Impact Assessment of Flood Extent on Agricultural Land and Communities Using Google Earth Engine and SAR Data: A Case Study of Indus River Basin
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La Cuenca del Río Indo ha experimentado cambios hidrológicos significativos debido al cambio climático, lo que ha llevado a un aumento en la frecuencia de inundaciones, representando un riesgo para la seguridad alimentaria basada en la agricultura. Este estudio se centró en eventos de inundación inducidos por la lluvia desde agosto hasta septiembre de 2022 a través de las provincias de Punjab, Sindh y Baluchistan utilizando Google Earth Engine, datos de Radar de Apertura Sintética (SAR) Sentinel-1A y conjuntos de datos de Cobertura del Suelo. Las inundaciones causaron daños considerables a las tierras agrícolas y a las comunidades, afectando 49,602.92 km2 de tierra. En Sindh, el total de tierra inundada es 2,042.1 km2 con 915.9 km2 de tierra agrícola y 609.8 km2 de áreas construidas afectadas. Por lo tanto, la evaluación de daños a nivel distrital incluye Sukkur (497.1 km2), Sanghar (565.2 km2) y Khairpur (979.9 km2). En la provincia de Baluchistan, el área inundada fue 10,733.4 km2. La tierra agrícola afectada fue 674.8 km2, y 47.8 km2 de terrenos construidos. La lluvia intensa extendió aún más las inundaciones afectando 1002.2 km2 en Jhal Magsi, 7,266.5 km2 en Khuzdar y 2,464.7 km2 en Lasbella. En Punjab, 4001.3 km2 de tierra se inundaron incluyendo 297.6 km2 de áreas construidas, y 776.9 km2 afectaron tierra agrícola. A nivel distrital las áreas afectadas fueron D.G. Khan (1,871.3 km2), Muzaffargarh (620 km2) y Rajanpur (1,509.7 km2). La integración de la teledetección y GEE proporcionó información crucial sobre inundaciones y estrategias de reducción del riesgo climático, especialmente en regiones con escasez de datos.
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Ahmad, D., Kanwal, M. and Afzal, M. (2023). Climate change effects on riverbank erosion Bait community flood-prone area of Punjab, Pakistan: an application of livelihood vulnerability index. Environment, Development and Sustainability, 25, 9387–9415. doi: https://doi.org/10.1007/s10668-022-02440-1
Ali, S., Liu, Y., Ishaq, M., Shah, T., Abdullah, Ilyas, A. and Din, I. (2017). Climate Change and Its Impact on the Yield of Major Food Crops: Evidence from Pakistan. Foods, 6(6), 39. doi: https://doi.org/10.3390/foods6060039
Amin, F., Luxmi, S., Ali, F., & Fareeduddin, M. (2023). Flood 2022 in Pakistan: Managing medical flood relief camps in a developing country. Journal of Family Medicine and Primary Care, 12(2), 194–200. doi: https://doi.org/10.4103/jfmpc.jfmpc_1919_22
Aqib, S., Seraj, M., Ozdeser, H., Khalid, S., Haseeb Raza, M., Ahmad, T. (2024). Assessing adaptive capacity of climate-vulnerable farming communities in flood-prone areas: Insights from a household survey in South Punjab, Pakistan. Climate Services, 33, 100444. doi: https://doi.org/10.1016/j.cliser.2023.100444
Arshad, M., Ma, X., Yin, J., Ullah, W., Liu, M. and Ullah, I. (2021). Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan. Weather and Climate Extremes, 33, 100373. doi: https://doi.org/10.1016/j.wace.2021.100373
Ashraf, I., Ahmad, S. R., Ashraf, U. and Khan, M. (2023). Community perspectives to improve flood management and socio-economic impacts of floods at Central Indus River, Pakistan. International Journal of Disaster Risk Reduction, 92, 103718. doi: https://doi.org/10.1016/j.ijdrr.2023.103718
Asim, M., Nadeem, M. and Saima, G. (2021). Empowering Communities to cope Flood Risk: Learning from Flood affected Community in Narowal District, Pakistan. Disaster Advances, 14(9), 23–33. doi: https://doi.org/10.25303/149da2333.
Bai, Y., Wu, W., Yang, Z., Yu, J., Zhao, B., Liu, X., Yang, H., Mas, E. and Koshimura, S. (2021). Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets. Remote Sensing, 13(11), 2220. doi: https://doi.org/10.3390/rs13112220
Carreño Conde, F. and De Mata Muñoz, M. (2019). Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study. Water, 11(12), 2454. doi: https://doi.org/10.3390/w11122454
Chan, E. Y. Y., Man, A. Y. T. and Lam, H. C. Y. (2019). Scientific evidence on natural disasters and health emergency and disaster risk management in Asian rural-based area. British Medical Bulletin, 129(1), 91–105. doi: https://doi.org/10.1093/bmb/ldz002
Chini, M., Pelich, R., Liu, Y., Renaud Hostache, Zhao, J., Concetta Di Mauro and Matgen, P. (2021). Sar-Based Flood Mapping, Where We Are and Future Challenges. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium. https://doi.org/10.1109/igarss47720.2021.9554975
Clement, M. A., Kilsby, C. G. and Moore, P. (2017). Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11(2), 152–168. doi: https://doi.org/10.1111/jfr3.12303
Dube, K., Nhamo, G. and Chikodzi, D. (2021). Flooding trends and their impacts on coastal communities of Western Cape Province, South Africa. GeoJournal, 87, 453–468. doi: https://doi.org/10.1007/s10708-021-10460-z
Ejeta, L. T. (2018). Community’s Emergency Preparedness for Flood Hazards in Dire-dawa Town, Ethiopia: A Qualitative Study. PLoS Currents, 10. doi: https://doi.org/10.1371/currents.dis.3843ad9fc823c8c853970148b350750c
Fankhauser, S. and McDermott, T. K. J. (2014). Understanding the adaptation deficit: Why are poor countries more vulnerable to climate events than rich countries? Global Environmental Change, 27, 9–18. doi: https://doi.org/10.1016/j.gloenvcha.2014.04.014
Fu, Z.-H., Zhou, W., Zhou, W., Xie, S.-P., Zhang, R. and Wang, X. (2024). Dynamic pathway linking Pakistan flooding to East Asian heatwaves. Science advances, 10(17). doi: https://doi.org/10.1126/sciadv.adk9250
Gan, T. Y., Zunic, F., Kuo, C.-C. and Strobl, T. (2012). Flood mapping of Danube River at Romania using single and multi-date ERS2-SAR images. International Journal of Applied Earth Observation and Geoinformation, 18, 69–81. doi: https://doi.org/10.1016/j.jag.2012.01.012
Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. doi: https://doi.org/10.1016/S0034-4257(96)00067-3
Ghouri, A. Y., Rehman, A. ur, Rasheed, F., Miandad, M. and Rehman, G. (2024). Flood Mapping Using the Sentinel-1 SAR Dataset and Application of the Change Detection Approach Technique (CDAT) to the Google Earth Engine In Sindh Province, Pakistan. Ecological Questions, 35(2), 1–18. doi: https://doi.org/10.12775/EQ.2024.024
Gupta, N., Mathew, A. and Khandelwal, S. (2019). Analysis of cooling effect of water bodies on land surface temperature in nearby region: A case study of Ahmedabad and Chandigarh cities in India. The Egyptian Journal of Remote Sensing and Space Science, 22(1), 81–93. doi: https://doi.org/10.1016/j.ejrs.2018.03.007
Haque, M. A., Yamamoto, S. S., Malik, A. A. and Sauerborn, R. (2012). Households’ perception of climate change and human health risks: A community perspective. Environmental Health, 11(1). doi: https://doi.org/10.1186/1476-069x-11-1
Hoang, L. P., Biesbroek, R., Tri, V. P. D., Kummu, M., van Vliet, M. T. H., Leemans, R., Kabat, P. and Ludwig, F. (2018). Managing flood risks in the Mekong Delta: How to address emerging challenges under climate change and socioeconomic developments. Ambio, 47(6), 635–649. doi: https://doi.org/10.1007/s13280-017-1009-4
Jane, R. A., Simmonds, D. J., Gouldby, B. P., Simm, J. D., Dalla Valle, L. and Raby, A. C. (2018). Exploring the Potential for Multivariate Fragility Representations to Alter Flood Risk Estimates. Risk Analysis, 38(9), 1847–1870. doi: https://doi.org/10.1111/risa.13007
Joyce, K., Belliss, S., Samsonov, S., McNeill, S. and Glassey, P. (2009). A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Semantic Scholar, 33(2). doi: https://doi.org/10.1177/0309133309339563
Khan, I., Lei, H., Shah, A. A., Khan, I. and Muhammad, I. (2021). Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future. Environmental Science and Pollution Research, 28(23). doi: https://doi.org/10.1007/s11356-021-12801-4
Kirsch, T. D., Wadhwani, C., Sauer, L., Doocy, S. and Catlett, C. (2012). Impact of the 2010 Pakistan Floods on Rural and Urban Populations at Six Months. PLoS Currents, 1. doi: https://doi.org/10.1371/4fdfb212d2432
Manzoor, Z., Ehsan, M., Khan, M. B., Manzoor, A., Akhter, M. M., Sohail, M. T., Hussain, A., Shafi, A., Abu-Alam, T. and Abioui, M. (2022). Floods and flood management and its socio-economic impact on Pakistan: A review of the empirical literature. Frontiers in Environmental Science, 10(10). doi: https://doi.org/10.3389/fenvs.2022.1021862
Mbah, M. F., Shingruf, A. and Molthan-Hill, P. (2022). Policies and practices of climate change education in South Asia: towards a support framework for an impactful climate change adaptation. Climate Action, 1(1). doi: https://doi.org/10.1007/s44168-022-00028-z
Nanditha, J. S., Kushwaha, A. P., Singh, R., Malik, I., Solanki, H., Chuphal, D. S., Dangar, S., Mahto, S. S., Vegad, U. and Mishra, V. (2023). The Pakistan Flood of August 2022: Causes and Implications. Earth’s Future, 11(3). doi: https://doi.org/10.1029/2022ef003230
Otto, Zachariah, M., Saeed, F., Siddiqi, A., Kamil, S., Mushtaq, H., Arulalan T, AchutaRao, K., T, C. S., Barnes, C., Philip, S., Kew, S. F., Vautard, R., Koren, G., Pinto, I., Wolski, P., Maja Vahlberg, Singh, R., Arrighi, J. and Maarten van Aalst (2023). Climate change increased extreme monsoon rainfall, flooding highly vulnerable communities in Pakistan. Environmental Research: Climate, 2(2), 025001–025001. doi: https://doi.org/10.1088/2752-5295/acbfd5
P Lama, A., & Tatu, U. (2022). Climate change and infections: lessons learnt from recent floods in Pakistan. New microbes and new infections, 49-50, 101052. doi: https://doi.org/10.1016/j.nmni.2022.101052
Patra, S., Ghosh, S. and Ghosh, A. (2011). Histogram thresholding for unsupervised change detection of remote sensing images. International Journal of Remote Sensing, 32(21), 6071–6089. doi: https://doi.org/10.1080/01431161.2010.507793
Pletzer, J. F., Hauglustaine, D., Cohen, Y., Jöckel, P. and Grewe, V. (2022). The Climate Impact of Hypersonic Transport. The Climate Impact of Hypersonic Transport, EGUsphere. doi: https://doi.org/10.5194/egusphere-2022-285
Psomiadis, E., Diakakis, M. and Soulis, K. X. (2020). Combining SAR and Optical Earth Observation with Hydraulic Simulation for Flood Mapping and Impact Assessment. Remote Sensing, 12(23), 3980. doi: https://doi.org/10.3390/rs12233980
Qamer, F. M., Abbas, S., Ahmad, B., Hussain, A., Salman, A., Muhammad, S., Nawaz, M., Shrestha, S., Iqbal, B. and Thapa, S. (2023). A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods. Scientific Reports, 13(1), 4240. doi: https://doi.org/10.1038/s41598-023-30347-y
Quintero, F., Sempere-Torres, D., Berenguer, M. and Baltas, E. (2012). A scenario-incorporating analysis of the propagation of uncertainty to flash flood simulations. Journal of Hydrology, 460-461, 90–102. doi: https://doi.org/10.1016/j.jhydrol.2012.06.045
Qureshi, H., Syed and Fang Yenn Teo (2023). Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab. Frontiers in water, 5. doi: https://doi.org/10.3389/frwa.2023.1194540
Rana, V. K. and Suryanarayana, T. M. V. (2021). Estimation of flood influencing characteristics of watershed and their impact on flooding in data-scarce region. Annals of GIS, 1–22. doi: https://doi.org/10.1080/19475683.2021.1960603
Roth, F., Bauer-Marschallinger, B., Tupas, M. E., Reimer, C., Salamon, P. and Wagner, W. (2023). Sentinel-1-based analysis of the severe flood over Pakistan 2022. Natural Hazards and Earth System Sciences, 23(10), 3305–3317. doi: https://doi.org/10.5194/nhess-23-3305-2023
Schumann, G., Giustarini, L., Tarpanelli, A., Jarihani, B. and Martinis, S. (2022). Flood Modeling and Prediction Using Earth Observation Data. Surveys in Geophysics, 44, 1553-1578. doi: https://doi.org/10.1007/s10712-022-09751-y
Shah, A. H., Shahid, M., Tahir, M., Natasha, N., Bibi, I., Tariq, T. Z., Khalid, S., Nadeem, M., Abbas, G., Saeed, M. F., Ansar, S. and Dumat, C. (2023). Risk assessment of trace element accumulation in soil and Brassica oleracea after wastewater irrigation. Environmental Geochemistry and Health, 45(12), 8929–8942. doi: https://doi.org/10.1007/s10653-022-01351-4
Siddiqui, M. J., Haider, S., Gabriel, H. F. and Shahzad, A. (2018). Rainfall–runoff, flood inundation and sensitivity analysis of the 2014 Pakistan flood in the Jhelum and Chenab river basin. Hydrological Sciences Journal, 63(13-14), 1976–1997. doi: https://doi.org/10.1080/02626667.2018.1546049
Tariq, M.A.U.R. and van de Giesen, N. (2012). Floods and flood management in Pakistan. Physics and Chemistry of the Earth, Parts A/B/C, 47-48, 11–20. https://doi.org/10.1016/j.pce.2011.08.014
Tarpanelli, A., Mondini, A. C. and Camici, S. (2022). Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe. Natural Hazards and Earth System Sciences, 22(8), 2473–2489. doi: https://doi.org/10.5194/nhess-22-2473-2022
Ting, M., Michela Biasutti and You, Y. (2023). Climate warming contributes to the record-shattering 2022 Pakistan rainfall. Research Square (Research Square). doi: https://doi.org/10.21203/rs.3.rs-2925453/v1
Try, S., Tanaka, S., Tanaka, K., Sayama, T., Hu, M., Sok, T. and Oeurng, C. (2020). Projection of extreme flood inundation in the Mekong River basin under 4K increasing scenario using large ensemble climate data. Hydrological Processes, 34(22), 4350–4364. doi: https://doi.org/10.1002/hyp.13859
Wang, L. (2022). A review of the flood management: from flood control to flood resilience. Heliyon. 8(11). doi: https://doi.org/10.1016/j.heliyon.2022.e11763
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. doi: https://doi.org/10.1080/01431160304987
Zhao, J., Chen, H., Liang, Q., Xia, X., Xu, J., Hoey, T., Barrett, B., Renaud, F.G., Bosher, L. and Zhou, X. (2021). Large-scale flood risk assessment under different development strategies: the Luanhe River Basin in China. Sustainability Science, 17, 1365–1384. doi: https://doi.org/10.1007/s11625-021-01034-6