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Equifinality and uncertainty in physically based soil erosion models : application of the GLUE methodology to WEPP-the water erosion prediction project-for sites in the UK and USA.

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>08/2000
<mark>Journal</mark>Earth Surface Processes and Landforms
Issue number8
Number of pages21
Pages (from-to)825-845
Publication StatusPublished
<mark>Original language</mark>English


Despite the wealth of soil erosion models available for the prediction of both runoff and soil loss at a variety of scales, little quantification is made of uncertainty and error associated with model output. This in part reflects the need to produce unequivocal or optimal results for the end user, which will often be an unrealistic goal. This paper presents a conceptually simple methodology, Generalized Likelihood Uncertainty Estimation (GLUE), for assessing the degree of uncertainty surrounding output from a physically based soil erosion model, the Water Erosion Prediction Project (WEPP). The ability not only to be explicit about model error but also to evaluate future improvements in parameter estimation, observed data or scientific understanding is demonstrated. This approach is applied to two sets of soil loss/runoff plot replicates, one in the UK and one in the USA. Although it is demonstrated that observations can be largely captured within uncertainty bounds, results indicate that these uncertainty bounds are often wide, reflecting the need to qualify results that derive from optimum parameter sets, and to accept the concept of equifinality within soil erosion models. Attention is brought to the problem of under-prediction of large events/over-prediction of small events, as an area where model improvements could be made, specifically in the case of relatively dry years. Finally it is proposed that such a technique of model evaluation be employed more widely within the discipline so as to aid the interpretation and understanding of complex model output.