Surface runoff estimation at the Densu River Basin using geographic information system (GIS) and remote sensing (RS)
Abstract
Accurate estimation of runoff depth and volume is essential for effective watershed management. Runoff, resulting from rainfall, is influenced by numerous factors, including soil type, vegetation, land use patterns, and rainfall characteristics. The Densu River Basin, located in the Greater Accra region of Ghana, has experienced flooding incidents, partly attributed to changes in land use and land cover. To address these challenges and facilitate proper flood management, drainage network design, hydropower generation, and other applications, this study aims to estimate surface runoff depth in the Densu River Basin, Ghana. The Natural Resources Conservation Services Curve Number (NRCS-CN) method, combined with Geographic Information System (GIS) and Remote Sensing (RS), is employed for runoff depth estimation. The research involves supervised classification of Landsat images from 2001, 2011, and 2022 to determine land use patterns, calculate grass cover percentages, identify hydrologic soil categories, extract rainfall intensity data, compute maximum soil storage, and estimate runoff depths for 10 year, 25 year, and 50 year return periods. The study reveals a significant increase in direct surface runoff depth, from 138.29 mm to 144.70 mm, for soil type D (Clay loam), the dominant soil type in the basin, during the 10 year return period, attributed to changes in land use and climate within the basin. The findings from this study hold valuable insights for mitigating environmental hazards in the area and improving water resource management.
Keyword : surface runoff depth, supervised classification, hydrologic, rainfall intensity, soil storage
This work is licensed under a Creative Commons Attribution 4.0 International License.
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