Geospatial analysis of soil erosion and associated geomorphic hazards to avert increasing disaster risk in environmentally stressed eastern Himalaya region

Geospatial analysis of soil erosion and associated geomorphic hazards to avert increasing disaster risk in environmentally stressed eastern Himalaya region | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Geospatial analysis of soil erosion and associated geomorphic hazards to avert increasing disaster risk in environmentally stressed eastern Himalaya region Pradeep Rawat, Khrieketouno Belho, M Rawat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3826948/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Geo-environmentally, the eastern Himalaya region is highly vulnerable to erosion and soil loss geomorphic hazard due to humid tropical to humid sub-temperate climate (receives 1600-3200mm mean rainfall), young and highly erodible rock formations (mainly comprised of sandstones, siltstones and shales), fragmented reshaping geomorphology, high erodibility of surface and sub-surface soils. Despite that, anthropogenic activities have been enhancing this geo-environmental vulnerability to erosion hazard through rapid unplanned urbanization with associated infrastructural development in urban to suburban areas and shifting cultivation practices in rural areas. Addressing this burning environmental problem, a geospatial technology-based case study of the Kohima district, Nagaland state (India) from eastern Himalaya is presented here. Various experiential models are available for computing soil erosion; however, a Revised Universal Soil Loss Equation (RUSLE) integrated with the GIS framework was applied in the current study due to its robustness and high accuracy level. Five key RUSLE factors such as erosivity of rainfall (RE), erodibility of soil (ES), erodibility of rock (ER), slope length (LS), crop management (CM) and conservation practice (CP) were calculated using required data sets in a GIS environment. RE ranges between 648.12–1294.15 MJ mm/ha/h/year, ES varies minimum of 0.10 to a maximum of 0.41 among the existing 15 classes of soils, ER factor values ranges 0.01–0.04, LS factor values range between 0 and 1.22, CM factor values vary from a minimum of 0.0 for dense forest area to maximum 1.80 for buildup areas whereas the CP value varies 0.1–1.0 across the study region to land use/cover pattern. The accumulated impact of these erosion and soil loss factors resulted in a quite higher average rate (about 16 t/ha/year) than the threshold value of soil erosion (< 10 t/ha/year). This value ranges from 1–92.18 t/ha/year and poses. Thus, it has been essential to minimize the high rate of erosion through intensifying CP factors at the government level, community level and even individual level by adopting scientific crop patterns, agro forestry and reforestation programs. If these necessary actions were not taken timely, it may lead to other erosion-induced geomorphic hazards such as land degradation, mass movement, landslides, slope failure etc. Geomorphic hazards Erosion Soil loss Geospatial RUSLE model Kohima India Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Erosion is one of the most serious geomorphic hazards in sub-tropical climatic ecosystems across the world in general (Yamusa and Ismail 2023 , Al-Sababhan 2024, Chettr 2023)) and eastern Himalayas in particular (Rawat and Pant 2014, 2016, 2017; Gupta, 2023 ). It affects not only the natural environment through the acceleration of mass movement, landslides, removal of vegetation cover, and siltation of reservoirs but also affects the agriculture-centric rural economy through the loss of the topmost fertile soil layer, causing to decline in crop yield (Rawat and Rawat 1994 , Rawat an Pant, 2016, Gupta 2018 , 2021 ). Erosion rates are affected by several geophysical (comprises of geology, structural lineaments, soil erodibility, relief pattern, slope gradient, drainage morphometry), hydro-meteorological (comprises of temperature, rainfall, evaporation, surface percolation, runoff) and anthropogenic (comprises of vegetation cover, land use pattern, excavation, unscientific agricultural practices, community awareness level and conservation support practices) factors (Abdo and Salloum 2017 ; Sharma and Singh 2017; Rawat 2003 , 2011 , 2012, 2017 , Rahman et al., 2023 ). So that, the erosion rates varies from place to place under different geo-ecological systems. Desert ecosystems, which lack protective vegetation covers, may erode obviously at rates multiple times higher than those for humid climatic ecosystems, which have thick evergreen vegetation cover (Miller and Donahue 1990 ; Rawat et al., 2011, 2011a, Rawat, 2013 , 2014 ). It means the removal of vegetation cover brings soil in direct contact with eroding agents such as water and wind thereby resulting in loss of surface soil layer thus negatively influencing the fertility of the soil (Rawat et al. 2012, 2012a, 2012b, Amare et al., 2023 , Hasan et al. 2023 . Considering it as a threatening geomorphic hazard, some researchers have pronounced it the 'creeping death of the soil' (Rama Rao 1962 ; Tripathi and Singh 1993 ; Angima et al. 2003 ; Sharma 2010). Pimentel et al. 1995 (1995) and El-Swaify (1997) have claimed in their studies that Asia is one of the highest (SE = 74 t/ac/year) soil erosion-prone areas. As far as the eastern Himalayas or northeastern region of India is concerned geo-physically it is characterized by rugged and undulating terrain composed mainly of high erodibility sandstone lithology. Moreover, across the region, unscientific traditional shifting (Jhum) agricultural practices have been accelerating soil sheet erosion thus causing adverse ecological (shrinking habitats, increasing man-wildlife conflicts, biodiversity loss, soil quality loss, low crop yield etc.) and hydrological (drying water spring, streams, watersheds during dry season and increasing frequency of extreme flood events during rainy season etc.) impacts (Rawat 2013 , 2013a ,). Subsequently, it has caused to increase in unproductive areas, to change livelihoods, and to migrate people to other places; thereby impacting the socio-economic structure of the rural population (Rawat and Haigh 1998 , Rawat et al. 1995 , 1999 , 2000). Therefore, there has been an urgent need to identify all erosion-affected areas so that suitable biological and mechanical measures can be implemented for the timely mitigation of erosion and erosion-induced geomorphic hazards such as mass movement, landslides, and removal of large-scale vegetation cover including forests. To address this burning environmental issue a case study of Kohima district, Nagaland (India), carried out through an integrated approach of the Revised Universal Soil Loss Equation (RUSLE) model and geospatial technology, is presented here (Fig. 1 ). RUSLE is one of the most widely applied experimental models for erosion rate assessment, developed by Wischmeier and Smith in 1978. The geospatial technology-based techniques (DEM, GIS and remote sensing), facilitate spatial data input to the RUSLE model for its effective application and to predict the accurate erosion rate from the selected study area, whether it is a watershed, river basin or large regional landscape (Renard et al. 1997; Youe-Qing et al. 2008 ; Kouli et al. 2009 ). Originally the model was introduced as Universal Soil Loss Equation (Wischmeier and Smith in 1978) while in some recent research work, it has been pronounced as the Revised Universal Soil Loss Equation (RUSLE) model just by integrating modern geospatial technology with it and advocating the large-scale application of the revised model for erosion hazard assessment (Prasannakumar et al. 2012 , Demirci and Karaburum 2012; Pradeep et al. 2015 ; Gelagy and Minale 2016; Ganasri and Ramesh 2016 ). Kohima is a district and capital city of Nagaland state of India. It represents the rich geo-biodiversity of the northeastern Himalaya Mountain within just about 978.96 km2 between 204m to 2953m altitude above mean sea level and lies between 25⁰31′21′′N to 25⁰54′30.06′′N latitudes and 93⁰54′17′′E to 94⁰16′4′′ E longitudes in northeastern Himalaya region of India (Fig. 1 ). The climate varies throughout the study area from humid tropical to humid sub-temperate, between the lowest and highest elevations, respectively. Consequently, the annual average temperature varies between less than 23⁰C within sub temperate zone to more than 28 ⁰C within the tropical climatic zone, whereas average precipitating varies between below 2360 and 3240 mm, respectively, from humid tropical to humid sub-temperate climatic conditions. Geo-environmentally the region is highly vulnerable to soil erosion and accelerated erosion-induced geomorphic hazards such as mass movement, landslide and slope failure because of rugged, steep terrain, tectonically active fragmented young geology, dynamic reshaping geomorphology, and high density of drainage network. Keep in view this; it has been essential to investigate erosion-affected areas to implement required mitigation measures so that accelerated erosion-induced geomorphic hazards can be averted. Materials and methods The study carried out through the integration of two broad GIS modules; these are geo-environmental GIS modelling and erosion hazard modelling as demonstrated in Fig. 2 and being described below. Geo-environmental GIS modeling Geo-environmental GIS module provides spatial and attribute data of topographical, geophysical, and ecological parameters. Thematic GIS maps of topographical (comprised relief pattern, surface slope gradient, wetness index and drainage morphometry) geophysical parameters (comprised of geology, geomorphic landforms and soil characteristics) and ecological parameters (comprised of climate, rainfall, runoff, vegetation cover and land use pattern) have carried out using geospatial techniques after required fieldwork and laboratory analysis. For geospatial analysis, Google Earth satellite imagery, Survey of India topographical sheets (83G/13, 83G/14, 83K/1, 83K/2, 83K/5) at scale 1:50000 used to carry out geological, tectonic, geomorphological and soil distribution maps. USGS-Landsat satellite imagery with 30m spatial resolution was used for mapping land use patterns, vegetation covers etc. NASA-STRM satellite imagery with 30 spatial resolutions was used for relief, slope, watershed demarcation, drainage pattern, drainage order, drainage density, drainage frequency, water flow direction, and flow accumulation through a digital elevation model (Fig. 1 ). Monthly rainfall data for a period of three decades (1991–2022) was collected for 12 locations at multiple elevations across the study area, out of which 8 locations represent web GIS technology-based rainfall data recorded by NASA Power Data Access Viewer (NASA-DAV: https://power.larc.nasa.gov/data-access-viewer/ ). Whereas remaining 4 are ground-based meteorological stations, run by the Indian Meteorological Department or IMD (2 stations) and, the soil and water conservation department of Nagaland state government (2 stations) (Fig. 1 ). Figure 2 demonstrates the process that how all necessary data used to appraise RUSLE factors and superimposed to attain the key objective of the study as being described below: Erosion hazard modelling The geospatial technology-based Revised Universal Soil Loss Equation (RUSLE) model test was followed to carry out an erosion hazard study of the study area (USDA 1993). To carry out the average rate of erosion and soil loss, the RUSLE model requires integration of the erosion-controlling factors using the following equation: A = RE × ES x ER x LS × CM × CP where A is the soil loss; RE is the rainfall and runoff erosivity factor; ES is the erodibility of soil and ER is erodifbility of bedrock factor; L is the slope length factor; S is the steepness factor; CM is the cover and management factor and CP is the conservation practices factor. Selected factors were integrated with RUSLE in the GIS environment to get the soil erosion rate. Methods to analyze all these erosion-controlling factors are discussed below. Rainfall runoff Erosivity (RE) factor Rainfall-runoff erosivity is considered to be a key factor of the USLE model to estimate soil erosion. The RE-factor of USLE indicates the possible potential of a rainstorm incident to soil erosion. In the present study, the RE-factor is analyzed using total storm energy and its maximum 30-min intensity following Wischmeier and Smith ( 1978 ). Mean annual rainfall data for 30 years was used to estimate RE-factor following Choudhury and Nayak ( 2003 ) R = 79 + 0.363 × Xa where RE is the Rainfall-runoff erosivity, and Xa is the Average annual rainfall (mm). Erodibility of soil (ES) factor ES expresses the resistance power of top surface soil against the rainstorm event, which depends on geo-chemical properties of soil such as dispersal of grain mass, organic matter and soil structure and absorbency (Wischmeier and Smith 1978 ). Based on the geological and pedological maps of the study area, ESR values of existing soil and rock types have been estimated from the nomogram (Wischmeier and Smith 1978 ) of RUSLE. The ES factor map of the study area has been prepared by plotting the ES/R values of each map unit. Erodibility of rock (ER) factor ER expresses residence power of bedrock (R) against the rainstorm event, which depends on geo-chemical properties of rock, such as dispersal of grain mass, organic matter and structure and absorbency (Wischmeier and Smith 1978 ). Based on the geological maps of the study area, ER values of existing rock types have been estimated from the nomogram (Wischmeier and Smith 1978 ) of RUSLE. The ER factor map of the study area has been prepared by plotting the ER values of each map unit. Slope Steepness and Length LS factor combines two sub-factors which are length (L) of slope and steepness or slope gradient (S) of slope. The slope length factor represents the combined effect of steepness and length of slope on soil loss rate in a region. The higher the LS factor, the higher the vulnerability to soil loss. LS facto was determined using SRTM DEM data following Moore and Burch (1986). Where LS is the length and steepness factor of the slope respectively, cell size is the pixel value of a single grid cell which also reflects the spatial resolution of the DEM satellite image and sin is the slope degree value in the Sin table. Cover management (CM) factor In general, it is a type of vegetation cover, which influences the rate of soil erosion as vegetal cover does not allow the rainstorm to interact directly with the top surface layer of soil and thereby averts the soil erosion. CM factor is expressed as the ratio of soil loss among different types of existing vegetation cover in the study area. The CM value ranges between 0 and 1 which was calculated using the following formula (Van der Knijff et al. 1999). Where α and β are the parameters that determine the shape of the NDVI-C curve. α value of 2 and β value of 1 was taken (Van der Knifjj et al. 1999). Conservation practice (CP) factor The CP factor represents different types of land use/cover patterns of a specific region. This factor controls soil erosion by shifting the flow pattern, pitch or course of runoff and by reducing the volume and runoff rate (Renard and Foster 1983 ). To that, Landsat satellite imagery with 30m spatial resolution of the study area classified with five (water bodies, dense forest area, degraded open forest, built-up land and agriculture land) major types of land use/cover pattern applying supervised classification method and then reclassified based on their CP values suggested in USDA Handbook (1981) list. Results and discussion Results are being discussed categorically in three broad sections: first section presents spatial and non spatial GIS database of geo-environmental setup, second sections is on RUSLE factor analysis and data integration to compute erosion and soil loss using the Algebra tool of ArcMap GIS program whereas third section discusses the status of erosion associated geomorphic hazards such as landslide, debris flow, mass moment slope failure etc. Geo-environmental setup As the RUSLE model requires detailed attributes of all key geo-environmental parameters of the study area, were carried out through extensive fieldwork and laboratory analysis and thereafter converted in a GIS environment. These geo-environmental parameters are: Topographical Characteristics The vulnerability level of soil loss whether it is in the form of sheet erosion or gully erosion is mainly driven by terrain characteristics such as relief pattern, slope gradient, slope aspects, topographic wetness index, drainage density and drainage order. So, these terrain morphometric parameters have been studied through the Digital Elevation Model (DEM) using SRTM satellite imagery with a spatial resolution of 30 m (Fig. 3 ). This DEM-based topographic analysis reveals that the elevation across the region varies from a minimum of 204m in the northwest part to a maximum of 2953m in the southern part (Fig. 3 a). The slope gradient ranges below 15° in the alluvial plain area in the southern part to 81° in the mountain area southern part respectively (Fig. 3 b). Slope aspect is highly controlled by geo-structural lineaments such as faults, thrusts, strike ridges etc (Fig. 3 c). Topographic wetness index is highly controlled by slope aspects, flow accumulation pattern of the terrain (Fig. 3 d). Drainage morphometry (stream density, stream frequency, stream ordering etc.) and flow direction are highly controlled by geology and geomorphological sections of the region (Fig. 3 e, 3 f respectively). Geophysical setup Key geophysical factors which determine the erosion rate within a region are geology, geomorphology and soils of the land surface. Keeping in view this, these three key factors were studied comprehensively. Geology of the area consists of highly erodible rocks such as sandstone, siltstone, shales of Barail, Tipam and Surma groups; slates and phyllites of Disang group (Fig. 4 a) Spatial distribution of rock pattern is highly influenced by geo-structural lineament density which varies < 0.40km/km 2 -2 km//km 2 across the region (Fig. 4 b). Rich diversity of geomorphic landforms resting on these geological groups can be categorized as fluvial landforms (comprises alluvial plain, piedmonts, flood plain, inter hill valley, rills, young fluvial fans, river terraces) and tectonic landforms (comprises strike ridge areas, high relief structural hills, tectonic scars, triangular facets which are moderately to highly dissected forms) which are highly controlled by number of regional and local geo-structural lineaments such as thrusts, faults, lithological joints sets and fractures (Fig. 5 a). The top layer of all geomorphic landforms consists of sub-surface and surface soil; therefore types of soils vary across the region in order to geomorphological and geological background. So considering geology, geomorphology and soil taxonomy and properties, a total of twelve types of soils were identified and mapped in the study area from lowest to highest elevation under different geomorphological and lithological backgrounds (Table 1 ). The spatial distribution map (Fig. 5 b) shows that the low relief geomorphology (Piedmonts, Foothills) comprises Fine, Umbric Dystrochrepts soil and Fine, Loamy Lithic Udorthent soils over lithology of Disang group rocks (shales, slates and phyllites). Moderate relief geomorphology (Upslope hills, downslope hills, terraces, Inter hill valley) comprises Fine Loamy Lithic Udorthent, Umbric Dystrochrepts, Fine, Typic Paleudults, Typic Hapludults and Fine-Loamy soils over lithology of Disang group rocks (shales, slates and phyllites). High relief geomorphology (Strike ridge areas, High relief structural hills, High-middle structural hills) comprises Coarse-loamy, Typic Dystrochrepts, Fine-loamy, Typic Dystrochrepts soils over lithology of Barail group rocks (Shales, sandstone). Ecological status Land use pattern, climate, rainfall, runoff, are the key ecological factors which influence the rate of soil erosion, indeed these ecological parameters are key drivers of the erosion geomorphic process. Therefore all ecological parameters were studied. The land use pattern map reveals that only 18% area is in the virgin stage or anthropogenically least degraded (dense forest), whereas 63% is moderately degraded (open forest which is exploited by inhabitants in the name of shifting agricultural practices, fodder, firewood etc.), 10% highly degraded (comprises 1% shrubs land, 1% wasteland and 8% agriculture land). The remaining 9% area is very highly degraded (built-up area including urban settlements, and rural settlements with associated infrastructural development such as roads, bridges, bridle paths, canals, dams etc.) (Fig. 6 a and Table 1 ). Other three ecological factors, namely climate, rainfall and runoff are deeply correlated. The region falls in the humid climatic region of India, further can be categorized into four micro-climatic zones considering local spatial variability in long-term meteorological characteristics; these are humid tropical, humid subtropical, temperate and humid temperate (Fig. 6 b). Subsequently, the region receives heavy annual rainfall which varies from 1600mm to 3250mm in the lowest relief areas (200m) to the highest relief (2950m) areas respectively (Table 1 and Fig. 6 c). Heavy rainfall throughout the year results in high flood surface runoff which ultimately accelerates the soil erosion. However, this flood runoff varies below 100 liter/km 2 in anthropogenically least degraded dense forest land to above 600 liter/km 2 in most degraded buildup land area (Fig. 6 d). RUSLE model test Rainfall-runoff Erosivity Factor (RE) The average annual rainfall erosivity factor (RE) for the selected last three decades period (1991–2021) ranges between 648.12–1294.15 MJ mm/ha/h/year across the Kohima district (Fig. 7 a). The higher values of rainfall erosivity (1000-1294.15MJ mm/ha/h/year) are found in the southern part of the region having elevation above 1400m from mean sea level and the northern part, having lower elevation below 800m from mean sea level has lower values of rainfall erosivity (648–800 MJ mm/ha/h/year) whereas the mid-altitude area having moderate rainfall erosivity between 800–1000 MJ mm/ha/h/year (Table 1 ). Table 1 Test values of RUSLE factors and sub-factors Factors Sub-factors Test value Average Rainfall Erosivity (RE) 1620–1975 648.124-848.124 1018.984 1976–2260 848.124-948.124 2261–2488 948.124-998.124 2489–2703 998.124-1148.12 2704–2919 1148.12-1248.12 2920–3236 1248.12-1294.15 Erodibility of Soil (ES) Coarse-loamy, 0.09 0.248 Typic Dystrochrepts 0.11 Fine-loamy 0.32 Umbric Dystrochrepts 0.38 Typic Hapludults 0.31 Fine, Typic Paleudults 0.28 Umbric Dystrochrepts soil 0.38 Fine Loamy Lithic Udorthent 0.41 Coarse-loamy, 0.09 Typic Dystrochrepts 0.11 Erodibility of Rock ER Barail group Shales, 0.02 0.02 0.03 Sandstone 0.04 Disang group Shales, 0.02 0.01 Slates, 0.01 Phyllites 0.01 Tipam group Shales, 0.02 0.03 Siltstones, 0.03 Sandstones 0.04 Surma group Sandstones 0.04 0.04 Slope Steepness (SL) 0–10 0–10 29.80 10–25 10–20 25–35 20–30 35–45 30–40 45–80 40–49 Cover Management (CM) Dense Forest (Least degraded) 0.01 0.25 Open Forest (Moderate degraded) 0.05 Water bodies (Moderate degraded) 0.00 Shrubs land (Highly degraded) 0.05 Build up area (Very highly degraded) 1.00 Waste Land (Very highly degraded) 0.08 Agricultural land (Highly degraded) 0.60 Conservation Practices (CP) Dense Forest (Least degraded) 1.00 0.52 Open Forest (Moderate degraded) 0.70 Water bodies (Moderate degraded) 0.00 Shrubs land (Highly degraded) 0.70 Build up area (Very highly degraded) 0.10 Waste Land (Very highly degraded) 0.70 Agricultural land (Highly degraded) 0.50 Erodibility Factor of Soil (ES) This factor is generally controlled by the texture of soil rather than other parameters such as structure and organic matter. ES value increases as the soil texture becomes smaller to finer, such as fine loamy soil which contains a maximum proportion of silt and smaller to finer sand due to, is highly vulnerable to erosion. ES value in Kohima district ranges in 15 classes concerning existing types of soil. Figure 7 b depicts the spatial variability of the soil erodibility factor of the region which varies minimum of 0.10 to a maximum of 0.41 among the existing 12 classes of soils. The range of the ES factor varies from a minimum of 0.00 to 0.41, where close to '0' indicates less susceptibility to soil erosion and close to '0.41' is an indication of high susceptibility to soil erosion (Table 1 and Fig. 6 b). Erodibility factor of rock (ER) Erodibility of exposed bedrocks depends on geo-chemical properties of rock, such as dispersal of grain mass, organic matter and structure and absorbency (Wischmeier and Smith 1978 ). Based on the geological maps of the study area, ER values of existing rock types have been estimated from the nomogram (Wischmeier and Smith 1978 ) of RUSLE. The average ER value of the study area stands on 0.03 whereas it is varies minimum 0.01 for Slates and Phyllites of Disang group to maximum 0.04 sandstones Barail, Tipam and Surma groups (Table 1 and Fig. 7 c). Slope Steepness and Length Factor (LS) LS is considered a significant factor in the RUSLE model as it influences the amount of soil erosion in a specific site. LS factor has been calculated by integrating the slope factor and flow accumulation factor in SRTM DEM. This exercise carried out values of slope steepness and length (LS) factor ranges between 0 and 49 across the study area (Fig. 10 ). Spatial distribution suggested that in most of the proportion of the study area, LS factor values range between 0 and 1.22 (Table 1 and Fig. 7 d). Crop Management Factor (CM) Crop management factor determines crop sequence, productivity level, depth of soil etc., thereby influencing rate of erosion and soil loss. Due to the rich diversity in the relief and slope pattern of the study area, land cover or cop pattern mapping was carried out to assess the CM factor. CM factor value varies minimum of 0.0 for dense forest areas to a maximum of 1.80 for buildup areas (Table 1 and Fig. 8 a). Most parts of the study area account for a CM value below 0.25, which represents an area of dense to open forest land. It is also noted that the CM value varies in different seasons and weather conditions throughout the year. Conservation Practices Factor (CP) Erosion-controlling biological and mechanical measures are considered a conservation practice in the RUSLE model. The frequency of these erosion mitigating measures influences the CP value and rate of soil erosion. To that, the CP values were assigned considering the land use pattern of the study region. Buildup area accounts for low CP value whereas dense forest area accounts for higher CP value. The CP value varies from 0.1–1.0 across the study region to land use/cover pattern (Table 1 and Fig. 8 b). Spatial variability of erosion and soil loss (A) Spatial variability of erosion and soil loss (A) has been computed by superimposing and multiplying the prepared raster map layers of all five RUSLE factors (A = RE x ES x ER x LS x CM x CP) through Map Algebra raster calculator spatial analysis tool of ArcMap GIS software. The outcome map of overlay operation shows that the spatial variability of annual erosion and soil loss rate ranges from 0–92.18 t/ha/year across the region. This range has been classified into four erosion intensity zones: these are low (below 20 t/ha/year), moderate (20–40 t/ha/year), high (40–60 t/ha/year), and very high (above 60 t/ha/year) erosion intensity zones (Fig. 9 ). Erosion induced geomorphic hazards susceptibility The high rate of erosion and associated geomorphic hazards during heavy rainfall is a serious geo-environmental problem in eastern Himalaya as the region receives more than enough annual average rainfall which varies from 2500-3200mm (Sengupta, 2010, Dikshit et al. 2019, Gariano et al. 2019, Teja 2019, Harilal et al 2019). There are several geospatial technologies based on modern studies on erosion and rain-induced geomorphic hazards (mass movement, debris fall, and landslide etc.) across the Himalayas but most of them are concentrated in central Himalaya (Dash et al., 2000, Naithani et al. 2002, Sarkar and Gupta, 2005, Shantanu et al. 2013, Martha et al. 2015) and western Himalaya (Mukhopadhyay et al. 2005, Roy et al. 2018, Kumar et al., 2018, Pradhan et al., 2019, Banerjee and Dimri 2019, Kumar et al. 2019) region whereas from eastern Himalaya region lack of studies are available (Umrao et al. 2017, Sarkar et al., 2017, Dikshit and Satyam 2018, Bera et al., 2019); as far as Nagaland state is concern it is about nil. Keep in view this; it has been essential to address rain and erosion-induced geomorphic hazard risk in the study area to represent Nagaland state as well as the eastern Himalaya region. However, studying each type of erosion and rainfall-induced geomorphic hazard whether it is a mass movement, landslide, slope failure or other, requires specific methods and techniques respectively. The present study discusses a general overview of geomorphic hazards which are being triggered by accelerated erosion during the rainy season in the study area. To that, during field validation of the erosion intensity zone map, several places have been identified across the study areas where a high rate of erosion has been triggering other associated geomorphic hazards such as mass movement, rock fall, debris fall, landslide and slope failure etc.; subsequently affecting infrastructural setup, agricultural land, settlements, rural livelihood, natural resources and wildlife ecosystem. Taking into consideration all such locations and spatial variability of erosion rate, geophysical characteristics, an integrated erosion-induced geomorphic hazard susceptibility zone map of the study area carried out which suggests four zones, namely low, moderate, high and very (Fig. 10 ). Area of low susceptibility to erosion-induced geomorphic hazards The areas having soil erosion rate below 20 t/ha/year and least stressed geophysical characteristics (comprised of fluvial plain and valleys over geology of shales, siltstones, sandstones rock) have been considered as low susceptibility zone of erosion induced geomorphic hazard (Fig. 10 ). No significant mass movement events are seen in such areas. Area of moderate susceptibility to erosion-induced geomorphic hazards The areas having soil erosion rate 20–40 t/ha/year and moderately stressed geophysical background (comprised of river terraces and gentle down-slope susceptibility zone of erosion induced geomorphic hazards (Fig. 10 ). Landslides and slope failure due to the erosion of slopes along rivers are common types of geomorphic hazards in this zone (Fig. 11 d). Area of high susceptibility to erosion-induced geomorphic hazards The areas having soil erosion rate 40–60 t/ha/year and moderate to highly stressed geophysical characteristics (comprised of steep up-slope hilly geomorphology over a geological background of shales, slates and phyllites) have been identified to high susceptibility zone of erosion induced geomorphic hazard (Fig. 10 ). Land subsidences with large spatial extension are found in this zone (Fig. 11 c). Anthropogenically accelerated erosion and subsequent landslide, rock fall, and debris flow along roads, and urban settlements of Kohima city are also found in this zone (Fig. 11 e and 11 f). Area of very high susceptibility to erosion-induced geomorphic hazards The areas having soil erosion rate above 60 t/ha/year and highly to extremely stressed geophysical characteristics (comprises of steep up-slope hills, strike ridge areas, high relief structural hills geomorphology over a geological background of Shales, sandstone.) have been identified as very high-risk zone of erosion induced geomorphic hazard (Fig. 10 ). Active landslides and slop failure geomorphic hazards are commonly seen in this zone (Fig. 11 a and 11 b). Conclusion The study concluded that unplanned urbanization with associated infrastructural development in urban to sub-urban areas and shifting cultivation practices in a rural area are the main anthropogenic factors of the high rate of erosion and soil loss; whereas young and highly erodible formation of rocks, fragmented reshaping geomorphology, high-intensity rainfall are key natural causes of high rate of erosion and soil loss. The sudden change in any one of above mentioned anthropogenic and natural factors must lead to accelerated erosion and soil loss. The output results of the RUSLE model on key drivers of erosion and soil loss show that the rainfall erosivity factor (RE) ranges between 648.12–1294.15 MJ mm/ha/h/year, soil erodibility factor varies minimum of 0.10 to a maximum of 0.41 among existing 15 classes of soils, ER factor values ranges 0.01–0.04, slope steepness (LS) factor values ranges between 0 and 1.22, cover management (CM) factor values varies minimum 0.0 for dense forest area to maximum 1.80 for buildup areas whereas the conservation practice (CP) value varies 0.1–1.0 across the study region according to land use/cover pattern. The accumulated impact of these erosion and soil loss factors, results in a quite high average rate (about 9 t/ha/year) of erosion than the threshold value of soil erosion (< 10t/ha/year). This value ranges from 0–92.18 t/ha/year. Thus, the high rate of erosion has been triggering several geomorphic hazards in the region such as mass movement, debris fall, landslide etc.; subsequently affecting infrastructural setup, agricultural land, settlements, rural livelihood, natural resources and wildlife ecosystem (Fig. 9 ). It has been essential to minimize the high rate of erosion and its associated geomorphic hazards by intensifying the CP factor or conservation measures at the government level, community level and even individual level. If these necessary actions are not taken as early as possible, it may lead to worsening ecological and socioeconomic impacts. Declarations Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. Pradeep Kumar Rawat and Dr. Khrieketouno Belho. The first draft of the paper was prepared by Dr. Pradeep Kumar Rawat. .All authors commented on previous versions of the manuscript. Supervision was carried out by Prof. M.S. Rawat. All authors read and approved the final manuscript. References Abdo H, Salloum J (2017) Spatial assessment of soil erosion in Alqer- daha basin, Syria. Model Earth Syst Environ 3:26 Angima SD, Stott DE, O’Neill MK, Ong CK, Weesies GA (2003) Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agric Ecosyst Environ 97(1–3):295–308 Al-Sababhah, N. (2024) Land Suitability and Capability Analysis for Sustainable Allocation of Agricultural Crops and Natural Plants, Northwest Jordan. J geovis spat anal 8 , 1. https://doi.org/10.1007/s41651-023-00150-4 Amare, M.T., Demissie, S.T., Beza, S.A. (2023) Land Cover Change Detection and Prediction in the Fafan Catchment of Ethiopia. J geovis spat anal 7 , 19. https://doi.org/10.1007/s41651-023-00148-y Chettry, V. A(2023) Critical Review of Urban Sprawl Studies. 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Rawat, M. S. and Haigh, M. J.(1998): Rainy season runoff and sediment yields of forested and non-forested catchments in Kumaun Himalaya. In: Proceedings of the 8 th International Conference on Soil and Water Conservation: Challenges and Opportunities (eds. L. S. Bhushan, I. P. Abrol and M. S. Ramamohan Rao). Oxford and IBH Publishing Co. Pvt. Ltd., New Delhi, Vol. II, p. 1458-1465. Rawat, M. S., Haigh, M. J. Krecek, J. and Rawat, J. S. (1999): Dissolved load of a Central Himalayan forest headwater in an experimental catchment, India. In: Biogeochemistry of Rivers in Tropical South and South East Asia. (eds. Ittekkot, et. al.), Mitt. Geog. Palaont. Inst. Univ. Hamburg/SCOPE Sonderband Heft 82, Germany, Marz, p. 185-195. Rawat, M. S. (2003): Pattern of headwater erosion in Animal Park catchment, Kumaun Himalaya. Nagaland University Research Journal (NURJ), 2003, Vol. 1, p. 37-40. Rawat, M. S., Haigh, M. J. Krecek, J. and Rawat, J. S. (1998): Dissolved sediment flow from a Central Himalayan forest headwater catchment. In: Recent Trends in Environmental Biogeochemistry. Proceedings of the International Workshop, New Delhi, Jawahar Lal Nehru University, ENVIS, p. 443-449. Rawat, M. S. (2011). Environmental Geomorphology and Watershed Management. Concept Publishing Company, New Delhi (ISBN-13:978-81-8069-758-6). Rawat, M. S. (2013). Sustainable development in Nagaland through integrated watershed management: A case study from Kiliki River Basin. International Journal of Development Studies and Research, Vol. 2, 60-80. Rawat, M. S. and Furkumzuk C. (2013a). Environmental management and sustainable development in the Kiliki watershed of Nagaland. Indian Journal of Environmental Studies, Vol. 01, (01), 26-42. Rawat, M. S. (2017). Environmental management in the headwater catchments of Kiliki river, Nagaland, North East India . In: Ecosystem Services of Headwater Catchments (Eds. Krecek et. al.). Capital Publishing Company and co-published by Springer, International Publishing Company, Cham, Switzerland, p. 105–115. Rawat Pradeep K, Tiwari PC and Pant CC (2011). Modeling of stream runoff and sediment output for erosion hazard assessment in Lesser Himalaya; Need for sustainable land use plane using Remote Sensing and GIS: A case study. Natural Hazards , Vol. 59:1277–1297 Rawat Pradeep K, Tiwari PC, Pant CC, Sharama AK and Pant PD (2011a). Spatial variability assessment of river-line floods and flash floods in Himalaya: A case study using GIS. International journal of Disaster Prevention and Management , 12(2): 135-159. Rawat Pradeep K and Sharma A.K. (2012). Geo-diversity and its hydrological response in relation to landslide susceptibility in the Himalaya: a GIS-based case study. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 6(4): 229-251. Rawat Pradeep K, Tiwari PC and Pant CC (2012a). Geo-hydrological database modelling for integrated multiple hazards and risk assessment in Lesser Himalaya: GIS based case study. Natural Hazards , 62:1233-1260. Rawat Pradeep K, Tiwari Prakash C and Pant Charu C (2012b). Climate Change accelerating land use dynamic and its environmental and socio-economic risks in Himalaya: Mitigation through sustainable land use. International Journal of Climate Change Strategy and Management, 4(4):452-471. Rawat, Pradeep K. (2013)GIS modeling on mountain geodiversity and its hydrological responses in view of climate change” , Lambert Academic Publishing , Heinrich-Böcking-Str. 6-8, 66121, Saarbrücken, Germany. ISBN: 978-3-659-34681-1 Rawat, Pradeep K. (2014). GIS Development to monitor climate change and its geohydrological consequences on Non-monsoon crop pattern in Lesser Himalaya.International Journal of Computers and Geosciences. Vol. 70:80-95. Rawat Pradeep K and Pant Charu C (2016). 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In: Dregne HE, Willis WO (eds) Dryland Agricul- ture. American Society of Agronomy, Soil Science Society of America, Madison, WI, USA, pp 155–176 Sharma A (2010) Integrating terrain and vegetation indices for identify- ing potential soil erosion risk area. Geospat Inf Sci 13(3):201–209 Sharma T, Singh O (2017) Soil erosion susceptibility assessment through geo-stastical multivariate approach in Panchkula district of Haryana, India. Model Earth Syst Environ 3(2):733–753 Srivastava,V., Nakhro, R., Pandey, N., (2013): Geometry of mesoscopic folds in the vicinity of Disang and Piphima thrust in Kohima district, Nagaland. Tripathi RP, Singh HP (1993) Soil erosion and conservation. New Age International Publishers, New Delhi, p 10 Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. Agriculture Handbook No. 537. US Department of Agriculture, Washington, DC, pp 285–291 Yamusa, I.B., Ismail, M.S. (2023) Futuristic Structural and Lithological Constraint Mapping of Landslides Using Structural Geology and Geospatial Techniques. J geovis spat anal 7 , 5. https://doi.org/10.1007/s41651-023-00137-1 Youe-Qing X, Xiao-Mei S, Xiang-Bin K, Jian P, Yun-Long C (2008) Adapting the RUSLE and GIS to model soil erosion risk in a mountains karst watershed, Guizhou Province, China. Environ Monit Assess 141(1–3):275–286 VanDer Knijff J, Jones RRJ, Montanarella L, Van der Knijff JM (1999) Soil erosion risk assessment in Italy. Office for Official Publi- cations of the European Communities, Luxembourg, p 32, EUR 19022(EN) Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning. Agriculture handbook, vol 537. US Department of Agriculture, US Government Printing Office, Washington, DC USDA (1981) Rainfall erosion losses from cropland east of the rocky mountain. Handbook no. 282. US Department of Agriculture, Washington, DC Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3826948","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265141544,"identity":"d5f550ad-8a32-40c8-bdc0-af51fb2229b5","order_by":0,"name":"Pradeep 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Kohima district of Nagaland state (India)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/0e9e0e6f0c8e3c08fab78803.png"},{"id":49240103,"identity":"456aacf0-81f4-4776-bd7b-378868a8ee8a","added_by":"auto","created_at":"2024-01-05 18:19:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112606,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology flow chart\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/9b47a33a43e85d9799f8818d.png"},{"id":49240107,"identity":"e7a2ad61-73e9-4a73-aa86-14d5007f30e7","added_by":"auto","created_at":"2024-01-05 18:19:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":417295,"visible":true,"origin":"","legend":"\u003cp\u003eSRTM-DEM showing pattern of relief (a), slope (b), Aspect (c), TWI (d), drainage density, stream orders\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/81a33dc8268e015752142352.png"},{"id":49240105,"identity":"a38fbd3d-798a-4e6c-bc50-0001cec80e4a","added_by":"auto","created_at":"2024-01-05 18:19:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":277627,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability of geology and geo-structural lineament density\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/30fb7f36ebeec09cbfd91692.png"},{"id":49243018,"identity":"eb6e3526-4af6-4d0e-9123-79edc48b46fb","added_by":"auto","created_at":"2024-01-05 18:35:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":312847,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability of geomorphology (a) and soils\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/414781ee6d2d1a24d5523de0.png"},{"id":49243019,"identity":"7d69c7e9-8f5a-42ce-bfcf-3bbd772ef42f","added_by":"auto","created_at":"2024-01-05 18:35:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":489411,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability of land use pattern (a), climate (b), Rainfall (c) and flood runoff(d)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/42e147fd41dcd3725ad7c2d7.png"},{"id":49241806,"identity":"eae5bd58-8a2b-43ff-8451-c5aea686bf4a","added_by":"auto","created_at":"2024-01-05 18:27:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":350101,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability of rainfall erosibity (RE), soil erodibility (ES), rock erodibility (ER) and slope length and steepness (LS)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/f22d2e39103bb706bf80352a.png"},{"id":49241807,"identity":"030a1008-c309-4cf6-93fd-eb96d9e97e83","added_by":"auto","created_at":"2024-01-05 18:27:23","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":469276,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability of cover management factor (a) and conservation practices factor (b)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/20a9c00a191c3a2910652d73.png"},{"id":49240106,"identity":"9ca958da-f880-4191-817c-db0096bca9fe","added_by":"auto","created_at":"2024-01-05 18:19:23","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":483841,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability Erosion and Soil loss (A)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/11a88502af6229e389814478.png"},{"id":49240110,"identity":"a630b50c-f5a5-479b-82b9-f8902b970194","added_by":"auto","created_at":"2024-01-05 18:19:23","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":496276,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variability of erosion induced geomorphic hazard susceptibility\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/8bf80b1521e4f5a7657a296c.png"},{"id":49240113,"identity":"2f7cd0fd-3b79-4be0-b5e9-4f7beade4a87","added_by":"auto","created_at":"2024-01-05 18:19:23","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1189750,"visible":true,"origin":"","legend":"\u003cp\u003eErosion and associated geomoorphic hazards affecting environment (a,b,c,d); settlements (e) and roads (f)\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/7c25af6bf3db8454d8e0c08a.png"},{"id":50654546,"identity":"1861237f-fee4-40cb-ab18-762524841935","added_by":"auto","created_at":"2024-02-05 09:58:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4952957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3826948/v1/80149bc7-6eae-4d8e-8799-085bbbc8b1c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial analysis of soil erosion and associated geomorphic hazards to avert increasing disaster risk in environmentally stressed eastern Himalaya region","fulltext":[{"header":"Introduction","content":"\u003cp\u003eErosion is one of the most serious geomorphic hazards in sub-tropical climatic ecosystems across the world in general (Yamusa and Ismail \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Al-Sababhan 2024, Chettr 2023)) and eastern Himalayas in particular (Rawat and Pant 2014, 2016, 2017; Gupta, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It affects not only the natural environment through the acceleration of mass movement, landslides, removal of vegetation cover, and siltation of reservoirs but also affects the agriculture-centric rural economy through the loss of the topmost fertile soil layer, causing to decline in crop yield (Rawat and Rawat \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, Rawat an Pant, 2016, Gupta \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Erosion rates are affected by several geophysical (comprises of geology, structural lineaments, soil erodibility, relief pattern, slope gradient, drainage morphometry), hydro-meteorological (comprises of temperature, rainfall, evaporation, surface percolation, runoff) and anthropogenic (comprises of vegetation cover, land use pattern, excavation, unscientific agricultural practices, community awareness level and conservation support practices) factors (Abdo and Salloum \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sharma and Singh 2017; Rawat \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, 2012, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Rahman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). So that, the erosion rates varies from place to place under different geo-ecological systems. Desert ecosystems, which lack protective vegetation covers, may erode obviously at rates multiple times higher than those for humid climatic ecosystems, which have thick evergreen vegetation cover (Miller and Donahue \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Rawat et al., 2011, 2011a, Rawat, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It means the removal of vegetation cover brings soil in direct contact with eroding agents such as water and wind thereby resulting in loss of surface soil layer thus negatively influencing the fertility of the soil (Rawat et al. 2012, 2012a, 2012b, Amare et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Hasan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e. Considering it as a threatening geomorphic hazard, some researchers have pronounced it the 'creeping death of the soil' (Rama Rao \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1962\u003c/span\u003e; Tripathi and Singh \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Angima et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sharma 2010). Pimentel et al. 1995 (1995) and El-Swaify (1997) have claimed in their studies that Asia is one of the highest (SE\u0026thinsp;=\u0026thinsp;74 t/ac/year) soil erosion-prone areas.\u003c/p\u003e \u003cp\u003eAs far as the eastern Himalayas or northeastern region of India is concerned geo-physically it is characterized by rugged and undulating terrain composed mainly of high erodibility sandstone lithology. Moreover, across the region, unscientific traditional shifting (Jhum) agricultural practices have been accelerating soil sheet erosion thus causing adverse ecological (shrinking habitats, increasing man-wildlife conflicts, biodiversity loss, soil quality loss, low crop yield etc.) and hydrological (drying water spring, streams, watersheds during dry season and increasing frequency of extreme flood events during rainy season etc.) impacts (Rawat \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e,). Subsequently, it has caused to increase in unproductive areas, to change livelihoods, and to migrate people to other places; thereby impacting the socio-economic structure of the rural population (Rawat and Haigh \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Rawat et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, 2000). Therefore, there has been an urgent need to identify all erosion-affected areas so that suitable biological and mechanical measures can be implemented for the timely mitigation of erosion and erosion-induced geomorphic hazards such as mass movement, landslides, and removal of large-scale vegetation cover including forests. To address this burning environmental issue a case study of Kohima district, Nagaland (India), carried out through an integrated approach of the Revised Universal Soil Loss Equation (RUSLE) model and geospatial technology, is presented here (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). RUSLE is one of the most widely applied experimental models for erosion rate assessment, developed by Wischmeier and Smith in 1978. The geospatial technology-based techniques (DEM, GIS and remote sensing), facilitate spatial data input to the RUSLE model for its effective application and to predict the accurate erosion rate from the selected study area, whether it is a watershed, river basin or large regional landscape (Renard et al. 1997; Youe-Qing et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kouli et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Originally the model was introduced as Universal Soil Loss Equation (Wischmeier and Smith in 1978) while in some recent research work, it has been pronounced as the Revised Universal Soil Loss Equation (RUSLE) model just by integrating modern geospatial technology with it and advocating the large-scale application of the revised model for erosion hazard assessment (Prasannakumar et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Demirci and Karaburum 2012; Pradeep et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gelagy and Minale 2016; Ganasri and Ramesh \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKohima is a district and capital city of Nagaland state of India. It represents the rich geo-biodiversity of the northeastern Himalaya Mountain within just about 978.96 km2 between 204m to 2953m altitude above mean sea level and lies between 25⁰31\u0026prime;21\u0026prime;\u0026prime;N to 25⁰54\u0026prime;30.06\u0026prime;\u0026prime;N latitudes and 93⁰54\u0026prime;17\u0026prime;\u0026prime;E to 94⁰16\u0026prime;4\u0026prime;\u0026prime; E longitudes in northeastern Himalaya region of India (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The climate varies throughout the study area from humid tropical to humid sub-temperate, between the lowest and highest elevations, respectively. Consequently, the annual average temperature varies between less than 23⁰C within sub temperate zone to more than 28 ⁰C within the tropical climatic zone, whereas average precipitating varies between below 2360 and 3240 mm, respectively, from humid tropical to humid sub-temperate climatic conditions. Geo-environmentally the region is highly vulnerable to soil erosion and accelerated erosion-induced geomorphic hazards such as mass movement, landslide and slope failure because of rugged, steep terrain, tectonically active fragmented young geology, dynamic reshaping geomorphology, and high density of drainage network. Keep in view this; it has been essential to investigate erosion-affected areas to implement required mitigation measures so that accelerated erosion-induced geomorphic hazards can be averted.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe study carried out through the integration of two broad GIS modules; these are geo-environmental GIS modelling and erosion hazard modelling as demonstrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and being described below.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eGeo-environmental GIS modeling\u003c/h2\u003e\n \u003cp\u003eGeo-environmental GIS module provides spatial and attribute data of topographical, geophysical, and ecological parameters. Thematic GIS maps of topographical (comprised relief pattern, surface slope gradient, wetness index and drainage morphometry) geophysical parameters (comprised of geology, geomorphic landforms and soil characteristics) and ecological parameters (comprised of climate, rainfall, runoff, vegetation cover and land use pattern) have carried out using geospatial techniques after required fieldwork and laboratory analysis. For geospatial analysis, Google Earth satellite imagery, Survey of India topographical sheets (83G/13, 83G/14, 83K/1, 83K/2, 83K/5) at scale 1:50000 used to carry out geological, tectonic, geomorphological and soil distribution maps. USGS-Landsat satellite imagery with 30m spatial resolution was used for mapping land use patterns, vegetation covers etc. NASA-STRM satellite imagery with 30 spatial resolutions was used for relief, slope, watershed demarcation, drainage pattern, drainage order, drainage density, drainage frequency, water flow direction, and flow accumulation through a digital elevation model (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Monthly rainfall data for a period of three decades (1991\u0026ndash;2022) was collected for 12 locations at multiple elevations across the study area, out of which 8 locations represent web GIS technology-based rainfall data recorded by NASA Power Data Access Viewer (NASA-DAV: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://power.larc.nasa.gov/data-access-viewer/\u003c/span\u003e\u003c/span\u003e). Whereas remaining 4 are ground-based meteorological stations, run by the Indian Meteorological Department or IMD (2 stations) and, the soil and water conservation department of Nagaland state government (2 stations) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the process that how all necessary data used to appraise RUSLE factors and superimposed to attain the key objective of the study as being described below:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eErosion hazard modelling\u003c/h2\u003e\n \u003cp\u003eThe geospatial technology-based Revised Universal Soil Loss Equation (RUSLE) model test was followed to carry out an erosion hazard study of the study area (USDA 1993). To carry out the average rate of erosion and soil loss, the RUSLE model requires integration of the erosion-controlling factors using the following equation:\u003c/p\u003e\n \u003cp\u003eA\u0026thinsp;=\u0026thinsp;RE \u0026times; ES x ER x LS \u0026times; CM \u0026times; CP\u003c/p\u003e\n \u003cp\u003ewhere A is the soil loss; RE is the rainfall and runoff erosivity factor; ES is the erodibility of soil and ER is erodifbility of bedrock factor; L is the slope length factor; S is the steepness factor; CM is the cover and management factor and CP is the conservation practices factor. Selected factors were integrated with RUSLE in the GIS environment to get the soil erosion rate. Methods to analyze all these erosion-controlling factors are discussed below.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRainfall runoff Erosivity (RE) factor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRainfall-runoff erosivity is considered to be a key factor of the USLE model to estimate soil erosion. The RE-factor of USLE indicates the possible potential of a rainstorm incident to soil erosion. In the present study, the RE-factor is analyzed using total storm energy and its maximum 30-min intensity following Wischmeier and Smith (\u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e). Mean annual rainfall data for 30 years was used to estimate RE-factor following Choudhury and Nayak (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eR\u0026thinsp;=\u0026thinsp;79\u0026thinsp;+\u0026thinsp;0.363 \u0026times; Xa\u003c/p\u003e\n \u003cp\u003ewhere RE is the Rainfall-runoff erosivity, and Xa is the Average annual rainfall (mm).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eErodibility of soil (ES) factor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eES expresses the resistance power of top surface soil against the rainstorm event, which depends on geo-chemical properties of soil such as dispersal of grain mass, organic matter and soil structure and absorbency (Wischmeier and Smith \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e). Based on the geological and pedological maps of the study area, ESR values of existing soil and rock types have been estimated from the nomogram (Wischmeier and Smith \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e) of RUSLE. The ES factor map of the study area has been prepared by plotting the ES/R values of each map unit.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eErodibility of rock (ER) factor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eER expresses residence power of bedrock (R) against the rainstorm event, which depends on geo-chemical properties of rock, such as dispersal of grain mass, organic matter and structure and absorbency (Wischmeier and Smith \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e). Based on the geological maps of the study area, ER values of existing rock types have been estimated from the nomogram (Wischmeier and Smith \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e) of RUSLE. The ER factor map of the study area has been prepared by plotting the ER values of each map unit.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSlope Steepness and Length\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLS factor combines two sub-factors which are length (L) of slope and steepness or slope gradient (S) of slope. The slope length factor represents the combined effect of steepness and length of slope on soil loss rate in a region. The higher the LS factor, the higher the vulnerability to soil loss. LS facto was determined using SRTM DEM data following Moore and Burch (1986).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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width=\"624\" height=\"60\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003cp\u003eWhere LS is the length and steepness factor of the slope respectively, cell size is the pixel value of a single grid cell which also reflects the spatial resolution of the DEM satellite image and sin is the slope degree value in the Sin table.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCover management (CM) factor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn general, it is a type of vegetation cover, which influences the rate of soil erosion as vegetal cover does not allow the rainstorm to interact directly with the top surface layer of soil and thereby averts the soil erosion. CM factor is expressed as the ratio of soil loss among different types of existing vegetation cover in the study area. The CM value ranges between 0 and 1 which was calculated using the following formula (Van der Knijff et al. 1999).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"279\" height=\"60\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003cp\u003eWhere \u0026alpha; and \u0026beta; are the parameters that determine the shape of the NDVI-C curve. \u0026alpha; value of 2 and \u0026beta; value of 1 was taken (Van der Knifjj et al. 1999).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConservation practice (CP) factor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe CP factor represents different types of land use/cover patterns of a specific region. This factor controls soil erosion by shifting the flow pattern, pitch or course of runoff and by reducing the volume and runoff rate (Renard and Foster \u003cspan class=\"CitationRef\"\u003e1983\u003c/span\u003e). To that, Landsat satellite imagery with 30m spatial resolution of the study area classified with five (water bodies, dense forest area, degraded open forest, built-up land and agriculture land) major types of land use/cover pattern applying supervised classification method and then reclassified based on their CP values suggested in USDA Handbook (1981) list.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eResults are being discussed categorically in three broad sections: first section presents spatial and non spatial GIS database of geo-environmental setup, second sections is on RUSLE factor analysis and data integration to compute erosion and soil loss using the Algebra tool of ArcMap GIS program whereas third section discusses the status of erosion associated geomorphic hazards such as landslide, debris flow, mass moment slope failure etc.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eGeo-environmental setup\u003c/h2\u003e\n \u003cp\u003eAs the RUSLE model requires detailed attributes of all key geo-environmental parameters of the study area, were carried out through extensive fieldwork and laboratory analysis and thereafter converted in a GIS environment. These geo-environmental parameters are:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTopographical Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe vulnerability level of soil loss whether it is in the form of sheet erosion or gully erosion is mainly driven by terrain characteristics such as relief pattern, slope gradient, slope aspects, topographic wetness index, drainage density and drainage order. So, these terrain morphometric parameters have been studied through the Digital Elevation Model (DEM) using SRTM satellite imagery with a spatial resolution of 30 m (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This DEM-based topographic analysis reveals that the elevation across the region varies from a minimum of 204m in the northwest part to a maximum of 2953m in the southern part (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). The slope gradient ranges below 15\u0026deg; in the alluvial plain area in the southern part to 81\u0026deg; in the mountain area southern part respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). Slope aspect is highly controlled by geo-structural lineaments such as faults, thrusts, strike ridges etc (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). Topographic wetness index is highly controlled by slope aspects, flow accumulation pattern of the terrain (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). Drainage morphometry (stream density, stream frequency, stream ordering etc.) and flow direction are highly controlled by geology and geomorphological sections of the region (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ef respectively).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeophysical setup\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eKey geophysical factors which determine the erosion rate within a region are geology, geomorphology and soils of the land surface. Keeping in view this, these three key factors were studied comprehensively. Geology of the area consists of highly erodible rocks such as sandstone, siltstone, shales of Barail, Tipam and Surma groups; slates and phyllites of Disang group (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea) Spatial distribution of rock pattern is highly influenced by geo-structural lineament density which varies\u0026thinsp;\u0026lt;\u0026thinsp;0.40km/km\u003csup\u003e2\u003c/sup\u003e-2 km//km\u003csup\u003e2\u003c/sup\u003e across the region (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Rich diversity of geomorphic landforms resting on these geological groups can be categorized as fluvial landforms (comprises alluvial plain, piedmonts, flood plain, inter hill valley, rills, young fluvial fans, river terraces) and tectonic landforms (comprises strike ridge areas, high relief structural hills, tectonic scars, triangular facets which are moderately to highly dissected forms) which are highly controlled by number of regional and local geo-structural lineaments such as thrusts, faults, lithological joints sets and fractures (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). The top layer of all geomorphic landforms consists of sub-surface and surface soil; therefore types of soils vary across the region in order to geomorphological and geological background. So considering geology, geomorphology and soil taxonomy and properties, a total of twelve types of soils were identified and mapped in the study area from lowest to highest elevation under different geomorphological and lithological backgrounds (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The spatial distribution map (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb) shows that the low relief geomorphology (Piedmonts, Foothills) comprises Fine, Umbric Dystrochrepts soil and Fine, Loamy Lithic Udorthent soils over lithology of Disang group rocks (shales, slates and phyllites). Moderate relief geomorphology (Upslope hills, downslope hills, terraces, Inter hill valley) comprises Fine Loamy Lithic Udorthent, Umbric Dystrochrepts, Fine, Typic Paleudults, Typic Hapludults and Fine-Loamy soils over lithology of Disang group rocks (shales, slates and phyllites). High relief geomorphology (Strike ridge areas, High relief structural hills, High-middle structural hills) comprises Coarse-loamy, Typic Dystrochrepts, Fine-loamy, Typic Dystrochrepts soils over lithology of Barail group rocks (Shales, sandstone).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEcological status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLand use pattern, climate, rainfall, runoff, are the key ecological factors which influence the rate of soil erosion, indeed these ecological parameters are key drivers of the erosion geomorphic process. Therefore all ecological parameters were studied. The land use pattern map reveals that only 18% area is in the virgin stage or anthropogenically least degraded (dense forest), whereas 63% is moderately degraded (open forest which is exploited by inhabitants in the name of shifting agricultural practices, fodder, firewood etc.), 10% highly degraded (comprises 1% shrubs land, 1% wasteland and 8% agriculture land). The remaining 9% area is very highly degraded (built-up area including urban settlements, and rural settlements with associated infrastructural development such as roads, bridges, bridle paths, canals, dams etc.) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Other three ecological factors, namely climate, rainfall and runoff are deeply correlated. The region falls in the humid climatic region of India, further can be categorized into four micro-climatic zones considering local spatial variability in long-term meteorological characteristics; these are humid tropical, humid subtropical, temperate and humid temperate (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). Subsequently, the region receives heavy annual rainfall which varies from 1600mm to 3250mm in the lowest relief areas (200m) to the highest relief (2950m) areas respectively (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec). Heavy rainfall throughout the year results in high flood surface runoff which ultimately accelerates the soil erosion. However, this flood runoff varies below 100 liter/km\u003csup\u003e2\u003c/sup\u003e in anthropogenically least degraded dense forest land to above 600 liter/km\u003csup\u003e2\u003c/sup\u003e in most degraded buildup land area (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eRUSLE model test\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eRainfall-runoff Erosivity Factor (RE)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe average annual rainfall erosivity factor (RE) for the selected last three decades period (1991\u0026ndash;2021) ranges between 648.12\u0026ndash;1294.15 MJ mm/ha/h/year across the Kohima district (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). The higher values of rainfall erosivity (1000-1294.15MJ mm/ha/h/year) are found in the southern part of the region having elevation above 1400m from mean sea level and the northern part, having lower elevation below 800m from mean sea level has lower values of rainfall erosivity (648\u0026ndash;800 MJ mm/ha/h/year) whereas the mid-altitude area having moderate rainfall erosivity between 800\u0026ndash;1000 MJ mm/ha/h/year (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest values of RUSLE factors and sub-factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSub-factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAverage\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\" rowspan=\"6\"\u003e\n \u003cp\u003eRainfall Erosivity\u003c/p\u003e\n \u003cp\u003e(RE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1620\u0026ndash;1975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e648.124-848.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"6\"\u003e\n \u003cp\u003e1018.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1976\u0026ndash;2260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e848.124-948.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2261\u0026ndash;2488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e948.124-998.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2489\u0026ndash;2703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998.124-1148.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2704\u0026ndash;2919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1148.12-1248.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2920\u0026ndash;3236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1248.12-1294.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eErodibility of Soil\u003c/p\u003e\n \u003cp\u003e(ES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCoarse-loamy,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"10\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTypic Dystrochrepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFine-loamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUmbric Dystrochrepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTypic Hapludults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFine, Typic Paleudults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUmbric Dystrochrepts soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFine Loamy Lithic Udorthent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCoarse-loamy,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTypic Dystrochrepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eErodibility of Rock\u003c/p\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBarail group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShales,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDisang group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShales,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlates,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhyllites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTipam group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShales,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiltstones,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandstones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurma group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandstones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSlope Steepness\u003c/p\u003e\n \u003cp\u003e(SL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"5\"\u003e\n \u003cp\u003e29.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e35\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e45\u0026ndash;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eCover Management\u003c/p\u003e\n \u003cp\u003e(CM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDense Forest (Least degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"7\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOpen Forest (Moderate degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWater bodies (Moderate degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShrubs land (Highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBuild up area (Very highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWaste Land (Very highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAgricultural land (Highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eConservation Practices\u003c/p\u003e\n \u003cp\u003e(CP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDense Forest (Least degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"7\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOpen Forest (Moderate degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWater bodies (Moderate degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShrubs land (Highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBuild up area (Very highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWaste Land (Very highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAgricultural land (Highly degraded)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eErodibility Factor of Soil (ES)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis factor is generally controlled by the texture of soil rather than other parameters such as structure and organic matter. ES value increases as the soil texture becomes smaller to finer, such as fine loamy soil which contains a maximum proportion of silt and smaller to finer sand due to, is highly vulnerable to erosion. ES value in Kohima district ranges in 15 classes concerning existing types of soil. Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb depicts the spatial variability of the soil erodibility factor of the region which varies minimum of 0.10 to a maximum of 0.41 among the existing 12 classes of soils. The range of the ES factor varies from a minimum of 0.00 to 0.41, where close to \u0026apos;0\u0026apos; indicates less susceptibility to soil erosion and close to \u0026apos;0.41\u0026apos; is an indication of high susceptibility to soil erosion (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eErodibility factor of rock (ER)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eErodibility of exposed bedrocks depends on geo-chemical properties of rock, such as dispersal of grain mass, organic matter and structure and absorbency (Wischmeier and Smith \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e). Based on the geological maps of the study area, ER values of existing rock types have been estimated from the nomogram (Wischmeier and Smith \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e) of RUSLE. The average ER value of the study area stands on 0.03 whereas it is varies minimum 0.01 for Slates and Phyllites of Disang group to maximum 0.04 sandstones Barail, Tipam and Surma groups (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSlope Steepness and Length Factor (LS)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLS is considered a significant factor in the RUSLE model as it influences the amount of soil erosion in a specific site. LS factor has been calculated by integrating the slope factor and flow accumulation factor in SRTM DEM. This exercise carried out values of slope steepness and length (LS) factor ranges between 0 and 49 across the study area (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Spatial distribution suggested that in most of the proportion of the study area, LS factor values range between 0 and 1.22 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ed).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCrop Management Factor (CM)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCrop management factor determines crop sequence, productivity level, depth of soil etc., thereby influencing rate of erosion and soil loss. Due to the rich diversity in the relief and slope pattern of the study area, land cover or cop pattern mapping was carried out to assess the CM factor. CM factor value varies minimum of 0.0 for dense forest areas to a maximum of 1.80 for buildup areas (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea). Most parts of the study area account for a CM value below 0.25, which represents an area of dense to open forest land. It is also noted that the CM value varies in different seasons and weather conditions throughout the year.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConservation Practices Factor (CP)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eErosion-controlling biological and mechanical measures are considered a conservation practice in the RUSLE model. The frequency of these erosion mitigating measures influences the CP value and rate of soil erosion. To that, the CP values were assigned considering the land use pattern of the study region. Buildup area accounts for low CP value whereas dense forest area accounts for higher CP value. The CP value varies from 0.1\u0026ndash;1.0 across the study region to land use/cover pattern (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eSpatial variability of erosion and soil loss (A)\u003c/h2\u003e\n \u003cp\u003eSpatial variability of erosion and soil loss (A) has been computed by superimposing and multiplying the prepared raster map layers of all five RUSLE factors (A\u0026thinsp;=\u0026thinsp;RE x ES x ER x LS x CM x CP) through Map Algebra raster calculator spatial analysis tool of ArcMap GIS software. The outcome map of overlay operation shows that the spatial variability of annual erosion and soil loss rate ranges from 0\u0026ndash;92.18 t/ha/year across the region. This range has been classified into four erosion intensity zones: these are low (below 20 t/ha/year), moderate (20\u0026ndash;40 t/ha/year), high (40\u0026ndash;60 t/ha/year), and very high (above 60 t/ha/year) erosion intensity zones (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eErosion induced geomorphic hazards susceptibility\u003c/h2\u003e\n \u003cp\u003eThe high rate of erosion and associated geomorphic hazards during heavy rainfall is a serious geo-environmental problem in eastern Himalaya as the region receives more than enough annual average rainfall which varies from 2500-3200mm (Sengupta, 2010, Dikshit et al. 2019, Gariano et al. 2019, Teja 2019, Harilal et al 2019). There are several geospatial technologies based on modern studies on erosion and rain-induced geomorphic hazards (mass movement, debris fall, and landslide etc.) across the Himalayas but most of them are concentrated in central Himalaya (Dash et al., 2000, Naithani et al. 2002, Sarkar and Gupta, 2005, Shantanu et al. 2013, Martha et al. 2015) and western Himalaya (Mukhopadhyay et al. 2005, Roy et al. 2018, Kumar et al., 2018, Pradhan et al., 2019, Banerjee and Dimri 2019, Kumar et al. 2019) region whereas from eastern Himalaya region lack of studies are available (Umrao et al. 2017, Sarkar et al., 2017, Dikshit and Satyam 2018, Bera et al., 2019); as far as Nagaland state is concern it is about nil. Keep in view this; it has been essential to address rain and erosion-induced geomorphic hazard risk in the study area to represent Nagaland state as well as the eastern Himalaya region. However, studying each type of erosion and rainfall-induced geomorphic hazard whether it is a mass movement, landslide, slope failure or other, requires specific methods and techniques respectively. The present study discusses a general overview of geomorphic hazards which are being triggered by accelerated erosion during the rainy season in the study area. To that, during field validation of the erosion intensity zone map, several places have been identified across the study areas where a high rate of erosion has been triggering other associated geomorphic hazards such as mass movement, rock fall, debris fall, landslide and slope failure etc.; subsequently affecting infrastructural setup, agricultural land, settlements, rural livelihood, natural resources and wildlife ecosystem. Taking into consideration all such locations and spatial variability of erosion rate, geophysical characteristics, an integrated erosion-induced geomorphic hazard susceptibility zone map of the study area carried out which suggests four zones, namely low, moderate, high and very (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eArea of low susceptibility to erosion-induced geomorphic hazards\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe areas having soil erosion rate below 20 t/ha/year and least stressed geophysical characteristics (comprised of fluvial plain and valleys over geology of shales, siltstones, sandstones rock) have been considered as low susceptibility zone of erosion induced geomorphic hazard (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). No significant mass movement events are seen in such areas.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eArea of moderate susceptibility to erosion-induced geomorphic hazards\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe areas having soil erosion rate 20\u0026ndash;40 t/ha/year and moderately stressed geophysical background (comprised of river terraces and gentle down-slope susceptibility zone of erosion induced geomorphic hazards (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Landslides and slope failure due to the erosion of slopes along rivers are common types of geomorphic hazards in this zone (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ed).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eArea of high susceptibility to erosion-induced geomorphic hazards\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe areas having soil erosion rate 40\u0026ndash;60 t/ha/year and moderate to highly stressed geophysical characteristics (comprised of steep up-slope hilly geomorphology over a geological background of shales, slates and phyllites) have been identified to high susceptibility zone of erosion induced geomorphic hazard (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Land subsidences with large spatial extension are found in this zone (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ec). Anthropogenically accelerated erosion and subsequent landslide, rock fall, and debris flow along roads, and urban settlements of Kohima city are also found in this zone (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ee and \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ef).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eArea of very high susceptibility to erosion-induced geomorphic hazards\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe areas having soil erosion rate above 60 t/ha/year and highly to extremely stressed geophysical characteristics (comprises of steep up-slope hills, strike ridge areas, high relief structural hills geomorphology over a geological background of Shales, sandstone.) have been identified as very high-risk zone of erosion induced geomorphic hazard (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Active landslides and slop failure geomorphic hazards are commonly seen in this zone (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study concluded that unplanned urbanization with associated infrastructural development in urban to sub-urban areas and shifting cultivation practices in a rural area are the main anthropogenic factors of the high rate of erosion and soil loss; whereas young and highly erodible formation of rocks, fragmented reshaping geomorphology, high-intensity rainfall are key natural causes of high rate of erosion and soil loss. The sudden change in any one of above mentioned anthropogenic and natural factors must lead to accelerated erosion and soil loss. The output results of the RUSLE model on key drivers of erosion and soil loss show that the rainfall erosivity factor (RE) ranges between 648.12\u0026ndash;1294.15 MJ mm/ha/h/year, soil erodibility factor varies minimum of 0.10 to a maximum of 0.41 among existing 15 classes of soils, ER factor values ranges 0.01\u0026ndash;0.04, slope steepness (LS) factor values ranges between 0 and 1.22, cover management (CM) factor values varies minimum 0.0 for dense forest area to maximum 1.80 for buildup areas whereas the conservation practice (CP) value varies 0.1\u0026ndash;1.0 across the study region according to land use/cover pattern. The accumulated impact of these erosion and soil loss factors, results in a quite high average rate (about 9 t/ha/year) of erosion than the threshold value of soil erosion (\u0026lt;\u0026thinsp;10t/ha/year). This value ranges from 0\u0026ndash;92.18 t/ha/year. Thus, the high rate of erosion has been triggering several geomorphic hazards in the region such as mass movement, debris fall, landslide etc.; subsequently affecting infrastructural setup, agricultural land, settlements, rural livelihood, natural resources and wildlife ecosystem (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). It has been essential to minimize the high rate of erosion and its associated geomorphic hazards by intensifying the CP factor or conservation measures at the government level, community level and even individual level. If these necessary actions are not taken as early as possible, it may lead to worsening ecological and socioeconomic impacts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. Pradeep Kumar Rawat and Dr. Khrieketouno Belho. The first draft of the paper was prepared by Dr. Pradeep Kumar Rawat. .All authors commented on previous versions of the manuscript. Supervision was carried out by Prof. M.S. Rawat. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdo H, Salloum J (2017) Spatial assessment of soil erosion in Alqer- daha basin, Syria. Model Earth Syst Environ 3:26\u003c/li\u003e\n\u003cli\u003eAngima SD, Stott DE, O\u0026rsquo;Neill MK, Ong CK, Weesies GA (2003) Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agric Ecosyst Environ 97(1\u0026ndash;3):295\u0026ndash;308\u003c/li\u003e\n\u003cli\u003eAl-Sababhah, N. 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US Department of Agriculture, US Government Printing Office, Washington, DC\u003c/li\u003e\n\u003cli\u003eUSDA (1981) Rainfall erosion losses from cropland east of the rocky mountain. Handbook no. 282. US Department of Agriculture, Washington, DC\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geomorphic hazards, Erosion, Soil loss, Geospatial RUSLE model, Kohima, India","lastPublishedDoi":"10.21203/rs.3.rs-3826948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3826948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeo-environmentally, the eastern Himalaya region is highly vulnerable to erosion and soil loss geomorphic hazard due to humid tropical to humid sub-temperate climate (receives 1600-3200mm mean rainfall), young and highly erodible rock formations (mainly comprised of sandstones, siltstones and shales), fragmented reshaping geomorphology, high erodibility of surface and sub-surface soils. Despite that, anthropogenic activities have been enhancing this geo-environmental vulnerability to erosion hazard through rapid unplanned urbanization with associated infrastructural development in urban to suburban areas and shifting cultivation practices in rural areas. Addressing this burning environmental problem, a geospatial technology-based case study of the Kohima district, Nagaland state (India) from eastern Himalaya is presented here. Various experiential models are available for computing soil erosion; however, a Revised Universal Soil Loss Equation (RUSLE) integrated with the GIS framework was applied in the current study due to its robustness and high accuracy level. Five key RUSLE factors such as erosivity of rainfall (RE), erodibility of soil (ES), erodibility of rock (ER), slope length (LS), crop management (CM) and conservation practice (CP) were calculated using required data sets in a GIS environment. RE ranges between 648.12\u0026ndash;1294.15 MJ mm/ha/h/year, ES varies minimum of 0.10 to a maximum of 0.41 among the existing 15 classes of soils, ER factor values ranges 0.01\u0026ndash;0.04, LS factor values range between 0 and 1.22, CM factor values vary from a minimum of 0.0 for dense forest area to maximum 1.80 for buildup areas whereas the CP value varies 0.1\u0026ndash;1.0 across the study region to land use/cover pattern. The accumulated impact of these erosion and soil loss factors resulted in a quite higher average rate (about 16 t/ha/year) than the threshold value of soil erosion (\u0026lt;\u0026thinsp;10 t/ha/year). This value ranges from 1\u0026ndash;92.18 t/ha/year and poses. Thus, it has been essential to minimize the high rate of erosion through intensifying CP factors at the government level, community level and even individual level by adopting scientific crop patterns, agro forestry and reforestation programs. If these necessary actions were not taken timely, it may lead to other erosion-induced geomorphic hazards such as land degradation, mass movement, landslides, slope failure etc.\u003c/p\u003e","manuscriptTitle":"Geospatial analysis of soil erosion and associated geomorphic hazards to avert increasing disaster risk in environmentally stressed eastern Himalaya region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-05 18:19:18","doi":"10.21203/rs.3.rs-3826948/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e51ddf7-df59-4fc1-be7d-33b1e76922b4","owner":[],"postedDate":"January 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-05T09:50:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-05 18:19:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3826948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3826948","identity":"rs-3826948","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}