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Quantification of Erosion in Selected Catchment Areas of the Ruzizi River (DRC) Using the (R)USLE Model

Author

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  • Joachim Eisenberg

    (Institute of Physical Geography, Geo-Center of the Goethe University Frankfurt, Altenhöferallee 1, 60438 Frankfurt am Main, Germany)

  • Fabrice A. Muvundja

    (Unité d’Enseignement et de Recherche en Hydrobiologie Appliquée (UERHA), Institut Supérieur Pédagogique (ISP) de Bukavu, 32, Avenue Kibombo, Ibanda, B.P. 854 Bukavu, Democratic Republic of the Congo
    Centre de Recherche en Environnement et Géoressources, Université Catholique de Bukavu (UCB), 2, Avenue de la Mission (Bugabo), B.P. 285 Bukavu, Democratic Republic of the Congo)

Abstract

Inappropriate land management leads to soil loss with destruction of the land’s resource and sediment input into the receiving river. Part of the sediment budget of a catchment is the estimation of soil loss. In the Ruzizi catchment in the Eastern Democratic Republic of the Congo (DRC), only limited research has been conducted on soil loss mainly dealing with local observations on geomorphological forms or river load measurements; a regional quantification of soil loss is missing so far. Such quantifications can be calculated using the Universal Soil Loss Equation (USLE). It is composed of four factors: precipitation (R), soil (K), topography (LS), and vegetation cover (C). The factors can be calculated in different ways according to the characteristics of the study area. In this paper, different approaches for calculating the single factors are reviewed and validated with field work in two sub-catchments of Ruzizi River supplying the water for the reservoirs of Ruzizi I and II hydroelectric dams. It became obvious that the (R)USLE model provides the best results with revised R and LS factors. C factor calculations required to conduct a supervised classification using the Maximum Likelihood Procedure. Different C factor values were assigned to the land cover classes. The calculations resulted in a soil loss rate for the predominantly occurring Ferralsols and Leptosols of around 576 kt/yr in both catchments, when 2016 landcover and precipitation are used. This represents an area-normalized value of 40.4 t/ha/yr for Ruzizi I and 50.5 t/ha/yr for Ruzizi II due to different landcover in the two sub-catchments. The mean value for the whole study area is 47.8 t/ha/yr or even 27.1 t/ha/yr when considering land management techniques like terracing on the slopes (P factor). This work has shown that the (R)USLE model can serve as an easy to handle tool for soil loss quantification when comprehensive field work results are sparse. The model can be implemented in Geographic Information Systems (GIS) with free data; hence, a validation is crucial. It becomes apparent that the use of high resolution Sentinel 2a MSI data as the basis for C factor calculations is an appropriate method for considering heterogeneous Land Use Land Cover (LULC) patterns. To transfer the approach to other regions, the calculation of factor R needs to be modified.

Suggested Citation

  • Joachim Eisenberg & Fabrice A. Muvundja, 2020. "Quantification of Erosion in Selected Catchment Areas of the Ruzizi River (DRC) Using the (R)USLE Model," Land, MDPI, vol. 9(4), pages 1-18, April.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:4:p:125-:d:349276
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    References listed on IDEAS

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    1. Tomislav Hengl & Gerard B M Heuvelink & Bas Kempen & Johan G B Leenaars & Markus G Walsh & Keith D Shepherd & Andrew Sila & Robert A MacMillan & Jorge Mendes de Jesus & Lulseged Tamene & Jérôme E Tond, 2015. "Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-26, June.
    2. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
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    1. Pietro De Marinis & Samuele De Petris & Filippo Sarvia & Giacinto Manfron & Evelyn Joan Momo & Tommaso Orusa & Gianmarco Corvino & Guido Sali & Enrico Mondino Borgogno, 2021. "Supporting Pro-Poor Reforms of Agricultural Systems in Eastern DRC (Africa) with Remotely Sensed Data: A Possible Contribution of Spatial Entropy to Interpret Land Management Practices," Land, MDPI, vol. 10(12), pages 1-22, December.
    2. Lucia Petrikovičová & Zuzana Rampašeková & Jaroslava Sobocká, 2020. "A Detailed Identification of Erosionally Endangered Agricultural Land in Slovakia (Case Study of Nitra Upland)," Sustainability, MDPI, vol. 12(12), pages 1-14, June.

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