Abstract
Study regionCatchments: Wölzerbach, Raab; Styria; Austria. Study focusThe study was carried out to investigate the fitness for purpose of the high-resolution Austrian National Climate datasets (ÖKS15) in hydrological applications. Provided as a standard for climate impact studies in Austria in 2016, the potential of the ÖKS15 data for hydrological impact studies has not previously been assessed. To fill this research gap, we evaluated the ÖKS15 datasets for the recent climate period (1961–2005) to determine their suitability for hydrological analyses. Hydro-meteorological indicators of the ÖKS15 datasets, as well as of the data used to obtain them by bias correcting and downscaling the EURO-CORDEX models, were assessed at different spatio-temporal scales and compared to the raw EURO-CORDEX data. The hydrological model WaSiM was driven with the different datasets to generate the hydrological simulations used for validation against long-term observations. New hydrological insights for the regionHydro-meteorological indicators obtained from ÖKS15 models are comparable to observations and associated with less uncertainty than the raw EURO-CORDEX data. We thus consider the ÖKS15 dataset basically appropriate for hydrological impact studies. However, despite bias correction, some biases in the ÖKS15 data remain and differences in timing, magnitude and spatial characteristics lead to deviations in hydrological indicators at different scales.
Highlights
Regional Climate Model (RCM) projections provide essential information for hydrological climate change impact assessments
The hydrological models for the Raab and the Wolzerbach catchments were driven by observational, uncorrected and biascorrected climate model data to assess the utility of the high-resolution O KS15 datasets in hydrological applications
Temperature data consistency at gridpoint level We found an average Mean Absolute Error (MAE) of 1.3 ◦C between the STATION and GRID daily temperature values for gridpoints in the alpine catchment for the period 1961–2005, with a positive bias for GRID temperatures observed in 12 out of 17 locations
Summary
Regional Climate Model (RCM) projections provide essential information for hydrological climate change impact assessments These are commonly used as input in hydrological models (e.g., Pasten-Zapata et al, 2020; Rossler et al, 2019; Smiatek and Kunst mann, 2019; Willkofer et al, 2018). The application of climate-hydrology modelling chains, for smaller sized basins (< 100 km2), remains challenging For this reason, such an application has been subject to numerous studies They showed that bias correction may enhance the validity of climate simulations for driving hydrological models, but it can deteriorate hydrological extremes They concluded that it might be reasonable to select a climate model subset instead of full ensembles in a regional context. Hakala et al (2018) confirmed the importance of bias correcting RCMs driven by General Circulation Models (GCMs), which improved discharge simulations in most cases in their study. They proposed using hydrological models to rank GCM-RCM simulations (i.e., simulations from RCMs forced by GCMs) according to their performance of discharge simulations before applying bias correction, which would allow the user to evaluate their output in a process-oriented framework
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