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<p>Geographically, Bangladesh is a low-lying country highly vulnerable to climate change, facing acute challenges from cyclones, floods, storm surges, droughts, riverbank erosion, and also deadly heatwaves. This susceptibility stems from its exposure to tropical cyclones and monsoon rains (June–October), which frequently cause severe flooding. ( INFORM Report. (2024). INFORM annual report 2024: Shared evidence for managing crises and disasters. European Union.) assigns Bangladesh high-risk scores: 8.2/10 for hazard exposure, 4.6/10 for vulnerability, and 4.8/10 for lack of coping capacity, highlighting the country’s susceptibility to extreme weather events. The August 2024 flash flood in the Gomti River basin, affecting eastern Bangladesh and Tripura (India), resulted from extreme rainfall exceeding 600&#xa0;mm, cloudbursts, geomorphological factors, and high tides coinciding with the full moon stage in the Bay on Bengal. Despite the basin’s history of flash floods, no prior study has systematically mapped flood-prone areas in this region. This study is the first to apply an integrated GIS-based Multi-Criteria Decision-Making (GIS-MCDM) model especially to the Gomti River basin, using nine critical flood risk determinants: rainfall, geomorphology, drainage density, flow accumulation, slope, Topographic Wetness Index (TWI), Normalized Difference Water Index (NDWI), Land Use and Land Cover (LULC), and Stream Power Index (SPI). The Analytic Hierarchy Process (AHP) assigned the highest weights to rainfall (30%), geomorphology (21%), and drainage density (15%), with a validated consistency ratio (CR = 0.052). Results indicate that 24% (4,674 km<sup>2</sup>) of the basin is highly flood-prone, 59% (11,654 km<sup>2</sup>) faces moderate risk, and 17% (3,467 km<sup>2</sup>) is at low risk, with the most affected areas being Comilla, Feni (Bangladesh), and Sipahijala, Gomati, and South Tripura (India). Long-term LULC analysis (2000–2023) reveals a 16% rise in urbanization and an 18% decline in cropland, exacerbating flood susceptibility by reducing natural water retention. Validation using Sentinel-1 SAR imagery confirms strong agreement between predicted high-risk zones and actual flood extents, reinforcing the model’s accuracy. This study fills a critical gap in flood risk research by integrating remote sensing validation, land-use dynamics, and a transboundary approach. It provides a robust framework for flood risk mitigation, emphasizing the need for early warning systems, sustainable land-use planning, and cross-border diplomacy to enhance resilience in the Gomti River basin and other vulnerable regions.</p>

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Flash flood risk zoning in Gomti River Basin in Eastern Bangladesh and Tripura State (India) using MCDM-GIS Tool

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • Md. Salman Arefin Alif,
  • Mong-E.-Sing Marma,
  • Md. Nahid Hassan,
  • Chowdhury Sarwar Jahan,
  • Rakib Howlader,
  • Tanoy Sarker,
  • Md. Ishtiak Ahmed Rasel,
  • Md. Mahibi Alom Mahim,
  • Roni Roy,
  • Quamrul Hasan Mazumdar

摘要

Geographically, Bangladesh is a low-lying country highly vulnerable to climate change, facing acute challenges from cyclones, floods, storm surges, droughts, riverbank erosion, and also deadly heatwaves. This susceptibility stems from its exposure to tropical cyclones and monsoon rains (June–October), which frequently cause severe flooding. ( INFORM Report. (2024). INFORM annual report 2024: Shared evidence for managing crises and disasters. European Union.) assigns Bangladesh high-risk scores: 8.2/10 for hazard exposure, 4.6/10 for vulnerability, and 4.8/10 for lack of coping capacity, highlighting the country’s susceptibility to extreme weather events. The August 2024 flash flood in the Gomti River basin, affecting eastern Bangladesh and Tripura (India), resulted from extreme rainfall exceeding 600 mm, cloudbursts, geomorphological factors, and high tides coinciding with the full moon stage in the Bay on Bengal. Despite the basin’s history of flash floods, no prior study has systematically mapped flood-prone areas in this region. This study is the first to apply an integrated GIS-based Multi-Criteria Decision-Making (GIS-MCDM) model especially to the Gomti River basin, using nine critical flood risk determinants: rainfall, geomorphology, drainage density, flow accumulation, slope, Topographic Wetness Index (TWI), Normalized Difference Water Index (NDWI), Land Use and Land Cover (LULC), and Stream Power Index (SPI). The Analytic Hierarchy Process (AHP) assigned the highest weights to rainfall (30%), geomorphology (21%), and drainage density (15%), with a validated consistency ratio (CR = 0.052). Results indicate that 24% (4,674 km2) of the basin is highly flood-prone, 59% (11,654 km2) faces moderate risk, and 17% (3,467 km2) is at low risk, with the most affected areas being Comilla, Feni (Bangladesh), and Sipahijala, Gomati, and South Tripura (India). Long-term LULC analysis (2000–2023) reveals a 16% rise in urbanization and an 18% decline in cropland, exacerbating flood susceptibility by reducing natural water retention. Validation using Sentinel-1 SAR imagery confirms strong agreement between predicted high-risk zones and actual flood extents, reinforcing the model’s accuracy. This study fills a critical gap in flood risk research by integrating remote sensing validation, land-use dynamics, and a transboundary approach. It provides a robust framework for flood risk mitigation, emphasizing the need for early warning systems, sustainable land-use planning, and cross-border diplomacy to enhance resilience in the Gomti River basin and other vulnerable regions.