Evaluating quantitative and qualitative models: An application for nationwide water erosion assessment in Ethiopia

  • Authors:
  • B. G. J. S. Sonneveld;M. A. Keyzer;L. Stroosnijder

  • Affiliations:
  • Centre for World Food Studies (SOW-VU), Vrije Universiteit, De Boelelaan, 1081 HV Amsterdam, Netherlands1;Centre for World Food Studies (SOW-VU), Vrije Universiteit, De Boelelaan, 1081 HV Amsterdam, Netherlands1;Land Degradation and Development Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, Netherlands

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2011

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Abstract

This paper tests the candidacy of one qualitative response model and two quantitative models for a nationwide water erosion hazard assessment in Ethiopia. After a descriptive comparison of model characteristics the study conducts a statistical comparison to evaluate the explanatory power of the models, using an Ethiopian soil erosion data set as reference. The study, therefore, introduces a generic transformation procedure, whereby qualitative models reproduce quantitative results, while the outcomes of quantitative models are mapped on an ordered (qualitative) classification. The evaluation yields the following results. Application of the USLE model in Ethiopia is restricted by data paucity, while it ranks lowest in the statistical evaluation. However, it provides reliable results in areas where water erosion incidence is low. The Expert model, based on easily available data and expert judgements, covers a wide variability of the explanatory variables, which makes it suitable for a nationwide assessment. It is the second-best model in the statistical evaluation. Yet, its qualitative output complicates the assessment of the dynamic changes in soil productivity characteristics, while the postulated additive form of the logit model is not appropriate to assess erosion hazard. The quantitative AccDat model has the highest predictive power and is based on easily available data, but has a frail empirical basis and its application at a nationwide scale requires a careful interpretation. The varying performances in the different areas of the data domain justify the selection of a combination of models for a nationwide erosion assessment, rather than a single 'best' model.