Modeling of Sediment Yield From Anjeni-Gauged Watershed, Ethiopia Using SWAT Model
2010 (English)In: Journal of the American Water Resources Association, ISSN 1093-474X, E-ISSN 1752-1688, Vol. 46, no 3, 514-526 p.Article in journal (Refereed) Published
The Soil and Water Assessment Tool (SWAT) was tested for prediction of sediment yield in Anjeni-gauged watershed, Ethiopia. Soil erosion and land degradation is a major problem on the Ethiopian highlands. The objectives of this study were to evaluate the performance and applicability of SWAT model in predicting monthly sediment yield and assess the impacts of subbasin delineation and slope discretization on the prediction of sediment yield. Ten years monthly meteorological, flow and sediment data were used for model calibration and validation. The annual average measured sediment yield was 24.6 tonnes/ha. The annual average simulated sediment yield was 27.8 and 29.5 tones/ha for calibration and validation periods, respectively. The study found that the observed values showed good agreement with the simulated sediment yield with Nash-Sutcliffe efficiency (NSE) = 0.81, percent bias (PBIAS) = 28%, RMSE-observations standard deviation ratio (RSR) = 0.23, and coefficient of determination (R superset of) = 0.86 for calibration and NSE = 0.79, PBIAS = 30%, RSR = 0.29, and R superset of = 0.84 for validation periods. The model can be used for further analysis of different management scenarios that could help different stakeholders to plan and implement appropriate soil and water conservation strategies.
Place, publisher, year, edition, pages
2010. Vol. 46, no 3, 514-526 p.
SWAT, SUFI-2, sediment yield, soil erosion, watershed modeling, Anjeni, hydrology, streamflows
Oceanography, Hydrology, Water Resources
IdentifiersURN: urn:nbn:se:kth:diva-12021DOI: 10.1111/j.1752-1688.2010.00431.xISI: 000278522700006ScopusID: 2-s2.0-77954615297OAI: oai:DiVA.org:kth-12021DiVA: diva2:294407
QC 20100719 Uppdaterad från in press till published (20101123).2010-02-172010-02-172010-11-23Bibliographically approved