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<p>Flooding remains a critical environmental challenge, particularly in regions with rapid urbanization and poor drainage infrastructure. Understanding flood susceptibility through geospatial analysis is essential for effective risk management and disaster preparedness. This study assesses flood risk and soil erosion dynamics in Southeastern Nigeria using Geographic Information Systems (GIS) and Remote Sensing (RS). The objective is to classify flood-prone areas and propose suitable mitigation strategies based on environmental parameters. A multi-criteria approach was applied using Digital Elevation Models (DEM), slope analysis, Topographic Wetness Index (TWI), drainage density, land use/land cover (LULC), and annual rainfall data. The weighted overlay method in ArcGIS was used to classify the study area into five flood risk categories: very low, low, moderate, high, and very high. The analysis revealed that moderate-risk areas constituting 41.10% cover the most significant portion (106.12&#xa0;km²) of the study area, while high-risk zones span 51.14&#xa0;km² (19.80%), primarily in low-lying regions with impervious surfaces. Very high-risk areas, totaling 6.03&#xa0;km² (2.33%), were concentrated along riverbanks and poorly drained areas. In contrast, low-risk areas covered 81.74&#xa0;km² (31.64%), benefiting from adequate drainage, and very low-risk zones accounted for 5.03% (13.30&#xa0;km²). The study highlights the influence of urbanization, topography, and hydrology on flood susceptibility. Effective land-use planning, afforestation, and improved drainage infrastructure are critical for mitigating flood risks. The integration of geospatial techniques provides valuable insights for flood risk assessment, aiding in proactive flood management strategies and urban planning. This research offers a spatially explicit flood risk classification, integrating multiple environmental variables to enhance predictive accuracy and inform sustainable flood mitigation efforts.</p>

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Geoenvironmental Assessment of flood risk and soil dynamics in parts of Southeastern Nigeria

  • 期刊论文

  • 发表日期：

    2026年4月14日

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  • Francis Begianpuye Akiang,
  • Alexander Iheanyichukwu Opara,
  • Chikwendu Njoku Okereke,
  • Diugo Okereke Ikoro,
  • Maureen Chioma Umeh,
  • Charles Uchenna Emmanuel,
  • Chukwuebuka Nnamdi Onwubuariri,
  • Michael Maduabuchi Emeh

摘要

Flooding remains a critical environmental challenge, particularly in regions with rapid urbanization and poor drainage infrastructure. Understanding flood susceptibility through geospatial analysis is essential for effective risk management and disaster preparedness. This study assesses flood risk and soil erosion dynamics in Southeastern Nigeria using Geographic Information Systems (GIS) and Remote Sensing (RS). The objective is to classify flood-prone areas and propose suitable mitigation strategies based on environmental parameters. A multi-criteria approach was applied using Digital Elevation Models (DEM), slope analysis, Topographic Wetness Index (TWI), drainage density, land use/land cover (LULC), and annual rainfall data. The weighted overlay method in ArcGIS was used to classify the study area into five flood risk categories: very low, low, moderate, high, and very high. The analysis revealed that moderate-risk areas constituting 41.10% cover the most significant portion (106.12 km²) of the study area, while high-risk zones span 51.14 km² (19.80%), primarily in low-lying regions with impervious surfaces. Very high-risk areas, totaling 6.03 km² (2.33%), were concentrated along riverbanks and poorly drained areas. In contrast, low-risk areas covered 81.74 km² (31.64%), benefiting from adequate drainage, and very low-risk zones accounted for 5.03% (13.30 km²). The study highlights the influence of urbanization, topography, and hydrology on flood susceptibility. Effective land-use planning, afforestation, and improved drainage infrastructure are critical for mitigating flood risks. The integration of geospatial techniques provides valuable insights for flood risk assessment, aiding in proactive flood management strategies and urban planning. This research offers a spatially explicit flood risk classification, integrating multiple environmental variables to enhance predictive accuracy and inform sustainable flood mitigation efforts.