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Estimation of surface runoff volume using GIS and remote sensing Case of Kirimbi River

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dc.contributor.author NDACYAYINSABA, Blandine
dc.date.accessioned 2025-05-22T15:19:18Z
dc.date.available 2025-05-22T15:19:18Z
dc.date.issued 2024
dc.identifier.uri http://hdl.handle.net/123456789/1129
dc.description.abstract Runoff is the water that flows across the land’s surface due to gravity, replenishing groundwater and surface water as it seeps into aquifers or moves into rivers, streams, and watersheds. This phenomenon occurs when the amount of water exceeds the land’s capacity to absorb it, leading to excess water flowing into nearby creeks, streams, or ponds. Runoff can result from both natural processes and human activities. This study aimed to estimate the surface runoff volume of the Kirimbi River using Geographic Information Systems (GIS) and remote sensing techniques. The specific objectives were to identify the factors influencing surface water runoff, assess the effects of surface runoff volume, evaluate runoff using the SCS-CN method, and analyse the relationship between rainfall and runoff. By utilizing advanced remote sensing and GIS techniques, the study estimated surface runoff based on various parameters, including land use/land cover (LULC), hydrological soil characteristics, rainfall data (P), potential maximum retention (S), antecedent moisture condition (AMC), and weighted curve number (CN). The LULC map was classified into five categories: water body, agriculture, built-up areas, forest, and bare soil, revealing that water bodies have low runoff potential while bare soil exhibits high runoff. The hydrologic soil groups (HSG) were classified into four categories: A, B, C, and D, with group a showing low runoff and group D showing high runoff. Integrating LULC and HSG allowed for the determination of CN values, with a weighted curve number calculated using normal antecedent moisture condition II (AMC II). The average CN under these conditions was found to be 8.42. Monthly rainfall data from 2022 was used to calculate runoff, revealing an average runoff volume of 462.182 mm³. The findings conclude that the integration of the SCS- CN method and GIS effectively evaluates surface runoff volume. It is recommended that government and non-government organizations promote the establishment of rainwater reservoirs, as water is scarce and essential for daily human activities. Additionally, further research should explore alternative methods for estimating runoff to compare -+*the results from different approaches. en_US
dc.language.iso en en_US
dc.publisher ULK en_US
dc.subject LULC en_US
dc.title Estimation of surface runoff volume using GIS and remote sensing Case of Kirimbi River en_US
dc.type Book en_US


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