Assessment of Suitability of Hydrological Models for Climate Change Impact Studies

Effective water resources management and mitigation of adverse effects of global warming requires accurate flow projections. These projections are generally focused on statistical changes in hydrologic signatures (e.g., 100-year floods, 30-year or 7-day minimum flows), which are obtained from statistical analyses of simulated flows under baseline and future conditions. However, hydrological models used for these simulations are traditionally calibrated to reproduce entire flow series, rather than statistical properties of the hydrologic signatures. Therefore, there is a dichotomy between criteria for hydrological model evaluation/selection and the actual requirements of climate change impact studies.

Here, we address this dichotomy by providing novel insights into the assessment of model suitability for climate change impact studies. Specifically, we analyse performance of numerous spatially-lumped, bucket-style hydrological models in reproducing observed distributions and trends in the annual series hydrologic signatures relevant for hydrologic impacts studies under climate change. Model performance in reproducing distributions of the signatures is evaluated by applying the Wilcoxon rank sum test. We consider that a model properly reproduces trends in the series of signatures if either series of observed and simulated signatures both exhibit lack of statistically significant trends, or both series exhibit statistically significant trends of the same sign. Statistical significance of the trends is estimated by applying the Man-Kendall test is used, while signs of the trends are obtained from the San slope. Model performance is also quantified in terms of commonly used numerical indicators, such as Nash-Sutcliffe or Kling-Gupta coefficients.

Our results, which are based on streamflow simulations in 50 high-latitude catchments in Sweden, show that high model performance quantified in terms of traditional performance indicators does not necessarily imply that distributions or trends in series of hydrologic signatures are well reproduced, and vice-versa. Therefore, these two aspects of model performance are distinct and complementary, and they require separate evaluation analyses. Accurate reproduction of statistical properties of hydrologic signatures relevant for climate change impact studies is essential for improving the credibility of future flow projections. We, therefore, recommend that the traditional process of selecting hydrological models for the impact studies should be enhanced with assessments of model ability to reproduce distributions and trends in the hydrologic signatures.