Percentile-Based Wind Field Analysis for Cyclone Return Period Estimation and Microzonation Mapping in Andaman and Nicobar Region

Percentile-Based Wind Field Analysis for Cyclone Return Period Estimation and Microzonation Mapping in Andaman and Nicobar 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 Percentile-Based Wind Field Analysis for Cyclone Return Period Estimation and Microzonation Mapping in Andaman and Nicobar Region Yuvaraj E, Aswanth KM, Lyyappan M, Shoaib Rassel, Adharsh AR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9038925/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The current study attempted to map wind field-based cyclone return period analysis and microzonation mapping using integrated geospatial techniques and percentile methods. The Andaman and Nicobar Islands (ANI) in the North Indian Ocean are taken as the study area. Cyclone track data from 1891 to 2022 were collected and collated from multiple sources, including the IMD, JTWC, and NOAA. In the study, grids with a spatial extent of 0.25 degrees are created throughout the study area using ArcGIS 10.8 software. Each grid’s centroid is considered a point of interest, which is used to generate the circle of influence. The wind speed data of cyclone tracks that intersect with each circle of influence are taken for statistical evaluation. The percentile method was used to analyse the cyclone return periods for 5, 10, 25, 50, 75, and 100 years. The outputs of the percentile analysis are used in ArcGIS to plot the return period maps using spatial interpolation techniques. The return period analysis indicates that the Andaman group of islands is highly prone to cyclones compared to the Nicobar group of Islands. The cyclonic category windspeed (34 Knots) was observed only after the 75-year return period, which was in the Andaman Group of Islands, whereas in the Nicobar Group of Islands, it was observed only in the 100-year return period. The maximum wind speeds in the 100-year return period are 85 Knots and 64 Knots in the Andaman and Nicobar Groups, respectively, which are comparatively less than the mainland Indian coast. The study demonstrates the integration of GIS, percentile analysis, and wind field-based cyclone microzonation mapping, providing a deeper understanding of return periods, which are useful for the inclusive development plans. Cyclone microzonation GIS Percentile analysis Wind-field Andaman and Nicobar Islands Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 INTRODUCTION Tropical cyclones are India's most common natural disasters, generating storm surges, floods, strong winds, flooding, and erosion (Nair et al., 2018). These hazards pose a risk to life, infrastructure, properties, socio-economy and the environment. The Bay of Bengal Sea in the North Indian Ocean has the most tropical cyclones in terms of frequency and intensity (Shaoo & Baskaran, 2015). Cyclones are commonly observed across the Bay of Bengal during the post-monsoon season, which inflicts substantial damage to life and property on the east coast of the Indian mainland (Mondal et al., 2022). Most cyclones originated or developed in the southeastern part of the Bay of Bengal Sea, where the sea surface temperature is comparatively warmer than in other regions (Kumar et al., 2023 ). This region is situated in close proximity to the Andaman and Nicobar Islands, which comprise sensitive ecosystems, tribal communities, and local populations that heavily rely on fisheries and tourism. Although major cyclonic events are rare in the ANI, the intensity of cyclogenesis in the region poses significant threats to fisheries, tourism activities, and transportation (Yadav and Das 2024 ). Over the last decade, numerous developmental activities have been underway in the ANI region, with additional plans for major developments in eco-tourism and the blue economy (Government of India, 2022; Department of Fisheries, 2025), which are highly sensitive to coastal hazards such as cyclones. It is necessary to conduct a detailed study of cyclone hazards in the ANI region to understand its various scenarios, which are crucial for sustainable development and building resilience in these islands. Zonation is a strategy for the classification of homogenous spatial zones with high similarity to differentiate from its heterogeneous zones (Mitchell, 1976). The term microzonation is derived from earthquake engineering and refers to the zoning of land at the micro level to assess earthquake susceptibility. Later, this method was leveraged in multiple hazard zonation studies, including those on landslides and floods (Anbalagan, 1992 ; Rahman & Saha, 2007 ). The GIS technique has been commonly used in microzonation studies over recent years; various methods, including multi-criteria evaluation, neural network algorithms, impact-based mapping, and spatial interpolation, are employed in most studies (Nath & Thingbaijam, 2009 ; Jahangir et al., 2019; Uddin & Matin, 2021 ). Cyclone microzonation enables site-specific hazard assessments, a significant technique in dealing with damage potential at fine resolution exposure to cyclonic impacts (Fang & Zhang, 2021 ). Cyclone zonation studies were conducted using various spatial clustering methods, including analytical clustering, weighted analysis, and spatial interpolation (Goyal & Datta, 2012 ; Fang & Zhang, 2021 ). The return period analysis for the cyclone risk is important to reduce the civilian casualties, economic loss and environmental damages and to understand the investment risk on coastal infrastructures (Wang et al., 2021) 2 STUDY AREA The union territory of India, the Andaman and Nicobar Islands, is composed of 572 islands with an aggregate land area of 8,249 square kilometres. Among these, 37 islands are inhabited, and the administration of the islands is divided into three districts based on population: South Andaman, North Andaman, and Middle Andaman and Nicobar. The union territory's capital, Sri Vijayapuram (Port Blair), is located on South Andaman Island. The study area is situated between the Bay of Bengal and the Andaman Sea, which spreads from 13°N to 7°N. Furthermore, the study area was divided into the Andaman group of islands and the Nicobar group of islands by the 10-degree channel, as shown in Fig. 1 . Due to its location, the Andaman and Nicobar (ANI) Islands are highly susceptible to cyclonic phenomena. The Bay of Bengal, where the ANI is situated, is more sensitive to cyclonic storms than the Arabian Sea (Shankar et al., 2015 ). In the last two decades, hardly three cyclones have hit the ANI with the intensity of a Cyclonic storm category. Among that, the catastrophic damage caused to life and property was reported during Cyclone Lehar in 2013. The cyclonic storm damaged residential buildings, roads, electrical poles, transformers, and other critical infrastructure, and resulted in the inundation of low-lying areas, beach erosion, and caused landslides in South Andaman (Yuvaraj et al., 2014). This cyclone also affected the coastal ecosystem, including seagrass, sandy beaches, and coral reefs on the North Andaman Island (Sachithanandam et al., 2014 ). 3 METHODOLOGY 3.1 Data The cyclone track data from various sources are collected from the Indian Meteorological Department (IMD) Best Tracks data for the periods 1982–2024, Joint Typhoon Warning Centre (JTWC) Best Tracks Data for the periods 1945–2023, and the National Ocean and Atmospheric Administration (NOAA) IBTrACS for the periods 1879–2020. The various categories of cyclone tracks, including depression and deep depression, are also considered for the study. The collected data were collated, and IMD data were considered primary data; NOAA and JTWC data were used wherever lagging was observed. The collated data shows the overall cyclonic events of the Bay of Bengal that occurred between 1879 and 2022, which are used to conduct the study. 3.2 Generation of Circle of Influence The grids are generated with an extent of 0.25 × 0.25 degrees to divide the study area for microzoning. There are 54 grids covered along the entire study area. The grid size is determined based on the spatial extent limitations of the island compared to the mainland. The centroid of each grid is considered a point of interest (Goyal & Datta, 2012 ), which is derived using ArcGIS tools. The distance of 134.6 km from the point of interest is considered the cyclonic wind radius in the Bay of Bengal with reference to Mohapatra and Sharma ( 2015 ), as shown in Fig. 2 . This is the distance of maximum wind radii for the cyclone at 34 Knots/hr in the cyclonic storm category. There are 54 circles of influence that are generated with reference to each point of interest. The cyclonic storm and the above categories were considered for the study. The cyclone tracks that intersect with the circle of influence are considered as influencing factors for the point of interest (Fig. 3) 3.3 Return Period Analysis Determining the number and wind speed of cyclones traversing each circle of influence is taken for the return period analysis. Since cyclones occur in irregular time series, the return periods are taken into account depending on the actual value of the annual frequency or the chosen percentile (Camufo et al., 2020). The percentile analysis is conducted to assess the return period analysis for 10, 25, 50, 75, and 100-year return periods. The wind speed of each return period value is attributed to the shapefile of the point of interest. To map the variables and create spatial zonation based on the point of interest, the IDW interpolation method was employed (Khouni et al., 2021 ). 4 RESULT AND DISCUSSION Cyclone data for the Bay of Bengal and the Andaman Sea from 1879 to 2022 is sourced from the IMD, NOAA, and JTWC, which shows 244 cyclonic systems that influenced the study area. Among these, 165 cyclonic systems passed through the Andaman group of Islands and 79 in the Nicobar group. This shows that the Andaman group of islands experiences cyclonic systems more frequently than the Nicobar group. 4.1 Return Period Analysis The return period analysis is calculated using the percentile technique, which reflects the cyclonic systems during the period of 1879–2022. The return periods are created using five distinct return periods 5, 10, 25, 50, 75, and 100 years. 4.2 5-years return period The micro zonation map of a 5-year return period shows that Nicobar Island has the lowest wind speed, ranging from 8 to 10 Knots, followed by 12 to 15 Knots observed at the south of and Little Andaman Islands, and 15 to 18 Knots in the northern part of South Andaman and the southern part of Middle Andaman. The maximum wind speed of 18–20 Knots was observed from the centre of the Middle Andaman to the North Andaman (Fig. 3). 4.3 10-year return period The 10-year return period map shows that Great Nicobar has the lowest wind speed, ranging from 10 to 12 Knots, followed by the South of South Andaman, Little Andaman, and Car Nicobar Islands, which have wind speeds between 13 and 16 Knots. The middle of South Andaman observed with wind speed of 16–19 Knots, and maximum at the northern part of South Andaman Island, towards North Andaman Island has the highest wind speed of 19–22 Knots (Fig. 4). 4.4 25-year return period The 25-year return period map shows wind speeds of 15–16 Knots in Great Nicobar, followed by all other Nicobar Islands with wind speeds of 18–20 Knots. Little Andaman observed with the wind speed of 22–24 Knots. The maximum wind speed observed throughout the Andaman Islands was 24–25 Knots (Fig. 5). 4.5 50- year return period The 50-year return period map shows that the Great Nicobar has the lowest wind speed of 20–22 Knots, and the rest of Nicobar group of Islands up to North of South Andaman shows the wind speed of 24–26 Knots. The northern part of the South Andaman and Little Andaman Islands shows the wind speed of 26–28 Knots. Middle Andaman to North Andaman Islands and the northern tip of Little Andaman has the highest wind speed of 28–30 Knots (Fig. 6). The pattern is slightly irregular when compared to other return periods. 4.6 75-year return period The 75-year return period indicates that the Nicobar Islands have the least wind speed, ranging from 25–26 Knots, except for Car Nicobar, which exhibits a wind speed of 28–30 Knots. From Little Andaman to the middle of South Andaman, the wind speed is 30–32 Knots. The northern part of the South Andaman to North Andaman Islands has the highest wind speed, ranging from 32 to 34 Knots. 4.7 100-year return period The 100-year return period map of ANI shows that the Great Nicobar shows the lowest wind speed of 45–48 Knots, followed by the Katchal group of Islands with 51–54 Knots, and Car Nicobar with a wind speed of 60–63 Knots in its east part and 63–66 Knots in the west part. The southern part of Little Andaman experiences wind speeds of 66–69 Knots. The South Andaman to North Andaman Islands and the northern part of Little Andaman have the highest wind speeds of 84–85 Knots (Fig. 8). The trend shows that the number of cyclonic wind disturbances is higher in the Andaman group of Islands than in the Nicobar Group of Islands. 4.8 DISCUSSION The return period analysis shows that North and Middle Andaman are highly susceptible to the potential damage of cyclones, followed by South Andaman and the Nicobar Group of Islands. When comparing the cyclone intensity from the return period maps, it is observed that the North Andaman Islands are highly exposed to cyclones, and Great Nicobar Island is least exposed. The intensity of cyclonic wind speed return periods is high to low from North to South of the study area. A wind speed of 34 Knots, indicating a cyclone category (Mohapatra & Sharma, 2015 ), was observed from a 75-year return period in the Andaman group of Islands. Whereas, in the Nicobar group of Islands, the cyclone category is observed only in a 100-year return period. The maximum wind speed of 84 kt was observed at the entire Andaman group of Islands. There are four cyclones that crossed over the Andaman Islands (Sridharan et al., 2000). Cyclone Lehar (2013) is the only severe cyclone storm that made landfall and crossed over the South Andaman Islands, resulting in the maximum wind speed in the region, which is considered a 100-year event. This cyclone caused significant damage to critical infrastructure, agriculture, and coastal ecosystems in the Andaman Islands (Yuvaraj et al., 2015 ; Sachithanandam et al., 2014 ). In the Nicobar group of Islands, only two cyclones cross over the region. The cyclones that occurred in 1966 and 1988, which passed parallel to the Little Andaman and Car Nicobar, resulted in maximum wind speeds of 69 Knots observed at the Car Nicobar Island. The maximum wind speed of 140 Knots was observed on the mainland Indian coast during the 1999 Odisha Super Cyclone (Fanchiotti et al., 2020 ), which is comparatively higher than ANI. The district-level cyclone risk maps of India also show that ANI are less prone to cyclones (Mohapatra, 2015 ). The detailed analysis of the return period reveals that, although very few cyclonic events occurred in ANI, the southeast Bay of Bengal near ANI has been most susceptible to cyclogenesis in recent decades. Most of the low-pressure systems of cyclones originate in the South of the Bay of Bengal region, but they intensify in the central and northern Parts of the Bay of Bengal region (Sharma et al., 2025 ). Among the intensified cyclones, most make landfall over the coasts of Odisha (India), West Bengal (India), Bangladesh, and Myanmar. Some of the cyclones intensify in the South of the Bay of Bengal and move towards the coasts of Andhra Pradesh (India), Tamil Nadu (India), and Sri Lanka (Mohapatra et al., 2012 ). Similarly, the cyclogenesis in the Andaman Sea is comparatively less than Bay of Bengal, but the system generated in the Andaman Sea are high intense and sustain maximum lifetime (Pentakota, 2018), which are mostly observed in the north part of the Andaman Sea results to maximum wind speed in the North and Middle Andaman in 5, 10, 25 and 50-year return periods. Further, these cyclonic systems are crossed by ANI mostly in depression and deep depression stages. Climate change and warming across the tropical oceans, particularly in the Bay of Bengal, increase the likelihood of intensified cyclonic events with high frequency (Knutson, 2019). Furthermore, the shift in frequency of tropical cyclone tracks towards the Northeast India, Bangladesh, Myanmar, and Thailand coasts (Kabir et al., 2022 ) may increase the chances of cyclonic events along the ANI in the future. In ANI, the infrastructure development, like the international container transhipment port at Great Nicobar Islands (Government of India 2022), eco-tourism development and plans for mariculture activities along the ANI coast (Directorate of Fisheries 2025), are significantly related to the cyclonic impacts. These developmental activities should consider and integrate cyclone microzonation into their development plans to enhance disaster risk reduction and infrastructure resilience. CONCLUSION In the present study, cyclone microzonation maps for various return periods were prepared for Andaman and Nicobar Islands using GIS techniques and percentile analysis. In the study, cyclone data from 1879 and 2022, comprising 244 cyclonic systems, are analysed. Among these, 165 occurred along the Andaman Island region and 79 in the Nicobar Island region. The microzonation maps for the various return periods 5, 10, 25, 50, 75, and 100 years show the high cyclonic disturbances in North Andaman and low towards Great Nicobar. The minimum wind speed in the 5-year return period is 8–10 Knots/hr at the Nicobar Group of Islands and 18–20 Knots in North Andaman Island. Whereas the maximum wind speed shows 84–85 Knots in a 100-year return period in the entire Andaman Group of Islands and 45–48 Knots/hr in Great Nicobar Island. The cyclone category wind speed was observed after a 75-year return period in the Andaman group of islands, whereas in the Nicobar group of islands, it was observed only in a 100-year return period. When compared to the mainland Indian coast, the maximum wind speed in a 100-year return period is very low in ANI. Although rare historical records of cyclonic impacts are reported in ANI, the vulnerability and remoteness indicate the need to integrate cyclone microzonation into developmental activities for improved resilience. Declarations The authors confirm that no financial support, grants, or funding were received during the preparation of this manuscript . Author Contributions Writing-original draft, conceptualisation, Investigation and GIS analysis: [Yuvaraj E]; GIS analysis, Statistical analysis: [Aswanth KM]; Conceptualisation, Review and editing : [ Iyyappan M]; GIS analysis and validation: [Shoaib Rassel]; Formal analysis, review and editing: [ Adharsh AR]. Data STATEMENTS All datasets used in this research are collected from open source. Author Information Authors and Affiliations School of Physical, Chemical and Applied Sciences, Department of Coastal Disaster Management, Pondicherry University Port Blair Campus, Sri Vijaya Puram, Andaman and Nicobar Islands,744211, India. Yuvaraj E, Aswanth KM, Shoaib Rassel and Adharsh AR. Indian Meteorological Department, Ministry of Earth Sciences, New Delhi. India Iyyappan M, Ethics declarations Ethics approval and consent to participate: The research process and findings are accurately represented without fabrication, falsification, or selective reporting, and every facet of the study including the design, methodology, and results is carried out with the highest integrity and transparency. The manuscript is unique it hasn't been cut or published in any other format or language. We declare that suitable software was used to avoid plagiarism in this work, and that all the content, including data, language, and theories, is original and appropriately attributed to the appropriate sources when applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. ACKNOWLEDGEMENT The authors express their sincere gratitude to Pondicherry University for providing the necessary infrastructure and invaluable support in conducting this study. Further, the authors extend their appreciation to Mr. Shiva Kumar (GIS specialist, RIMES-Regional Integrated Multi-Hazard Early Warning System, Africa & Asia) for his valuable suggestions, which significantly contributed to the research . References Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277. https://doi.org/10.1016/0013-7952(92)90053-2 Deo AA, Ganer DW (2021) Decadal changes of cyclone tracks in the Bay of Bengal. Mausam 72(1):199–206. https://doi.org/10.54302/mausam.v72i1.135 Fanchiotti M, Dash J, Tompkins E.L, Hutton C.W. (2020) The 1999 super cyclone in Odisha, India: A systematic review of documented losses, International Journal of Disaster Risk Reduction, 51, 101790. https://doi.org/10.1016/j.ijdrr.2020.101790 . Fang W, Zhang H (2021) Zonation and scaling of tropical cyclone hazards based on spatial clustering for coastal China. Nat Hazards 109:1271–1295. https://doi.org/10.1007/s11069-021-04878-4 Goyal PK, Datta TK (2012) Cyclonic microzonation. Nat Hazards 61:911–929. https://doi.org/10.1007/s11069-011-9943-0 Hall TM, Jewson S (2007) Statistical modelling of North Atlantic tropical cyclone tracks. Tellus A Dyn Meteorol Oceanogr 59(4):486–498. https://doi.org/10.1111/j.1600-0870.2007.00240.x Kabir, R.; Ritchie, E.A.; Stark, C. Tropical Cyclone Exposure in the North Indian Ocean. Atmosphere 2022, 13, 1421. https://doi.org/10.3390/atmos13091421 Khouni I, Louhichi G, Ghrabi A (2021) Use of GIS-based Inverse Distance Weighted interpolation to assess surface water quality: case of Wadi El Bey, Tunisia. Environ Technol Innov 24:101892. https://doi.org/10.1016/j.eti.2021.101892 Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., & Wu, L. (2019). Tropical Cyclones and Climate Change Assessment: Part I: Detection and Attribution. Bulletin of the American Meteorological Society , 100 (10), 1987–2007. https://doi.org/10.1175/BAMS-D-18-0189.1 Kumar S, Chakraborty A, Chandrakar R, et al. (2023) Analysis of marine heatwaves over the Bay of Bengal during 1982–2021. Sci Rep 13:14235. https://doi.org/10.1038/s41598-023-39884-y Kumar VS, Babu VR, Babu MT, Dhinakaran G, Rajamanickam GV (2008) Assessment of storm surge disaster potential for the Andaman Islands. J Coast Res 24(10024):171–177. https://doi.org/10.2112/05-0506.1 Mandal GS, Mohapatra M (2010) Cyclone hazard prone districts of India: a report. Natl Disaster Manag Auth Gov India, New Delhi, p 44. Mohapatra M & Sharma M. (2015) Characteristics of surface wind structure of tropical cyclones over the north Indian Ocean. J. Earth Syst. Sci. 124 (7) 1573–1598. https://doi.org/10.1007/s12040-015-0613-6 Mohapatra M (2015) Cyclone hazard proneness of districts of India. J Earth Syst Sci 124:515–526. https://doi.org/10.1007/s12040-015-0556-y Mohapatra M, Mandal GS, Bandyopadhyay BK, Tyagi A, Mohanty UC (2012) Classification of cyclone hazard prone districts of India. Nat Hazards 63:1601–1620. https://doi.org/10.1007/s11069-011-9891-8 Mohapatra M. (2015) Cyclone hazard proneness of districts of India. J. Earth Syst. Sci. 124(3), 515–526 Nair AG, Annadurai R (2018) A study on various tropical cyclone hits in India – through GIS approach. Int J Pure Appl Math 119:589–595. Nath SK, Thingbaijam KKS (2009) Seismic hazard assessment—a holistic microzonation approach. Nat Hazards Earth Syst Sci 9:1445–1459. https://doi.org/10.5194/nhess-9-1445-2009 Paliwal M, Patwardhan A (2014) Analysis of cyclone tracks of North Indian Ocean using cluster analysis. In: Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change. Springer, pp 89–96. https://doi.org/10.1007/978-94-007-7720-0_9 Pentakota, S., Vivek, S. & Seshagiri Rao, K. Role of Andaman Sea in the intensification of cyclones over Bay of Bengal. Nat Hazards 91, 1113–1125 (2018). https://doi.org/10.1007/s11069-018-3170-x . Raghavan S, Rajesh S (2003) Trends in tropical cyclone impact: a study in Andhra Pradesh, India. Bull Am Meteorol Soc 84(5):635–644. https://doi.org/10.1175/BAMS-84-5-635 Rahman R, Saha SK (2007) Flood hazard zonation—a GIS-aided multi-criteria evaluation (MCE) approach with remotely sensed data. Int J Geoinform 3(3):1–13 Rumpf J, Weindl H, Höppe P, Rauch E, Schmidt V (2009) Tropical cyclone hazard assessment using model-based track simulation. Nat Hazards 48:383–398. https://doi.org/10.1007/s11069-008-9268-9 Sachithanandam V, Mageswaran T, Sridhar R, Purvaja R, Ramesh R (2014) Assessment of Cyclone Lehar’s impact on seagrass meadows in Ross and Smith Islands, North Andaman. Nat Hazards 72:1253–1258. https://doi.org/10.1007/s11069-014-1040-8 Sarangi RK, Shrinidhi SK, Chauhan P, Raghavan BR (2018) Remote sensing and in situ platform-based study on impact of Bay of Bengal cyclones (Phailin, Helen, Lehar, and Madi) on ocean chlorophyll and associated physical parameters. Nat Hazards 93:413–451. https://doi.org/10.1007/s11069-018-3307-y Shankar SV, Dharanirajan K, Narshimulu G (2015) Cyclone vulnerability zonation of southern part of South Andaman, India using multi-criteria weighted overlay analysis techniques. J Indian Soc Remote Sens 43(4):1181–1190 Sharma M, Mohapatra M, Suneetha P. (2025) Evaluation of operational extended range forecast of cyclogenesis over the north Indian Ocean. Tropical Cyclone Research and Review, 14(1), 82–103. https://doi.org/10.1016/j.tcrr.2025.02.006 . Toushif J, Dharanirajan K, Sohel R (2020) Lehar cyclone hazard impact assessment for South Andaman using remote sensing and GIS. Disaster Adv 13(10):62–71 Uddin K, Matin MA (2021) Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology. Prog Disaster Sci 11:100185. https://doi.org/10.1016/j.pdisas.2021.100185 Venkat Ratnam M, Ravindra Babu S, Das SS, Basha G, Krishnamurthy BV, Venkateswararao B (2016) Effect of tropical cyclones on the stratosphere–troposphere exchange observed using satellite observations over the north Indian Ocean. Atmos Chem Phys 16(13):8581–8591. https://doi.org/10.5194/acp-16-8581-2016 Vivek G, Srinivasa Kumar T (2015) Impact assessment of tropical cyclone Hud Hud on coastal region of Visakhapatnam, Andhra Pradesh, India. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2:123–130. https://doi.org/10.5194/isprsannals-II-2-W2-123-2015 Vongvisessomjai S (2009) Tropical cyclone disasters in the Gulf of Thailand. Songklanakarin J Sci Technol 31(2) Wahiduzzaman M, Oliver EC, Wotherspoon SJ, Holbrook NJ (2017) A climatological model of North Indian Ocean tropical cyclone genesis, tracks and landfall. Clim Dyn 49:2585–2603. https://doi.org/10.1007/s00382-016-3461-4 Yadav M, Das L (2024) Characteristics of cyclonic disturbance on India and their impact analysis on human and economic losses. Rev Climatol 24:64–73. https://doi.org/10.59427/rcli/2024/v24.64-73 Yuvaraj E, Dharanirajan K, Narshimulu S, Narshimulu G (2015) Post-disaster assessment of impact of cyclone Lehar in South Andaman Island. Curr Sci 85–90. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 21 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 05 Mar, 2026 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-9038925","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621683678,"identity":"cede6c0e-c8b3-47d2-8265-3041c2c71150","order_by":0,"name":"Yuvaraj E","email":"","orcid":"","institution":"Pondicherry University","correspondingAuthor":false,"prefix":"","firstName":"Yuvaraj","middleName":"","lastName":"E","suffix":""},{"id":621683679,"identity":"5f62a413-775c-4abb-85ac-fd153f87039e","order_by":1,"name":"Aswanth KM","email":"","orcid":"","institution":"Pondicherry University","correspondingAuthor":false,"prefix":"","firstName":"Aswanth","middleName":"","lastName":"KM","suffix":""},{"id":621683680,"identity":"1c114d76-8f8f-41fe-bb94-ee31a3833b0b","order_by":2,"name":"Lyyappan M","email":"","orcid":"","institution":"Indian Meteorological Department","correspondingAuthor":false,"prefix":"","firstName":"Lyyappan","middleName":"","lastName":"M","suffix":""},{"id":621683681,"identity":"432fa6b6-5c41-4627-abd9-06c30bb26fe3","order_by":3,"name":"Shoaib Rassel","email":"data:image/png;base64,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","orcid":"","institution":"Pondicherry University","correspondingAuthor":true,"prefix":"","firstName":"Shoaib","middleName":"","lastName":"Rassel","suffix":""},{"id":621683682,"identity":"6239db5d-2e06-4303-b587-979228a44282","order_by":4,"name":"Adharsh AR","email":"","orcid":"","institution":"Pondicherry University","correspondingAuthor":false,"prefix":"","firstName":"Adharsh","middleName":"","lastName":"AR","suffix":""}],"badges":[],"createdAt":"2026-03-05 10:11:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9038925/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9038925/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106790236,"identity":"339a3b95-56ea-4b6a-8432-7c94110c82ec","added_by":"auto","created_at":"2026-04-13 13:12:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92873,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area Map - Andaman and Nicobar Islands\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/5d0a081b1db2dcac47b17723.jpeg"},{"id":106790304,"identity":"5d98f0c5-f7de-4072-958f-895eb81f4a2a","added_by":"auto","created_at":"2026-04-13 13:13:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":986725,"visible":true,"origin":"","legend":"\u003cp\u003eMap shows the circle of influence of each point of interest of ANI. Fig.3 Map shows the cyclones passing through the circle of influence of point of interest of 92°34’57” E and 11°37’46” Of ANI during 1979-2022\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/46452c0dbbcdcacc702d4746.png"},{"id":106961356,"identity":"53f0adcd-c19b-490d-b7e0-508d4839b858","added_by":"auto","created_at":"2026-04-15 09:25:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141414,"visible":true,"origin":"","legend":"\u003cp\u003eshows the 5 years return period map of ANI\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/3364143f19a4e01f8e8fd8bc.jpeg"},{"id":106790175,"identity":"6247ec31-27f5-40ac-849e-2b5f37c19f2b","added_by":"auto","created_at":"2026-04-13 13:12:33","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145783,"visible":true,"origin":"","legend":"\u003cp\u003eshows the 10 years return period map of ANI\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/c38b51ee2f1962d52aee15e2.jpeg"},{"id":106790288,"identity":"4a495d19-ef25-43dc-8eb4-ea4ca5e23f04","added_by":"auto","created_at":"2026-04-13 13:12:45","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132917,"visible":true,"origin":"","legend":"\u003cp\u003eshows the 25 years return period map of ANI\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/f783fc38e306723465c62249.jpeg"},{"id":106790283,"identity":"3317ee00-242d-4e54-a8c5-17d16f94f49b","added_by":"auto","created_at":"2026-04-13 13:12:44","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131236,"visible":true,"origin":"","legend":"\u003cp\u003eshows the 50 years return period map of ANI\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/e8f3b1463521147b60f66c7d.jpeg"},{"id":106790303,"identity":"41a2c1ea-b7da-46fd-b66b-59e20f41a0a6","added_by":"auto","created_at":"2026-04-13 13:13:00","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":122801,"visible":true,"origin":"","legend":"\u003cp\u003eshows the 75 years return period map of ANI\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/ffaa719ea386c1e4b536fe61.jpeg"},{"id":106790180,"identity":"bbe270b2-4786-43b6-aeec-2f227c3260fb","added_by":"auto","created_at":"2026-04-13 13:12:34","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":148985,"visible":true,"origin":"","legend":"\u003cp\u003eshows the 100 years return period map of ANI\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/c146ec85a57f930d59ceff21.jpeg"},{"id":106963283,"identity":"c2223c1b-4baf-4e7b-9282-b2db16e76249","added_by":"auto","created_at":"2026-04-15 09:43:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2288796,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9038925/v1/f1856b03-26fd-4e5e-9804-063a3b3f1c35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Percentile-Based Wind Field Analysis for Cyclone Return Period Estimation and Microzonation Mapping in Andaman and Nicobar Region","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eTropical cyclones are India's most common natural disasters, generating storm surges, floods, strong winds, flooding, and erosion (Nair et al., 2018). These hazards pose a risk to life, infrastructure, properties, socio-economy and the environment. The Bay of Bengal Sea in the North Indian Ocean has the most tropical cyclones in terms of frequency and intensity (Shaoo \u0026amp; Baskaran, 2015). Cyclones are commonly observed across the Bay of Bengal during the post-monsoon season, which inflicts substantial damage to life and property on the east coast of the Indian mainland (Mondal et al., 2022). Most cyclones originated or developed in the southeastern part of the Bay of Bengal Sea, where the sea surface temperature is comparatively warmer than in other regions (Kumar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This region is situated in close proximity to the Andaman and Nicobar Islands, which comprise sensitive ecosystems, tribal communities, and local populations that heavily rely on fisheries and tourism. Although major cyclonic events are rare in the ANI, the intensity of cyclogenesis in the region poses significant threats to fisheries, tourism activities, and transportation (Yadav and Das \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Over the last decade, numerous developmental activities have been underway in the ANI region, with additional plans for major developments in eco-tourism and the blue economy (Government of India, 2022; Department of Fisheries, 2025), which are highly sensitive to coastal hazards such as cyclones. It is necessary to conduct a detailed study of cyclone hazards in the ANI region to understand its various scenarios, which are crucial for sustainable development and building resilience in these islands.\u003c/p\u003e \u003cp\u003eZonation is a strategy for the classification of homogenous spatial zones with high similarity to differentiate from its heterogeneous zones (Mitchell, 1976). The term microzonation is derived from earthquake engineering and refers to the zoning of land at the micro level to assess earthquake susceptibility. Later, this method was leveraged in multiple hazard zonation studies, including those on landslides and floods (Anbalagan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Rahman \u0026amp; Saha, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The GIS technique has been commonly used in microzonation studies over recent years; various methods, including multi-criteria evaluation, neural network algorithms, impact-based mapping, and spatial interpolation, are employed in most studies (Nath \u0026amp; Thingbaijam, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jahangir et al., 2019; Uddin \u0026amp; Matin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCyclone microzonation enables site-specific hazard assessments, a significant technique in dealing with damage potential at fine resolution exposure to cyclonic impacts (Fang \u0026amp; Zhang, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cyclone zonation studies were conducted using various spatial clustering methods, including analytical clustering, weighted analysis, and spatial interpolation (Goyal \u0026amp; Datta, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fang \u0026amp; Zhang, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The return period analysis for the cyclone risk is important to reduce the civilian casualties, economic loss and environmental damages and to understand the investment risk on coastal infrastructures (Wang et al., 2021)\u003c/p\u003e"},{"header":"2 STUDY AREA","content":"\u003cp\u003eThe union territory of India, the Andaman and Nicobar Islands, is composed of 572 islands with an aggregate land area of 8,249 square kilometres. Among these, 37 islands are inhabited, and the administration of the islands is divided into three districts based on population: South Andaman, North Andaman, and Middle Andaman and Nicobar. The union territory's capital, Sri Vijayapuram (Port Blair), is located on South Andaman Island.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study area is situated between the Bay of Bengal and the Andaman Sea, which spreads from 13\u0026deg;N to 7\u0026deg;N. Furthermore, the study area was divided into the Andaman group of islands and the Nicobar group of islands by the 10-degree channel, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Due to its location, the Andaman and Nicobar (ANI) Islands are highly susceptible to cyclonic phenomena. The Bay of Bengal, where the ANI is situated, is more sensitive to cyclonic storms than the Arabian Sea (Shankar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the last two decades, hardly three cyclones have hit the ANI with the intensity of a Cyclonic storm category. Among that, the catastrophic damage caused to life and property was reported during Cyclone Lehar in 2013. The cyclonic storm damaged residential buildings, roads, electrical poles, transformers, and other critical infrastructure, and resulted in the inundation of low-lying areas, beach erosion, and caused landslides in South Andaman (Yuvaraj et al., 2014). This cyclone also affected the coastal ecosystem, including seagrass, sandy beaches, and coral reefs on the North Andaman Island (Sachithanandam et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"3 METHODOLOGY","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cp\u003eThe cyclone track data from various sources are collected from the Indian Meteorological Department (IMD) Best Tracks data for the periods 1982\u0026ndash;2024, Joint Typhoon Warning Centre (JTWC) Best Tracks Data for the periods 1945\u0026ndash;2023, and the National Ocean and Atmospheric Administration (NOAA) IBTrACS for the periods 1879\u0026ndash;2020. The various categories of cyclone tracks, including depression and deep depression, are also considered for the study. The collected data were collated, and IMD data were considered primary data; NOAA and JTWC data were used wherever lagging was observed. The collated data shows the overall cyclonic events of the Bay of Bengal that occurred between 1879 and 2022, which are used to conduct the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Generation of Circle of Influence\u003c/h2\u003e \u003cp\u003eThe grids are generated with an extent of 0.25 \u0026times; 0.25 degrees to divide the study area for microzoning. There are 54 grids covered along the entire study area. The grid size is determined based on the spatial extent limitations of the island compared to the mainland. The centroid of each grid is considered a point of interest (Goyal \u0026amp; Datta, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which is derived using ArcGIS tools. The distance of 134.6 km from the point of interest is considered the cyclonic wind radius in the Bay of Bengal with reference to Mohapatra and Sharma (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This is the distance of maximum wind radii for the cyclone at 34 Knots/hr in the cyclonic storm category. There are 54 circles of influence that are generated with reference to each point of interest. The cyclonic storm and the above categories were considered for the study. The cyclone tracks that intersect with the circle of influence are considered as influencing factors for the point of interest (Fig.\u0026nbsp;3)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Return Period Analysis\u003c/h2\u003e \u003cp\u003eDetermining the number and wind speed of cyclones traversing each circle of influence is taken for the return period analysis. Since cyclones occur in irregular time series, the return periods are taken into account depending on the actual value of the annual frequency or the chosen percentile (Camufo et al., 2020). The percentile analysis is conducted to assess the return period analysis for 10, 25, 50, 75, and 100-year return periods. The wind speed of each return period value is attributed to the shapefile of the point of interest. To map the variables and create spatial zonation based on the point of interest, the IDW interpolation method was employed (Khouni et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 RESULT AND DISCUSSION","content":"\u003cp\u003eCyclone data for the Bay of Bengal and the Andaman Sea from 1879 to 2022 is sourced from the IMD, NOAA, and JTWC, which shows 244 cyclonic systems that influenced the study area. Among these, 165 cyclonic systems passed through the Andaman group of Islands and 79 in the Nicobar group. This shows that the Andaman group of islands experiences cyclonic systems more frequently than the Nicobar group.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Return Period Analysis\u003c/h2\u003e\n \u003cp\u003eThe return period analysis is calculated using the percentile technique, which reflects the cyclonic systems during the period of 1879\u0026ndash;2022. The return periods are created using five distinct return periods 5, 10, 25, 50, 75, and 100 years.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 5-years return period\u003c/h2\u003e\n \u003cp\u003eThe micro zonation map of a 5-year return period shows that Nicobar Island has the lowest wind speed, ranging from 8 to 10 Knots, followed by 12 to 15 Knots observed at the south of and Little Andaman Islands, and 15 to 18 Knots in the northern part of South Andaman and the southern part of Middle Andaman. The maximum wind speed of 18\u0026ndash;20 Knots was observed from the centre of the Middle Andaman to the North Andaman (Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 10-year return period\u003c/h2\u003e\n \u003cp\u003eThe 10-year return period map shows that Great Nicobar has the lowest wind speed, ranging from 10 to 12 Knots, followed by the South of South Andaman, Little Andaman, and Car Nicobar Islands, which have wind speeds between 13 and 16 Knots. The middle of South Andaman observed with wind speed of 16\u0026ndash;19 Knots, and maximum at the northern part of South Andaman Island, towards North Andaman Island has the highest wind speed of 19\u0026ndash;22 Knots (Fig.\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 25-year return period\u003c/h2\u003e\n \u003cp\u003eThe 25-year return period map shows wind speeds of 15\u0026ndash;16 Knots in Great Nicobar, followed by all other Nicobar Islands with wind speeds of 18\u0026ndash;20 Knots. Little Andaman observed with the wind speed of 22\u0026ndash;24 Knots. The maximum wind speed observed throughout the Andaman Islands was 24\u0026ndash;25 Knots (Fig. 5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 50- year return period\u003c/h2\u003e\n \u003cp\u003eThe 50-year return period map shows that the Great Nicobar has the lowest wind speed of 20\u0026ndash;22 Knots, and the rest of Nicobar group of Islands up to North of South Andaman shows the wind speed of 24\u0026ndash;26 Knots. The northern part of the South Andaman and Little Andaman Islands shows the wind speed of 26\u0026ndash;28 Knots. Middle Andaman to North Andaman Islands and the northern tip of Little Andaman has the highest wind speed of 28\u0026ndash;30 Knots (Fig.\u0026nbsp;6). The pattern is slightly irregular when compared to other return periods.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.6 75-year return period\u003c/h2\u003e\n \u003cp\u003eThe 75-year return period indicates that the Nicobar Islands have the least wind speed, ranging from 25\u0026ndash;26 Knots, except for Car Nicobar, which exhibits a wind speed of 28\u0026ndash;30 Knots. From Little Andaman to the middle of South Andaman, the wind speed is 30\u0026ndash;32 Knots. The northern part of the South Andaman to North Andaman Islands has the highest wind speed, ranging from 32 to 34 Knots.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.7 100-year return period\u003c/h2\u003e\n \u003cp\u003eThe 100-year return period map of ANI shows that the Great Nicobar shows the lowest wind speed of 45\u0026ndash;48 Knots, followed by the Katchal group of Islands with 51\u0026ndash;54 Knots, and Car Nicobar with a wind speed of 60\u0026ndash;63 Knots in its east part and 63\u0026ndash;66 Knots in the west part. The southern part of Little Andaman experiences wind speeds of 66\u0026ndash;69 Knots. The South Andaman to North Andaman Islands and the northern part of Little Andaman have the highest wind speeds of 84\u0026ndash;85 Knots (Fig.\u0026nbsp;8). The trend shows that the number of cyclonic wind disturbances is higher in the Andaman group of Islands than in the Nicobar Group of Islands.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.8 DISCUSSION\u003c/h2\u003e\n \u003cp\u003eThe return period analysis shows that North and Middle Andaman are highly susceptible to the potential damage of cyclones, followed by South Andaman and the Nicobar Group of Islands. When comparing the cyclone intensity from the return period maps, it is observed that the North Andaman Islands are highly exposed to cyclones, and Great Nicobar Island is least exposed. The intensity of cyclonic wind speed return periods is high to low from North to South of the study area.\u003c/p\u003e\n \u003cp\u003eA wind speed of 34 Knots, indicating a cyclone category (Mohapatra \u0026amp; Sharma, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), was observed from a 75-year return period in the Andaman group of Islands. Whereas, in the Nicobar group of Islands, the cyclone category is observed only in a 100-year return period. The maximum wind speed of 84 kt was observed at the entire Andaman group of Islands. There are four cyclones that crossed over the Andaman Islands (Sridharan et al., 2000). Cyclone Lehar (2013) is the only severe cyclone storm that made landfall and crossed over the South Andaman Islands, resulting in the maximum wind speed in the region, which is considered a 100-year event. This cyclone caused significant damage to critical infrastructure, agriculture, and coastal ecosystems in the Andaman Islands (Yuvaraj et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sachithanandam et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the Nicobar group of Islands, only two cyclones cross over the region. The cyclones that occurred in 1966 and 1988, which passed parallel to the Little Andaman and Car Nicobar, resulted in maximum wind speeds of 69 Knots observed at the Car Nicobar Island. The maximum wind speed of 140 Knots was observed on the mainland Indian coast during the 1999 Odisha Super Cyclone (Fanchiotti et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is comparatively higher than ANI. The district-level cyclone risk maps of India also show that ANI are less prone to cyclones (Mohapatra, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe detailed analysis of the return period reveals that, although very few cyclonic events occurred in ANI, the southeast Bay of Bengal near ANI has been most susceptible to cyclogenesis in recent decades. Most of the low-pressure systems of cyclones originate in the South of the Bay of Bengal region, but they intensify in the central and northern Parts of the Bay of Bengal region (Sharma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among the intensified cyclones, most make landfall over the coasts of Odisha (India), West Bengal (India), Bangladesh, and Myanmar. Some of the cyclones intensify in the South of the Bay of Bengal and move towards the coasts of Andhra Pradesh (India), Tamil Nadu (India), and Sri Lanka (Mohapatra et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similarly, the cyclogenesis in the Andaman Sea is comparatively less than Bay of Bengal, but the system generated in the Andaman Sea are high intense and sustain maximum lifetime (Pentakota, 2018), which are mostly observed in the north part of the Andaman Sea results to maximum wind speed in the North and Middle Andaman in 5, 10, 25 and 50-year return periods. Further, these cyclonic systems are crossed by ANI mostly in depression and deep depression stages.\u003c/p\u003e\n \u003cp\u003eClimate change and warming across the tropical oceans, particularly in the Bay of Bengal, increase the likelihood of intensified cyclonic events with high frequency (Knutson, 2019). Furthermore, the shift in frequency of tropical cyclone tracks towards the Northeast India, Bangladesh, Myanmar, and Thailand coasts (Kabir et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) may increase the chances of cyclonic events along the ANI in the future. In ANI, the infrastructure development, like the international container transhipment port at Great Nicobar Islands (Government of India 2022), eco-tourism development and plans for mariculture activities along the ANI coast (Directorate of Fisheries 2025), are significantly related to the cyclonic impacts. These developmental activities should consider and integrate cyclone microzonation into their development plans to enhance disaster risk reduction and infrastructure resilience.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn the present study, cyclone microzonation maps for various return periods were prepared for Andaman and Nicobar Islands using GIS techniques and percentile analysis. In the study, cyclone data from 1879 and 2022, comprising 244 cyclonic systems, are analysed. Among these, 165 occurred along the Andaman Island region and 79 in the Nicobar Island region. The microzonation maps for the various return periods 5, 10, 25, 50, 75, and 100 years show the high cyclonic disturbances in North Andaman and low towards Great Nicobar. The minimum wind speed in the 5-year return period is 8\u0026ndash;10 Knots/hr at the Nicobar Group of Islands and 18\u0026ndash;20 Knots in North Andaman Island. Whereas the maximum wind speed shows 84\u0026ndash;85 Knots in a 100-year return period in the entire Andaman Group of Islands and 45\u0026ndash;48 Knots/hr in Great Nicobar Island. The cyclone category wind speed was observed after a 75-year return period in the Andaman group of islands, whereas in the Nicobar group of islands, it was observed only in a 100-year return period. When compared to the mainland Indian coast, the maximum wind speed in a 100-year return period is very low in ANI. Although rare historical records of cyclonic impacts are reported in ANI, the vulnerability and remoteness indicate the need to integrate cyclone microzonation into developmental activities for improved resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors confirm that no financial support, grants, or funding were received during the preparation of this manuscript\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting-original draft, conceptualisation, Investigation and GIS analysis: [Yuvaraj E]; GIS analysis, Statistical analysis: [Aswanth KM]; Conceptualisation, Review and editing : [ Iyyappan M]; GIS analysis and validation: [Shoaib Rassel]; Formal analysis, review and editing: [ Adharsh AR].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData STATEMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used in this research are collected from open source.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchool of Physical, Chemical and Applied Sciences, Department of Coastal Disaster Management, Pondicherry University Port Blair Campus, Sri Vijaya Puram, Andaman and Nicobar Islands,744211, India.\u003c/p\u003e\n\u003cp\u003eYuvaraj E, Aswanth KM, Shoaib Rassel and Adharsh AR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndian Meteorological Department, Ministry of Earth Sciences, New Delhi. India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIyyappan M,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research process and findings are accurately represented without fabrication, falsification, or selective reporting, and every facet of the study including the design, methodology, and results is carried out with the highest integrity and transparency. The manuscript is unique it hasn\u0026apos;t been cut or published in any other format or language. We declare that suitable software was used to avoid plagiarism in this work, and that all the content, including data, language, and theories, is original and appropriately attributed to the appropriate sources when applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere gratitude to Pondicherry University for providing the necessary infrastructure and invaluable support in conducting this study. Further, the authors extend their appreciation to Mr. Shiva Kumar (GIS specialist, RIMES-Regional Integrated Multi-Hazard Early Warning System, Africa \u0026amp; Asia) for his valuable suggestions, which significantly contributed to the research\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269\u0026ndash;277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0013-7952(92)90053-2\u003c/span\u003e\u003cspan address=\"10.1016/0013-7952(92)90053-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeo AA, Ganer DW (2021) Decadal changes of cyclone tracks in the Bay of Bengal. Mausam 72(1):199\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.54302/mausam.v72i1.135\u003c/span\u003e\u003cspan address=\"10.54302/mausam.v72i1.135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFanchiotti M, Dash J, Tompkins E.L, Hutton C.W. (2020) The 1999 super cyclone in Odisha, India: A systematic review of documented losses, International Journal of Disaster Risk Reduction, 51, 101790. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijdrr.2020.101790\u003c/span\u003e\u003cspan address=\"10.1016/j.ijdrr.2020.101790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang W, Zhang H (2021) Zonation and scaling of tropical cyclone hazards based on spatial clustering for coastal China. Nat Hazards 109:1271\u0026ndash;1295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-021-04878-4\u003c/span\u003e\u003cspan address=\"10.1007/s11069-021-04878-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoyal PK, Datta TK (2012) Cyclonic microzonation. Nat Hazards 61:911\u0026ndash;929.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-011-9943-0\u003c/span\u003e\u003cspan address=\"10.1007/s11069-011-9943-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall TM, Jewson S (2007) Statistical modelling of North Atlantic tropical cyclone tracks. Tellus A Dyn Meteorol Oceanogr 59(4):486\u0026ndash;498.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0870.2007.00240.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0870.2007.00240.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKabir, R.; Ritchie, E.A.; Stark, C. Tropical Cyclone Exposure in the North Indian Ocean. Atmosphere 2022, 13, 1421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos13091421\u003c/span\u003e\u003cspan address=\"10.3390/atmos13091421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhouni I, Louhichi G, Ghrabi A (2021) Use of GIS-based Inverse Distance Weighted interpolation to assess surface water quality: case of Wadi El Bey, Tunisia. Environ Technol Innov 24:101892.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eti.2021.101892\u003c/span\u003e\u003cspan address=\"10.1016/j.eti.2021.101892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., \u0026amp; Wu, L. (2019). Tropical Cyclones and Climate Change Assessment: Part I: Detection and Attribution. \u003cem\u003eBulletin of the American Meteorological Society\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(10), 1987\u0026ndash;2007. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/BAMS-D-18-0189.1\u003c/span\u003e\u003cspan address=\"10.1175/BAMS-D-18-0189.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Chakraborty A, Chandrakar R, et al. (2023) Analysis of marine heatwaves over the Bay of Bengal during 1982\u0026ndash;2021. Sci Rep 13:14235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-39884-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-39884-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar VS, Babu VR, Babu MT, Dhinakaran G, Rajamanickam GV (2008) Assessment of storm surge disaster potential for the Andaman Islands. J Coast Res 24(10024):171\u0026ndash;177.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2112/05-0506.1\u003c/span\u003e\u003cspan address=\"10.2112/05-0506.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandal GS, Mohapatra M (2010) Cyclone hazard prone districts of India: a report. Natl Disaster Manag Auth Gov India, New Delhi, p 44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohapatra M \u0026amp; Sharma M. (2015) Characteristics of surface wind structure of tropical cyclones over the north Indian Ocean. J. Earth Syst. Sci. 124 (7) 1573\u0026ndash;1598. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12040-015-0613-6\u003c/span\u003e\u003cspan address=\"10.1007/s12040-015-0613-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohapatra M (2015) Cyclone hazard proneness of districts of India. J Earth Syst Sci 124:515\u0026ndash;526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12040-015-0556-y\u003c/span\u003e\u003cspan address=\"10.1007/s12040-015-0556-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohapatra M, Mandal GS, Bandyopadhyay BK, Tyagi A, Mohanty UC (2012) Classification of cyclone hazard prone districts of India. Nat Hazards 63:1601\u0026ndash;1620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-011-9891-8\u003c/span\u003e\u003cspan address=\"10.1007/s11069-011-9891-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohapatra M. (2015) Cyclone hazard proneness of districts of India. J. Earth Syst. Sci. 124(3), 515\u0026ndash;526\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair AG, Annadurai R (2018) A study on various tropical cyclone hits in India \u0026ndash; through GIS approach. Int J Pure Appl Math 119:589\u0026ndash;595.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNath SK, Thingbaijam KKS (2009) Seismic hazard assessment\u0026mdash;a holistic microzonation approach. Nat Hazards Earth Syst Sci 9:1445\u0026ndash;1459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/nhess-9-1445-2009\u003c/span\u003e\u003cspan address=\"10.5194/nhess-9-1445-2009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaliwal M, Patwardhan A (2014) Analysis of cyclone tracks of North Indian Ocean using cluster analysis. In: Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change. Springer, pp 89\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-94-007-7720-0_9\u003c/span\u003e\u003cspan address=\"10.1007/978-94-007-7720-0_9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePentakota, S., Vivek, S. \u0026amp; Seshagiri Rao, K. Role of Andaman Sea in the intensification of cyclones over Bay of Bengal. \u003cem\u003eNat Hazards\u003c/em\u003e 91, 1113\u0026ndash;1125 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-018-3170-x\u003c/span\u003e\u003cspan address=\"10.1007/s11069-018-3170-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghavan S, Rajesh S (2003) Trends in tropical cyclone impact: a study in Andhra Pradesh, India. Bull Am Meteorol Soc 84(5):635\u0026ndash;644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/BAMS-84-5-635\u003c/span\u003e\u003cspan address=\"10.1175/BAMS-84-5-635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman R, Saha SK (2007) Flood hazard zonation\u0026mdash;a GIS-aided multi-criteria evaluation (MCE) approach with remotely sensed data. Int J Geoinform 3(3):1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRumpf J, Weindl H, H\u0026ouml;ppe P, Rauch E, Schmidt V (2009) Tropical cyclone hazard assessment using model-based track simulation. Nat Hazards 48:383\u0026ndash;398. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-008-9268-9\u003c/span\u003e\u003cspan address=\"10.1007/s11069-008-9268-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSachithanandam V, Mageswaran T, Sridhar R, Purvaja R, Ramesh R (2014) Assessment of Cyclone Lehar\u0026rsquo;s impact on seagrass meadows in Ross and Smith Islands, North Andaman. Nat Hazards 72:1253\u0026ndash;1258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-014-1040-8\u003c/span\u003e\u003cspan address=\"10.1007/s11069-014-1040-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarangi RK, Shrinidhi SK, Chauhan P, Raghavan BR (2018) Remote sensing and in situ platform-based study on impact of Bay of Bengal cyclones (Phailin, Helen, Lehar, and Madi) on ocean chlorophyll and associated physical parameters. Nat Hazards 93:413\u0026ndash;451. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-018-3307-y\u003c/span\u003e\u003cspan address=\"10.1007/s11069-018-3307-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShankar SV, Dharanirajan K, Narshimulu G (2015) Cyclone vulnerability zonation of southern part of South Andaman, India using multi-criteria weighted overlay analysis techniques. J Indian Soc Remote Sens 43(4):1181\u0026ndash;1190\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma M, Mohapatra M, Suneetha P. (2025) Evaluation of operational extended range forecast of cyclogenesis over the north Indian Ocean. Tropical Cyclone Research and Review, 14(1), 82\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tcrr.2025.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tcrr.2025.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToushif J, Dharanirajan K, Sohel R (2020) Lehar cyclone hazard impact assessment for South Andaman using remote sensing and GIS. Disaster Adv 13(10):62\u0026ndash;71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUddin K, Matin MA (2021) Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology. Prog Disaster Sci 11:100185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pdisas.2021.100185\u003c/span\u003e\u003cspan address=\"10.1016/j.pdisas.2021.100185\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkat Ratnam M, Ravindra Babu S, Das SS, Basha G, Krishnamurthy BV, Venkateswararao B (2016) Effect of tropical cyclones on the stratosphere\u0026ndash;troposphere exchange observed using satellite observations over the north Indian Ocean. Atmos Chem Phys 16(13):8581\u0026ndash;8591. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/acp-16-8581-2016\u003c/span\u003e\u003cspan address=\"10.5194/acp-16-8581-2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVivek G, Srinivasa Kumar T (2015) Impact assessment of tropical cyclone Hud Hud on coastal region of Visakhapatnam, Andhra Pradesh, India. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2:123\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/isprsannals-II-2-W2-123-2015\u003c/span\u003e\u003cspan address=\"10.5194/isprsannals-II-2-W2-123-2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVongvisessomjai S (2009) Tropical cyclone disasters in the Gulf of Thailand. Songklanakarin J Sci Technol 31(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahiduzzaman M, Oliver EC, Wotherspoon SJ, Holbrook NJ (2017) A climatological model of North Indian Ocean tropical cyclone genesis, tracks and landfall. Clim Dyn 49:2585\u0026ndash;2603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-016-3461-4\u003c/span\u003e\u003cspan address=\"10.1007/s00382-016-3461-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav M, Das L (2024) Characteristics of cyclonic disturbance on India and their impact analysis on human and economic losses. Rev Climatol 24:64\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59427/rcli/2024/v24.64-73\u003c/span\u003e\u003cspan address=\"10.59427/rcli/2024/v24.64-73\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuvaraj E, Dharanirajan K, Narshimulu S, Narshimulu G (2015) Post-disaster assessment of impact of cyclone Lehar in South Andaman Island. Curr Sci 85\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Hazards](https://link.springer.com/journal/44475)","snPcode":"44475","submissionUrl":"https://submission.nature.com/new-submission/44475/3","title":"Discover Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cyclone microzonation, GIS, Percentile analysis, Wind-field, Andaman and Nicobar Islands","lastPublishedDoi":"10.21203/rs.3.rs-9038925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9038925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current study attempted to map wind field-based cyclone return period analysis and microzonation mapping using integrated geospatial techniques and percentile methods. The Andaman and Nicobar Islands (ANI) in the North Indian Ocean are taken as the study area. Cyclone track data from 1891 to 2022 were collected and collated from multiple sources, including the IMD, JTWC, and NOAA. In the study, grids with a spatial extent of 0.25 degrees are created throughout the study area using ArcGIS 10.8 software. Each grid\u0026rsquo;s centroid is considered a point of interest, which is used to generate the circle of influence. The wind speed data of cyclone tracks that intersect with each circle of influence are taken for statistical evaluation. The percentile method was used to analyse the cyclone return periods for 5, 10, 25, 50, 75, and 100 years. The outputs of the percentile analysis are used in ArcGIS to plot the return period maps using spatial interpolation techniques. The return period analysis indicates that the Andaman group of islands is highly prone to cyclones compared to the Nicobar group of Islands. The cyclonic category windspeed (34 Knots) was observed only after the 75-year return period, which was in the Andaman Group of Islands, whereas in the Nicobar Group of Islands, it was observed only in the 100-year return period. The maximum wind speeds in the 100-year return period are 85 Knots and 64 Knots in the Andaman and Nicobar Groups, respectively, which are comparatively less than the mainland Indian coast. The study demonstrates the integration of GIS, percentile analysis, and wind field-based cyclone microzonation mapping, providing a deeper understanding of return periods, which are useful for the inclusive development plans.\u003c/p\u003e","manuscriptTitle":"Percentile-Based Wind Field Analysis for Cyclone Return Period Estimation and Microzonation Mapping in Andaman and Nicobar Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 13:11:32","doi":"10.21203/rs.3.rs-9038925/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"130336549025725837361314603693019123843","date":"2026-05-17T17:44:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72359871504903374549482864191805776475","date":"2026-05-15T08:17:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176750127680999619167146724001651255482","date":"2026-05-14T00:48:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51738684071626905580221081156623416328","date":"2026-05-13T08:30:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59294322773271594589088589122544334397","date":"2026-04-16T03:54:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T06:28:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-21T07:49:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T12:57:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Hazards","date":"2026-03-05T10:01:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Hazards](https://link.springer.com/journal/44475)","snPcode":"44475","submissionUrl":"https://submission.nature.com/new-submission/44475/3","title":"Discover Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8318e7c-29ff-486c-b799-c95602dba7e5","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"130336549025725837361314603693019123843","date":"2026-05-17T17:44:50+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"72359871504903374549482864191805776475","date":"2026-05-15T08:17:07+00:00","index":67,"fulltext":""},{"type":"reviewerAgreed","content":"176750127680999619167146724001651255482","date":"2026-05-14T00:48:45+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"51738684071626905580221081156623416328","date":"2026-05-13T08:30:35+00:00","index":64,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T13:11:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 13:11:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9038925","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9038925","identity":"rs-9038925","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}