Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory

Abstract

A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies structural deficiency. This leads to a formal definition of model structure deficiency (or rigidity). Model structure deficiency introduces a bias in the prediction of an observed quantile, which is often not equal across quantiles. Structure deficiency is therefore diagnosed when any two quantile predictions for a given model structure cross since unequal bias across quantiles result in quantile predictions crossing. The analysis further suggests that the optimal value of quantile specific loss functions order different model structures by its structure deficiencies over a range of quantiles. In addition to such novelties, quantile hydrologic model selection is a frequentist approach that seeks to complement existing Bayesian approaches to hydrological model uncertainty. Key Points Different quantile models for a given structure has non-overlapping information Quantile predictions do not cross when models are monotonous in parameters Quantile model selection can be used to assess model structure deficiency ©2013. American Geophysical Union. All Rights Reserved.