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Identification of Erosion Hotspot Area using GIS and MCE Technique for Koga Watershed in the Upper Blue Nile Basin, Ethiopia | Science Publications

机译:使用GIS和MCE技术识别埃塞俄比亚上蓝尼罗河流域科加流域的侵蚀热点区域|科学出版物

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> >Soil erosion is a serious threat in Ethiopian highlands. Continuous land degradation resulted in loss of fertile top soil leading to low agricultural productivity. In addition, excessive soil erosion from Koga Watershed in upper catchment to an artificial reservoir (Koga Dam reservoir) is substantially reducing its service life. Community participatory based effective watershed management strategies may have tremendous potential to reduce soil erosion. However, it is not practical to implement management interventions in the entire basin. This study aims to identify and map erosion hotspot areas in Koga Watershed to assist local government decision towards implementing watershed management strategies. Multi Criteria Evaluation (MCE) technique was integrated with Geographic Information System (GIS). For these analysis four major factors: Topography, soil, land use and potential location of gullies were considered. Each of these was processed and analyzed for its potential contribution to erosion on a pixel by pixel basis. The factors were weighted using pair-wise comparison matrix and weights were combined using Weighted Overlay Tool of ArcGIS Spatial Analyst Toolbox to obtain the final erosion hotspot map. The results found that 2% (440 ha) to be highly sensitive, 43% (9,460 ha) to be moderately sensitive, 16% (3,520 ha) to be marginally sensitive and 32% (7,040 ha) currently not sensitive. The remaining 7% of the watershed area (22,000 ha) was constraint to erosion. The lowland area near the dam was found to be found most sensitive for erosion and sedimentation.
机译: > >土壤侵蚀是埃塞俄比亚高地的严重威胁。土地的持续退化导致肥沃的表层土壤流失,导致农业生产力下降。此外,从上游流域的Koga流域到人工水库(Koga大坝水库)的过度土壤侵蚀大大缩短了其使用寿命。基于社区参与的有效流域管理策略可能具有减少土壤侵蚀的巨大潜力。但是,在整个流域实施管理干预措施是不切实际的。这项研究旨在识别和绘制Koga流域的侵蚀热点地区,以协助地方政府决定实施流域管理策略。多标准评估(MCE)技术与地理信息系统(GIS)集成在一起。对于这些分析,考虑了四个主要因素:地形,土壤,土地利用和潜在的沟壑位置。对每个像素进行了处理和分析,以逐像素为基础对腐蚀的潜在贡献进行了分析。使用成对比较矩阵对因子进行加权,并使用ArcGIS Spatial Analyst Toolbox的加权叠加工具对权重进行组合,以获得最终的侵蚀热点图。结果发现,2%(440公顷)为高敏感度,43%(9,460公顷)为中度敏感,16%(3,520公顷)为边际敏感,32%(7,040公顷)当前不敏感。流域面积的剩余7%(22,000公顷)受到侵蚀的限制。大坝附近的低地地区被发现对侵蚀和沉积最敏感。

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