Journal of Water and Climate Change (Feb 2024)

Decadal mapping of flood inundation and damage assessment in the confluence region of Rivers Niger and Benue using multi-sensor data and Google Earth Engine

  • Caleb Odiji,
  • Godstime James,
  • Ademuyiwa Oyewumi,
  • Shomboro Karau,
  • Belinda Odia,
  • Halima Idris,
  • Olaide Aderoju,
  • Abubakar Taminu

DOI
https://doi.org/10.2166/wcc.2024.166
Journal volume & issue
Vol. 15, no. 2
pp. 348 – 369

Abstract

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Climate change has made weather patterns more extreme, causing floods in Nigeria. Flooding is the most frequent and serious natural hazard in the confluence region of Rivers Niger and Benue, impacting lives, agriculture, and socio-economic activities significantly. Advancements in satellite technology and computational capabilities have enhanced rapid information about flood extent for monitoring, mitigation, and planning. However, there is a dearth of information based on time series analysis of flood inundation and monitoring in the confluence region. In this study, Sentinel-1 Synthetic Aperture Radar, Sentinel-2, and Landsat-7 and Landsat-8 data were used to extract flood inundation for 10 years (2012–2022) in the confluence region of Rivers Niger and Benue. Flood extent/surface waterbodies were extracted using the Google Earth Engine platform, modified normalized difference water index, and normalized difference water index methods. The findings revealed that within 10 years, four significant flooding incidents occurred in 2012, 2018, 2020, and 2022, inundating 60.57, 48.24, 39.98, and 84.39 km2 of the area, respectively. The study underscores the need for the establishment of a decision support system for monitoring flood inundation and providing decision-makers necessary information for flood disaster preparedness, mitigation, and adaptation. HIGHLIGHTS Mapping and monitoring flood inundation were done by intersecting Sentinel-1 Synthetic Aperture Radar, Landsat-7 and Landsat-8, and Sentinel-2 datasets.; Surface water was extracted using the modified normalized difference vegetation index and Google Earth Engine.; These data are used to conduct time series flood inundation mapping, which is lacking in most studies.;

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