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

估计气候变化的水文后果通常涉及校准当前气候的水文模型，并使用气候模型的输入来运行它们。然而，目前这一代水文模型是否适合模拟变化仍存在争议。一些研究表明，在变化的气候条件下，水文模型参数可能不会保持不变。该项目的目的是促进我们对气候变化下山区水文系统行为的理解。通过调查过去观察到的变化，可以获得关于这个主题的有价值的见解。奥地利将被选为试验区，因为过去几十年的强烈气候信号和优秀的数据库。提出的项目的第一个目标是通过分析和修改模型结构来减少气候变化下水文模型的参数不稳定性。在此背景下，还将评估积雪数据的多目标校准是否可以减少参数的不稳定性。该项目的第二个目标是促进我们对气候变量变化对山区集水区水文循环的影响的理解。特别地，它将分析观测到的水流变化在多大程度上可归因于气候变量的变化。这将使用两种互补的方法进行调查。一方面，将采用基于模型的方法，使用改进的水文模型和具有非趋势气候输入时间序列的模拟。另一方面，将采用基于数据的多元线性回归方法。所有分析都将在区域范围内进行，以便研究对气候变量变化及其控制的敏感性的空间变异性。将具有相似变化的集水区集中在一起可以提高统计分析的信噪比。由于对大量集水区进行了重点研究，预计研究结果将超越个案研究，并得出可推广的结论。

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.