Soil Erosion Risk Assessment in Nashe Dam Reservoir Using Remote Sensing, GIS and RUSLE Model Techniques in Horro Guduru Wollega Zone, Oromia Region, Ethiopia
Journal of Civil, Construction and Environmental Engineering
Volume 4, Issue 1, February 2019, Pages: 1-18
Received: Jan. 1, 2019;
Accepted: Feb. 14, 2019;
Published: Mar. 11, 2019
Views 962 Downloads 275
Ayana Abera Beyene, Surveying Engineering, Institute of Technology, Wollega University, Nekemte, Ethiopia
Follow on us
Soil degradation is wide spread and serious throughout the Ethiopian Highlands. It is also a major watershed problem in many developing countries causing significant loss of soil fertility, loss of productivity and environmental degradation. This research has, therefore, been carried out to evaluate the soil erosion risk and quantify the major land use land cover changes over the past 20 years (1996-2016) in the Nashe watershed. The research integrates the Revised Universal Soil Loss Equation (RUSLE) with a Geographic Information System (GIS) and Remote Sensing (RS) to quantify the potential soil erosion risk and land use land cover changes. Rainfall data, soil data, DEM data and satellite image were used as input data sets to generate RUSLE factor values. Raster calculator was used to interactively calculate potential soil loss and prepare soil erosion risk map. For the land use land cover change calculation two satellite images of two year interval ( Landsat TM 1996 and Landsat 2016) has been utilized. As a result the potential soil erosion risk and land use land cover map of 1996 and 2016 of the study area was generated. The result showed that the potential annual soil loss of the watershed ranges from 0.00 to 243..065ton/ha/yr. and the mean annual soil loss rate is 45.7ton/ha/yr. Concerning the land use land cover change Grass land decline from (8.85%) to (6.85.4%), open forest changes from (47.10%) to (22.75 %) and settlement land changes from (4.42%) to (7.59%). On the contrary farm land changes from (27.18%) to (45.55%), bare lands increase from (5.40%) to (5.55%) and water body changes from (7.06%) to (12.10 %). By the LULC analysis it has been found that the grass land and forest land declined from 1996-2016. On other hand, the rest of the land cover types have increased.
Nashe Dam, Soil Erosion Risk, Watershed, RUSLE, GIS
To cite this article
Ayana Abera Beyene,
Soil Erosion Risk Assessment in Nashe Dam Reservoir Using Remote Sensing, GIS and RUSLE Model Techniques in Horro Guduru Wollega Zone, Oromia Region, Ethiopia, Journal of Civil, Construction and Environmental Engineering.
Vol. 4, No. 1,
2019, pp. 1-18.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Deore. (2005). Prioritization of Micro-watersheds of Upper Bhama Basin on the Basis of Soil Erosion Risk Using Remote Sensing and GIS Technology. PhD thesis, University of Pune, Pune.
Morgan. ((1995).). Morgan, R. P. C., (1995). Soil Erosion and Conservation. Edinburgh: Addison-Wesley Longman.
Saavedra. (2005). Estimating spatial patterns of soil erosion and deposition in the Andean region using geo-information techniques: a case study in Cochabamba, Bolivia Ph.D dissertation, Wageningen University, The Netherlands.
Mulugeta. (2004). Effects of land use change on soil quality and native flora degradation and restoration in the high lands of Ethiopia. Implication for sustainable land management. Swedish University of Agricultural Science. Uppsala, Sweden.
Wischmeier and Smith. (1978). W. H., D. D., (1978). Predicting rainfall erosion losses, a guide to conservation planning. Agric. Hand B. No. 537, US Department of Agriculture,. conservation planning. Agric. Hand B. No. 537, US Department of Agriculture, Washington, DC.
Bobe (2004). Evaluation of Soil Erosion in the Harerge Region of Ethiopia, Using Soil Loss Models, Rainfall Simulation and Field Trials, PhD thesis, University of Pretoria, etd.
Breiby. (2006). Assessment of Soil Erosion Risk within a Sub watershed using GIS and RUSLE with a Comparative Analysis of the use of STATSGO and SSURGO Soil Databases. Volume 8, Papers in Resource Analysis. Saint Mary’s University of Minnesota Central SMinnesota Central S.
FAO 1986; Sutcliff. (1993). Economic assessment of land degradation in the Ethiopian highlands: Acase study. National conservation strategy secretariat, Ministry of planning and economicdevelopment, Addis Ababa, Ethiopia.
Hurni. (1985). The Design and Construction of Small-scale Earth Micro-dams. A fieldmanual for assistant technicians working under the supervision of agriculturalor irrigation engineers. Addis Ababa: Soil Conservation Research Project, Ministry of Agricultur.
Lillesand & Kiefer. (1994). Remote Sensing and Image Interpretation. Third edition. Printed in the United States of America.
De Asis and Omasa. (2007). Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS Journal of Photogrammetry & Remote Sensing 62, 309–324.
Hellden. (1987). An Assessment of Woody Biomass, Community Forests, Land Use and Soil Erosion in Ethiopia. Lund University Press.
Renard. (1996). Renard, K. G., Foster, G. R., Weesies, G. A., Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture Handbook No. 703. Agricultural Research Service, Washington, DC.
Hudson. (1981). The Factors Determining the extent of Soil Erosion; ‖ In Green Land J. D and Lal R. (ed), Soil Conservation and Management in the Humid Tropics; John Wiley &Sons Ltd, Great Britain.