compiling procedure and data analysis techniques The collected events for individual disaster types, with all the information pertaining to the six selected variables (Table 1 ), were systematically documented into tables using Excel in a chronological order starting from 1900 up to 2020. Any sort of repetition in the event entry or allied information were removed from the database by proofreading. The excel sheets were used to analyse temporal variability, frequency distribution (Section 4.1.3.), and impacts of the events in the form of casualties (Section 4.1.2.). Further analysis was done by making use of the ArcGIS software. The spatial information of each event was made specific by adding geographic coordinates for earthquakes, landslides and snow avalanches, and then plotted using an SRTM DEM and a district shape file of the Kashmir valley as a base map to generate spatial distribution maps. While, in case of floods spatial extent was represented by the spread of and inundation levels of the 2014 Kashmir floods. Further, thematic maps for district wise susceptibility of all four hazard types were generated from the spatial distribution maps based on the number of occurrences per district for earthquakes, landslides and snow avalanches and on total area (in square kilometres) inundated per district for floods. Events reported with substantial damage and loss were discussed in detail in the study to get a clear picture of the hazard and disaster scenario of the Kashmir Valley in the 120 years long time frame (1900-2020) (Section 4.1.1.). 4. Results And Discussion 4.1. Hazard and disaster profiling A meticulous review of the consulted secondary archival data sources enabled us to discover a spectrum of hazardous events their spatial extent, magnitude, cause, and impact in Kashmir throughout the selected timeline. With the aid of an exhaustive research, comparative analysis, and data presented in the form of catalogues, graphs, and maps, an incisive insight into the disaster and hazard scenario across the valley of Kashmir in the entire twentieth century and early twenty-first century has been achieved. The period under review has experienced repeated natural hazard events of different types, several of which have turned into devastating disasters. In our analysis, basic trends concerning 1854 natural hazards witnessed by the Kashmir valley consisting of 1693 earthquakes, 39 floods, 65 landslides and 57 snow avalanches have been represented. Out of the total hazard events, 91.31% comprised of earthquakes, 2.10% floods, 3.50 landslides and 3.07 snow avalanches. Some of the entries in the table concern more than one phenomenon occurring concurrently, as cascading disasters amplifying the intensity (damage and loss) of the primary disasters, like the Kashmir Basin flood of 1900 which was succeeded by a Cholera epidemic killing 4225 people; the magnitude 7.8 earthquake of 4th April, 1905 (having epicentre in Kangra Valley, H.P.) that triggered landslides and caused large number of casualties and damage to buildings and hillside aqueduct networks; the flood of 1957 (August-September) which almost submerged the entire valley causing colossal damage to crops that in turn led to a famine; the September flood of 1992 which took place in the NW border districts of Kashmir and parts of PoK, was unprecedented in terms of fury and most devastating in terms of casualties, caused land sliding as an associated secondary disaster; 19th February, 2005 Waltengu snow avalanche triggered multiple landslides across the affected area adding to the damage and loss; 8th October, 2005 largest instrumented earthquake with epicenter in Muzzafarabad, PoK (Mw 7.6) lead to extensive land sliding causing large scale damage and loss in N-W border districts; 2006 (August-September) floods in J&K lead to associated disasters in the form of land and mud slides; 24th January, 2012 snow avalanche in Kupwara triggered landslides; 2nd September, 2014 floods land and mud slides; 26th July, 2015 cloud burst triggered landslides along the Baltal route to Amarnath; 20th March, 2017 flooding in Chadoora, Budgam induced mud slides; 6th April, 2017 snow avalanches along higher reaches in Kashmir and Ladakh caused landslides; and 14th January, 2020 snow avalanches in Ganderbal and Kupwara triggered land sliding events but no damage and loss was witnessed. Table 2 Annual Distribution of the total number of events from 1900 to 2020 (E= Earthquakes, F=Floods, L=Landslides and SA=Snow Avalanches) Year E F L SA Year E F L SA Year E F L SA Year E F L SA Year E F L SA 1900 - 01 - - 1924 02 -- - - 1948 01 - - - 1972 22 01 - - 1996 52 01 - - 1901 - - - - 1925 01 - - - 1949 01 - - - 1973 18 01 - - 1997 20 01 - 01 1902 01 01 - - 1926 02 - - - 1950 08 01 - - 1974 19 - - - 1998 34 - - 01 1903 - 01 - - 1927 05 - - - 1951 03 - - - 1975 35 01 - - 1999 31 - - - 1904 - - - - 1928 03 01 - - 1952 03 - - - 1976 121 01 - - 2000 49 - - - 1905 01 01 01 - 1929 02 - - - 1953 04 - - - 1977 16 - - - 2001 42 - - - 1906 01 - - - 1930 03 - - - 1954 01 01 - - 1978 14 - - - 2002 49 - - - 1907 - - - - 1931 01 01 - - 1955 04 - - - 1979 16 - - - 2003 36 - - - 1908 - - - - 1932 - - - - 1956 03 - - - 1980 18 - - - 2004 43 - - - 1909 - 01 - - 1933 03 - - - 1957 - - - - 1981 21 01 - - 2005 69 - 02 - 1910 01 - - - 1934 01 - - - 1958 - - - - 1982 16 - - - 2006 57 02 01 - 1911 - - - - 1935 01 - - - 1959 01 01 - - 1983 09 - - - 2007 47 - 03 - 1912 - 01 - - 1936 01 - - - 1960 01 01 - - 1984 18 - - - 2008 43 - 06 04 1913 - - - - 1937 03 - - - 1961 05 - - - 1985 18 01 - - 2009 27 - 10 03 1914 01 - - - 1938 02 - - - 1962 04 - - - 1986 21 01 - 01 2010 23 01 16 04 1915 - - - - 1939 - - - - 1963 06 - - - 1987 16 07 - - 2011 25 - 03 02 1916 01 - - - 1940 - - - - 1964 27 01 - - 1988 17 - - - 2012 52 - 01 05 1917 02 - - - 1941 01 - - - 1965 13 01 - - 1989 09 - - - 2013 47 - - 01 1918 - - - - 1942 01 - - - 1966 6 01 - - 199 25 - - - 2014 39 01 05 01 1919 01 - - - 1943 01 - - - 1967 09 - - - 1991 23 - - - 2015 34 03 02 - 1920 - - - - 1944 - - - - 1968 10 - - - 1992 43 01 01 - 2016 28 - 01 01 1921 01 - - - 1945 02 - - - 1969 03 01 - - 1993 20 01 - - 2017 39 01 02 13 1922 - - - - 1946 03 - - - 1970 07 - - - 1994 17 - - 01 2018 24 01 07 11 1923 01 - - - 1947 01 - - - 1971 07 - - - 1995 29 01 - - 2019 38 01 - 02 - - - - - - - - - - - - - - - - - - - - 2020 77 - 04 02 4.1.1. Extreme events and their impacts The study discusses in detail disaster events witnessed within the timeline for which damage and loss have been reported in the form of fatalities, injuries, loss of cattle, damage to structures and crops, population affected, and other associated impacts. These comprise 121 events out of the total 1854, including 7 earthquakes, 17 floods, 49 landslides and 47 snow avalanches, which constitute 5.78%, 14.04%, 40.49% and 38.84% of the total severe events, respectively. This shows that even though the total number of earthquake events (1693) is very high only a small number (7) of these events actually turn into disasters i.e., 0.41% of the total occurrences. In case of floods out of the total 39 events 17 have turned into disasters which is about 43.58% of the total occurrences. Whereas, for landslide hazard, 49 events i.e., 75.38% of the total 65 occurrences and for snow avalanches, 47 events i.e., 82.45% of the total 57 occurrences show impacts. Although, a larger portion of the total landslide and snow avalanche events have impacts recorded but the magnitude of these impacts is far lesser than that of both earthquakes and floods individually, as can be established from the tables discussed in the following sections. This could be because landslides and snow avalanches are small scale and localized events with limited extent and impact as compared to earthquakes and floods thus, proving an inverse relationship between the magnitude and frequency of the hazard events. Standing true for the generalization, that the magnitude of a natural hazard event varies in its frequency of occurrence over time in an inverse power relationship (Jackson, 2013 ). Earthquakes History shows earthquakes don’t occur randomly but follow a general pattern and are distributed along geological faults across the globe (Bolt, 2003). The NEIC (National Earthquake Information Centre) locates about 20,000 earthquakes in the world each year and approximately 55 per day. According to records (since 1900), 16 major earthquakes are expected in a year, 15 in the magnitude 7 range and 1 magnitude 8.0 or greater (Bolt, 2003; USGS, 2020 ) which have been responsible for millions of deaths and an incalculable amount of damage to property over centuries. India has a long history of disastrous earthquakes, majorly documented from 1800’s (Iyenger et al., 1999) and about 59% of its total land area is prone to seismic hazards (BMTPC, 2006; MHA Report, 2015). The Himalayas originated due to continental collision between the Indian and the Eurasian plates (Searle et al., 1987 , Le Fort, 1989 , Searle, 1991 , Thakur, 1992 , 1998 ) and this orogenic process continues till date, as is indicated by significant small to moderate earthquakes and neo-tectonic movements along several thrusts and faults located in the region (Valdiya, 1998 , 2001 ; Bilham, 2001). A major risk lies for more than 50 million people living near the seismically active Himalayan region (Bilham, 2001). The Himalayan zone is divided into three seismic gaps – Kashmir gap, Central gap and Assam gap. The Jammu and Kashmir, Himachal Pradesh and Uttarakhand fall under Kashmir gap which is the highest earthquake prone zone (Gupta, 2012; Sharma, 2013). Jammu and Kashmir, the western most extension of the Himalayan Mountain range in India, lies atop a web of active geological faults and thrusts on the boundary of the two colliding tectonic plates (Gavillot, et al., 2016 ; Shah, 2016 ), many of which have and are capable of producing earthquakes of magnitude 8.0 or greater (Seeber and Armbruster 1981 ; Ni and Barazangi 1984 ; Thakur and Kumar 2002 ; Kayal, 2007 ). As a result of active participation of some faults in the ongoing collisional deformity the region shows active seismicity through small to moderate magnitude earthquakes at a continuous rate and occasionally large magnitude ones (Burbank and Johnson, 1983; Ambraseys and Bilham, 2000 ; Yin, 2006 ; Shah, 2018). According to seismic zonation map of India, the entire region has been classified as very high damage risk zone V (MSK IX or more) and high damage risk zone IV (MSK-VIII) (BIS Map, 2002; SDMP, 2017; Mahajan et al., 2010 ). A major portion of the districts in Jammu and Kashmir fall under seismic zone V. Kathua, Leh, Ladakh and Tribal Territory districts lie in Zone IV, the districts Anantnag, Budgam, Bandipora, Baramulla, Ganderbal, Kishtwar, Kulgam, Kupwara, Pulwama, Ramban, Shopian and Srinagar occupy seismic V zone and the remaining under seismic IV zone (SDMP, 2017). Kashmir region is very important in relation to seismic activity in the Great Himalayas. Earthquakes in the Himalaya, in general, and in Kashmir, in particular, pose serious challenges. Historical records of the past centuries show that several big earthquakes have destroyed parts of the Himalayan settlements (and many earthquakes have possibly gone unrecorded). The history of earthquakes dates back to 1505 in this region (Ghaffar and Abbas, 2010 ) and the record of the past decades shows that the Kashmir region has been hit at least by one earthquake of magnitude 5 or larger every year or two (Sorkhabi, 2006 ). Among the most notable earthquake occurrences of the region are the N-W Kashmir earthquake of 2005 (Mw 7.6); 2002 Astore, PoK (Mw 6.4), Pattan earthquake of 1974 (Mw 7.4), Kangra earthquake of 1905 (Mw 7.8), 1885 (Magnitude 7.5), 1842 (Magnitude 7.5), 1555 (magnitude more than 8), 1505 (Magnitude 7.6) etc., (Sharma, 2013). Earthquakes, if strong enough, are extensive events, with far-reaching impacts which cannot be contained by political and geographical boundaries, therefore, earthquakes with epicentres in and around the valley have been considered for this study while events with their epicentres within the Valley numbered 58 for the selected timeline (e.g., 1963 and 1967). In the present study of 120 years, the region witnessed intensive seismic activity where earthquakes were felt across the entire valley (Table 2 ), including 1693 events of magnitude 2.0 to 8.0, out of which 34 were strong earthquakes with magnitude greater than Mw 6.0 and 7 events have been reported with severe impacts (Table 3 ). The highest magnitude episode recorded for the time period is the earthquake of 4th April, 1905 with its epicentre in Kangra Valley, Himachal Pradesh and magnitude Mw 8.0. The record also shows some incidents of magnitude 7 and above viz., 1974 Pattan earthquake with 7.4 magnitude, 1975/19/01 Kinnaur District, HP (M 7.0), 19th January, 1996 Aksai Chin (M 7.1), 8th October, 2005 Muzzafarbad, Pakistan (M 7.6) and 26th October, 2015 Hindukush Mountain region, Afghanistan (M 7.5). Table 3 Major earthquake events located in and around Jammu & Kashmir for which damage and loss were reported. Date of occurrence Location Places affected Magnitude (Mw) Casualties Associated impacts Year DD/MM Place of occurrence Long Lat Fatalities Injuries 1905 04/04 Kangra Valley 76.16 34.04 J&K and Himachal Pradesh. Shocks felt in Leh, Kargil, Drass and Muzafarabad 8.0 20,000 - 53,000 domestic animals killed. 100,000 buildings damaged. Damage to hillside aqueducts networks. 1963 02/09 Budgam, Kashmir 74.64 33.93 Budgam, Kashmir 5.2 100 - - 1975 19/01 Kinnaur District 78.43 32.45 J&K and Himachal Pradesh 7.0 47 - - 1981 12/09 Gilgit Wazarat (Pakistan occupied Kashmir). 73.59 35.69 Shocks were felt in Srinagar (J&K, India) and in Peshawar and Rawalpindi (Pakistan) 6.3 220 2500 Unconfirmed reports of surface faulting. 2002 20/11 Astore Valley, Pakistan occupied Kashmir 74.60 35.00 Astore Valley, Pakistan occupied Kashmir 6.3 23 - Damage to property. 2005 08/10 Muzzafarabad Kashmir-Kohistan, 73.58 34.53 Indo-Pak Border Region. Strongly felt in much of Pakistan, North-India, East-Afghanistan. Tremors felt as far as Delhi and Punjab in India. 7.6 80,000 1350 (J&K) 70,000 6266 (J&K) Largest instrumented in the area. 4 million homeless. Secondary disasters: landslides, fires. 32, 000 buildings completely or partially damaged, blocked roads. Series of hundreds of aftershocks. [Homeless=150000; Affected=156622 (J&K)] 2015 26/10 Hindukush mountain region of Afghanistan. 78.15E 37.45 Afghanistan, India and Pakistan. Tremors felt in J&K, Delhi, Lucknow and parts of Pakistan, Afghanistan and China. 7.5 399; 4 (J&K) 2536; 20 (J&K) Damage to property. Cracks appeared in most multi-storied buildings. (53 houses damaged in J&K.) Floods Floods with natural and anthropogenic triggers are among the most common and devastating natural disasters and the leading cause of deaths, responsible for 6.8 million deaths in the 20th Century worldwide, impacting about two-thirds of the total population affected by natural disasters (1991-2000) (UNISDR, 2001, 2015; Doocey, et al., 2013 ; CRED Report, 2018, 2020). In agreement with the global pattern, the disasters with the largest human impact in Asia were floods during the year 2015 (Guha-Sapir et al., 2015). The occurrences and impacts of flooding are expected to rise due to increase in population, unscientific development and climate change (Tanoue et al., 2016 ; Bhat et al., 2019 ). 12% of the total land area of India faces the threat of flooding (MHA Report, 2015). Kashmir, a highly populated, Himalayan intermontane basin flanked by mountains, drained by major rivers such as Jhelum, Chenab, and Indus, and mainly divided into three physiographic units: floodplains, karewas and mountains, (SDMP, 2017), is prone to floods, widely established through historical records. The structure of the Valley, hydrographic features and drainage characteristics of its river systems viz., bowl shape (elongated trough), variation in altitudes with consequent reduced lag time and sudden peak flows in rivers along low-lying areas during heavy rainfalls make the region specifically prone to floods and congenial for inundation (Bhat et al., 2019 ). Pertinently, most of the population and socio-economic activity is hosted by the area prone to floods and is one of the major urban centres of the region, Srinagar, where the number of wetlands that act as natural sponges, have come down severely, resulting in frequent flooding (Gupta, 2014 ; Meraj, 2015; Bhat et al., 2017, 2019 ). In terms of impact, frequency and economic loss, floods are the largest of all the natural hazards to which the Kashmir Valley is prone (Bhat et al., 2017). Historical reports reveal that flooding is a recurrent phenomenon and owing to River Jhelum, the valley has witnessed a series of floods, dating back to 635 A.D., many among which were disastrous with widespread socio-economic and environmental impacts (Lawrence, 1895; Uppal, 1956 ; Bhat et al., 2017). Research indicates two major reasons responsible for the flood vulnerability in the Kashmir valley – inadequate carrying capacity of the River Jhelum from Sangam (Anantnag) to Khandanyar (Baramulla) and naturally flat topography of the Jhelum Basin (Meraj, 2015; Bhat et al., 2017). Maximum topography of the valley is precipitous, exposing low-lying areas to frequent inundation especially during extended hours of precipitation (seasonally) however, some intensifying factors such as enormous population growth and the resultant expansion of human settlements, ill-planned urban sprawl, modification of floodplain, including, encroachment of waterways, landfilling, and road/railway construction in the floodplain, changes in river morphology and reduced water holding capacity of rivers, erosion and subsequent alluvial deposition in water bodies leading to degradation and extinction of wetlands and waterways have amplified the existing flood risk (Alam et al., 2018 ; Meraj, 2015). It has also been observed that as a result of climate variability, the frequency of floods in Kashmir Valley is likely to increase in future (Bhat et al., 2019 ). Although, most of the flood events in Kashmir have meteorological origin, historical records bear instances where flooding has been associated to a primary disaster event like floods triggered by damming of the Jhelum caused by landslides or earthquake-triggered landslides and dam failures (856 AD and 635 AD) (Kalhana, 1149 ; Chaudora, 1620 ; Khanyari, 1857 ; Khoihami, 1885; Stein, 1891; Bamzai, 1962 ; Ahmad & Bano, 1984 ; Bilham and Bali, 2014 ; Meraj, 2015). A total of 17 severe flood incidents which show a substantial impact on society were compiled in detail (Table 4 ). For certain events no figures were available to record impacts but the severity of the situation was described as “the entire valley being completely submerged in water” or “resembling a vast lake” (1903 and 1957) which gives us the idea that major portions of the valley are susceptible to floods and how wide spread can flooding be in Kashmir. Few notable flood events of the recent past recorded in the study were 1903, 1950, 1957, 1959, 1963, 1992, 1994, 1996, 2004, 2006, and 2014 (Raza et al., 1978 ; Koul, 1993 ; Meraj et al., 2015 ; Kumar, 2016 ; Bhatt et al., 2017 ; Rather et al., 2017 ; Alam et al., 2018 ). Table 4 Major flood events witnessed by the Kashmir Valley for which damage and loss were reported Date of occurrence Location/Area affected Cause/ trigger Casualties Magnitude (water levels) Associated impacts Year DD/MM Fatalities Injuries 1900 - Kashmir Basin Continuous rains caused floods. - - Water level was 9 feet lower at Munshibagh than previous flood (1893- R.L. 5197.0). Breaches in right bank above Sherghari. Succeeded by cholera killing 4,225 people 1903 23 July Srinagar City, Kashmir valley 5″ of rainfall recorded between 11-17 July and 8″ between 21-23 July. Large number of deaths - River rose to max R.L. 5200.37 on 24 July (three points higher than 1893 flood). Whole valley converted to one great expanse of water. 7,000 dwellings marooned in city. 83 villages affected; 26 villages lost entire kharif harvest. 421 houses completely destroyed. 1905 - Kashmir Valley - 6 - - 74 villages affected, heavy loss to government records. Extensive loss to crops. 1909 - Kashmir Valley - - - - Disastrous for crops. Total estimated loss: Rs. 98,393. 1912 May Kashmir Valley - 21 - - Spill channel minimized extent of damage. Many bridges from Baramulla to Chakoti (across LoC) were washed away. 1928 - Kashmir Valley - 76 - - Total 1,750 houses partially damaged and 282 houses fully damaged. Loss of 2,228 domestic animals. Agricultural sector affected badly. 1950 01-17 Sep Jhelum Basin, J&K - 100 - Water of Jhelum was flowing at 10-15 feet over the banks in Srinagar. More than 15,000 houses collapsed or heavily damaged. River bank breached at multiple places posing threat to civil lines of city. About 70 miles of the area of valley was under water. 1957 Aug-Sep Kashmir Valley Natural Hydrological Flood 92 - Highest water level ever recorded (till that time) in state (roughly 90,000 cusecs to 1,20,000 cusecs) at Sangam. Almost submerged entire valley. Jhelum overflowed right bank in uptown Srinagar, submerging low-lying areas. Colossal damage to crops and property. Led to famine. 600 villages inundated. Estimated damage: 4.2 crores. 1959 July Kashmir Valley Natural Hydrological Flood. Four days of incessant rains in valley. 104 - Flood water level touched 30.25 feet on July 5 in Jhelum. Jhelum was assumed to be 80,000 to 1,00,000 cusecs. Damage to public utility services: 20 million; damage to crops: 15.6 million. 1973 6–10 Aug J&K - 70 (50 in Jammu and 20 in Kashmir) - - Flooded 40 villages impacting 20% of population. Damages amounted to Rs. 12.18 crore. 1992 September N-W border districts Kashmir Recording highest rainfall (of that time) 200 (IoK) 2000 (PoK) - - Unprecedented in terms of fury and most devastating in terms of casualties. Over 60,000 people were affected in several NW border districts. Parts of POK bore the brunt. Secondary disaster: landslides 1996 23, Aug Kashmir Valley 29600 Km2 (Dis. Mag. Value). Natural Hydrological Flood 23 226 - Homeless:70,000; Affected: 70,000. Water level in Jhelum not as high as earlier floods but water didn’t recede for long period after rains stopped causing heavy damage to houses. In Srinagar city and outskirts, about 10,000 houses were flooded for over a fortnight. 2006 24 Jul-22 Aug Jhelum-Chenab basins, J&K Provinces Lat/Long: 34.61-73.20 Natural Hydrological Flood; Riverine flood; Monsoonal rains 15 800 - Affected: 800; 2006 31 Aug-11 Sep J&K Provinces; Jhelum, Sutlej, Lidder, Chenab, Tavi basins Natural Hydrological Flood; Flash Flood; Monsoonal rains; 19 - - Homeless:15000 Affected:15000 Secondary disasters: Slides (land, mud, snow, rock). 2014 02 Sep Kashmir region, India-Pakistan Worst affected districts: Srinagar, Anantnag, Baramulla, Pulwama, Ganderbal, Kulgam, Budgam, Rajouri, Poonch and Reasi. Natural Hydrological Flood; Riverine flood, Monsoonal Rains Caused by torrential rainfall. 557 (277 India, 280 Pakistan) (190 Jammu, 78 Kashmir) -- - Affected:275000; Homeless: 275000; Damage:16000000. 60 major and minor roads were cut off and 30 bridges washed away, hampering relief and rescue. 80,000 people evacuated. 390 villages in Kashmir completely submerged. 1225 villages affected partially and 1000 villages affected in Jammu. Secondary Disasters: Slides (land, mud, snow, rock) 2015 25 July Amarnath, Pahalgam, South Kashmir Flash floods; Cloudburst 2 9 - Yatra suspended, tents washed away. Secondary disaster: land and mud slides 2015 04 Sep Various parts of the Kashmir Valley - 55 25 - 862 cattle killed, and 12565 structures damaged. 211 camps set up to house 2907 evacuated families. 2017 20-31 March Chadoora village Budgam district; Jhelum river basin; Lat/Long: 33.1767-76.41; Disaster magnitude value: 70288 Km2. Natural Hydrological Flood; Flash flood; Heavy rains 44 16 in mudslides 25 - Homeless: 2097; Affected: 2122; Damage: 76000 Secondary disasters: Slides (land, mud, snow, rock). Deaths in mudslides and house collapse. Hundreds moved to safety. Flood alert issued. Schools closed. Relief camps set up. Some major flood events have been of regional scale spreading across international borders like flooding in the years 1912, 1992 and 2014. The Kashmir Flood of September, 2014 has been declared the highest magnitude flood recorded instrumentally on Jhelum with a discharge of 72585 cusecs (recorded by the Department of Irrigation and Flood control) and inundated maximum part of the floodplain, resulting in colossal loss of life and property but could still not reach the highest flood levels (HFLs) as documented for events of years 1144, 1360, 1462, 1747, 1903 and 1929 (Alam et al., 2018 ). However, the event recorded with the maximum number of casualties i.e., 200 deaths in IoK and 2000 deaths in PoK, was the 1992 flood. Floods, as we can make out from the data, have shown some episodes of domino effect by being a cause to secondary disasters, like, epidemics, landslides, famines, etc., (Table 4 ) viz., 1900 (Cholera epidemic), 1893, 1929,1957 (famines) and 1992, 2006, 2014, 2017 (landslides) (Mehran, 2015 ). Consequently, the Valley was affected in a very passive way, exhausting the stores and destroying the assets of the dwelling population (Ahmad et al., 2021 ) in the past century, through impacts like deaths and injuries (1950, 1957, 1959, 1973, 1992, 2014, 2015), long periods of inundation (1957, 1996, 2014), marooned settlements and entire villages (1973), partial or total damage to infrastructure and washing away of structures (1912, 1928, 1950, 1959, 2014), devastating crops and agricultural sector (1903,1909, 1928, 1959), loss of domestic animals and cattle (1928, 2015), causing health (1900) and food insecurities (1957), and damage and loss of government records (1905). From the developed catalogue we establish the fact that flooding is a prominent recurring phenomenon of the Kashmir Valley and floods generally occur in the summer months (June to September) when heavy rain is followed by the bright sun, which melts the snow cover and occasionally in springtime (March, April and May). Landslides Landslides, more widespread than any other geological event, are localized in nature often with small to medium scale impacts, and can either occur as an individual primary disaster, as a result of a wide array of processes and therefore, be a geological, hydro-meteorological and an anthropogenic hazard, or happen to be an associated secondary disaster to some major disasters like earthquakes, floods, droughts, volcanic eruptions, etc. and thus, worsen their impacts (Chingkhei, et al., 2013 ; Parkash, 2011 ). It is apparent from prior research that in recent years the abundance, activity, frequency, socio-economic consequences of, and vulnerability to landslides have increased (Guzzetti, 2000 ; Gariano & Guzzetti, 2016; Haque et al., 2019 ) and landslides have been ranked as the 4th deadliest among natural disasters, after floods, storms, and earthquakes (Lacasse et al., 2005 ). In India about 12.6% of the total land area is prone to landslides, consisting of the Himalayas and the Western Ghats, in which many slopes also fall in high seismically active zones, including Jammu and Kashmir, Himachal Pradesh, Uttarakhand, and the entire North-East (NIDM, 2011; MHA, 2015). Located in the N-W Himalayas, a major portion of Kashmir is mountainous, the complex, young and continuously changing topography along with the prevalent climate and various anthropogenic drivers interfering in the fragile ecosystem make it vulnerable to landslide hazard (Shah et al., 2018 ; SDMP, 2017), varying in magnitude from soil creep to landslides and solifluction (mass movement) common in higher snow-covered ranges of the region. Almost every year the region is affected by one or more major landslide events affecting the society in many ways like loss of life, damage to settlements, roads, means of communication, agricultural land, and floods. Heavy rainfall, cloudburst and consequent flash floods particularly in narrow river gorges are one of the main causes of major landslides in Kashmir (SDMP, 2017). Doda, Udhampur, Kathua, Kishtwar, Gulmarg, Dawar, Gurez, Tangdhar, Rajouri and Kargil are some areas of the erstwhile state highly prone to landslide hazard, also areas along major highways particularly Ramban, Panthal, Banihal, Qazigund (NH 44), and Baltal, Sonmarg, Zogila (NH 1) are vulnerable (Chingkhei et al., 2013 ). The rugged topography of the region makes it highly susceptible to major landslides triggered by flash floods along narrow river gorges eventually jeopardizing the whole hill systems. The geologically young, unstable and fragile rocks of the region have witnessed an increase in vulnerability by manifolds in the recent past due to various unscientific developmental activities like deforestation, road cutting, settlement construction and terracing, quarrying practices, indiscriminate encroachment on steep hill slopes, etc., increasing the frequency and intensity of landslides which is also evident from the events recorded from the data (Table 5 and Fig. 3 (c)) (SDMP, 2017). The Jammu-Srinagar national highway gets blocked at number of places during the monsoon and winter seasons, due to landslides of which the Ramban-Banihal stretch has become one of the most affected portions (Chingkhei et al., 2013 ). Table 5 Major Landslide events witnessed by the Kashmir Valley for which damage and loss were reported. Date of occurrence Location Place of occurrence Long/ Lat Cause/ trigger Casualties Associated impacts Year DD/MM Fatalities Injuries 1905 04/04 J&K and HP Earthquake induced - - Damage to structures and network of hillside aqueducts feeding water to affected areas. 1992 Sept NW border districts of valley Kupwara and Baramulla Flood induced - - Huge loss to life and property at the hands of floods and associated landslides 2005 19/02 Waltengu, kund and nar villages Kulgam and Anantnag snow- avalanche induced - - Large scale loss of life and damage to property, loss of connectivity and hindrance in rescue and relief 2005 08/10 Multiple locations of NW districts, J&k (Tangdhar, Uri) Kupwara and Baramulla earthquake induced - - Splitting of earth, landslides, rockfalls, complete and partial damage to roads, and hillside structures. 2007 25/06 Ganderbal and Srinagar - 3 - - 2007 17/12 Srinagar and Ganderbal - 2 6 Temple, bridge and army bunker damaged 2008 09/01 Banihal-Ramban HW 44 - - - 200 vehicles stranded 2008 08/02 Ramban-Banihal HW 44 - 3 15 Vehicles stranded 2008 18/02 Uri Baramulla - 1 1 - 2008 31/03 Qazigund Anantnag - 2 1 - 2008 26/10 Srinagar - 2 - 32 cattle lost 2008 20/11 Gurez Bandipora - 6 - - 2009 06/02 Srinagar - 4 4 - 2009 17/06 Amarnath Anantnag - 1 - 4000 pilgrims stranded 2009 29/07 Srinagar-Ladakh HW 1 and Baltal road Ganderbal - 3-4 - Pilgrims buried, tourism affected 2009 29/07 Kupwara - 3 pilgrims - Tourism affected 2009 02/08 Amarnath Anantnag - 2 - - 2009 12/12 Keran Sector Kupwara - 1 BRO porter - - 2009 17/06 Gurez, Bandipora - 1 - - 2009 17/06 Railpathri, Baltal base camp Ganderbal - 1 porter 1 - 2010 09/01 Kupwara District - 4 - - 2010 08/02 Uri, near LoC Baramulla - 1 7 5 Houses collapsed, cattle affected (7 cows, 30 goats killed) 2010 08/02 Uri, Gharkote Baramulla - - 1 army man Shooting stones 2010 10/02 Gulmarg Baramulla - 3 - - 2010 22/02 Chairvani village, Ganderbal, Srinagar - 6 6 - 2010 22/02 Uri Baramulla - 1 6 - 2010 20/04 Zojila HW 1 Heavy rains - - - 2010 28/04 Ganderbal, Srinagar - 1 - Srinagar-Leh highway closed 2010 28/04 Srg-Leh HW 1 - 1 BRO laborer - Highway closed 2010 20/05 Srinagar - 1 - - 2010 04/06 Baramulla - - 1 - 2010 06/06 Uri Baramulla - 6 - Traffic disrupted for several days 2010 10/10 Uri Baramulla - 1 7 - 2010 23/10 Uri Baramulla - 3 (Army men) - Hampered traffic movement 2011 04/03 Uri Baramulla - 1 - 4 houses damaged 2011 18/04 Phimram, Shangus Anantnag - 5 family members 1 - 2011 09/12 Gurez Bandipora - 3 - 8 shops and 10 kiosks destroyed, dozen vehicles damaged 2014 02/09 Multiple sites in J&K Flood induced - - Colossal damage to life and property. Blocked river channels and caused flash floods, aggravated flood situation. 2014 12/03 Kulgam district Avalanche induced 13 - houses collapsed 2014 12/03 Balsaran Danaukandimarg village, Kulgam district - 4 - - 2014 12/03 Qazigund (Anantnag), Kulgam - 1 - House collapsed 2014 12/03 Shopian district - 1 - House collapsed 2015 06/03 Sunergund, Awantipora, Pulwama, Anantnag - 1 - - 2017 20/03 Chadoora, Budgam flood induced and heavy rains 16 - mudslides and house collapse 2018 05/01 Sadna pass, Kupwara - 10 - Avalanche and landslides hit camp 2018 18/01 Happat Koal, Happat nar Anantnag - 1 Swedish skier - - 2018 31/03 Ladden Chadoora Budgam - 16 - Traffic disrupted for more than 10 days 2018 04/07 Railpathri and Brarimarg Ganderbal - 10 (4 pilgrims) - Amarnath yatra was suspended 2020 11/06 Salar Pahalgham, Anantnag District - 1 - - Table 6 Major Snow avalanches witnessed by the Kashmir Valley for which damage and loss were reported. Date of occurrence Location Casualties Associated impacts Year DD/MM Place of occurrence Fatalities Injuries/ missing/rescued 1986 January Zojila, Srg-Leh National HW 60 - - 1994 February Jawahar tunnel, Banihal HW 98 - Passengers trapped and perished on both sides of the tunnel 1997 28/03 Monang Post, Uri Baramulla 4 soldiers - A patrol party of 4 was swept and later bodies recovered 1998 25/02 Mou Mangat, Banihal HW 11 civilians - House buried located far from the main village 2005 10/02 Kund and Waltengu Nar villages, Qazigund Anantnag Kulgam 175 60 civilians rescued Hundreds trapped 2008 10/01 Uri Baramulla 15 - - 2008 08/02 Qazigund Anantnag 3 15 injured 500 trucks stranded 2008 08/02 Jammu -Srinagar HW 25 - - 2008 08/02 Ramban-Banihal, HW 3 - 400-500 trucks stranded 2008 18/02 Uri Baramulla 1 - - 2009 13/01 Kashmir valley 2 - - 2009 06/02 Kupwara 4 - - 2009 06/02 Srinagar 4 - - 2009 14/04 Kupwara 7 - - 2010 09/01 Kupwara 4 - - 2010 08/02 Gulmarg Baramulla 17 Soldiers 17 soldiers injured - 2010 09/02 Kupwara 2 - - 2010 10/02 Gulmarg Baramulla 3 - - 2011 12/02 Phiram Shangus, Anantnag 2 1 injured - 2012 24/01 Kupwara 7 (army and BSF) - Associated slides 2012 23/02 Ganderbal & Bandipora 16 Army personnel Many injured - 2012 24/02 Gurez Bandipora 13 army personnel - - 2012 21/03 Gurez Bandipora 2 3 people rescued. 1 person missing. Civilian vehicle caught in the avalanche. 2013 23/12 Gurez Bandipora 2 - - 2014 13/03 Batalik 3 soldiers 2 rescued - 2016 14/03 Kupwara 10 73 civilians rescued. People stranded in vehicles. 2017 25/01 Gurez Bandipora 24 (4 civilians; 20 soldiers) - Series of 3 avalanches. Army camp and patrol party was hit. 2017 25/01 Sonmarg Ganderbal 4 civilians - - 2017 26/01 Gurez Bandipora 10 soldiers - - 2017 26/01 Sonmarg Ganderbal 4 soldiers injured - 2017 28/01 Machil, Kupwara 5 soldiers rescued - 2017 06/04 J&k, higher reaches of Kashmir and Ladakh. Batalik, Kargil, Kupwara, Kokernag, (Anantnag) Rajori, etc. 9 (6 civilians, 3 army men) - Avalanches, minor flooding, landslides, rise in water levels in Jhelum and tributaries. 2017 13/12 Gurez (Bandipora) & Naugam, (Kupwara) 5 soldiers - Trapped after snow track caved in. 2018 06/01 Sadhna top, Tangdhar Sector (C-T), karnah, Kupwara (khooni nallah) 11 (civilians) - 2 avalanches. Vehicle hit by avalanche. 2018 25/01 Sonmarg (Ganderbal), Gurez (Bandipora) & Kupwara 6 (5 civilians, 1 army major) 4 soldiers missing, 6 soldiers rescued alive Camp hit, House collapsed, family of four died. 2018 26/01 Bandipora 11 (7 soldiers, 4 civilians) Several missing A camp and patrol party got hit. 2018 02/02 Kupwara 2 soldiers 1 injured Avalanche struck army post 2018 16/02 Gulmarg Baramulla 5(tourists- 1 international, 4 national) - - 2018 24/02 Guchibal Behak, Kupwara 3 civilians 2 missing - 2018 01/03 Tulail Bandipora 1 injured - 2018 09/09 Kolahoi 2 local trekkers - - 2019 04/12 Tangdhar Kupwara 3 soldiers - Army post hit 2019 04/12 Dawar, Gurez, Bandipora 1 1 injured Foot patrol of army was hit 2020 14/01 Kulan, Sonmarg Ganderbal 5 - Several houses damaged when village was hit by avalanche 2020 14/01 Machil sector, Kupwara 4 soldiers 5 trapped Army post hit 2020 14/01 Naugam sector, along LoC Kupwara 1 soldier 6 rescued alive - 2020 18/11 Roshan post, Tangdhar Kupwara 1 2 injured - For landslide hazard out of the total 65 events collected, 49 events with substantial damage and loss were discussed in detail (Table 5 ). The events with highest number of deaths in the databset are the landslide event of 20th March, 2007 Chdoora budgam and 31st March, 2018 Ladden, Chadoora, Budgam with a death toll of 16 persons each. Land sliding can have varied triggers, heavy rains appear to be the most frequent cause of landslides and therefore, many a times landslides coincide with floods and flash floods (1992, 20th April, 20th March, 2007 and 2010, 2nd September, 2014), earthquakes (4th April, 1905, 8th October, 2005) and snow avalanches (19th February, 2005 and 12th March, 2014) are also a common cause for land sliding. Landslides frequently lead to road blockade, disrupted traffic movement (of people and goods) as can be seen from the data, along with other impacts like deaths and injuries, loss of cattle, damage to hillside settlements, infrastructures, roads and bridges causing loss of connectivity, hamper pilgrimage activities, accidents and damage to vehicles, affect tourism, and daming of rivers causing flash floods. National hihgway is the main link of the valley to the rest of the country which gets blocked ever so frequently during rainy and winter seasons leaving the valley without accessibility for days at a strech having impacts like shortage in supplies, availability of goods, inflation, hampered movement of people, etc (Prakash, 2011). From the data we can establish a pattern in the seasonal variability of landslide occurrences, with maximum number of events (9 each) in the months of February and March, which account for 34.04% of the total occurrences, and minimum (1 each) in May and November, rest occassional slides occur throughout the year. 53.19% of the total land sliding activity occures in the months of January, February, March and April, but a substantial number of occurrences have also been recorded for the month of June, which accounts for 14.89% of the total incidents recorded. Snow Avalanches An endemic feature of snow-covered mountain ranges (Spencer, 2011 ; Bruno, 2013 ), avalanches are both widespread and one among the most destructive natural hazards (Keylock, 1997 ), causing fewer casualties globally, on an average several hundred people per year (Birkeland, 2021), than many other natural hazards, but overall fatalities have been on the rise over the past several decades (Bruno, 2013 ). The overwhelming expansion of tourism and increasing popularity of winter sports and climate warming has escalated the number of people exposed to avalanches by influencing the behaviour, uncertainty and increasing frequency of snow avalanches (Martin et al., 2001 ; Bruno, 2013 ; Castebrunet et al., 2014 ). The Himalayas (Indian Himalayas) are highly vulnerable to snow avalanches (Sethi, 2000 ; Ganju, 2002; Mc Clung, 2016) and with increased communication to isolated mountain villages, the number of incidents and casualties recorded has enhanced substantially in the last few decades (Sethi, 2000 ). Snow-covered regions of Jammu and Kashmir, Himachal Pradesh, Uttaranchal and Western Uttar Pradesh are significantly susceptible (Sethi, 2000 ; Ganju, 2002) while eastern states witness occasional incidents. In erstwhile, Jammu and Kashmir higher reaches of Kashmir division (Kashmir valley, Gurez valleys, Kargil and Ladakh), areas of Jammu region (Doda, Ramban, Udhampur, Reasi, Kishtwar, Banihal), some of the major roads (long stretches of national highway connecting J&K to the rest of the country, from Ladakh through Srinagar to Jammu, Mughal Road, etc.) (Kelman, 2018; RMSI Report, 2018) and famous pilgrim centres (Amarnath, Phalgham and Baltal, and Vaishnu Devi, Katra) (Sethi, 2000 ) are highly vulnerable to snow avalanches (SDMP, 2017). J&K, as compared to the other vulnerable regions of the country has taken the major brunt of avalanche accidents in the past (Ganju, 2002) with both civilians and the army being severely impacted as can be established from the recorded events (Gusain et al., 2018 ). Table 6 discusses 47 incidents in detail most of which have been recorded for the period 2000-2020. An evident increase in the number of avalanche occurrences and subsequent casualties can be seen in the past three decades which can be primarily attributed to insufficient knowledge about the terrain, lack of forecasting mechanism, and ill-equipped adventures taken-up by army as well civilians like construction of roads, enhancement in tourism and increased patrolling activity in the region post 1990’s (Ganju, 2002). Snow avalanches have substantial adverse impact on human activity (Keylock, 1997 ) threatening human life directly by causing death or injury, or by detaining them and indirectly by obstructing the overall development, disrupting ecosystems, damaging built structures and landscapes in mountainous regions (Ganju, 2002; Choubin, 2019). A considerable portion of the total fatalities seem to occur when people are in movement, as opposed to when they are static (like in their houses, barns etc.,), and majority of the accidents take place during snowfall or immediately after cessation of snow storm, as can also be confirmed from the reported events in this study (Table 6) (Sethi, 2000 ; Ganju et al., 2002). The incident showing highest number of casualties is the avalanche of 10th February, 2005 in Kund and Waltengu Nar Villages killing a total of 175 people while 60 were rescued alive. The record shows a few more severe incidents with large number of casualties like 1986 Zojila (60 deaths), 1994 Jawahar Tunnel (98 deaths), 8th February, 2008 Jammu-Srinagar National HW (25 deaths), 8th February, 2010 Gulmarg (17 deaths and 17 injuries), and 25th January, 2017 Gurez (24 deaths). Recorded casualties show a greater number of army personnel than civilians which is due to the proximity of the region to the international border that mostly stretches across avalanche-prone snow-covered slopes, therefore, the presence of army posts along the LoC makes them highly susceptible to snow avalanches (Gusain et al., 2018 ). The data reveals that areas like Tangdhar, Drass, Gurez, Keran, Machhal, Gulmarg, Naugam and Banihal are highly avalanche prone sites in the valley (Table 6) (Kelman, 2018; Gusain et al., 2018 ). The major roads of the region, winding up on some of the prominent passes on the Pir Panjal and the Greater Himalayan Range, are often closed due to landslides and avalanches during the winter and early spring seasons, with a number of casualties every year, frequent suspension of vehicular movement and confinement of pedestrian movement to only village level for long periods creating various socio-economic problems, but with no systematic compilation of incidents or casualties (Kelman, 2018). The important road axes susceptible to avalanche activity include: Jammu-Srinagar, Naugam-Kaiyan, Chowkibal-Tangdhar, Srinagar-Leh and Bandipora-Gurez. Tangdhar is one of the regions studied well for avalanches and many researchers have reported various prediction techniques for this road axis in the past (Gusain et al., 2018 ). The avalanche activity for major portions of mountain areas of Kashmir is most pronounced in the months of January to March, but may stretch over the months of November to April (Sethi, 2000 ), while in the high alpine areas the avalanche season continues all year-round (9th September, Kolahoi) (Ganju, 2002). In Kashmir 72.34% of the total avalanches occur in January and February followed by the months of March and April accounting for about 17.02% of the total. 4.1.2. Casualties Casualties form one of the most important aspects to study the intensity and extent of impact caused by a particular disaster event. A casualty can be any person who becomes a victim to an adverse impact caused by any hazardous event, which may include persons killed, injured, trapped, missing, evacuated, or rescued. However, the present study only includes two forms of casualties viz., fatalities and injuries. The trend shows that maximum number of reported casualties are related to earthquakes (30,530 i.e., 90.94%), followed by floods (2,194 i.e., 6.53%), snow avalanches (642 i.e., 1.91%) and least for landslides (204 i.e., 0.60%). Although many of the events have a regional and cross border extent, only the casualties reported for the area under focus i.e., the Kashmir Valley, whereever provided, were considered for analysis. The total number of severe events shows an inverse relation to the total casualties reported, for example only 7 severe earthquake events were reported for the entire timeline for which the count of casualties far surpasses that caused by any other hazard type, whereas a total of 49 landslide events were of the magnitude to cause substantial damage and loss, for which the total count of casualties stands the least among all the four hazard types. 4.1.3. Temporal variability and distribution The temporal variation and frequency (annual and decadal events distribution) of hazard and disaster events of all four types for the time period 1900 to 2020 (Fig. 3 and Fig. 4 ) shows a general increasing trend in the last few decades depicted by a sharp rise in the graph line between 1980 to 2020, establishing the fact that the number of disaster events recorded has considerably increased in the past few decades. This indicates that the frequency of hazard and disaster occurrences, due to various natural (climate change and global warming, geological endogenic and exogenic processes) and anthropogenic factors has increased to a great extent. Another factor responsible for the rise in recorded incidents possibly could be the enhanced and improved recording and reporting of disaster events globally and nationally in recent times. Also, due to dramatic population growth and rapid urban expansion, overburdening of regions takes place which forces people to move to and settle in unsafe conditions and vulnerable areas, increasing the chance of human interaction with these potential hazards and therefore, elevate the levels of exposure to which populations and societies are subjected to, thus, consequently increasing the number and frequency of these adverse events. In case of earthquakes, although, major events are well recorded throughout the timeline, small to medium scale incidents, which need technological intervention to be detected find more reliable and frequent reporting post 1960s which can be backed up by the gradual but evident increase in the number of events recorded per year (Table 2 and Fig. 3 a). The history of instrumental monitoring of earthquakes in India dates back to 1898 when the first seismological observatory of the country was established in Calcutta after the great Shillong plateau earthquake of 1897. Other similar occurrences like 1905 in Kangra Valley, necessitated the strengthening of the national seismological network with 1960s marking a landmark in history of seismic monitoring when the WWSSN (World Wide Standardized Seismic Network) stations started functioning globally, post which the number of reported earthquake events shows a drastic increase (Table 2 and Fig. 3 a). For the given timeline of 120 years (1900-2020) the highest number of earthquake events i.e., 121 were recorded in the year 1976. The dataset reveals that there is a strong earthquake (magnitude 6.0 and above) every few years, in and around the Kashmir region, with occasional episodes of continued occurrences without any gap like 1963, 1964, 1965 and 1972, 1973, 1974 and 1975. Sometimes more than one strong earthquake seems to have jolted the region in a single year (1950, 1975, 1990, 2005 and 2015). Earthquakes with magnitude lesser than 6 occur more frequently all the year-round. Flood events on the other hand show the most consistent trend of occurrence throughout the timeline, with occasional periods of no flood occurrences. Although, a slight increase can be detected through the slope of the trend line in the graph 2000-2020, indicating more than one flooding event in the same year (Fig. 3 , b). Floods, in general, show a repeated pattern through the 20th century arriving at regular intervals (Table 2 and Fig. 3 b) and during the years 1900 to 1965, the valley experienced about 15 major floods (Razdan, 2014). 3 flood events in a single year 2015 were the highest number of flood events recorded for any year. Detection of a flood event is more evident through the rise in water levels, which are easier to measure and record, and therefore, are available for most of the times when water levels have crossed the flood mark, dating back to 635 A.D. Landslide and snow avalanche events are localized events and have limited spread. From the graph we can make out a comparatively less consistent trend of occurrence and frequency throughout the timeline, which hints at a dramatic increase in landslide and snow avalanche incidents in the recent past which could be attributed to, firstly, increase in exposure of populations to these hazards through tourism and adventurous activities, communication to and settling in remote susceptible areas, increased movement of traffic in these areas, and also, large scale patrolling activity post-1990s, and secondly, better reporting and recording of these events, which was found almost negligible for the twentieth century except for incidents with greater human impact and those along the national highway, (Ganju, 2002) and the increasing trend can be seen continuing in the 21st Century (Table 2 and Fig. 3 c, 3 d). A total number of 16 landslide events in a single year 2010 were the highest recorded and for snow avalanches the highest number of events recorded in a single year were 13. Looking at the dataset we infer that on an average 2 to 3 low to moderate intensity avalanche events occur in a year. Also, some very high impact events with a large number of casualties keep repeating once in few years (Table 5 and 6). 4.1.4. Spatial distribution The spatial distribution of the selected four hazard types was represented through maps developed in ArcGis. For earthquake hazard only the events with epicentres lying within the Kashmir Valley were plotted using the geographic coordinates provided in the secondary sources consulted, similarly, the landslides and snow avalanches reported within the area of interest were plotted by making use of the latitudinal-longitudinal information collected for the reported events, but for flood hazard spatial distribution was represented through the extent of inundation experienced by the Valley in the Kashmir Flood of September, 2014 (declared as the highest magnitude flood recorded instrumentally on Jhelum by the Department of Irrigation and Flood control), in order to give an idea about the area of Kashmir Valley which may be at risk of inundation during a Flood event of similar magnitude. The entire valley is about equally prone to earthquake hazard with a somewhat homogenous distribution of earthquake incidents in and around the region, which coincides with its zonation into high to very high seismic intensity zones (zones IV and V) (Fig. 5 a). For flood hazard, however, flood plains and low-lying areas, on both sides of the Jhelum River, across the length of the entire valley, are under the threat of inundation (Fig. 5 b). Analysis of recent available data suggests that the left bank of the Jhelum is more vulnerable to inundation than the right bank (Ram 1895 , 1928 ; Bhat, 2019). As for the landslides and snow avalanches, incidents are limited to higher reaches, unstable slopes, with pockets of high, moderate and low frequencies throughout the mountainous stretches of the Valley (Fig. 5 c and Fig. 5 d), especially along the roadways running through these hilly terrains. The plot distribution and inundation extent data were further used to generate susceptibility maps of all four hazard types for the districts of the Kashmir Valley (Fig. 6 ) based on the concentration of events in each district for earthquakes, landslides and avalanches and for floods the extent of area inundated in each district, as factors for classification of the districts as less to more vulnerable to specific hazards. This gives us a general idea about the proneness of the districts towards different hazards, from which we can make out that all the districts are susceptible to two or more of the selected hazard types. Based on the choropleth/thematic maps (Fig. 6 ), maximum concentration of earthquake incidents is seen in the district Kupwara, whereas, Shopian shows the lowest concentration (Fig. 6 a). For flood hazard, districts with maximum flooded area appear to be Baramulla and Bandipora whereas, Kupwara and Shopian show least area affected (Fig. 6 b). There can be seen pockets of concentration depicting landslide incidents, located predominantly in the mountainous terrain running along the periphery of the valley (Fig. 5 c and d). In figure 6 c, it is evident that districts Baramulla and Anantnag have witnessed the maximum number of landslide events in the time period and therefore, exhibit the highest susceptibility, while the districts with the least susceptibility are Pulwama and Shopian. For snow avalanches, the events are spread over the snow-covered ranges of the valley, especially towards the north and northeast (Fig. 5 d). In some spots, locations of high-concentration of avalanche events coincide with those of the landslide events. Among the districts, Kupwara shows the highest susceptibility towards snow avalanches, whereas, Budgam, Pulwama and Shopian show the least susceptibility towards avalanche hazards (Fig. 6 d). 5. Data Uncertainty And Limitations Disaster data has been found to be scattered and broken, showing intermittent and discontinuous coverage in the major portion of the 20th Century. While large impactful events find a place into literature through one way or the other most of the small-scale incidents go unreported. In the later part of the 20th Century disaster events can be seen recorded more continuously with more accurate details, which continues in the 21st Century, attributed to improved technology for detection, reporting and disseminating information. There has been no specific literature dealing with what can be generally termed as disaster data base in the historical perspective in the former part of the timeline but things have begun to change ever since the inception of databases like CRED-EM-DAT, Munich RE, ADRC, IDNDR, etc. Consequently, information about natural events and resulting processes were littered in the vast corpses of literature making them difficult to assemble. Ambiguity and unreliability are produced by relying upon online digital sources in the process of data accumulation for extended periods as these usually get deactivated or removed from the web after a certain period of time. Similarly, digital newspaper archives are also only available for 5-8 years which poses a challenge to data collection retrospectively. Additionally, websites which work as supplementary resources to global and regional hazard databases are mostly region and country specific, especially to developed nations (Sultana, 2020 ). Also, databases have specific criteria which limit the recording of all disaster events specifically to those which fulfil these particulars, e.g., EM-DAT. As a result, many events don’t make it to such databases and thereby, forming a corrupt picture of the hazard or disaster scenario and making data collection a challenge. Some hurdles arise when it comes to characterizing disaster events. Generally, information can fluctuate widely regarding frequency, magnitude, impacts, exact location (latitude-longitude), etc. which results in several biases and overlaps. Like in case of earthquakes, the epicentres or the magnitudes showed variation from one source to another. Similarly, figures relating to damage and loss can also be erroneous, example death of someone injured in the main event a few days after the event. Sometimes, the events with minimal consequences go unreported which results in inaccurate figures of total disaster episodes. An over or under estimation is encountered when news of multiple events occurring on the same date at same or different locations are reported as a single event. Therefore, in terms of impacts and other specifications, multiple sources were consulted for a single episode and the data from best reported and more reliable source was selected in that case. In some cases, casualties and economic losses pertaining to a single hazard type may be higher than what is commonly documented (Schuster, 1996 , 2001 ; Guzzetti et al., 1999 ; Anderson et al., 2011 ). There is widespread recognition that the overall consequence of landslides and avalanches is usually underestimated (Kjekstad & Highland, 2009; Petley, 2012 ). Data is not systematically reported or readily available (Petley, 2005, 2010 ; Corominas et al., 2014 ) unlike other natural disasters. Casualties are often recorded under the label of triggering events, which are usually larger events such as flood, hurricanes, or tropical storms, earthquake, etc. (UNDP, 2004; Froude & Petley, 2018). Also, in most of the cases, exact reporting is hindered by the comparative smaller scale on which they occur, lack of close observation, short span of occurrence and that too at specific, remote locations and during specific seasons, which limit their impacts and is unrecognized in most natural disaster’s registers (Holcombe & Anderson, 2010). All this may cause flawed estimations pertaining to the hazard impact and susceptibility. In these records, for landslides and snow avalanches it was seen that data pertaining to army accidents is more or less complete as compared to that of the civilian data, which has often gone unreported, therefore giving a false idea of the actual hazard susceptibility. 6. Conclusion The aspect of utmost importance for preparing a mitigation plan is to understand the hazards facing a community. Understanding the various hazard risks and their subsequent consequences is of prime concern while mitigating the adverse effects of potential hazard events. A hazard profile is an account and analysis of different types of hazards specific to a place/community. It is performed for each natural hazard and based off certain criteria such as frequency, location, duration, speed of onset, and impacts. A hazard profile enables decision makers to compare the physical aspects that all hazards share. By comparing the characteristics of hazard events, they are able to identify and prioritize the hazards for mitigation, risk reduction, policy and decision making, and funding. From the above discussion, we assert that the valley of Kashmir is subjected to a threat of multiple hazards that have the potential to turn into devastating disaster events and jeopardize the lives of millions residing in this area. The analysis finds out that the frequency of disaster events has drastically increased over the past few decades, enhancing the threat of natural hazards to which the population and society of Kashmir are subjected to. The results reveal that these four natural hazards individually, can have devastating impacts and also, a catastrophic event may take form if multiple hazards overlap or cascade after one another. Therefore, it becomes essential to gauge this impending threat, mitigate the risks and prepare for the worst through sustainable Disaster Risk Reduction which the present study aims to facilitate by developing a multi-hazard profile of Kashmir based on the disaster database compiled. The disaster catalogues prepared in the study can prove of great help for various stakeholders and policymakers, to assess the local risk and hazard scenario of the valley, in order to be incorporated in planning, to reduce disaster risk and subsequently create sustainable environments. Information availability and ease of access to data regarding the events were found to vary with the type of hazard, like in the case of earthquakes, data was readily available in national and international databases as well as on open access portals and websites, because of improved recording technologies and dissemination platforms. Similarly, for floods, a good amount of data was found from various existing research studies, especially, due to a boon in flood research post-2014 Kashmir floods. On the contrary, for landslides and snow avalanches, it was encountered that data recording, reporting and access was limited due to various factors. Both landslide and snow avalanche events have not received as much recognition as is the case with floods and earthquakes. The data for earthquake incidents, their magnitudes and for flood incidents, the experienced water levels and damage and loss for either of the two types, have been more or less recorded and have found place in literature either through official record or historical literature as both events can have colossal adverse impacts and spread over a vast expanse, which is not the case with landslides and snow avalanches. The fact that events data of landslides and snow avalanches, for major portion of the selected timeline is missing from the consulted data sources could draw a misleading picture of the hazard and disaster scenario of the Kashmir valley, making the valley appear little to not vulnerable to these hazards, which is very contradictory to the actual scenario. Declarations 7. Acknowledgement I wish to show my gratitude to all the authors who contributed towards the conceptualization of the idea, collection of the historical events data and compilation of the manuscript. 8. Conflict of Interests/ Disclosure Statement The authors declare that there is no conflict of financial or personal interests or beliefs that could affect the objectivity of the research. References Ahmad B, Alam A, Bhat MS, Bhat KA, Haq ul, Ahmad JI, Qadir J (2021) Retracing Realistic Disaster Scenarios from Archival Sources: A Key Tool for Disaster Risk Reduction.International Journal of Disaster Risk Science,1–14 Ahmad B, Bhat MI, Bali BS (2009) Historical record of earthquakes in the Kashmir Valley. Himalayan Geol 30(1):75–84 Ahmad SM (1984) Historical Geography of Kashmir: Based on Arabic and Persian Sources from AD 800 to 1900. Ariana Publishing House Ahmad S, Alam A, Ahmad B (2016) Comment on: “Earthquake geology of Kashmir Basin and its implications for future large earthquakes” by Shah (2013); “Kashmir Basin Fault and its tectonic significance in NW Himalaya, Jammu and Kashmir, India” by Shah (2015). Int J Earth Sci 105(2):681–687 Ahmad M, Bano R (1984) Historical geography of Kashmir (AD 800–1900). Ariana, New Delhi, India Alam A, Ahmad S, Bhat MS, Ahmad B (2015) Tectonic evolution of Kashmir basin in northwest Himalayas. Geomorphology 239:114–126 Alam A, Bhat MS, Farooq H, Ahmad B, Ahmad S, Sheikh AH (2018) Flood risk assessment of Srinagar city in Jammu and Kashmir, India. International Journal of Disaster Resilience in the Built Environment 2:9. doi.org/10.1108/IJDRBE-02-2017-0012 Albinia A (2010) Empires of the Indus: The story of a river. WW Norton & Company Ambraseys NN, Douglas J (2004) Magnitude calibration of north Indian earthquakes. Geophys J Int 159(1):165–206 Ambraseys N, Bilham R (2000) A note on the Kangra M s= 7.8 earthquake of 4 April 1905. Curr Sci 79(1):45–50 Anderson MG, Holcombe E, Blake JR, Ghesquire F, Holm-Nielsen N, Fisseha T (2011) Reducing landslide risk in communities: Evidence from the Eastern Caribbean. Appl Geogr 31(2):590–599 Anees SUM, Bhat MS (2016) History of natural disasters in Kashmir valley, Jammu and Kashmir with special reference to earthquakes.History, 5(9) Ballesteros-Cánovas JA, Koul T, Bashir A, Del Pozo JMB, Allen S, Guillet S, Stoffel M (2020) Recent flood hazards in Kashmir put into context with millennium-long historical and tree-ring records. Sci Total Environ 722:137875 Bamzai PNK (1962) Socio-economic history of Kashmir: 1846–1925(p. 771). Metropolitan Book, New Delhi, India Bankoff G (2007) Comparing vulnerabilities: toward charting an historical trajectory of disasters. Historical Social Research/Historische Sozialforschung, pp 103–114 Below R, Vos F, Guha-Sapir D (2010) Moving towards harmonization of disaster data: a study of six Asian databases. Centre for Research on the Epidemiology of Disasters, Brussels Bhat MI (1987) Spasmodic rift reactivation and its role in the pre-orogenic evolution of the Himalayan region. Tectonophysics 134(1–3):103–127 Bhat MS, Ahmad B, Alam A, Farooq H, Ahmad S (2019) Flood hazard assessment of the Kashmir Valley using historical hydrology.Journal of Flood Risk Management, 12, e12521 Bhat MS, Alam A, Ahmad B, Kotlia BS, Farooq H, Taloor AK, Ahmad S (2019) Flood frequency analysis of river Jhelum in Kashmir basin. Quatern Int 507:288–294 Bhatt CM, Rao GS, Farooq M, Manjusree P, Shukla A, Sharma SVSP, Dadhwal VK (2017) Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India. Geomatics, Natural Hazards and Risk, 8(2), 309-327 Bilham R, Bali BS (2014) A ninth century earthquake-induced landslide and flood in the Kashmir Valley, and earthquake damage to Kashmir’s Medieval temples. Bull Earthq Eng 12(1):79–109 Bilham R, Bali BS, Bhat MI, Hough S (2010) Historical earthquakes in Srinagar, Kashmir: Clues from the Shiva temple at Pandrethan. Geol Soc Am Spec Pap 471:107–117 Bilham R, Gaur VK, Molnar P (2001) Himalayan seismic hazard. Science 293(5534):1442–1444 Birkeland KW (2021, July 26) avalanche. Encyclopaedia Britannica. https://www.britannica.com/science/avalanche BIS (2002) Seismic zonation map of India prepared by the Bureau of Indian Standards BMTPC ( https://www.bmtpc.org/topics. aspx?mid=43&Mid1=56) Bolt A, Bruce (eds) (October 2003) Earthquake. Encyclopaedia Britannica. https://www.britannica.com/science/earthquake-geology/Earthquake-magnitude Bruno A (2013) Tumbling snow: Vulnerability to avalanches in the Soviet North. Environmental History 18(4):683–709 Burbank and Jhonson 1983: Burbank, D. W., & Johnson, G. D. (1983). The late Cenozoic chronologic and stratigraphic development of the Kashmir intermontane basin, northwestern Himalaya. Palaeogeography, Palaeoclimatology, Palaeoecology, 43(3-4), 205-235 Burton I (2010) Forensic disaster investigations in depth: a new case study model. Environment 52(5):36–41 Castebrunet H, Eckert N, Giraud G, Durand Y, Morin S (2014) Projected changes of snow conditions and avalanche activity in a warming climate: the French Alps over the 2020–2050 and 2070–2100 periods. The Cryosphere 8(5):1673–1697 Chaudora MH (1620) Tarikh-i-Kashmir (Persian). Folios 48:184 Chingkhei RK, Shiroyleima A, Singh LR, Kumar A (2013) Landslide hazard zonation in NH-1A in Kashmir Himalaya, India. International Journal of Geosciences, 2013 Choubin B, Borji M, Mosavi A, Sajedi-Hosseini F, Singh VP, Shamshirband S (2019) Snow avalanche hazard prediction using machine learning methods. J Hydrol 577:123929 Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73(2):209–263 CRED U (2020) Human cost of disasters: an overview of the last 20 years 2000-2019. Dikshit A, Sarkar R, Pradhan B, Segoni S, Alamri AM (2020) Rainfall induced landslide studies in Indian Himalayan region: A critical review. Applied Sciences 10(7):2466 Doocey S, Daniels A, Murray S, Kirsch TD (2013) The human impact of floods: a historical review of events 1980-2009 and systematic literature review.PLoS currents,5 Economic, Losses Poverty and Disaster 1998- 2017 (2018). Report for the United Nations Office for Disaster Risk Reduction,Geneva, and Center for Research on the Epidemiology of Disasters, Brussels Froude, M. J., & Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18(8), 2161-2181 Ganjoo RK (2014) The vale of Kashmir: landform evolution and processes. Landscapes and landforms of India. Springer, Dordrecht, pp 125–133 Ganju A, Thakur NK, Rana V (2002, September) Characteristics of avalanche accidents in western Himalayan region, India. In Proceedings of the International Snow Science Workshop, Penticton, BC, Canada (pp. 200-207) Gansser A (1964) Geology of the Himalayas Gariano and Guzzetti 2016: Gariano, S. L., & Guzzetti, F. (2016). Landslides in a changing climate. Earth-Science Reviews, 162, 227-252 Gavillot YG, Meigs A, Rittenour TM, Malik MOA (2016, December) Active folding of fluvial terraces across a blind 'Himalayan deformation front in the Kashmir Himalaya, northwest India. In AGU Fall Meeting Abstracts (Vol. 2016, pp. T11G-05) Gavillot Y, Meigs A, Yule D, Heermance R, Rittenour T, Madugo C, Malik M (2016) Shortening rate and Holocene surface rupture on the Riasi fault system in the Kashmir Himalaya: active thrusting within the Northwest Himalayan orogenic wedge. Bulletin 128(7–8):1070–1094 Ghaffar A, Abbas S (2010) An Overview of Past History Based on Seismicity Pattern of Kashmir Region, An Interpretation from 2005 Earthquake. The Journal of Animal and Plant Sciences 20(4):297–304 Ghosh SK, Veeraraghavan K (1975) Severe floods in Jammu & Kashmir in August I973. MAUSAM 26(2):203–207 Gupta S, Muralikrishna M (2010) South Asia disaster risk management programme: Synthesis report on SAR countries disaster risks. RMSI Private Limited, Noida Gupta S, Kumar S, Wason HR, Das R (2012) A statistical analysis of completeness of earthquake data around Dehradun city and its implications for seismicity evaluation. 15WCEE (Accepted). Gupta AK (2014) Loss of Life in Kashmir: A Tale of Man-Made Disaster. The Water Digest Report, New Delhi, pp 8–25 Gusain HS, Mishra VD, Singh DK (2018) Study of a snow avalanche accident along Chowkibal--Tangdhar road in Kupwara district, Jammu and Kashmir, India.Current Science(00113891),115(5) Guzzetti F (2000) Landslide fatalities and the evaluation of landslide risk in Italy. Eng Geol 58(2):89–107 Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1–4):181–216 Habibullah H (1905) Syllab Nama Haque U, Da Silva PF, Devoli G, Pilz J, Zhao B, Khaloua A, Glass GE (2019) The human cost of global warming: Deadly landslides and their triggers (1995–2014). Science of the Total Environment, 682, 673-684 Holcombe, E., & Anderson, M. (2010). Tackling landslide risk: Helping land use policy to reflect unplanned housing realities in the Eastern Caribbean. Land Use Policy,27(3), 798-800 Hough S, Bilham R, Bhat I (2009) Kashmir valley mega-earthquakes: estimates of the magnitudes of past seismic events foretell a very shaky future for this pastoral valley. Am Sci 97(1):42–49 IRDR Annual Report (2015) (2016) Integrated Research on Disaster Risk. https://www.irdrinternational.org/knowledge_pool/publications/602 Iyengar RN, Sharma D, Siddiqui JM (1999) Earthquake history of India in medieval times. Indian Journal of history of science 34:181–238 Jackson LE Jr (2013) Frequency and magnitude of events Encyclopaedia of Natural Hazards Peter Brobrowsky. (ed.) Springer-Verlag Jahn T, Wehling P (1998) Gesellschaftliche Naturverhältnisse—Konturen eines theoretischen Konzepts. Soziologie und Natur. VS Verlag für Sozialwissenschaften, Wiesbaden, pp 75–93 Joshi M, Thakur VC (2016) Signatures of 1905 Kangra and 1555 Kashmir earthquakes in medieval period temples of Chamba region, northwest Himalaya. Seismol Res Lett 87(5):1150–1160 Kalhana P (1149) Rajtarangani (Sanskrit) Kapur A (2010) Vulnerable India: a geographical study of disasters. SAGE Publications India Bhatt S (2004) Kashmir Ecology and environment: New Concerns and Strategies. APH Publishing, Delhi Kayal JR (2007) Recent large earthquakes in India: seismotectonic perspective. IAGR Memoir 10:189–199 Kelman I, Field J, Suri K, Bhat GM (2018) Disaster diplomacy in Jammu and Kashmir. International journal of disaster risk reduction 31:1132–1140 Keylock C (1997) Snow avalanches. Prog Phys Geogr 21(4):481–500 Khanyari GN (1857) Wajeez-ul-Tawarikh (in Persian) Khoihami, P. G. H. (1885). Tarikh-i-Hassan. Vol. I & II (Persian), pp. 394–395 Kjekstad and highland 2009: Kjekstad, O., & Highland, L. (2009). Economic and social impacts of landslides. In Landslides–disaster risk reduction (pp. 573-587).Springer, Berlin, Heidelberg Koul PA (1993) Geography of Jammu and Kashmir state, Thacker spink, Calcutta, India Kumar R, Acharya P (2016) Flood hazard and risk assessment of 2014 floods in Kashmir Valley: a space-based multi-sensor approach. Nat Hazards 84(1):437–464 Rajesh K (2016) Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu & Kashmir): An application of geoinformatics. Remote Sensing Applications: Society and Environment 4:188–203 Lacasse S, Nadim F, Kalsnes B (2005) Living with landslide risk. World Lawrence WR (1967) The Valley of Kashmir (1895). Kesar, Srinagar Le Fort P (1989) The Himalayan orogenic segment. Tectonic evolution of the Tethyan region. Springer, Dordrecht, pp 289–386 Lin Q, Wang Y (2018) Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016. Landslides 15(12):2357–2372 Mahajan AK, Thakur VC, Sharma ML, Chauhan M (2010) Probabilistic seismic hazard map of NW Himalaya and its adjoining area, India. Nat Hazards 53(3):443–457 Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Proc Land 29(6):687–711 Malik IH, Hashmi SNI (2020) Ethnographic account of flooding in North-Western Himalayas: a study of Kashmir Valley. GeoJournal, pp 1–19 Martin E, Giraud G, Lejeune Y, Boudart G (2001) Impact of a climate change on avalanche hazard. Ann Glaciol 32:163–167 Mason K (1929) Indus floods and Shyok glaciers. Himalayan Journal 1:10–29 McClung DM (2016) Avalanche character and fatalities in the high mountains of Asia. Ann Glaciol 57(71):114–118 Mehran D (2015) FOOD SHORTAGES IN KASHMIR: RESPONSE OF SOCIETY.Journal of Central Asian Studies,22 Meraj G, Romshoo SA, Yousuf AR, Altaf S, Altaf F (2015) Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya. Nat Hazards 77(1):153–175 Mukhopadhyay B, Dasgupta S (2015) Seismic hazard assessment of Kashmir and Kangra valley region, Western Himalaya, India. Geomatics, Natural Hazards and Risk, 6(2), 149-183 National Research Council (2012) Himalayan glaciers: Climate change, water resources, and water security. National Academies Press Ni J, Barazangi M (1984) Seismotectonics of the Himalayan collision zone: Geometry of the under thrusting Indian plate beneath the Himalaya. Journal of Geophysical Research: Solid Earth 89(B2):1147–1163 NIDM. India Disaster Report (2011) https://nidm.gov.in/PDF/pubs/India Disaster Report 2011.pdf Oliver-Smith A, Hoffman SM (2002) Introduction: Why anthropologists should study disasters. Catastrophe and culture: The anthropology of disaster, 3-22 Oliver-Smith A, Alcántara-Ayaia I, Burton I, Lavell A (2017) Forensic Investigations of Disaster (FORIN): towards the understanding of root causes of disasters. Policy brief. Integrated Research on Disaster Risk, ICSU, Beijing Parkash S (2011) Historical records of socio-economically significant landslides in India. J South Asia Disaster Studies 4(2):177–204 Parkash S, Kathait A (2014) A selected annotated bibliography and bibliography on landslides in India. National Institute of Disaster Management, Ministry of Home Affairs, Govt. of India, New Delhi, p 172 Patel SK, Nanda A, Singh G, Patel S (2020) A review of disasters in Jammu and Kashmir, and Ladakh region in India. International Journal of Population Studies 6(1):69–81 Petley D (2012) Global patterns of loss of life from landslides. Geology 40(10):927–930 Petley DN (2010) On the impact of climate change and population growth on the occurrence of fatal landslides in South, East and SE Asia. Q J Eng GeolHydrogeol 43(4):487–496 Petley DN, Dunning SA, Rosser NJ (2005) The analysis of global landslide risk through the creation of a database of worldwide landslide fatalities. Landslide risk management. CRC Press, pp 377–384 Rafiq M, Mishra AK (2018) A study of heavy snowfall in Kashmir, India in January 2017. Weather 73(1):15–17 Ram G (1928) Floods in Jammu and Kashmir. Jammu and Kashmir government flood management report (pp. 2–3) Ram PB (1895) Report on the administration of the Jammu and Kashmir State for 1892–93. Rambir Prakash Press, Jammu, India, pp 132–133 Rather MA, Kumar JS, Farooq M, Rashid H (2017) Assessing the influence of watershed characteristics on soil erosion susceptibility of Jhelum basin in Kashmir Himalayas. Arab J Geosci 10(3):59 Raza M, Ahmad A, Mohammad A (1978) The Valley of Kashmir: A Geographical Interpretation, Vol,1: the Land. Vikas Publishing House Pvt, Ltd., New Delhi, pp 1–59 Razdan Vinayak. September 6, 2014 “Sylabnama: Great Floods of Kashmir”. https://searchkashmir.org/2014/09/sylabnama-great-floods-of-kashmir.html Riede F (2014) Towards a science of past disasters. Nat Hazards 71(1):335–362 Riede F (2017) Past-forwarding ancient calamities. Pathways for making archaeology relevant in disaster risk reduction research. Humanities 6(4):79 RMSI Private Limited India (2018) Multi Hazard Risk Assessment of the State of Jammu and Kashmir, Data Inventory Report, November,2018. RMSI Limited, India (http://jtfrp.in) Saleh SF, Rather FF, Jabbar MJ (2017) Floods and mitigation techniques with reference to Kashmir. Int J Eng Sci Comput 7(4):6359–6363 Schenk GJ (2014) Common grounds in early modern disaster experiences? Some remarks on new trends. historical disaster research as part of environmental history and climate history. An environmental history of the early modern period: Experiments and perspectives. LIT, Zürich, pp 11–18 Schuster RL, Highland L (2001) Socioeconomic and environmental impacts of landslides in the western hemisphere. US Department of the Interior, US Geological Survey, Denver (CO), pp 1–50 Schuster RL (1996) Socioeconomic significance of landslides. In: Turner AK, Schuster RL (eds) Landslides: Investigation and mitigation, vol 247. National Academy Press.Transportation Research Board Special Report, pp 12–35 Searle MP (1991) Geology and tectonics of the Karakoram Mountains. John Wiley & Sons Incorporated Searle MP, Windley BF, Coward MP, Cooper DJW, Rex AJ, Rex D, Kumar S (1987) The closing of Tethys and the tectonics of the Himalaya. Geol Soc Am Bull 98(6):678–701 Seeber L, Armbruster JG (1981) Great detachment earthquakes along the Himalayan arc and long-term forecasting. Earthquake prediction: an international review 4:259–277 Sethi DN, Satyawali PK (2000) Snow and Avalanche Problem in Indian Himalaya and its Mitigation Shah A (2016) The Kashmir Basin fault and its influence on fluvial flooding in the Kashmir Basin, NW Himalaya. Geol Soc Am Spec Pap 520:321–334 Shah AA, Khwaja S, Shah BA, Reduan Q, Jawi Z (2018) Living with earthquake and flood hazards in Jammu and Kashmir, NW Himalaya. Frontiers in Earth Science 6:179 Sharma S, Kumar A, Ghangas V (2013) Seismicity in Jammu and Kashmir region with special reference to Kishtwar. International Journal of Scientific and Research Publications 3(9):1–5 Sorkhabi R (2006) The great Himalayan earthquakes.The Himalayan Journal, 62(5) Spencer JM, Ashley WS (2011) Avalanche fatalities in the western United States: a comparison of three databases. Nat Hazards 58(1):31–44 State Disaster Management Plan, J&K (2017) State for 1892–93. Rambir Prakash Press, Jammu, India, pp 132–133Developed by Tata Institute of Social Sciences, Mumbai Stein MA (1899) Memoir on Maps Illustrating Ancient Geography of Kashmir (p.235). British Mission Press, Culcutta, India Sultana N (2020) Analysis of landslide-induced fatalities and injuries in Bangladesh: 2000-2018. Cogent Social Sciences 6(1):1737402 Swinburne TR (1907) A holiday in the happy valley with pen and pencil. Indy Publish. com Tanoue M, Hirabayashi Y, Ikeuchi H (2016) Global scale river flood vulnerability in the last 50 years. Sci Rep 6(1):1–9 Taylor FE, Malamud BD, Freeborough K, Demeritt D (2015) Enriching Great Britain's national landslide database by searching newspaper archives. Geomorphology 249:52–68 Thakur VC (1992) Geology of western Himalaya. Phys Chem Earth 19:1–355 Thakur VC (1998) Structure of the Chamba nappe and position of the Main Central Thrust in Kashmir Himalaya. J Asian Earth Sci 16(2–3):269–282 Thakur VC, Kumar S (2002) Seismotectonics of the Chamoli Earthquake of March 29, 1999 and Earthquake Hazard Assessment of Garhwal – Kumaon Region, NW Himalaya. Himalayan Geol 23(1–2):113 UNISDR (2004) ‘Living with risk: a global review of disaster reduction initiatives’ launched at United Nations Headquarters. 14/07/2004. Press Release IHA/922 Uppal HL (1956) Flood Control, Drainage, and Reclamation in Kashmir Valley. Central Water & Power Commission USGS (2020) ( https://www.usgs.gov/faqs/why-are-we-having-so-many-earthquakes-has-naturally-occurring-earthquake-activity-been?qt-news_science_products=0#qt-news_science_products ) Valdiya (1998) Valdiya, K. S. (1998). Dynamic Himalaya. Universities press Valdiya KS (2001) Reactivation of terrane-defining boundary thrusts in central sector of the Himalaya: implications. Curr Sci 81(11):1418–1431 Van Westen C (2012) Landslide risk assessments for decision-making. The World Bank, pp 67–71 Van Westen CJ (2002) Remote sensing and geographic information systems for natural disaster management. Environmental modelling with GIS and remote sensing. Taylor & Francis, London, pp 200–226 Van Westen CJ (2004) Geo-information tools for landslide risk assessment: an overview of recent developments. Landslides Eval Stab 1:39–56 Van Westen CJ (2013) Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise on geomorphology 3:259–298 Venugopal R, Yasir S (2017) The politics of natural disasters in protracted conflict: the 2014 flood in Kashmir. Oxf Dev Stud 45(4):424–442 Yin A (2006) Cenozoic tectonic evolution of the Himalayan orogen as constrained by along strike variation of structural geometry, exhumation history, and foreland sedimentation. Earth Sci Rev 76(1–2):1–131 Supplementary Files supplementarydata.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Dec, 2021 Reviewers invited by journal 15 Dec, 2021 Editor invited by journal 14 Dec, 2021 Editor assigned by journal 14 Dec, 2021 First submitted to journal 06 Dec, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYLCCBAYGHgb2BiDLwIIULTwHQFokSLFKIgFMElZozt577MGDim0y5jOfX93wo0CCgb+9OwGvFsuec+kGCWdu88jczim72QN0mMSZsxvwajG4kWMmkdh2m0dCOiftBg9Qi4FELjFa/gG1SJ5Ju/mHeC0NQC0S7MduE2fLmTNmEglAxRI8OWy3ZQwkeAj75XiPmeSPmtv2EuzHn91888dGjr+9F78WJMBjACaJVQ4C7A9IUT0KRsEoGAUjCAAAa2hERIS8iqoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9879-0957","institution":"University of Kashmir","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Noureen","middleName":"","lastName":"Ali","suffix":""},{"id":70289761,"identity":"2cef2dcd-b75a-4f1d-992b-72a4eaa31f2c","order_by":1,"name":"Akhtar Alam","email":"","orcid":"","institution":"University of Kashmir","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Akhtar","middleName":"","lastName":"Alam","suffix":""},{"id":70289762,"identity":"e3320539-ff34-428a-9270-bbf04809ea0a","order_by":2,"name":"M Sultan Bhat","email":"","orcid":"","institution":"University of Kashmir","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"M","middleName":"Sultan","lastName":"Bhat","suffix":""},{"id":70289763,"identity":"a9af682a-d115-456b-8d65-e218daf24e83","order_by":3,"name":"Bilquis Shah","email":"","orcid":"","institution":"University of Kashmir","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bilquis","middleName":"","lastName":"Shah","suffix":""}],"badges":[],"createdAt":"2021-12-07 09:55:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1148421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1148421/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16520324,"identity":"0e280480-152d-49ef-97dd-8ca8aa90a4e5","added_by":"auto","created_at":"2021-12-16 15:43:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":658291,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/7d5673715541ac213cbabffb.png"},{"id":16520249,"identity":"8fafd375-682d-43ae-bb26-c9b9450ad02b","added_by":"auto","created_at":"2021-12-16 15:40:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12014,"visible":true,"origin":"","legend":"\u003cp\u003eCasualties (fatalities and injuries) caused by each hazard type\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/5a9a88a5ea85659f1b4098ec.png"},{"id":16520104,"identity":"317ff8a2-a826-4081-805d-cdcf046053eb","added_by":"auto","created_at":"2021-12-16 15:37:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112299,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variability and frequency trends of each hazard type for the time-period 1900-2020 (a) Earthquakes (b) Floods (c) Landslides (d) Snow Avalanches and (e) Comparative analysis of all four hazard types\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/9f62159985b153fdbe21fe17.png"},{"id":16520103,"identity":"14c9ca1d-9553-4cfd-8053-b8e8d84adf95","added_by":"auto","created_at":"2021-12-16 15:37:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29765,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of Decadal trend of all four hazard types\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/49419d250d4ebc8c345910ca.png"},{"id":16520108,"identity":"4d94a112-31e3-442b-ad72-7f5d2ab50462","added_by":"auto","created_at":"2021-12-16 15:37:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2162974,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of hazards in the Valley (a) Earthquakes with epicentres within the Valley (b) Spatial extent of the 2014 flood (c) Landslide events and (d) Snow avalanche events\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/67557f5ffa0527b18e454c72.png"},{"id":16520105,"identity":"1fe80e60-4e35-4173-9371-708bcefb7328","added_by":"auto","created_at":"2021-12-16 15:37:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2157466,"visible":true,"origin":"","legend":"\u003cp\u003eThematic maps for district-wise susceptibility of Kashmir Valley to hazards (a) Earthquakes (b) Floods (c) Landslides and (d) Snow avalanches\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/8128b1da40e844c401ac2101.png"},{"id":16520331,"identity":"e30df10b-1714-4e83-a6b4-ad62af1c8e22","added_by":"auto","created_at":"2021-12-16 15:43:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1754177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/f4c74fbe-7f05-4fc0-a3ee-39db671bb2d5.pdf"},{"id":16520112,"identity":"29546775-9e55-42fb-a3ae-2ed5a689be84","added_by":"auto","created_at":"2021-12-16 15:37:28","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":60526,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-1148421/v1/eb01aa797bc08c038bdb5044.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDeveloping a Hazard Profile of The Kashmir Valley Through Historical Data Analysis For The Period 1900-2020\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDisasters appear in the news headlines almost every day. Most happen in remote places, with lesser human interaction or limited extent and impact, are forgotten easily, while others have compelling consequences and leave a mark in history. Disasters are an outcome of hazardous events which cross the threshold of human endurance capacity and bring about devastating consequences upon interaction with human existence (vulnerable populations) (UN-ISDR, 2004). Whereas a hazard is defined as a potentially damaging physical event, phenomenon, or human activity that may cause loss of life or injury, property damage, social and economic disruption, or environmental degradation (UNISDR, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistics show that the world has experienced an increasing impact of disasters in the past decades, the main cause of which is attributed to a higher frequency of extreme hazardous events (especially, hydro-meteorological events, mostly related to climate change) and to an increase in vulnerable population (as a result of enhanced exposure) (Westen, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; CRED Report, 2018, 2020). To reduce disaster losses more efforts should be put into Disaster Risk Management, primarily based on a comprehensive and detailed Risk Assessment. Hazard Identification and disaster profiling is an essential element and the first step of the entire process of evaluating risks (i.e., risk assessment) and of disaster management (Weston, 2004, 2013). Developing disaster scenarios through historical perspective represents a valid back analysis tool that offers useful insights to understand the occurrence and impacts of natural disasters, establish existing and potential hazards, the conditions within which a given hazard takes place, determine the degree of vulnerability and the capabilities of response of the society subjected to that hazard (Jahn and Wehling \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is typically contextual to 'forensic investigation of disasters' to indicate the root causes and risk drivers (Burton, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; IRDR, 2015); or like the 'science of past disasters' (Riede, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); or similar to 'charting a historical trajectory of disasters' (Bankoff, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCataloguing disaster data of different hazards has picked great pace at international, regional, and national scale in order to facilitate various activities of assessment, policy and decision making, mitigation and management of disasters, relief, rehabilitation, risk reduction, development and research (Oliver-Smith and Hoffman, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Schenk, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Riede, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oliver-Smith et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). National (Indian Statistical Institute, Kolkata; Vulnerability Atlas of India) and international databases (EM-DAT International; Munich RE NATHAN Database; Dartmouth Flood Observatory Database), regional (Asian Disaster Preparedness Centre (ADPC); Asian Disaster Reduction and Response Network (ADRRN)) and intergovernmental (SAARC\u0026mdash;Disaster Knowledge Network) and international nongovernmental organizations and programs (UNDP\u0026rsquo;s Global Risk Identification Program; CRED; UN Office for Coordination of Humanitarian Affairs), and national disaster agencies (NIDM) play a pivotal role in keeping a track of disaster events taking place worldwide (Gupta and Muralikrishna, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; National Research Council, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These Datasets differ in coverage and data quality, have different filters, strengths, and limitations (Beckman, 2009; Gall et al., 2009), and despite some overlap, each offers different information and insights into disasters (Below et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; National Research Council, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInventorying disasters and hazard profiling have been attempted in various research works which add substantially to the disaster database. Compiling and analysing data from existing research publications can be sought as a reliable means to provide insight into different natural disasters to which a specific area is subjected to (Kapur, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Chronological archival records can promote an understanding of social and economic consequences of natural disasters to a place (Malamud, 2004; Prakash, 2011; Prakash and Kathait, 2014).\u003c/p\u003e \u003cp\u003eFrom the historical perspective Kashmir Valley has been vulnerable to multiple natural disasters and intermittently subjected to their consequent impacts which can be established from its rich archival data sources (Kelman et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ahmad, 2021). Historical events data have been utilized to study the patterns of seismicity and trends of earthquake occurrences in the Kashmir region (Ghaffar and Abbas, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Anees and Bhat, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and to illustrate hotspots for seismic activity (Sharma, Kumar and Ghangas, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Reconstruction of chronology of floods in Kashmir Valley by employing historical hydrology has been attempted to overcome the deficiency of sufficient time-series database for better flood hazard assessment (Bhat et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Historical natural hazards were profiled by Ahmad (2021) to have better insight into what vulnerable populations were subjected to under severe natural and deprived socio-economic conditions in Nineteenth Century Kashmir. An intersection of vulnerability to environmental hazards and to socio-political conflict to provide an overview of the disaster diplomacy of Jammu and Kashmir throughout history was studied by Kelman (2018) by compiling events of both environmental hazards and socio-political violence.\u003c/p\u003e \u003cp\u003eThe valley of Kashmir is subjected to several natural hazards for example, earthquakes, floods, landslides, snow avalanches, droughts, wildfires, extreme temperatures, lightning and thunderstorms, snowstorms, hailstorms, etc., (SDMP, 2017; Patel et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Henceforth, a case study on the recent past emphasizes the need to know how the valley of Kashmir has been impacted by natural hazards and disasters during the entire Twentieth Century and early Twenty-first Century through historical review and to stress its utility in disaster preparedness (Reide, 2014, 2017; Ahmad, 2021). Therefore, the present study attempts to develop a hazard and disaster profile of the Kashmir Valley focusing on the four most prominent natural hazards viz., earthquakes, floods, landslides, and snow avalanches.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eKashmir, a separate geographical entity, is an oval shaped valley, and one of the mesoregions of erstwhile Jammu and Kashmir, located in the North-Western Himalayas, spanning over 15,984 km\u003csup\u003e2\u003c/sup\u003e (Ganjoo, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The region is one of the most unfortunate portions across the globe where natural disasters and political unrest have greatly challenged the progressive development (Shah, 2018). Its physiography typically consists of mountain ranges on all three sides\u0026ndash; Zanskar (~ 6000m amsl) and Pir Panjal (~5000m amsl) on Northeast and South-Southeast, respectively, contrary to which the valley floor drops to a minimum elevation of ~1570 m amsl (as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The geological past suggests that it was formed when Indian tectonic plate collided with the Eurasian plate during the Eocene epoch, which lead to the development of some intermontane basins and a prehistoric lake, by the uplift of mountains between the present Indian and Pakistan Administered Kashmir which over geological time, silted in and the alluvium from the mountains became the fertile soil of the valley floor, which itself is a peculiar combination of depositional and erosional features (Gansser, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1964\u003c/span\u003e; Bhat, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A characteristic feature of valley floor is the presence of trunk river Jhelum which stretches over almost the entire length of the valley, originating at its southern end, near Verinag and flowing in a north-west direction receiving numerous tributaries before entering Wular Lake (Albinia, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In general, the valley is spread over three major physiographic divisions i.e., mountains, karewa uplands (Plio-Pliestocene deposits), and floodplains. The altitude and climate of the region favour plenty of precipitation both in the form of rains and snow and thus, snow bearing peaks and glaciers are a dominant feature of the mountainous stretches (Ahmad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Every thousand feet of elevation brings some new phase of topography, climate, and vegetation (Lawrence, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). As the tectonics is still actively shaping the topography, geology, geomorphology, and climate of the region, the occurrence of hazards like, earthquakes, landslides, floods, snow avalanches, etc. in the area is potentially unavoidable. More than 5.5 million people reside in areas prone to multiple risks and are posed by various geophysical hazards. The presence of active faults, the river drainage network, physiology, topography, lithology, geomorphology, climate, and demography all make the region vulnerable to different types of hazards and pose potential threat to the population of the valley (Shah, 2018).\u003c/p\u003e"},{"header":"3. Methods And Materials","content":"\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e3.1 Search strategy and data sources\u003c/h2\u003e\n \u003cp\u003eThe study has compiled a hazard and disaster events database of the Kashmir Valley to generate a profile focussing on four potentially most prevalent and majorly impacting hazards viz., earthquakes, floods, landslides, and snow avalanches, for the time-period 1900 to 2020, which includes charting information on disaster events in the form of various attributes (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (Lin and Wang, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kelman et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Identifying and inventorying various hazard events can be performed through some defined means and sources which include analysing historical data, government records/documents, newspaper reports, research literature, primary field surveys and geological study of the region (Westen et al., 2002; Taylor et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sultana, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). For the present research we studied and incorporated information from mixed sources of data, which primarily include secondary data sources like national and international open access databases, portals and websites, government and non-government documents and reports, existing research literature, news reports, private and public online blogs, portals and websites, books, personal and travel accounts, etc. In general, we used a diverse range of data sources in collecting information on natural hazards and disasters to assess and establish their authenticity. Despite the scattered nature of information dispersed across numerous sources of varied types and credibility we have been able to condense 1854 events in time and their trends spanning over a century and more.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of variables collected for the database and their description\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription of variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelevance to each hazard type\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDate of occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear (for all events), month and date (wherever available)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt gives an idea of the distribution and occurrence of events throughout the time period. It may be used to estimate the increase or decrease in hazard/disaster events over time and also, which time (season) of the year is more likely to witness a particular hazard type like floods, snow avalanches and landslides.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeographic or spatial information as name of the place (village, block, sector, district, etc) or latitudes and longitudes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt gives an idea of the place of occurrence/onset of any hazard event. For earthquakes epicentres were considered within or near Kashmir valley. While, for landslide and snow avalanches geographic coordinates were assigned based on the place of occurrence, and for floods, names of the area were used.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlaces affected\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncludes the places impacted by the event (villages, sectors, blocks, districts, etc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt denotes the extent and spread of hazard/disaster. It includes places where ground shaking or any damage or casualty is witnessed in case of an earthquake or the extent of inundation during a flood or areas impacted by avalanches and landslides.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCause/ triggering mechanism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt could either be an environmental or anthropogenic factor acting as trigger or a primary disaster leading to secondary events.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt helps determine the causes that make the area prone to any particular hazard.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMagnitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt is one of the factors to measure the strength and size of a disaster event.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnitude recorded on the Richter scale for earthquake hazard type and water level for flood hazard type.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCasualties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncluding fatalities and injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt reflects loss to human life and injury. It is one of the important determinants of the severity of a hazard or disaster event.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociated impacts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImpacts other than casualties, including damage to property, economic losses, missing, trapped, dislocated and evacuated/rescued people, secondary disaster events, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot available for all the events.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e3.2. Compiling procedure and data analysis techniques\u003c/h2\u003e\n \u003cp\u003eThe collected events for individual disaster types, with all the information pertaining to the six selected variables (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), were systematically documented into tables using Excel in a chronological order starting from 1900 up to 2020. Any sort of repetition in the event entry or allied information were removed from the database by proofreading. The excel sheets were used to analyse temporal variability, frequency distribution (Section 4.1.3.), and impacts of the events in the form of casualties (Section 4.1.2.). Further analysis was done by making use of the ArcGIS software. The spatial information of each event was made specific by adding geographic coordinates for earthquakes, landslides and snow avalanches, and then plotted using an SRTM DEM and a district shape file of the Kashmir valley as a base map to generate spatial distribution maps. While, in case of floods spatial extent was represented by the spread of and inundation levels of the 2014 Kashmir floods. Further, thematic maps for district wise susceptibility of all four hazard types were generated from the spatial distribution maps based on the number of occurrences per district for earthquakes, landslides and snow avalanches and on total area (in square kilometres) inundated per district for floods. Events reported with substantial damage and loss were discussed in detail in the study to get a clear picture of the hazard and disaster scenario of the Kashmir Valley in the 120 years long time frame (1900-2020) (Section 4.1.1.).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e4.1. Hazard and disaster profiling\u003c/h2\u003e\n \u003cp\u003eA meticulous review of the consulted secondary archival data sources enabled us to discover a spectrum of hazardous events their spatial extent, magnitude, cause, and impact in Kashmir throughout the selected timeline. With the aid of an exhaustive research, comparative analysis, and data presented in the form of catalogues, graphs, and maps, an incisive insight into the disaster and hazard scenario across the valley of Kashmir in the entire twentieth century and early twenty-first century has been achieved. The period under review has experienced repeated natural hazard events of different types, several of which have turned into devastating disasters. In our analysis, basic trends concerning 1854 natural hazards witnessed by the Kashmir valley consisting of 1693 earthquakes, 39 floods, 65 landslides and 57 snow avalanches have been represented. Out of the total hazard events, 91.31% comprised of earthquakes, 2.10% floods, 3.50 landslides and 3.07 snow avalanches. Some of the entries in the table concern more than one phenomenon occurring concurrently, as cascading disasters amplifying the intensity (damage and loss) of the primary disasters, like the Kashmir Basin flood of 1900 which was succeeded by a Cholera epidemic killing 4225 people; the magnitude 7.8 earthquake of 4th April, 1905 (having epicentre in Kangra Valley, H.P.) that triggered landslides and caused large number of casualties and damage to buildings and hillside aqueduct networks; the flood of 1957 (August-September) which almost submerged the entire valley causing colossal damage to crops that in turn led to a famine; the September flood of 1992 which took place in the NW border districts of Kashmir and parts of PoK, was unprecedented in terms of fury and most devastating in terms of casualties, caused land sliding as an associated secondary disaster; 19th February, 2005 Waltengu snow avalanche triggered multiple landslides across the affected area adding to the damage and loss; 8th October, 2005 largest instrumented earthquake with epicenter in Muzzafarabad, PoK (Mw 7.6) lead to extensive land sliding causing large scale damage and loss in N-W border districts; 2006 (August-September) floods in J\u0026amp;K lead to associated disasters in the form of land and mud slides; 24th January, 2012 snow avalanche in Kupwara triggered landslides; 2nd September, 2014 floods land and mud slides; 26th July, 2015 cloud burst triggered landslides along the Baltal route to Amarnath; 20th March, 2017 flooding in Chadoora, Budgam induced mud slides; 6th April, 2017 snow avalanches along higher reaches in Kashmir and Ladakh caused landslides; and 14th January, 2020 snow avalanches in Ganderbal and Kupwara triggered land sliding events but no damage and loss was witnessed.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnnual Distribution of the total number of events from 1900 to 2020 (E= Earthquakes, F=Floods, L=Landslides and SA=Snow Avalanches)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"25\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1924\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1948\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1972\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1901\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1925\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1949\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1973\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1997\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1902\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1926\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1950\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1974\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1903\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1927\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1951\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1975\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1999\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1904\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1928\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1952\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1976\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1905\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1929\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1953\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1977\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1906\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1930\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1954\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1978\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1907\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1931\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1955\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1979\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1932\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1956\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1980\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1909\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1933\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1957\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1981\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1910\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1934\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1958\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1982\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1911\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1935\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1959\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1983\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1912\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1936\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1960\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1984\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1913\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1937\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1961\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1985\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1914\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1938\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1962\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1986\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1915\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1939\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1963\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1987\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1916\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1940\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1964\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1917\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1941\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1965\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1989\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1918\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1942\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1966\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e199\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1919\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1943\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1967\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1991\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1920\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1944\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1968\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1992\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1921\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1945\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1969\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1993\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1922\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1946\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1970\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1994\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1923\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1947\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1971\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1995\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec8\"\u003e\n \u003ch2\u003e4.1.1. Extreme events and their impacts\u003c/h2\u003e\n \u003cp\u003eThe study discusses in detail disaster events witnessed within the timeline for which damage and loss have been reported in the form of fatalities, injuries, loss of cattle, damage to structures and crops, population affected, and other associated impacts. These comprise 121 events out of the total 1854, including 7 earthquakes, 17 floods, 49 landslides and 47 snow avalanches, which constitute 5.78%, 14.04%, 40.49% and 38.84% of the total severe events, respectively. This shows that even though the total number of earthquake events (1693) is very high only a small number (7) of these events actually turn into disasters i.e., 0.41% of the total occurrences. In case of floods out of the total 39 events 17 have turned into disasters which is about 43.58% of the total occurrences. Whereas, for landslide hazard, 49 events i.e., 75.38% of the total 65 occurrences and for snow avalanches, 47 events i.e., 82.45% of the total 57 occurrences show impacts. Although, a larger portion of the total landslide and snow avalanche events have impacts recorded but the magnitude of these impacts is far lesser than that of both earthquakes and floods individually, as can be established from the tables discussed in the following sections. This could be because landslides and snow avalanches are small scale and localized events with limited extent and impact as compared to earthquakes and floods thus, proving an inverse relationship between the magnitude and frequency of the hazard events. Standing true for the generalization, that the magnitude of a natural hazard event varies in its frequency of occurrence over time in an inverse power relationship (Jackson, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eEarthquakes\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eHistory shows earthquakes don\u0026rsquo;t occur randomly but follow a general pattern and are distributed along geological faults across the globe (Bolt, 2003). The NEIC (National Earthquake Information Centre) locates about 20,000 earthquakes in the world each year and approximately 55 per day. According to records (since 1900), 16 major earthquakes are expected in a year, 15 in the magnitude 7 range and 1 magnitude 8.0 or greater (Bolt, 2003; USGS, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) which have been responsible for millions of deaths and an incalculable amount of damage to property over centuries. India has a long history of disastrous earthquakes, majorly documented from 1800\u0026rsquo;s (Iyenger et al., 1999) and about 59% of its total land area is prone to seismic hazards (BMTPC, 2006; MHA Report, 2015). The Himalayas originated due to continental collision between the Indian and the Eurasian plates (Searle et al., \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e, Le Fort, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e, Searle, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e, Thakur, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e) and this orogenic process continues till date, as is indicated by significant small to moderate earthquakes and neo-tectonic movements along several thrusts and faults located in the region (Valdiya, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bilham, 2001). A major risk lies for more than 50 million people living near the seismically active Himalayan region (Bilham, 2001). The Himalayan zone is divided into three seismic gaps \u0026ndash; Kashmir gap, Central gap and Assam gap. The Jammu and Kashmir, Himachal Pradesh and Uttarakhand fall under Kashmir gap which is the highest earthquake prone zone (Gupta, 2012; Sharma, 2013).\u003c/p\u003e\n \u003cp\u003eJammu and Kashmir, the western most extension of the Himalayan Mountain range in India, lies atop a web of active geological faults and thrusts on the boundary of the two colliding tectonic plates (Gavillot, et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shah, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), many of which have and are capable of producing earthquakes of magnitude 8.0 or greater (Seeber and Armbruster \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e; Ni and Barazangi \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e; Thakur and Kumar \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kayal, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). As a result of active participation of some faults in the ongoing collisional deformity the region shows active seismicity through small to moderate magnitude earthquakes at a continuous rate and occasionally large magnitude ones (Burbank and Johnson, 1983; Ambraseys and Bilham, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Yin, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Shah, 2018). According to seismic zonation map of India, the entire region has been classified as very high damage risk zone V (MSK IX or more) and high damage risk zone IV (MSK-VIII) (BIS Map, 2002; SDMP, 2017; Mahajan et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eA major portion of the districts in Jammu and Kashmir fall under seismic zone V. Kathua, Leh, Ladakh and Tribal Territory districts lie in Zone IV, the districts Anantnag, Budgam, Bandipora, Baramulla, Ganderbal, Kishtwar, Kulgam, Kupwara, Pulwama, Ramban, Shopian and Srinagar occupy seismic V zone and the remaining under seismic IV zone (SDMP, 2017). Kashmir region is very important in relation to seismic activity in the Great Himalayas. Earthquakes in the Himalaya, in general, and in Kashmir, in particular, pose serious challenges. Historical records of the past centuries show that several big earthquakes have destroyed parts of the Himalayan settlements (and many earthquakes have possibly gone unrecorded). The history of earthquakes dates back to 1505 in this region (Ghaffar and Abbas, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the record of the past decades shows that the Kashmir region has been hit at least by one earthquake of magnitude 5 or larger every year or two (Sorkhabi, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Among the most notable earthquake occurrences of the region are the N-W Kashmir earthquake of 2005 (Mw 7.6); 2002 Astore, PoK (Mw 6.4), Pattan earthquake of 1974 (Mw 7.4), Kangra earthquake of 1905 (Mw 7.8), 1885 (Magnitude 7.5), 1842 (Magnitude 7.5), 1555 (magnitude more than 8), 1505 (Magnitude 7.6) etc., (Sharma, 2013).\u003c/p\u003e\n \u003cp\u003eEarthquakes, if strong enough, are extensive events, with far-reaching impacts which cannot be contained by political and geographical boundaries, therefore, earthquakes with epicentres in and around the valley have been considered for this study while events with their epicentres within the Valley numbered 58 for the selected timeline (e.g., 1963 and 1967). In the present study of 120 years, the region witnessed intensive seismic activity where earthquakes were felt across the entire valley (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), including 1693 events of magnitude 2.0 to 8.0, out of which 34 were strong earthquakes with magnitude greater than Mw 6.0 and 7 events have been reported with severe impacts (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest magnitude episode recorded for the time period is the earthquake of 4th April, 1905 with its epicentre in Kangra Valley, Himachal Pradesh and magnitude Mw 8.0. The record also shows some incidents of magnitude 7 and above viz., 1974 Pattan earthquake with 7.4 magnitude, 1975/19/01 Kinnaur District, HP (M 7.0), 19th January, 1996 Aksai Chin (M 7.1), 8th October, 2005 Muzzafarbad, Pakistan (M 7.6) and 26th October, 2015 Hindukush Mountain region, Afghanistan (M 7.5).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMajor earthquake events located in and around Jammu \u0026amp; Kashmir for which damage and loss were reported.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDate of occurrence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePlaces affected\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMagnitude (Mw)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCasualties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAssociated impacts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD/MM\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLat\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatalities\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjuries\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKangra Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJ\u0026amp;K and Himachal Pradesh. Shocks felt in Leh, Kargil, Drass and Muzafarabad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,000 domestic animals killed. 100,000 buildings damaged. Damage to hillside aqueducts networks.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02/09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBudgam, Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBudgam, Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKinnaur District\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJ\u0026amp;K and Himachal Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGilgit Wazarat (Pakistan occupied Kashmir).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShocks were felt in Srinagar (J\u0026amp;K, India) and in Peshawar and Rawalpindi (Pakistan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnconfirmed reports of surface faulting.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAstore Valley, Pakistan occupied Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAstore Valley, Pakistan occupied Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDamage to property.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuzzafarabad Kashmir-Kohistan,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndo-Pak Border Region. Strongly felt in much of Pakistan, North-India, East-Afghanistan.\u003c/p\u003e\n \u003cp\u003eTremors felt as far as Delhi and Punjab in India.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80,000\u003c/p\u003e\n \u003cp\u003e1350 (J\u0026amp;K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70,000\u003c/p\u003e\n \u003cp\u003e6266 (J\u0026amp;K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLargest instrumented in the area. 4 million homeless. Secondary disasters: landslides, fires. 32, 000 buildings completely or partially damaged, blocked roads. Series of hundreds of aftershocks. [Homeless=150000; Affected=156622 (J\u0026amp;K)]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHindukush mountain region of Afghanistan.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.15E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfghanistan, India and Pakistan. Tremors felt in J\u0026amp;K, Delhi, Lucknow and parts of Pakistan, Afghanistan and China.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399;\u003c/p\u003e\n \u003cp\u003e4 (J\u0026amp;K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2536;\u003c/p\u003e\n \u003cp\u003e20 (J\u0026amp;K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDamage to property. Cracks appeared in most multi-storied buildings. (53 houses damaged in J\u0026amp;K.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eFloods\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eFloods with natural and anthropogenic triggers are among the most common and devastating natural disasters and the leading cause of deaths, responsible for 6.8 million deaths in the 20th Century worldwide, impacting about two-thirds of the total population affected by natural disasters (1991-2000) (UNISDR, 2001, 2015; Doocey, et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; CRED Report, 2018, 2020). In agreement with the global pattern, the disasters with the largest human impact in Asia were floods during the year 2015 (Guha-Sapir et al., 2015). The occurrences and impacts of flooding are expected to rise due to increase in population, unscientific development and climate change (Tanoue et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bhat et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). 12% of the total land area of India faces the threat of flooding (MHA Report, 2015).\u003c/p\u003e\n \u003cp\u003eKashmir, a highly populated, Himalayan intermontane basin flanked by mountains, drained by major rivers such as Jhelum, Chenab, and Indus, and mainly divided into three physiographic units: floodplains, karewas and mountains, (SDMP, 2017), is prone to floods, widely established through historical records. The structure of the Valley, hydrographic features and drainage characteristics of its river systems viz., bowl shape (elongated trough), variation in altitudes with consequent reduced lag time and sudden peak flows in rivers along low-lying areas during heavy rainfalls make the region specifically prone to floods and congenial for inundation (Bhat et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Pertinently, most of the population and socio-economic activity is hosted by the area prone to floods and is one of the major urban centres of the region, Srinagar, where the number of wetlands that act as natural sponges, have come down severely, resulting in frequent flooding (Gupta, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Meraj, 2015; Bhat et al., 2017, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In terms of impact, frequency and economic loss, floods are the largest of all the natural hazards to which the Kashmir Valley is prone (Bhat et al., 2017).\u003c/p\u003e\n \u003cp\u003eHistorical reports reveal that flooding is a recurrent phenomenon and owing to River Jhelum, the valley has witnessed a series of floods, dating back to 635 A.D., many among which were disastrous with widespread socio-economic and environmental impacts (Lawrence, 1895; Uppal, \u003cspan class=\"CitationRef\"\u003e1956\u003c/span\u003e; Bhat et al., 2017). Research indicates two major reasons responsible for the flood vulnerability in the Kashmir valley \u0026ndash; inadequate carrying capacity of the River Jhelum from Sangam (Anantnag) to Khandanyar (Baramulla) and naturally flat topography of the Jhelum Basin (Meraj, 2015; Bhat et al., 2017).\u003c/p\u003e\n \u003cp\u003eMaximum topography of the valley is precipitous, exposing low-lying areas to frequent inundation especially during extended hours of precipitation (seasonally) however, some intensifying factors such as enormous population growth and the resultant expansion of human settlements, ill-planned urban sprawl, modification of floodplain, including, encroachment of waterways, landfilling, and road/railway construction in the floodplain, changes in river morphology and reduced water holding capacity of rivers, erosion and subsequent alluvial deposition in water bodies leading to degradation and extinction of wetlands and waterways have amplified the existing flood risk (Alam et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Meraj, 2015). It has also been observed that as a result of climate variability, the frequency of floods in Kashmir Valley is likely to increase in future (Bhat et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAlthough, most of the flood events in Kashmir have meteorological origin, historical records bear instances where flooding has been associated to a primary disaster event like floods triggered by damming of the Jhelum caused by landslides or earthquake-triggered landslides and dam failures (856 AD and 635 AD) (Kalhana, \u003cspan class=\"CitationRef\"\u003e1149\u003c/span\u003e; Chaudora, \u003cspan class=\"CitationRef\"\u003e1620\u003c/span\u003e; Khanyari, \u003cspan class=\"CitationRef\"\u003e1857\u003c/span\u003e; Khoihami, 1885; Stein, 1891; Bamzai, \u003cspan class=\"CitationRef\"\u003e1962\u003c/span\u003e; Ahmad \u0026amp; Bano, \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e; Bilham and Bali, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Meraj, 2015).\u003c/p\u003e\n \u003cp\u003eA total of 17 severe flood incidents which show a substantial impact on society were compiled in detail (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). For certain events no figures were available to record impacts but the severity of the situation was described as \u0026ldquo;the entire valley being completely submerged in water\u0026rdquo; or \u0026ldquo;resembling a vast lake\u0026rdquo; (1903 and 1957) which gives us the idea that major portions of the valley are susceptible to floods and how wide spread can flooding be in Kashmir. Few notable flood events of the recent past recorded in the study were 1903, 1950, 1957, 1959, 1963, 1992, 1994, 1996, 2004, 2006, and 2014 (Raza et al., \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e; Koul, \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e; Meraj et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kumar, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bhatt et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rather et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alam et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMajor flood events witnessed by the Kashmir Valley for which damage and loss were reported\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDate of occurrence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLocation/Area affected\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCause/ trigger\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCasualties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMagnitude (water levels)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAssociated impacts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD/MM\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatalities\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjuries\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Basin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous rains caused floods.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater level was 9 feet lower at Munshibagh than previous flood (1893- R.L. 5197.0).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreaches in right bank above Sherghari.\u003c/p\u003e\n \u003cp\u003eSucceeded by cholera killing 4,225 people\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 July\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar City, Kashmir valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026Prime; of rainfall recorded between 11-17 July and 8\u0026Prime; between 21-23 July.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge number of deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver rose to max R.L. 5200.37 on 24 July (three points higher than 1893 flood).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhole valley converted to one great expanse of water. 7,000 dwellings marooned in city.\u003c/p\u003e\n \u003cp\u003e83 villages affected; 26 villages lost entire kharif harvest. 421 houses completely destroyed.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 villages affected, heavy loss to government records. Extensive loss to crops.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisastrous for crops. Total estimated loss: Rs. 98,393.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpill channel minimized extent of damage. Many bridges from Baramulla to Chakoti (across LoC) were washed away.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal 1,750 houses partially damaged and 282 houses fully damaged.\u003c/p\u003e\n \u003cp\u003eLoss of 2,228 domestic animals.\u003c/p\u003e\n \u003cp\u003eAgricultural sector affected badly.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01-17 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJhelum Basin, J\u0026amp;K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater of Jhelum was flowing at 10-15 feet over the banks in Srinagar.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than 15,000 houses collapsed or heavily damaged.\u003c/p\u003e\n \u003cp\u003eRiver bank breached at multiple places posing threat to civil lines of city. About 70 miles of the area of valley was under water.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAug-Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Hydrological Flood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest water level ever recorded (till that time) in state (roughly 90,000 cusecs to 1,20,000 cusecs) at Sangam.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlmost submerged entire valley.\u003c/p\u003e\n \u003cp\u003eJhelum overflowed right bank in uptown Srinagar, submerging low-lying areas. Colossal damage to crops and property. Led to famine.\u003c/p\u003e\n \u003cp\u003e600 villages inundated. Estimated damage: 4.2 crores.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Hydrological Flood.\u003c/p\u003e\n \u003cp\u003eFour days of incessant rains in valley.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlood water level touched 30.25 feet on July 5 in Jhelum.\u003c/p\u003e\n \u003cp\u003eJhelum was assumed to be 80,000 to 1,00,000 cusecs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDamage to public utility services: 20 million; damage to crops: 15.6 million.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;10 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJ\u0026amp;K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003cp\u003e(50 in Jammu and 20 in Kashmir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlooded 40 villages impacting 20% of population. Damages amounted to Rs. 12.18 crore.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN-W border districts\u003c/p\u003e\n \u003cp\u003eKashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecording highest rainfall (of that time)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (IoK)\u003c/p\u003e\n \u003cp\u003e2000 (PoK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnprecedented in terms of fury and most devastating in terms of casualties.\u003c/p\u003e\n \u003cp\u003eOver 60,000 people were affected in several NW border districts.\u003c/p\u003e\n \u003cp\u003eParts of POK bore the brunt.\u003c/p\u003e\n \u003cp\u003eSecondary disaster: landslides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23, Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir Valley\u003c/p\u003e\n \u003cp\u003e29600 Km2 (Dis. Mag. Value).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Hydrological Flood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHomeless:70,000; Affected: 70,000.\u003c/p\u003e\n \u003cp\u003eWater level in Jhelum not as high as earlier floods but water didn\u0026rsquo;t recede for long period after rains stopped causing heavy damage to houses. In Srinagar city and outskirts, about 10,000 houses were flooded for over a fortnight.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 Jul-22 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJhelum-Chenab basins, J\u0026amp;K Provinces\u003c/p\u003e\n \u003cp\u003eLat/Long: 34.61-73.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Hydrological Flood; Riverine flood; Monsoonal rains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffected: 800;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 Aug-11 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJ\u0026amp;K Provinces; Jhelum, Sutlej, Lidder, Chenab, Tavi basins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Hydrological Flood; Flash Flood; Monsoonal rains;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHomeless:15000\u003c/p\u003e\n \u003cp\u003eAffected:15000\u003c/p\u003e\n \u003cp\u003eSecondary disasters: Slides (land, mud, snow, rock).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir region, India-Pakistan\u003c/p\u003e\n \u003cp\u003eWorst affected districts: Srinagar, Anantnag, Baramulla, Pulwama, Ganderbal, Kulgam, Budgam, Rajouri, Poonch and Reasi.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Hydrological Flood; Riverine flood, Monsoonal Rains\u003c/p\u003e\n \u003cp\u003eCaused by torrential rainfall.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e557 (277 India, 280 Pakistan)\u003c/p\u003e\n \u003cp\u003e(190 Jammu, 78 Kashmir)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAffected:275000; Homeless: 275000;\u003c/p\u003e\n \u003cp\u003eDamage:16000000.\u003c/p\u003e\n \u003cp\u003e60 major and minor roads were cut off and 30 bridges washed away, hampering relief and rescue.\u003c/p\u003e\n \u003cp\u003e80,000 people evacuated. 390 villages in Kashmir completely submerged. 1225 villages affected partially and 1000 villages affected in Jammu.\u003c/p\u003e\n \u003cp\u003eSecondary Disasters: Slides (land, mud, snow, rock)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 July\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmarnath, Pahalgam, South Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlash floods; Cloudburst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYatra suspended, tents washed away. Secondary disaster: land and mud slides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVarious parts of the Kashmir Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e862 cattle killed, and 12565 structures damaged. 211 camps set up to house 2907 evacuated families.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20-31 March\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChadoora village Budgam district;\u003c/p\u003e\n \u003cp\u003eJhelum river basin; Lat/Long: 33.1767-76.41;\u003c/p\u003e\n \u003cp\u003eDisaster magnitude value: 70288 Km2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatural Hydrological Flood; Flash flood; Heavy rains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e16 in mudslides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHomeless: 2097;\u003c/p\u003e\n \u003cp\u003eAffected: 2122;\u003c/p\u003e\n \u003cp\u003eDamage: 76000\u003c/p\u003e\n \u003cp\u003eSecondary disasters: Slides (land, mud, snow, rock). Deaths in mudslides and house collapse. Hundreds moved to safety. Flood alert issued. Schools closed. Relief camps set up.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSome major flood events have been of regional scale spreading across international borders like flooding in the years 1912, 1992 and 2014. The Kashmir Flood of September, 2014 has been declared the highest magnitude flood recorded instrumentally on Jhelum with a discharge of 72585 cusecs (recorded by the Department of Irrigation and Flood control) and inundated maximum part of the floodplain, resulting in colossal loss of life and property but could still not reach the highest flood levels (HFLs) as documented for events of years 1144, 1360, 1462, 1747, 1903 and 1929 (Alam et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the event recorded with the maximum number of casualties i.e., 200 deaths in IoK and 2000 deaths in PoK, was the 1992 flood. Floods, as we can make out from the data, have shown some episodes of domino effect by being a cause to secondary disasters, like, epidemics, landslides, famines, etc., (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) viz., 1900 (Cholera epidemic), 1893, 1929,1957 (famines) and 1992, 2006, 2014, 2017 (landslides) (Mehran, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eConsequently, the Valley was affected in a very passive way, exhausting the stores and destroying the assets of the dwelling population (Ahmad et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) in the past century, through impacts like deaths and injuries (1950, 1957, 1959, 1973, 1992, 2014, 2015), long periods of inundation (1957, 1996, 2014), marooned settlements and entire villages (1973), partial or total damage to infrastructure and washing away of structures (1912, 1928, 1950, 1959, 2014), devastating crops and agricultural sector (1903,1909, 1928, 1959), loss of domestic animals and cattle (1928, 2015), causing health (1900) and food insecurities (1957), and damage and loss of government records (1905).\u003c/p\u003e\n \u003cp\u003eFrom the developed catalogue we establish the fact that flooding is a prominent recurring phenomenon of the Kashmir Valley and floods generally occur in the summer months (June to September) when heavy rain is followed by the bright sun, which melts the snow cover and occasionally in springtime (March, April and May).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLandslides\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eLandslides, more widespread than any other geological event, are localized in nature often with small to medium scale impacts, and can either occur as an individual primary disaster, as a result of a wide array of processes and therefore, be a geological, hydro-meteorological and an anthropogenic hazard, or happen to be an associated secondary disaster to some major disasters like earthquakes, floods, droughts, volcanic eruptions, etc. and thus, worsen their impacts (Chingkhei, et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Parkash, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is apparent from prior research that in recent years the abundance, activity, frequency, socio-economic consequences of, and vulnerability to landslides have increased (Guzzetti, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Gariano \u0026amp; Guzzetti, 2016; Haque et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and landslides have been ranked as the 4th deadliest among natural disasters, after floods, storms, and earthquakes (Lacasse et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn India about 12.6% of the total land area is prone to landslides, consisting of the Himalayas and the Western Ghats, in which many slopes also fall in high seismically active zones, including Jammu and Kashmir, Himachal Pradesh, Uttarakhand, and the entire North-East (NIDM, 2011; MHA, 2015). Located in the N-W Himalayas, a major portion of Kashmir is mountainous, the complex, young and continuously changing topography along with the prevalent climate and various anthropogenic drivers interfering in the fragile ecosystem make it vulnerable to landslide hazard (Shah et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; SDMP, 2017), varying in magnitude from soil creep to landslides and solifluction (mass movement) common in higher snow-covered ranges of the region.\u003c/p\u003e\n \u003cp\u003eAlmost every year the region is affected by one or more major landslide events affecting the society in many ways like loss of life, damage to settlements, roads, means of communication, agricultural land, and floods. Heavy rainfall, cloudburst and consequent flash floods particularly in narrow river gorges are one of the main causes of major landslides in Kashmir (SDMP, 2017). Doda, Udhampur, Kathua, Kishtwar, Gulmarg, Dawar, Gurez, Tangdhar, Rajouri and Kargil are some areas of the erstwhile state highly prone to landslide hazard, also areas along major highways particularly Ramban, Panthal, Banihal, Qazigund (NH 44), and Baltal, Sonmarg, Zogila (NH 1) are vulnerable (Chingkhei et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe rugged topography of the region makes it highly susceptible to major landslides triggered by flash floods along narrow river gorges eventually jeopardizing the whole hill systems. The geologically young, unstable and fragile rocks of the region have witnessed an increase in vulnerability by manifolds in the recent past due to various unscientific developmental activities like deforestation, road cutting, settlement construction and terracing, quarrying practices, indiscriminate encroachment on steep hill slopes, etc., increasing the frequency and intensity of landslides which is also evident from the events recorded from the data (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(c)) (SDMP, 2017). The Jammu-Srinagar national highway gets blocked at number of places during the monsoon and winter seasons, due to landslides of which the Ramban-Banihal stretch has become one of the most affected portions (Chingkhei et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMajor Landslide events witnessed by the Kashmir Valley for which damage and loss were reported.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDate of occurrence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003cp\u003ePlace of occurrence\u003c/p\u003e\n \u003cp\u003eLong/\u003c/p\u003e\n \u003cp\u003eLat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCause/ trigger\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCasualties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAssociated impacts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD/MM\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatalities\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjuries\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJ\u0026amp;K and HP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarthquake induced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDamage to structures and network of hillside aqueducts feeding water to affected areas.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNW border districts of valley\u003c/p\u003e\n \u003cp\u003eKupwara and Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlood induced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuge loss to life and property at the hands of floods and associated landslides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaltengu, kund and nar villages Kulgam and Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esnow- avalanche induced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge scale loss of life and damage to property, loss of connectivity and hindrance in rescue and relief\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple locations of NW districts, J\u0026amp;k\u003c/p\u003e\n \u003cp\u003e(Tangdhar, Uri)\u003c/p\u003e\n \u003cp\u003eKupwara and Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eearthquake induced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSplitting of earth, landslides, rockfalls, complete and partial damage to roads, and hillside structures.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGanderbal and Srinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar and Ganderbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemple, bridge and army bunker damaged\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBanihal-Ramban HW 44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 vehicles stranded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRamban-Banihal HW 44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVehicles stranded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQazigund Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 cattle lost\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmarnath Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000 pilgrims stranded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29/07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar-Ladakh HW 1 and Baltal road Ganderbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePilgrims buried, tourism\u003c/p\u003e\n \u003cp\u003eaffected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29/07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 pilgrims\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTourism affected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02/08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmarnath Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKeran Sector Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 BRO porter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez, Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRailpathri, Baltal base camp Ganderbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 porter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara District\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri, near LoC Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 Houses collapsed, cattle affected (7 cows, 30 goats killed)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri, Gharkote Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 army man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShooting stones\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGulmarg Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChairvani village, Ganderbal, Srinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZojila HW 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeavy rains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGanderbal, Srinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar-Leh highway closed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrg-Leh HW 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 BRO laborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighway closed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20/05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraffic disrupted for several days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (Army men)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHampered traffic movement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 houses damaged\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhimram, Shangus Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 family members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 shops and 10 kiosks destroyed, dozen vehicles damaged\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02/09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple sites in J\u0026amp;K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlood induced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColossal damage to life and property. Blocked river channels and caused flash floods, aggravated flood situation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKulgam district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvalanche induced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehouses collapsed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBalsaran Danaukandimarg village, Kulgam district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQazigund (Anantnag), Kulgam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouse collapsed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShopian district\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouse collapsed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSunergund, Awantipora, Pulwama, Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChadoora, Budgam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eflood induced and heavy rains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emudslides and house collapse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSadna pass, Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvalanche and landslides hit camp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHappat Koal, Happat nar Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 Swedish skier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLadden Chadoora Budgam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraffic disrupted for more than 10 days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04/07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRailpathri and Brarimarg Ganderbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (4 pilgrims)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmarnath yatra was suspended\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11/06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalar Pahalgham, Anantnag District\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable 6 Major Snow avalanches witnessed by the Kashmir Valley for which damage and loss were reported.\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Taba\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDate of occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCasualties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociated impacts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD/MM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatalities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjuries/ missing/rescued\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZojila, Srg-Leh National HW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFebruary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJawahar tunnel, Banihal HW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassengers trapped and perished on both sides of the tunnel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonang Post, Uri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA patrol party of 4 was swept and later bodies recovered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMou Mangat, Banihal HW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 civilians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouse buried located far from the main village\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKund and Waltengu Nar villages, Qazigund Anantnag Kulgam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e60 civilians rescued\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHundreds trapped\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQazigund Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15 injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 trucks stranded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJammu -Srinagar HW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRamban-Banihal, HW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400-500 trucks stranded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUri Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKashmir valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSrinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGulmarg Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 Soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17 soldiers injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGulmarg Baramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhiram Shangus, Anantnag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (army and BSF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssociated slides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGanderbal \u0026amp; Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 Army personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMany injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 army personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 people rescued.\u003c/p\u003e\n \u003cp\u003e1 person missing.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCivilian vehicle caught in the avalanche.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBatalik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 rescued\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e73 civilians rescued.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeople stranded in vehicles.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (4 civilians; 20 soldiers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeries of 3 avalanches.\u003c/p\u003e\n \u003cp\u003eArmy camp and patrol party was hit.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSonmarg\u003c/p\u003e\n \u003cp\u003eGanderbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 civilians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez\u003c/p\u003e\n \u003cp\u003eBandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSonmarg Ganderbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 soldiers injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachil, Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 soldiers rescued\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06/04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJ\u0026amp;k, higher reaches of Kashmir and Ladakh.\u003c/p\u003e\n \u003cp\u003eBatalik, Kargil, Kupwara, Kokernag, (Anantnag) Rajori, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6 civilians, 3 army men)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvalanches, minor flooding, landslides, rise in water levels in Jhelum and tributaries.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGurez (Bandipora) \u0026amp; Naugam, (Kupwara)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrapped after snow track caved in.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSadhna top, Tangdhar Sector (C-T), karnah, Kupwara (khooni nallah)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (civilians)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 avalanches. Vehicle hit by avalanche.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSonmarg (Ganderbal), Gurez (Bandipora) \u0026amp; Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (5 civilians, 1 army major)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 soldiers missing, 6 soldiers rescued alive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCamp hit, House collapsed, family of four died.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (7 soldiers, 4 civilians)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSeveral missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA camp and patrol party got hit.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvalanche struck army post\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGulmarg\u003c/p\u003e\n \u003cp\u003eBaramulla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(tourists- 1 international, 4 national)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuchibal Behak, Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 civilians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTulail Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09/09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKolahoi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 local trekkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTangdhar Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArmy post hit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDawar, Gurez, Bandipora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFoot patrol of army was hit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKulan, Sonmarg\u003c/p\u003e\n \u003cp\u003eGanderbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeveral houses damaged when village was hit by avalanche\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMachil sector, Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 soldiers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 trapped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArmy post hit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNaugam sector, along LoC Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 soldier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6 rescued alive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoshan post, Tangdhar Kupwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 injured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eFor landslide hazard out of the total 65 events collected, 49 events with substantial damage and loss were discussed in detail (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The events with highest number of deaths in the databset are the landslide event of 20th March, 2007 Chdoora budgam and 31st March, 2018 Ladden, Chadoora, Budgam with a death toll of 16 persons each. Land sliding can have varied triggers, heavy rains appear to be the most frequent cause of landslides and therefore, many a times landslides coincide with floods and flash floods (1992, 20th April, 20th March, 2007 and 2010, 2nd September, 2014), earthquakes (4th April, 1905, 8th October, 2005) and snow avalanches (19th February, 2005 and 12th March, 2014) are also a common cause for land sliding. Landslides frequently lead to road blockade, disrupted traffic movement (of people and goods) as can be seen from the data, along with other impacts like deaths and injuries, loss of cattle, damage to hillside settlements, infrastructures, roads and bridges causing loss of connectivity, hamper pilgrimage activities, accidents and damage to vehicles, affect tourism, and daming of rivers causing flash floods. National hihgway is the main link of the valley to the rest of the country which gets blocked ever so frequently during rainy and winter seasons leaving the valley without accessibility for days at a strech having impacts like shortage in supplies, availability of goods, inflation, hampered movement of people, etc (Prakash, 2011). From the data we can establish a pattern in the seasonal variability of landslide occurrences, with maximum number of events (9 each) in the months of February and March, which account for 34.04% of the total occurrences, and minimum (1 each) in May and November, rest occassional slides occur throughout the year. 53.19% of the total land sliding activity occures in the months of January, February, March and April, but a substantial number of occurrences have also been recorded for the month of June, which accounts for 14.89% of the total incidents recorded.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSnow Avalanches\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAn endemic feature of snow-covered mountain ranges (Spencer, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bruno, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), avalanches are both widespread and one among the most destructive natural hazards (Keylock, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), causing fewer casualties globally, on an average several hundred people per year (Birkeland, 2021), than many other natural hazards, but overall fatalities have been on the rise over the past several decades (Bruno, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The overwhelming expansion of tourism and increasing popularity of winter sports and climate warming has escalated the number of people exposed to avalanches by influencing the behaviour, uncertainty and increasing frequency of snow avalanches (Martin et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bruno, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Castebrunet et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Himalayas (Indian Himalayas) are highly vulnerable to snow avalanches (Sethi, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ganju, 2002; Mc Clung, 2016) and with increased communication to isolated mountain villages, the number of incidents and casualties recorded has enhanced substantially in the last few decades (Sethi, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). Snow-covered regions of Jammu and Kashmir, Himachal Pradesh, Uttaranchal and Western Uttar Pradesh are significantly susceptible (Sethi, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ganju, 2002) while eastern states witness occasional incidents. In erstwhile, Jammu and Kashmir higher reaches of Kashmir division (Kashmir valley, Gurez valleys, Kargil and Ladakh), areas of Jammu region (Doda, Ramban, Udhampur, Reasi, Kishtwar, Banihal), some of the major roads (long stretches of national highway connecting J\u0026amp;K to the rest of the country, from Ladakh through Srinagar to Jammu, Mughal Road, etc.) (Kelman, 2018; RMSI Report, 2018) and famous pilgrim centres (Amarnath, Phalgham and Baltal, and Vaishnu Devi, Katra) (Sethi, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e) are highly vulnerable to snow avalanches (SDMP, 2017).\u003c/p\u003e\n \u003cp\u003eJ\u0026amp;K, as compared to the other vulnerable regions of the country has taken the major brunt of avalanche accidents in the past (Ganju, 2002) with both civilians and the army being severely impacted as can be established from the recorded events (Gusain et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Table 6 discusses 47 incidents in detail most of which have been recorded for the period 2000-2020. An evident increase in the number of avalanche occurrences and subsequent casualties can be seen in the past three decades which can be primarily attributed to insufficient knowledge about the terrain, lack of forecasting mechanism, and ill-equipped adventures taken-up by army as well civilians like construction of roads, enhancement in tourism and increased patrolling activity in the region post 1990\u0026rsquo;s (Ganju, 2002). Snow avalanches have substantial adverse impact on human activity (Keylock, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e) threatening human life directly by causing death or injury, or by detaining them and indirectly by obstructing the overall development, disrupting ecosystems, damaging built structures and landscapes in mountainous regions (Ganju, 2002; Choubin, 2019).\u003c/p\u003e\n \u003cp\u003eA considerable portion of the total fatalities seem to occur when people are in movement, as opposed to when they are static (like in their houses, barns etc.,), and majority of the accidents take place during snowfall or immediately after cessation of snow storm, as can also be confirmed from the reported events in this study (Table 6) (Sethi, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ganju et al., 2002). The incident showing highest number of casualties is the avalanche of 10th February, 2005 in Kund and Waltengu Nar Villages killing a total of 175 people while 60 were rescued alive. The record shows a few more severe incidents with large number of casualties like 1986 Zojila (60 deaths), 1994 Jawahar Tunnel (98 deaths), 8th February, 2008 Jammu-Srinagar National HW (25 deaths), 8th February, 2010 Gulmarg (17 deaths and 17 injuries), and 25th January, 2017 Gurez (24 deaths). Recorded casualties show a greater number of army personnel than civilians which is due to the proximity of the region to the international border that mostly stretches across avalanche-prone snow-covered slopes, therefore, the presence of army posts along the LoC makes them highly susceptible to snow avalanches (Gusain et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The data reveals that areas like Tangdhar, Drass, Gurez, Keran, Machhal, Gulmarg, Naugam and Banihal are highly avalanche prone sites in the valley (Table 6) (Kelman, 2018; Gusain et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe major roads of the region, winding up on some of the prominent passes on the Pir Panjal and the Greater Himalayan Range, are often closed due to landslides and avalanches during the winter and early spring seasons, with a number of casualties every year, frequent suspension of vehicular movement and confinement of pedestrian movement to only village level for long periods creating various socio-economic problems, but with no systematic compilation of incidents or casualties (Kelman, 2018). The important road axes susceptible to avalanche activity include: Jammu-Srinagar, Naugam-Kaiyan, Chowkibal-Tangdhar, Srinagar-Leh and Bandipora-Gurez. Tangdhar is one of the regions studied well for avalanches and many researchers have reported various prediction techniques for this road axis in the past (Gusain et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe avalanche activity for major portions of mountain areas of Kashmir is most pronounced in the months of January to March, but may stretch over the months of November to April (Sethi, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), while in the high alpine areas the avalanche season continues all year-round (9th September, Kolahoi) (Ganju, 2002). In Kashmir 72.34% of the total avalanches occur in January and February followed by the months of March and April accounting for about 17.02% of the total.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec9\"\u003e\n \u003ch2\u003e4.1.2. Casualties\u003c/h2\u003e\n \u003cp\u003eCasualties form one of the most important aspects to study the intensity and extent of impact caused by a particular disaster event. A casualty can be any person who becomes a victim to an adverse impact caused by any hazardous event, which may include persons killed, injured, trapped, missing, evacuated, or rescued. However, the present study only includes two forms of casualties viz., fatalities and injuries. The trend shows that maximum number of reported casualties are related to earthquakes (30,530 i.e., 90.94%), followed by floods (2,194 i.e., 6.53%), snow avalanches (642 i.e., 1.91%) and least for landslides (204 i.e., 0.60%). Although many of the events have a regional and cross border extent, only the casualties reported for the area under focus i.e., the Kashmir Valley, whereever provided, were considered for analysis. The total number of severe events shows an inverse relation to the total casualties reported, for example only 7 severe earthquake events were reported for the entire timeline for which the count of casualties far surpasses that caused by any other hazard type, whereas a total of 49 landslide events were of the magnitude to cause substantial damage and loss, for which the total count of casualties stands the least among all the four hazard types.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec10\"\u003e\n \u003ch2\u003e4.1.3. Temporal variability and distribution\u003c/h2\u003e\n \u003cp\u003eThe temporal variation and frequency (annual and decadal events distribution) of hazard and disaster events of all four types for the time period 1900 to 2020 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) shows a general increasing trend in the last few decades depicted by a sharp rise in the graph line between 1980 to 2020, establishing the fact that the number of disaster events recorded has considerably increased in the past few decades. This indicates that the frequency of hazard and disaster occurrences, due to various natural (climate change and global warming, geological endogenic and exogenic processes) and anthropogenic factors has increased to a great extent. Another factor responsible for the rise in recorded incidents possibly could be the enhanced and improved recording and reporting of disaster events globally and nationally in recent times. Also, due to dramatic population growth and rapid urban expansion, overburdening of regions takes place which forces people to move to and settle in unsafe conditions and vulnerable areas, increasing the chance of human interaction with these potential hazards and therefore, elevate the levels of exposure to which populations and societies are subjected to, thus, consequently increasing the number and frequency of these adverse events.\u003c/p\u003e\n \u003cp\u003eIn case of earthquakes, although, major events are well recorded throughout the timeline, small to medium scale incidents, which need technological intervention to be detected find more reliable and frequent reporting post 1960s which can be backed up by the gradual but evident increase in the number of events recorded per year (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). The history of instrumental monitoring of earthquakes in India dates back to 1898 when the first seismological observatory of the country was established in Calcutta after the great Shillong plateau earthquake of 1897. Other similar occurrences like 1905 in Kangra Valley, necessitated the strengthening of the national seismological network with 1960s marking a landmark in history of seismic monitoring when the WWSSN (World Wide Standardized Seismic Network) stations started functioning globally, post which the number of reported earthquake events shows a drastic increase (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). For the given timeline of 120 years (1900-2020) the highest number of earthquake events i.e., 121 were recorded in the year 1976. The dataset reveals that there is a strong earthquake (magnitude 6.0 and above) every few years, in and around the Kashmir region, with occasional episodes of continued occurrences without any gap like 1963, 1964, 1965 and 1972, 1973, 1974 and 1975. Sometimes more than one strong earthquake seems to have jolted the region in a single year (1950, 1975, 1990, 2005 and 2015). Earthquakes with magnitude lesser than 6 occur more frequently all the year-round.\u003c/p\u003e\n \u003cp\u003eFlood events on the other hand show the most consistent trend of occurrence throughout the timeline, with occasional periods of no flood occurrences. Although, a slight increase can be detected through the slope of the trend line in the graph 2000-2020, indicating more than one flooding event in the same year (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, b). Floods, in general, show a repeated pattern through the 20th century arriving at regular intervals (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb) and during the years 1900 to 1965, the valley experienced about 15 major floods (Razdan, 2014). 3 flood events in a single year 2015 were the highest number of flood events recorded for any year. Detection of a flood event is more evident through the rise in water levels, which are easier to measure and record, and therefore, are available for most of the times when water levels have crossed the flood mark, dating back to 635 A.D.\u003c/p\u003e\n \u003cp\u003eLandslide and snow avalanche events are localized events and have limited spread. From the graph we can make out a comparatively less consistent trend of occurrence and frequency throughout the timeline, which hints at a dramatic increase in landslide and snow avalanche incidents in the recent past which could be attributed to, firstly, increase in exposure of populations to these hazards through tourism and adventurous activities, communication to and settling in remote susceptible areas, increased movement of traffic in these areas, and also, large scale patrolling activity post-1990s, and secondly, better reporting and recording of these events, which was found almost negligible for the twentieth century except for incidents with greater human impact and those along the national highway, (Ganju, 2002) and the increasing trend can be seen continuing in the 21st Century (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). A total number of 16 landslide events in a single year 2010 were the highest recorded and for snow avalanches the highest number of events recorded in a single year were 13. Looking at the dataset we infer that on an average 2 to 3 low to moderate intensity avalanche events occur in a year. Also, some very high impact events with a large number of casualties keep repeating once in few years (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and 6).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec11\"\u003e\n \u003ch2\u003e4.1.4. Spatial distribution\u003c/h2\u003e\n \u003cp\u003eThe spatial distribution of the selected four hazard types was represented through maps developed in ArcGis. For earthquake hazard only the events with epicentres lying within the Kashmir Valley were plotted using the geographic coordinates provided in the secondary sources consulted, similarly, the landslides and snow avalanches reported within the area of interest were plotted by making use of the latitudinal-longitudinal information collected for the reported events, but for flood hazard spatial distribution was represented through the extent of inundation experienced by the Valley in the Kashmir Flood of September, 2014 (declared as the highest magnitude flood recorded instrumentally on Jhelum by the Department of Irrigation and Flood control), in order to give an idea about the area of Kashmir Valley which may be at risk of inundation during a Flood event of similar magnitude.\u003c/p\u003e\n \u003cp\u003eThe entire valley is about equally prone to earthquake hazard with a somewhat homogenous distribution of earthquake incidents in and around the region, which coincides with its zonation into high to very high seismic intensity zones (zones IV and V) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). For flood hazard, however, flood plains and low-lying areas, on both sides of the Jhelum River, across the length of the entire valley, are under the threat of inundation (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). Analysis of recent available data suggests that the left bank of the Jhelum is more vulnerable to inundation than the right bank (Ram \u003cspan class=\"CitationRef\"\u003e1895\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e1928\u003c/span\u003e; Bhat, 2019). As for the landslides and snow avalanches, incidents are limited to higher reaches, unstable slopes, with pockets of high, moderate and low frequencies throughout the mountainous stretches of the Valley (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed), especially along the roadways running through these hilly terrains.\u003c/p\u003e\n \u003cp\u003eThe plot distribution and inundation extent data were further used to generate susceptibility maps of all four hazard types for the districts of the Kashmir Valley (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) based on the concentration of events in each district for earthquakes, landslides and avalanches and for floods the extent of area inundated in each district, as factors for classification of the districts as less to more vulnerable to specific hazards. This gives us a general idea about the proneness of the districts towards different hazards, from which we can make out that all the districts are susceptible to two or more of the selected hazard types.\u003c/p\u003e\n \u003cp\u003eBased on the choropleth/thematic maps (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), maximum concentration of earthquake incidents is seen in the district Kupwara, whereas, Shopian shows the lowest concentration (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). For flood hazard, districts with maximum flooded area appear to be Baramulla and Bandipora whereas, Kupwara and Shopian show least area affected (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). There can be seen pockets of concentration depicting landslide incidents, located predominantly in the mountainous terrain running along the periphery of the valley (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec and d). In figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec, it is evident that districts Baramulla and Anantnag have witnessed the maximum number of landslide events in the time period and therefore, exhibit the highest susceptibility, while the districts with the least susceptibility are Pulwama and Shopian. For snow avalanches, the events are spread over the snow-covered ranges of the valley, especially towards the north and northeast (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed). In some spots, locations of high-concentration of avalanche events coincide with those of the landslide events. Among the districts, Kupwara shows the highest susceptibility towards snow avalanches, whereas, Budgam, Pulwama and Shopian show the least susceptibility towards avalanche hazards (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Data Uncertainty And Limitations","content":"\u003cp\u003eDisaster data has been found to be scattered and broken, showing intermittent and discontinuous coverage in the major portion of the 20th Century. While large impactful events find a place into literature through one way or the other most of the small-scale incidents go unreported. In the later part of the 20th Century disaster events can be seen recorded more continuously with more accurate details, which continues in the 21st Century, attributed to improved technology for detection, reporting and disseminating information. There has been no specific literature dealing with what can be generally termed as disaster data base in the historical perspective in the former part of the timeline but things have begun to change ever since the inception of databases like CRED-EM-DAT, Munich RE, ADRC, IDNDR, etc. Consequently, information about natural events and resulting processes were littered in the vast corpses of literature making them difficult to assemble.\u003c/p\u003e \u003cp\u003eAmbiguity and unreliability are produced by relying upon online digital sources in the process of data accumulation for extended periods as these usually get deactivated or removed from the web after a certain period of time. Similarly, digital newspaper archives are also only available for 5-8 years which poses a challenge to data collection retrospectively. Additionally, websites which work as supplementary resources to global and regional hazard databases are mostly region and country specific, especially to developed nations (Sultana, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Also, databases have specific criteria which limit the recording of all disaster events specifically to those which fulfil these particulars, e.g., EM-DAT. As a result, many events don\u0026rsquo;t make it to such databases and thereby, forming a corrupt picture of the hazard or disaster scenario and making data collection a challenge.\u003c/p\u003e \u003cp\u003eSome hurdles arise when it comes to characterizing disaster events. Generally, information can fluctuate widely regarding frequency, magnitude, impacts, exact location (latitude-longitude), etc. which results in several biases and overlaps. Like in case of earthquakes, the epicentres or the magnitudes showed variation from one source to another. Similarly, figures relating to damage and loss can also be erroneous, example death of someone injured in the main event a few days after the event. Sometimes, the events with minimal consequences go unreported which results in inaccurate figures of total disaster episodes. An over or under estimation is encountered when news of multiple events occurring on the same date at same or different locations are reported as a single event. Therefore, in terms of impacts and other specifications, multiple sources were consulted for a single episode and the data from best reported and more reliable source was selected in that case.\u003c/p\u003e \u003cp\u003eIn some cases, casualties and economic losses pertaining to a single hazard type may be higher than what is commonly documented (Schuster, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Guzzetti et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Anderson et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). There is widespread recognition that the overall consequence of landslides and avalanches is usually underestimated (Kjekstad \u0026amp; Highland, 2009; Petley, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Data is not systematically reported or readily available (Petley, 2005, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Corominas et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) unlike other natural disasters. Casualties are often recorded under the label of triggering events, which are usually larger events such as flood, hurricanes, or tropical storms, earthquake, etc. (UNDP, 2004; Froude \u0026amp; Petley, 2018). Also, in most of the cases, exact reporting is hindered by the comparative smaller scale on which they occur, lack of close observation, short span of occurrence and that too at specific, remote locations and during specific seasons, which limit their impacts and is unrecognized in most natural disaster\u0026rsquo;s registers (Holcombe \u0026amp; Anderson, 2010). All this may cause flawed estimations pertaining to the hazard impact and susceptibility.\u003c/p\u003e \u003cp\u003eIn these records, for landslides and snow avalanches it was seen that data pertaining to army accidents is more or less complete as compared to that of the civilian data, which has often gone unreported, therefore giving a false idea of the actual hazard susceptibility.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe aspect of utmost importance for preparing a mitigation plan is to understand the hazards facing a community. Understanding the various hazard risks and their subsequent consequences is of prime concern while mitigating the adverse effects of potential hazard events. A hazard profile is an account and analysis of different types of hazards specific to a place/community. It is performed for each natural hazard and based off certain criteria such as frequency, location, duration, speed of onset, and impacts. A hazard profile enables decision makers to compare the physical aspects that all hazards share. By comparing the characteristics of hazard events, they are able to identify and prioritize the hazards for mitigation, risk reduction, policy and decision making, and funding.\u003c/p\u003e \u003cp\u003eFrom the above discussion, we assert that the valley of Kashmir is subjected to a threat of multiple hazards that have the potential to turn into devastating disaster events and jeopardize the lives of millions residing in this area. The analysis finds out that the frequency of disaster events has drastically increased over the past few decades, enhancing the threat of natural hazards to which the population and society of Kashmir are subjected to. The results reveal that these four natural hazards individually, can have devastating impacts and also, a catastrophic event may take form if multiple hazards overlap or cascade after one another. Therefore, it becomes essential to gauge this impending threat, mitigate the risks and prepare for the worst through sustainable Disaster Risk Reduction which the present study aims to facilitate by developing a multi-hazard profile of Kashmir based on the disaster database compiled. The disaster catalogues prepared in the study can prove of great help for various stakeholders and policymakers, to assess the local risk and hazard scenario of the valley, in order to be incorporated in planning, to reduce disaster risk and subsequently create sustainable environments.\u003c/p\u003e \u003cp\u003eInformation availability and ease of access to data regarding the events were found to vary with the type of hazard, like in the case of earthquakes, data was readily available in national and international databases as well as on open access portals and websites, because of improved recording technologies and dissemination platforms. Similarly, for floods, a good amount of data was found from various existing research studies, especially, due to a boon in flood research post-2014 Kashmir floods. On the contrary, for landslides and snow avalanches, it was encountered that data recording, reporting and access was limited due to various factors. Both landslide and snow avalanche events have not received as much recognition as is the case with floods and earthquakes.\u003c/p\u003e \u003cp\u003eThe data for earthquake incidents, their magnitudes and for flood incidents, the experienced water levels and damage and loss for either of the two types, have been more or less recorded and have found place in literature either through official record or historical literature as both events can have colossal adverse impacts and spread over a vast expanse, which is not the case with landslides and snow avalanches. The fact that events data of landslides and snow avalanches, for major portion of the selected timeline is missing from the consulted data sources could draw a misleading picture of the hazard and disaster scenario of the Kashmir valley, making the valley appear little to not vulnerable to these hazards, which is very contradictory to the actual scenario.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e7. Acknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI wish to show my gratitude to all the authors who contributed towards the conceptualization of the idea, collection of the historical events data and compilation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Conflict of Interests/ Disclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of financial or personal interests or beliefs that could affect the objectivity of the research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad B, Alam A, Bhat MS, Bhat KA, Haq ul, Ahmad JI, Qadir J (2021) Retracing Realistic Disaster Scenarios from Archival Sources: A Key Tool for Disaster Risk Reduction.International Journal of Disaster Risk Science,1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad B, Bhat MI, Bali BS (2009) Historical record of earthquakes in the Kashmir Valley. Himalayan Geol 30(1):75\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad SM (1984) Historical Geography of Kashmir: Based on Arabic and Persian Sources from AD 800 to 1900. Ariana Publishing House\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad S, Alam A, Ahmad B (2016) Comment on: \u0026ldquo;Earthquake geology of Kashmir Basin and its implications for future large earthquakes\u0026rdquo; by Shah (2013); \u0026ldquo;Kashmir Basin Fault and its tectonic significance in NW Himalaya, Jammu and Kashmir, India\u0026rdquo; by Shah (2015). Int J Earth Sci 105(2):681\u0026ndash;687\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad M, Bano R (1984) Historical geography of Kashmir (AD 800\u0026ndash;1900). Ariana, New Delhi, India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam A, Ahmad S, Bhat MS, Ahmad B (2015) Tectonic evolution of Kashmir basin in northwest Himalayas. Geomorphology 239:114\u0026ndash;126\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam A, Bhat MS, Farooq H, Ahmad B, Ahmad S, Sheikh AH (2018) Flood risk assessment of Srinagar city in Jammu and Kashmir, India. International Journal of Disaster Resilience in the Built Environment 2:9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1108/IJDRBE-02-2017-0012\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbinia A (2010) Empires of the Indus: The story of a river. WW Norton \u0026amp; Company\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmbraseys NN, Douglas J (2004) Magnitude calibration of north Indian earthquakes. Geophys J Int 159(1):165\u0026ndash;206\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmbraseys N, Bilham R (2000) A note on the Kangra M s= 7.8 earthquake of 4 April 1905. Curr Sci 79(1):45\u0026ndash;50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson MG, Holcombe E, Blake JR, Ghesquire F, Holm-Nielsen N, Fisseha T (2011) Reducing landslide risk in communities: Evidence from the Eastern Caribbean. Appl Geogr 31(2):590\u0026ndash;599\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnees SUM, Bhat MS (2016) History of natural disasters in Kashmir valley, Jammu and Kashmir with special reference to earthquakes.History, 5(9)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallesteros-C\u0026aacute;novas JA, Koul T, Bashir A, Del Pozo JMB, Allen S, Guillet S, Stoffel M (2020) Recent flood hazards in Kashmir put into context with millennium-long historical and tree-ring records. Sci Total Environ 722:137875\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamzai PNK (1962) Socio-economic history of Kashmir: 1846\u0026ndash;1925(p.\u0026nbsp;771). Metropolitan Book, New Delhi, India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBankoff G (2007) Comparing vulnerabilities: toward charting an historical trajectory of disasters. Historical Social Research/Historische Sozialforschung, pp 103\u0026ndash;114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelow R, Vos F, Guha-Sapir D (2010) Moving towards harmonization of disaster data: a study of six Asian databases. Centre for Research on the Epidemiology of Disasters, Brussels\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat MI (1987) Spasmodic rift reactivation and its role in the pre-orogenic evolution of the Himalayan region. Tectonophysics 134(1\u0026ndash;3):103\u0026ndash;127\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat MS, Ahmad B, Alam A, Farooq H, Ahmad S (2019) Flood hazard assessment of the Kashmir Valley using historical hydrology.Journal of Flood Risk Management, 12, e12521\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat MS, Alam A, Ahmad B, Kotlia BS, Farooq H, Taloor AK, Ahmad S (2019) Flood frequency analysis of river Jhelum in Kashmir basin. Quatern Int 507:288\u0026ndash;294\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatt CM, Rao GS, Farooq M, Manjusree P, Shukla A, Sharma SVSP, Dadhwal VK (2017) Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu \u0026amp; Kashmir, India. Geomatics, Natural Hazards and Risk, 8(2), 309-327\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilham R, Bali BS (2014) A ninth century earthquake-induced landslide and flood in the Kashmir Valley, and earthquake damage to Kashmir\u0026rsquo;s Medieval temples. Bull Earthq Eng 12(1):79\u0026ndash;109\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilham R, Bali BS, Bhat MI, Hough S (2010) Historical earthquakes in Srinagar, Kashmir: Clues from the Shiva temple at Pandrethan. Geol Soc Am Spec Pap 471:107\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilham R, Gaur VK, Molnar P (2001) Himalayan seismic hazard. Science 293(5534):1442\u0026ndash;1444\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirkeland KW (2021, July 26) avalanche. Encyclopaedia Britannica. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.britannica.com/science/avalanche\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBIS (2002) Seismic zonation map of India prepared by the Bureau of Indian Standards\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBMTPC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bmtpc.org/topics.\u003c/span\u003e\u003c/span\u003easpx?mid=43\u0026amp;Mid1=56)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolt A, Bruce (eds) (October 2003) Earthquake. Encyclopaedia Britannica. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.britannica.com/science/earthquake-geology/Earthquake-magnitude\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruno A (2013) Tumbling snow: Vulnerability to avalanches in the Soviet North. Environmental History 18(4):683\u0026ndash;709\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurbank and Jhonson 1983: Burbank, D. W., \u0026amp; Johnson, G. D. (1983). The late Cenozoic chronologic and stratigraphic development of the Kashmir intermontane basin, northwestern Himalaya. Palaeogeography, Palaeoclimatology, Palaeoecology, 43(3-4), 205-235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurton I (2010) Forensic disaster investigations in depth: a new case study model. Environment 52(5):36\u0026ndash;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastebrunet H, Eckert N, Giraud G, Durand Y, Morin S (2014) Projected changes of snow conditions and avalanche activity in a warming climate: the French Alps over the 2020\u0026ndash;2050 and 2070\u0026ndash;2100 periods. The Cryosphere 8(5):1673\u0026ndash;1697\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaudora MH (1620) Tarikh-i-Kashmir (Persian). Folios 48:184\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChingkhei RK, Shiroyleima A, Singh LR, Kumar A (2013) Landslide hazard zonation in NH-1A in Kashmir Himalaya, India. International Journal of Geosciences, 2013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoubin B, Borji M, Mosavi A, Sajedi-Hosseini F, Singh VP, Shamshirband S (2019) Snow avalanche hazard prediction using machine learning methods. J Hydrol 577:123929\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73(2):209\u0026ndash;263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCRED U (2020) Human cost of disasters: an overview of the last 20 years 2000-2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDikshit A, Sarkar R, Pradhan B, Segoni S, Alamri AM (2020) Rainfall induced landslide studies in Indian Himalayan region: A critical review. Applied Sciences 10(7):2466\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoocey S, Daniels A, Murray S, Kirsch TD (2013) The human impact of floods: a historical review of events 1980-2009 and systematic literature review.PLoS currents,5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEconomic, Losses Poverty and Disaster 1998- 2017 (2018). Report for the United Nations Office for Disaster Risk Reduction,Geneva, and Center for Research on the Epidemiology of Disasters, Brussels\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFroude, M. J., \u0026amp; Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18(8), 2161-2181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanjoo RK (2014) The vale of Kashmir: landform evolution and processes. Landscapes and landforms of India. Springer, Dordrecht, pp 125\u0026ndash;133\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanju A, Thakur NK, Rana V (2002, September) Characteristics of avalanche accidents in western Himalayan region, India. In Proceedings of the International Snow Science Workshop, Penticton, BC, Canada (pp.\u0026nbsp;200-207)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGansser A (1964) Geology of the Himalayas\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGariano and Guzzetti 2016: Gariano, S. L., \u0026amp; Guzzetti, F. (2016). Landslides in a changing climate. Earth-Science Reviews, 162, 227-252\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavillot YG, Meigs A, Rittenour TM, Malik MOA (2016, December) Active folding of fluvial terraces across a blind 'Himalayan deformation front in the Kashmir Himalaya, northwest India. In AGU Fall Meeting Abstracts (Vol.\u0026nbsp;2016, pp. T11G-05)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavillot Y, Meigs A, Yule D, Heermance R, Rittenour T, Madugo C, Malik M (2016) Shortening rate and Holocene surface rupture on the Riasi fault system in the Kashmir Himalaya: active thrusting within the Northwest Himalayan orogenic wedge. Bulletin 128(7\u0026ndash;8):1070\u0026ndash;1094\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhaffar A, Abbas S (2010) An Overview of Past History Based on Seismicity Pattern of Kashmir Region, An Interpretation from 2005 Earthquake. The Journal of Animal and Plant Sciences 20(4):297\u0026ndash;304\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh SK, Veeraraghavan K (1975) Severe floods in Jammu \u0026amp; Kashmir in August I973. MAUSAM 26(2):203\u0026ndash;207\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Muralikrishna M (2010) South Asia disaster risk management programme: Synthesis report on SAR countries disaster risks. RMSI Private Limited, Noida\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Kumar S, Wason HR, Das R (2012) A statistical analysis of completeness of earthquake data around Dehradun city and its implications for seismicity evaluation. 15WCEE (Accepted).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta AK (2014) Loss of Life in Kashmir: A Tale of Man-Made Disaster. The Water Digest Report, New Delhi, pp 8\u0026ndash;25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGusain HS, Mishra VD, Singh DK (2018) Study of a snow avalanche accident along Chowkibal--Tangdhar road in Kupwara district, Jammu and Kashmir, India.Current Science(00113891),115(5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzzetti F (2000) Landslide fatalities and the evaluation of landslide risk in Italy. Eng Geol 58(2):89\u0026ndash;107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1\u0026ndash;4):181\u0026ndash;216\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabibullah H (1905) Syllab Nama\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaque U, Da Silva PF, Devoli G, Pilz J, Zhao B, Khaloua A, Glass GE (2019) The human cost of global warming: Deadly landslides and their triggers (1995\u0026ndash;2014). Science of the Total Environment, 682, 673-684\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolcombe, E., \u0026amp; Anderson, M. (2010). Tackling landslide risk: Helping land use policy to reflect unplanned housing realities in the Eastern Caribbean. Land Use Policy,27(3), 798-800\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHough S, Bilham R, Bhat I (2009) Kashmir valley mega-earthquakes: estimates of the magnitudes of past seismic events foretell a very shaky future for this pastoral valley. Am Sci 97(1):42\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIRDR Annual Report (2015) (2016) Integrated Research on Disaster Risk. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.irdrinternational.org/knowledge_pool/publications/602\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIyengar RN, Sharma D, Siddiqui JM (1999) Earthquake history of India in medieval times. Indian Journal of history of science 34:181\u0026ndash;238\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson LE Jr (2013) Frequency and magnitude of events Encyclopaedia of Natural Hazards Peter Brobrowsky. (ed.) Springer-Verlag\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJahn T, Wehling P (1998) Gesellschaftliche Naturverh\u0026auml;ltnisse\u0026mdash;Konturen eines theoretischen Konzepts. Soziologie und Natur. VS Verlag f\u0026uuml;r Sozialwissenschaften, Wiesbaden, pp 75\u0026ndash;93\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi M, Thakur VC (2016) Signatures of 1905 Kangra and 1555 Kashmir earthquakes in medieval period temples of Chamba region, northwest Himalaya. Seismol Res Lett 87(5):1150\u0026ndash;1160\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalhana P (1149) Rajtarangani (Sanskrit)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapur A (2010) Vulnerable India: a geographical study of disasters. SAGE Publications India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatt S (2004) Kashmir Ecology and environment: New Concerns and Strategies. APH Publishing, Delhi\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKayal JR (2007) Recent large earthquakes in India: seismotectonic perspective. IAGR Memoir 10:189\u0026ndash;199\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelman I, Field J, Suri K, Bhat GM (2018) Disaster diplomacy in Jammu and Kashmir. International journal of disaster risk reduction 31:1132\u0026ndash;1140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeylock C (1997) Snow avalanches. Prog Phys Geogr 21(4):481\u0026ndash;500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanyari GN (1857) Wajeez-ul-Tawarikh (in Persian)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhoihami, P. G. H. (1885). Tarikh-i-Hassan. Vol.\u0026nbsp;I \u0026amp; II (Persian), pp.\u0026nbsp;394\u0026ndash;395\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKjekstad and highland 2009: Kjekstad, O., \u0026amp; Highland, L. (2009). Economic and social impacts of landslides. In Landslides\u0026ndash;disaster risk reduction (pp.\u0026nbsp;573-587).Springer, Berlin, Heidelberg\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoul PA (1993) Geography of Jammu and Kashmir state, Thacker spink, Calcutta, India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Acharya P (2016) Flood hazard and risk assessment of 2014 floods in Kashmir Valley: a space-based multi-sensor approach. Nat Hazards 84(1):437\u0026ndash;464\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajesh K (2016) Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu \u0026amp; Kashmir): An application of geoinformatics. Remote Sensing Applications: Society and Environment 4:188\u0026ndash;203\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLacasse S, Nadim F, Kalsnes B (2005) Living with landslide risk. World\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawrence WR (1967) The Valley of Kashmir (1895). Kesar, Srinagar\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Fort P (1989) The Himalayan orogenic segment. Tectonic evolution of the Tethyan region. Springer, Dordrecht, pp 289\u0026ndash;386\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Q, Wang Y (2018) Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016. Landslides 15(12):2357\u0026ndash;2372\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan AK, Thakur VC, Sharma ML, Chauhan M (2010) Probabilistic seismic hazard map of NW Himalaya and its adjoining area, India. Nat Hazards 53(3):443\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Proc Land 29(6):687\u0026ndash;711\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik IH, Hashmi SNI (2020) Ethnographic account of flooding in North-Western Himalayas: a study of Kashmir Valley. GeoJournal, pp 1\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin E, Giraud G, Lejeune Y, Boudart G (2001) Impact of a climate change on avalanche hazard. Ann Glaciol 32:163\u0026ndash;167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMason K (1929) Indus floods and Shyok glaciers. Himalayan Journal 1:10\u0026ndash;29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcClung DM (2016) Avalanche character and fatalities in the high mountains of Asia. Ann Glaciol 57(71):114\u0026ndash;118\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehran D (2015) FOOD SHORTAGES IN KASHMIR: RESPONSE OF SOCIETY.Journal of Central Asian Studies,22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeraj G, Romshoo SA, Yousuf AR, Altaf S, Altaf F (2015) Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya. Nat Hazards 77(1):153\u0026ndash;175\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukhopadhyay B, Dasgupta S (2015) Seismic hazard assessment of Kashmir and Kangra valley region, Western Himalaya, India. Geomatics, Natural Hazards and Risk, 6(2), 149-183\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Research Council (2012) Himalayan glaciers: Climate change, water resources, and water security. National Academies Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi J, Barazangi M (1984) Seismotectonics of the Himalayan collision zone: Geometry of the under thrusting Indian plate beneath the Himalaya. Journal of Geophysical Research: Solid Earth 89(B2):1147\u0026ndash;1163\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNIDM. India Disaster Report (2011) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nidm.gov.in/PDF/pubs/India\u003c/span\u003e\u003c/span\u003e Disaster Report 2011.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver-Smith A, Hoffman SM (2002) Introduction: Why anthropologists should study disasters. Catastrophe and culture: The anthropology of disaster, 3-22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver-Smith A, Alc\u0026aacute;ntara-Ayaia I, Burton I, Lavell A (2017) Forensic Investigations of Disaster (FORIN): towards the understanding of root causes of disasters. Policy brief. Integrated Research on Disaster Risk, ICSU, Beijing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParkash S (2011) Historical records of socio-economically significant landslides in India. J South Asia Disaster Studies 4(2):177\u0026ndash;204\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParkash S, Kathait A (2014) A selected annotated bibliography and bibliography on landslides in India. National Institute of Disaster Management, Ministry of Home Affairs, Govt. of India, New Delhi, p 172\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel SK, Nanda A, Singh G, Patel S (2020) A review of disasters in Jammu and Kashmir, and Ladakh region in India. International Journal of Population Studies 6(1):69\u0026ndash;81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetley D (2012) Global patterns of loss of life from landslides. Geology 40(10):927\u0026ndash;930\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetley DN (2010) On the impact of climate change and population growth on the occurrence of fatal landslides in South, East and SE Asia. Q J Eng GeolHydrogeol 43(4):487\u0026ndash;496\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetley DN, Dunning SA, Rosser NJ (2005) The analysis of global landslide risk through the creation of a database of worldwide landslide fatalities. Landslide risk management. CRC Press, pp 377\u0026ndash;384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafiq M, Mishra AK (2018) A study of heavy snowfall in Kashmir, India in January 2017. Weather 73(1):15\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRam G (1928) Floods in Jammu and Kashmir. Jammu and Kashmir government flood management report (pp.\u0026nbsp;2\u0026ndash;3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRam PB (1895) Report on the administration of the Jammu and Kashmir State for 1892\u0026ndash;93. Rambir Prakash Press, Jammu, India, pp 132\u0026ndash;133\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRather MA, Kumar JS, Farooq M, Rashid H (2017) Assessing the influence of watershed characteristics on soil erosion susceptibility of Jhelum basin in Kashmir Himalayas. Arab J Geosci 10(3):59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaza M, Ahmad A, Mohammad A (1978) The Valley of Kashmir: A Geographical Interpretation, Vol,1: the Land. Vikas Publishing House Pvt, Ltd., New Delhi, pp 1\u0026ndash;59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazdan Vinayak. September 6, 2014 \u0026ldquo;Sylabnama: Great Floods of Kashmir\u0026rdquo;. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://searchkashmir.org/2014/09/sylabnama-great-floods-of-kashmir.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiede F (2014) Towards a science of past disasters. Nat Hazards 71(1):335\u0026ndash;362\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiede F (2017) Past-forwarding ancient calamities. Pathways for making archaeology relevant in disaster risk reduction research. Humanities 6(4):79\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRMSI Private Limited India (2018) Multi Hazard Risk Assessment of the State of Jammu and Kashmir, Data Inventory Report, November,2018. RMSI Limited, India (http://jtfrp.in)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleh SF, Rather FF, Jabbar MJ (2017) Floods and mitigation techniques with reference to Kashmir. Int J Eng Sci Comput 7(4):6359\u0026ndash;6363\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchenk GJ (2014) Common grounds in early modern disaster experiences? Some remarks on new trends. historical disaster research as part of environmental history and climate history. An environmental history of the early modern period: Experiments and perspectives. LIT, Z\u0026uuml;rich, pp 11\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuster RL, Highland L (2001) Socioeconomic and environmental impacts of landslides in the western hemisphere. US Department of the Interior, US Geological Survey, Denver (CO), pp 1\u0026ndash;50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuster RL (1996) Socioeconomic significance of landslides. In: Turner AK, Schuster RL (eds) Landslides: Investigation and mitigation, vol 247. National Academy Press.Transportation Research Board Special Report, pp 12\u0026ndash;35\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSearle MP (1991) Geology and tectonics of the Karakoram Mountains. John Wiley \u0026amp; Sons Incorporated\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSearle MP, Windley BF, Coward MP, Cooper DJW, Rex AJ, Rex D, Kumar S (1987) The closing of Tethys and the tectonics of the Himalaya. Geol Soc Am Bull 98(6):678\u0026ndash;701\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeeber L, Armbruster JG (1981) Great detachment earthquakes along the Himalayan arc and long-term forecasting. Earthquake prediction: an international review 4:259\u0026ndash;277\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSethi DN, Satyawali PK (2000) Snow and Avalanche Problem in Indian Himalaya and its Mitigation\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah A (2016) The Kashmir Basin fault and its influence on fluvial flooding in the Kashmir Basin, NW Himalaya. Geol Soc Am Spec Pap 520:321\u0026ndash;334\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah AA, Khwaja S, Shah BA, Reduan Q, Jawi Z (2018) Living with earthquake and flood hazards in Jammu and Kashmir, NW Himalaya. Frontiers in Earth Science 6:179\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma S, Kumar A, Ghangas V (2013) Seismicity in Jammu and Kashmir region with special reference to Kishtwar. International Journal of Scientific and Research Publications 3(9):1\u0026ndash;5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorkhabi R (2006) The great Himalayan earthquakes.The Himalayan Journal, 62(5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpencer JM, Ashley WS (2011) Avalanche fatalities in the western United States: a comparison of three databases. Nat Hazards 58(1):31\u0026ndash;44\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eState Disaster Management Plan, J\u0026amp;K (2017) State for 1892\u0026ndash;93. Rambir Prakash Press, Jammu, India, pp 132\u0026ndash;133Developed by Tata Institute of Social Sciences, Mumbai\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStein MA (1899) Memoir on Maps Illustrating Ancient Geography of Kashmir (p.235). British Mission Press, Culcutta, India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultana N (2020) Analysis of landslide-induced fatalities and injuries in Bangladesh: 2000-2018. Cogent Social Sciences 6(1):1737402\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwinburne TR (1907) A holiday in the happy valley with pen and pencil. Indy Publish. com\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanoue M, Hirabayashi Y, Ikeuchi H (2016) Global scale river flood vulnerability in the last 50 years. Sci Rep 6(1):1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor FE, Malamud BD, Freeborough K, Demeritt D (2015) Enriching Great Britain's national landslide database by searching newspaper archives. Geomorphology 249:52\u0026ndash;68\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur VC (1992) Geology of western Himalaya. Phys Chem Earth 19:1\u0026ndash;355\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur VC (1998) Structure of the Chamba nappe and position of the Main Central Thrust in Kashmir Himalaya. J Asian Earth Sci 16(2\u0026ndash;3):269\u0026ndash;282\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur VC, Kumar S (2002) Seismotectonics of the Chamoli Earthquake of March 29, 1999 and Earthquake Hazard Assessment of Garhwal \u0026ndash; Kumaon Region, NW Himalaya. Himalayan Geol 23(1\u0026ndash;2):113\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNISDR (2004) \u0026lsquo;Living with risk: a global review of disaster reduction initiatives\u0026rsquo; launched at United Nations Headquarters. 14/07/2004. Press Release IHA/922\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUppal HL (1956) Flood Control, Drainage, and Reclamation in Kashmir Valley. Central Water \u0026amp; Power Commission\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSGS (2020) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.usgs.gov/faqs/why-are-we-having-so-many-earthquakes-has-naturally-occurring-earthquake-activity-been?qt-news_science_products=0#qt-news_science_products\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValdiya (1998) Valdiya, K. S. (1998). Dynamic Himalaya. Universities press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValdiya KS (2001) Reactivation of terrane-defining boundary thrusts in central sector of the Himalaya: implications. Curr Sci 81(11):1418\u0026ndash;1431\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Westen C (2012) Landslide risk assessments for decision-making. The World Bank, pp 67\u0026ndash;71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Westen CJ (2002) Remote sensing and geographic information systems for natural disaster management. Environmental modelling with GIS and remote sensing. Taylor \u0026amp; Francis, London, pp 200\u0026ndash;226\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Westen CJ (2004) Geo-information tools for landslide risk assessment: an overview of recent developments. Landslides Eval Stab 1:39\u0026ndash;56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Westen CJ (2013) Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise on geomorphology 3:259\u0026ndash;298\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenugopal R, Yasir S (2017) The politics of natural disasters in protracted conflict: the 2014 flood in Kashmir. Oxf Dev Stud 45(4):424\u0026ndash;442\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin A (2006) Cenozoic tectonic evolution of the Himalayan orogen as constrained by along strike variation of structural geometry, exhumation history, and foreland sedimentation. Earth Sci Rev 76(1\u0026ndash;2):1\u0026ndash;131\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hazard profile, Kashmir Valley, Earthquakes, Landslides, Floods, Snow avalanches, Historical data analysis","lastPublishedDoi":"10.21203/rs.3.rs-1148421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1148421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDisasters not only cause high mortality and suffering, but thwart developmental activities and damage local economies in process of formation. A part of the NW Himalayas, the Kashmir Valley is very distinct with respect to its location, topography, climate, socioeconomic structure, and strategic geopolitical nature owing to which it has witnessed a multitude of disasters ranging from local incidents of rockfalls to catastrophic earthquakes, and has often paid heavily in terms of loss of life and property. However, the information on most of the events is either partially reported or exaggerated or sometimes not recorded at all and largely scattered. Availability of organized and reliable record of past hazards and disasters is essential for tackling the risks and mitigating the future disasters. In this context, the present study attempts to address the lack of data availability by focusing on developing a dependable hazard and disaster catalogue of the Kashmir Valley by investigating into the existing literature and the available secondary data sources. A record of natural hazards and disasters most prevalent in the valley viz., earthquakes, floods, landslides and snow avalanches, has been compiled for the time period 1900 to 2020 by making use of various secondary sources, comprising of 1854 events with a range of triggers and impacts reported in the valley, which provide an insight into the spatial and temporal (frequency and distribution) trends of different hazard types for the selected time-period. Developing a catalogue of events reported in the Kashmir Valley can help in building a hazard and disaster scenario which serves as a reliable information source and is of great value from the perspective of regional design, planning and policy responses to promote disaster risk reduction.\u003c/p\u003e","manuscriptTitle":"Developing a Hazard Profile of The Kashmir Valley Through Historical Data Analysis For The Period 1900-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-16 15:37:25","doi":"10.21203/rs.3.rs-1148421/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-12-15T09:46:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-12-15T09:03:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2021-12-14T17:16:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-12-14T13:45:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2021-12-07T04:54:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3378052b-f67c-400f-9d92-4cc87830bc14","owner":[],"postedDate":"December 16th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":9195439,"name":"Atmospheric Sciences"},{"id":9195440,"name":"Planetary Science"}],"tags":[],"updatedAt":"2022-05-30T15:55:49+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-16 15:37:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1148421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1148421","identity":"rs-1148421","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}