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Abstract/Summary
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Flat lakes with a large catchment area are especially affected by sediment inputs. The Kolleru Lake catchment (south-eastern India) with a surface area of approximately 6121 km² belongs to such types of lake basins. The study’s main objective was the assessment of both soil erosion and sediment yield concentration rate of the Kolleru catchment. The study was conducted using the revised universal soil loss equation (RUSLE) model due to its simple and good applicability for soil erosion estimation. Data such as rainfall, soil texture, topography, crop cover management, and support practice factor were integrated into the modeling using RUSLE and ArcGIS. Field data were used both to analyze the soil texture and the slope length factor. The results showed that average annual soil loss was estimated with 13.6 t/ha/year, classifying the Kolleru Lake Basin under a very high erosion rate category. About 38% of the catchment area has encountered slight soil loss. Areas covered with moderate, strong, severe, very severe erosion potential zones are 29%, 17%, 9%, and 5.5%, respectively. This study identified that upland areas with less vegetation cover exported high potential erosion rates. Unlike the soil loss, the sediment delivery ratio values for the catchment were not affected by land use, while it showed a strong relationship with the catchment drainage system. Whereas, the average annual sediment yield was determined with 7.61 t/ha/year, had identified with the same pattern of the soil erosion. Catchment topography, vegetation, drainage system, soil properties, and land use cover played a major role in exporting the highest sedimentation. The outcome of these studies can be used among others to identify critical erosion areas on a pixel basis for the planning of erosion management practices.
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1000
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Hinweis
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DeepGreen-ID: 2ba60a71daaf4adab62d50d898e7bb9f ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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