Determination of Soil Erosion and Sediment Yield by RUSLE using GIS and Remote Sensing- A Case study of Lake Tana

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Research ID B6NR9

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Abstract

Soil erosion is a severe and uninterrupted problem that threatens the environment all over the world. Sedimentation has a great effect on basins with large surface areas. Lake Tana basin is one of these lake basins with a surface area of about 15935 km2. The main objective of this study was to assess soil erosion and concentration of sediment yield of lake Tana basin and map their spatial distribution. The study was conducted by integrating Geographic Information System (GIS), remote sensing, and the Revised Universal Soil Loss equation (RUSLE). Data sets of rainfall, soil, topography, cover management, and conservation practices were integrated and modeled in GIS. Soil loss, sediment delivery ratio (SDR), and sediment yield were estimated. The results showed that the computed mean annual soil loss was 79.3 t.ha-1.y-1. Six classes of soil erosion were classified: very low (0-5), low (5-10), moderate (10-15), moderately high (15-20), high (20-40) and very high (>40) t.ha-1.y-1. About 57.5% of the study area is under low erosion risk (0-10 t.ha-1.y-1), whereas about 40.3% of the study area is under high and very high erosion risk. SDR was estimated, based on the mainstream slope, and found to be 0.281. The findings of the study also showed that the average annual sediment yield of the lake Tana basin is approximately 22.28 t.ha-1.y-1. The results of this study can benefit policymakers to investigate erosional risk areas and take the best appropriate decisions to control soil erosion risk in these regions.

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Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

Not applicable

Data Availability

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

Funding

This work did not receive any external funding.

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  • Classification

    FOR CODE-050399

  • Version of record

    v1.0

  • Issue date

    20 October 2022

  • Language

    en

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