Hydrological change in mountain basins: understanding observed changes and robust modeling

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Estimating hydrologic consequences of climate change typically involves calibrating hydrologic models for the present climate and running them with inputs from climate models. However, it is under debate whether the current generation of hydrologic models is suitable for simulating change. Several studies showed that hydrologic model parameters may not remain constant under changed climatic conditions. The objective of this project is to advance our understanding of the behavior of hydrologic systems in mountain regions under climate variations. Valuable insights on this topic may be gained from investigating past observed changes. Austria will be selected as a test bed region due to the strong climate signal over the last decades and the excellent database. The first aim of the proposed project is reducing parameter instabilities of a hydrological model under climate variations by analyzing and modifying the model structure. In this context, it will also be evaluated whether multiobjective calibration with snow cover data can reduce the parameter instabilities. The second aim of the project is advancing our understanding of the influence of variations in the climate variables on the hydrological cycle of mountain catchments. In particular, it will be analyzed to what extent observed streamflow changes can be attributed to changes in climate variables. This will be investigated using two complementary approaches. On the one hand, a model-based approach using the improved hydrological model and simulations with detrended climate input time series will be applied. On the other hand, a data-based approach using multilinear regression will be employed. All analyses will be performed in a regional context to enable studying the spatial variability of the sensitivity to changes in the climate variables and its controls. Pooling the catchments into clusters with similar changes may enhance the signal-to noise ratio of the statistical analyses. Due to the focus on a large number of catchments, the results are expected to go beyond a case study and to allow generalizable conclusions.