Abstract
The coastal region of West Bengal, India, is vulnerable to the tropical cyclones from the Bay of Bengal often causing loss of life, infrastructure damage, agricultural devastation, and environmental degradation exemplified by the devastating impacts of Amphan in May 2020 and Yaas in May 2021. Considering the importance of accurately mapping flood extent, a study was conducted to evaluate the inundation caused by Amphan and Yaas in three coastal districts of West Bengal: East Medinipur, North 24 Parganas, and South 24 Parganas, by using satellite-derived data products and Google Earth Engine (GEE). The changes in backscattering signal of dual-polar Sentinel-1-C-band Synthetic Aperture Radar (SAR) due to flooding were analyzed to detect the inundation extent. The Copernicus Digital Elevation Model (GLO30) and JRC Global Surface Water Mapping Layers were used for terrain correction, masking of high slope areas and seasonal water bodies. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) revealed rainfall increases of 47–135% above the long-term average in these districts. Amphan caused widespread flooding in East Medinipur (17.56% of total area and 35% of croplands) affecting 349,097 people while the other districts were moderately affected. The impact of Yaas was comparatively lower. The study provides detailed Block-wise (third-level administrative unit) inundation map due to Amphan and Yaas of the study region. The methodological framework outlined herein, presents a scientifically rigorous method involving open-source satellite data and cloud computing platform to make informed decisions for flood management in the coastal plains.
Highlights
-
• Sentinel-1 SAR imageries of pre and post cyclonic inundation extents are analyzed for VH backscatter thresholding to estimate the inundation caused by cyclones Amphan and Yaas in West Bengal.
-
• Significant rainfall deviation was observed during both the cyclones calculated from past 30 years data (1991-2020).
-
• Impact on cropland, urban areas and population estimated from Google Dynamic World and JRC Human Settlement layer database.
-
• Maximum flooding of 17.56% area occurred in East Medinipur district affecting 349,097 people during Amphan, while impact of Yaas was comparatively less. About 35% of cultivable land was inundated.
-
• South 24 Parganas district suffered more than other coastal districts during Yaas.
This is a preview of subscription content, log in via an institution to check access.
(Source: CHIRPS daily, https://data.chc.ucsb.edu/products/CHIRPS-2.0/, Standard reference period 1991 to 2020) (Color)
(Source: CHIRPS daily, https://data.chc.ucsb.edu/products/CHIRPS-2.0/) (Color)
Similar content being viewed by others
Data availability
All open-source data have been used in this research. The links will be provided upon specific request.
Code availability
The code used for data access and analysis will be shared upon specific request.
References
ADS (2022) Copernicus DEM. Product Handbook. Airbus Defense and Space, https://spacedata.copernicus.eu/documents/20123/121239/GEO1988-CopernicusDEM-SPE-002_ProductHandbook_I4.0.pdf, Retrieved March 2024
Ahammed KB, Pandey AC (2021) Characterization and impact assessment of super cyclonic storm AMPHAN in the Indian subcontinent through space borne observations. Ocean Coast Manag 205:105532
Behera MD, Prakash J, Paramanik S, Mudi S, Dash J, Varghese R, Roy PS, Abhilash PC, Gupta AK, Srivastava PK (2022) Assessment of tropical cyclone Amphan affected inundation areas using sentinel-1 satellite data. Trop Ecol, 1–11
Bessis JL, Bequignon J, Mahmood A (2004) The international charter space and major disasters initiative. Acta Astronaut 54(3):183–190
Bhattacharyya S, Sreekesh S (2022) Assessments of multiple gridded-rainfall datasets for characterizing the precipitation concentration index and its trends in India. Int J Climatol 42(5):3147–3172
Boni G, Ferraris L, Pulvirenti L, Squicciarino G, Pierdicca N, Candela L, Pisani AR, Zoffoli S, Onori R, Proietti C, Pagliara P (2016) A prototype system for flood monitoring based on flood forecast combined with COSMO-SkyMed and Sentinel-1 data. IEEE J Sel Top Appl Earth Obs Remote Sens 9(6):2794–2805
Brown CF, Brumby SP, Guzder-Williams B, Birch T, Hyde SB, Mazzariello J, Czerwinski W, Pasquarella VJ, Haertel R, Ilyushchenko S, Schwehr K, Weisse M, Stolle F, Hanson C, Guinan O, Moore R, Tait AM (2022) Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9:251
Census data, Govt of India (2011) https://www.census2011.co.in/census/state/districtlist/west+bengal.html, Retrieved March 2024
Chakraborty A, Srikanth P, Murthy CS, Rao PVN, Chowdhury S (2021) Assessing lodging damage of jute crop due to super cyclone amphan using multi-temporal Sentinel-1 and Sentinel-2 data over parts of West Bengal, India. Environ Monit Assess 193(8):464
Chowdhury KMMH (2002) Cyclone preparedness and management in Bangladesh. Improvement of early warning system and responses in Bangladesh towards Total Disaster Risk Management Approach. Dhaka, BPATC (ed). BPATC, pp 115–119
Cyclone Amphan: A trail of death, misery and destruction. Hindustan Times. May 28 (2020) https://www.hindustantimes.com/india-news/cyclone-Amphan-a-trail-of-death-misery-and-destruction/story-Y5DVaWIW63ZBhSiA1QVFUJ.html Retrieved March 2024
Cyclone Yaas makes landfall over Odisha, West Bengal coasts. Jayashree Nandi. Hindustan Times. May 26 (2021) https://www.hindustantimes.com/india-news/very-severe-cyclone-Yaas-close-to-north-odisha-west-bengal-coasts-101621997052559.html, Retrieved March 2024
The Indian Express (2021) Bengal coast: three killed, villages flooded. Indian Express. https://indianexpress.com/article/india/cyclone-Yaas-west-bengal-odisha-jharkhand-weather-7331444/, Retrieved on March 2024
Department of Agriculture & Farmers Welfare, Govt. of India (2019) West Bengal-Agriculture Contingency Plan, https://agriwelfare.gov.in/en/AgricultureContigencyPlan/WEST%20BENGAL Retrieved March 2024
Dutta RK (2020) Landfall of tropical cyclone-amphan in the Indian Sundarban area under pandemic situation. Rethinking 21st Century (Covid-19 Special Edition) Vol-II, Kultali Dr. B.R. Ambedkar College, 74–88
Fekete A, Tzavella K, Armas I, Binner J, Garschagen M, Giupponi C, Mojtahed V, Pettita M, Schneiderbauer S, Serre D (2015) Critical data source; tool or even infrastructure? Challenges of geographic information systems and remote sensing for disaster risk governance. ISPRS Int J Geoinf 4(4):1848–1869
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2(1):1–21
Ghosh S, Mistri B (2021) Assessing coastal vulnerability to environmental hazards of Indian Sundarban delta using multi-criteria decision-making approaches. Ocean Coast Manag 209:105641
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27
IMD (2021) Very Severe Cyclonic Storm YAAS‟ over Bay of Bengal (23rd – 28th May, 2021): A Report. Indian Meteorological Department. https://rsmcnewdelhi.imd.gov.in/uploads/report/26/26_77afd4_Preliminary%20Report%20YAAS%20during%2023-27%20May%202021.pdf, Retrieved March 2024
Impact of Cyclone Yaas on Indian economy. Times of India. June 1 (2021) Sikdar A. https://timesofindia.indiatimes.com/blogs/economic-update/impact-of-cyclone-Yaas-on-indian-economy/ Retrieved March 2024
Khatun M, Rahaman SK, Garai S, Ranjan A, Ghosh BG, Kumar A, Tiwari S (2022) Assessing the impact of Super Cyclone Amphan on Indian Sundarban Biosphere Reserve. Indian J Ecol 49(6):2236–2242
Kumar L, Mutanga O (2018) Google Earth Engine applications since inception: usage, trends, and potential. Remote Sens 10(10):1509
Kumar R, Rani S, Maharana P (2021a) Assessing the impacts of Amphan cyclone over West Bengal, India: a multi-sensor approach. Environ Monit Assess 193:1–21
Kumar A, Singh AK, Giri RK, Tripathi JN (2021b) Inundation mapping using Sentinel-1 data in the aftermath of super cyclone Amphan: a case study. Mausam 72(1):253–264
Mondal B, Doloi G, Islam ST, Bera MM (2021) Damage estimation using shock zones: a case study of Amphan tropical cyclone. https://doi.org/10.21203/rs.3.rs-962664/v2
Mondal P, Dutta T, Qadir A, Sharma S (2022) Radar and optical remote sensing for near real-time assessments of cyclone impacts on coastal ecosystems. Remote Sens Ecol 8(4):506–520
Mullissa A, Vollrath A, Odongo-Braun C, Slagter B, Balling J, Gou YQ, Gorelick N, Reiche J (2021) Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine. Remote Sens, 13(10)
Notti D, Giordan D, Caló F, Pepe A, Zucca F, Galve JP (2018) Potential and limitations of open satellite data for flood mapping. Remote Sens 10(11):1673
Pekel JF, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633):418–422
Pesaresi M, Politis P, GHS-BUILT-C, R2023A - (2023) GHS Settlement Characteristics, derived from Sentinel2 composite (2018) and other GHS R2023A data. European Commission, Joint Research Centre (JRC) http://data.europa.eu/89h/3c60ddf6-0586-4190-854b-f6aa0edc2a30, Retrieved March 2024
Puotinen ML (2007) Modelling the risk of cyclone wave damage to coral reefs using GIS: a case study of the great barrier reef, 1969–2003. Int J Geogr Inf Syst, 97–120
Quegan S, Yu JJ (2001) Filtering of multichannel SAR images. IEEE Trans Geosci Remote Sens 39:2373–2379
Sharma SSP, Rao KD, Shukla AK (2020) Near real-time delineation, mapping and monitoring of floods in West Bengal, India due to extremely severe cyclone ‘Amphan’ using multi-mission satellite data. Curr. Sci., 119(12), 1939
Torres R, Snoeij P, Geudtner D, Bibby D, Davidson M, Attema E, Potin P, Rommen B, Floury N, Brown M, Traver IN, Rostan F (2012) GMES Sentinel-1 mission. Remote Sens Environ 120:9–24
Twele A, Cao W, Plank S, Martinis S (2016) Sentinel-1-based flood mapping: a fully automated processing chain. Int J Remote Sens 37(13):2990–3004
Vanama VSK, Rao YS, Bhatt CM (2021) Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform. Eur J Remote Sens 54(1):589–609
Voigt S, Giulio-Tonolo F, Lyons J, Kučera J, Jones B, Schneiderhan T, Platzeck G, Kaku K, Hazarika MK, Czaran L, Li S, Guha-Sapir D (2016) Global trends in satellite-based emergency mapping. Sci 353(6296):247–252
West Bengal: Knocked down by the virus, flattened by winds- The Hindu.com. May 30 (2020) https://www.thehindu.com/news/national/other-states/west-bengal-knocked-down-by-the-coronavirus-flattened-by-winds-of-cyclone-Amphan /article31705148.ece Retrieved March 2024
West Bengal govt shares Amphan damage details of over Rs 1 lakh crore with Centre. TimesNow.com. June 07 (2020) https://www.timesnownews.com/india/article/west-bengal-govt-shares-amphan-damage-details-of-over-rs-1-lakh-crore-with-centre/602606#:~:text=The%20damage%20report%20extract%20pins,15%2C860%20crore%20to%20the%20loss. Retrieved March 2024
Yaas Cyclone effect one crore people hit, three lakh homes. The Times of India. May 27 (2021). https://timesofindia.indiatimes.com/city/kolkata/Yaas-effect-1-crore-people-hit-3-lakh-homes-wrecked-in-state/articleshow/82988553.cms. Retrieved March 2024
Acknowledgements
The authors sincerely acknowledge Australian Centre for International Agricultural Research (ACIAR) through the project CSI4CZ running in Bidhan Chandra Krishi Viswavidyalaya (BCKV) for providing all kinds of support for conducting this research work.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
M. Mondal and M. K. Nanda conceptualized and analyzed the data for flood mapping and loss assessment as well as prepared the initial draft of the manuscript. J. Peña-Arancibia has contributed in improvising the methodology of satellite data processing and complying the response to the reviewers. D. Sarkar, A. Saha and A. Mukherjee have surveyed the reference literature. A. Ghosh, S. Sarkar and. K. Brahmachari contributed for compilation of the results and preparation of tables and graphs. R. Goswami and Md. Mainuddin have improvised the writing. All the authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval/declarations
We declare that the research reported in this manuscript is our original work.
Consent to participate
Not applicable.
Consent for publication
We give consent to the journal of Theoretical and Applied Climatology to publish our article.
Conflict of interest
The authors declare that there is no conflict of interest with respect to the research article.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mondal, M., Nanda, M.K., Peña-Arancibia, J.L. et al. Assessment of inundation extent due to super cyclones Amphan and Yaas using Sentinel-1 SAR imagery in Google Earth Engine. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04948-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00704-024-04948-0