Advancing traditional strategies for testing hydrological model fitness in a changing climate

Abstract

Mitigation of adverse effects of global warming relies on accurate flow projections under climate change. These projections usually focus on changes in hydrologic signatures, such as 100-year floods, which are estimated through statistical analyses of simulated flows under baseline and future conditions. However, models used for these simulations are traditionally calibrated to reproduce entire flow series, rather than statistics of hydrologic signatures. Here, we consider this dichotomy by testing whether performance indicators (e.g., Nash-Sutcliffe coefficient) are informative about model ability to reproduce distributions and trends in the signatures. Results of streamflow simulations in 50 high-latitude catchments with the 3DNet-Catch model show that high model performances according to traditional indicators do not assure that distributions or trends in hydrologic signatures are well reproduced. We, therefore, suggest that performance in reproducing distributions and trends in hydrologic signatures should be included in the process of model selection for climate change impacts studies.