Evaluation of WEPP versus EGEM and empirical model efficiencies in predicting ephemeral gully erosion around Mubi area, Northeast Nigeria

Evaluation of prediction models is crucial to achieving valid information on erosion processes and their management choices. WEPP model efficiency in predicting ephemeral gully (EG) erosion was recently tested and compared with both EGEM and empirical models. The models abilities to predict EG erosion were validated using measured estimates at the 6 eroding locations around Mubi area in Northeast Nigeria between April 2008 and October 2009. Each location consisted of 3 watersheds where data on soils, climate, slope, management practices, EG shapes and dimensions were collected. Data on relevant soil properties were collected in the field and then analyzed in laboratory. The mass of soil loss (MSL) predicted by empirical, EGEM and WEPP models were compared with the measured using paired T-test, regression graphs (r(2)-values), error analysis, and analysis of variance (ANOVA) in a completely randomized design. The EG erosion losses varied significantly (P <= 0.05) between sites and years. No significant (P <= 0.05) differences were observed between measured and the empirically predicted aggregate MSL. The measured aggregate MSL strongly correlated with those predicted by empirical (r(2) = 0.67), than with EGEM (r(2) = 0.57), and WEPP (r(2) = 0.53) models. Slight over and under-prediction instances against the measured erosion were noted with all the models. The WEPP model was found to slightly overpredict MSL when compared to either the empirical or EGEM model. The prediction quality of the models was generally impressive. Future works should focus more on local inputs such as climate, plants, management, and tillage data for use with WEPP. (C) 2020 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. Production and Hosting by Elsevier B.V.

Automated summary

Evaluation of prediction models is crucial in obtaining valid information on erosion processes and management choices, involving comparisons and validations between different models and predicted values.