Fit-for-purpose analysis of uncertainty using split-sampling evaluations

An uncertainty assessment method for evaluating models, the Sources of UNcertainty GLobal Assessment using Split SamplES (SUNGLASSES), is presented, which assesses predictive uncertainty that is not captured by parameter or other input uncertainties. The method uses the split sample approach to generate a quantitative estimate of the fit-for-purpose of the model, thus focusing on the purpose for which the model is used. It operates by comparing the output to be used for decision making to its observed counterpart and the associated uncertainty. The described method is applied on a Soil Water Assessment Tool (SWAT) model of Honey Creek, a tributary of the Sandusky catchment in Ohio, USA. Water flow and sediment loads are analysed. In this case study the uncertainty estimated by the proposed method is much larger than the typically estimated parameter uncertainty.