Abstract:There are several indications that changes in land cover have influenced the hydrological regime of various river basins. In addition, the effects of climate change on the hydrological cycle and on the runoff behaviour of river catchments have been discussed extensively in recent years. However, it is at present rather uncertain how, how much and at which spatial scale these environmental changes are likely to affect the generation of storm runoff, and consequently the flood discharges of rivers. Firstly, this paper gives an overview of the possible effects of climatic and land-use change on storm runoff generation. Secondly, it discusses models dealing with the hydrological response to climate and land-use variations, including both the downscaling of climate information from global circulation models and the way flood forecasting models represent land-use conditions. Finally, two modelling studies of meso-scale catchments in Germany are presented: the first shows the possible impacts of climate change on storm runoff production, and the second the impacts of land-use changes.

Five statistical downscaling methods [automated regression-based statistical downscaling (ASD), bias correction spatial disaggregation (BCSD), quantile regression neural networks (QRNN), TreeGen (TG), and expanded downscaling (XDS)] are compared with respect to representing climatic extremes. The tests are conducted at six stations from the coastal, mountainous, and taiga region of British Columbia, Canada, whose climatic extremes are measured using the 27 Climate Indices of Extremes (ClimDEX; http://www.climdex. org/climdex/index.action) indices. All methods are calibrated from data prior to 1991, and tested against the two decades from 1991 to 2010. A three-step testing procedure is used to establish a given method as reliable for any given index. The first step analyzes the sensitivity of a method to actual index anomalies by correlating observed and NCEP-downscaled annual index values; then, whether the distribution of an index corresponds to observations is tested. Finally, this latter test is applied to a downscaled climate simulation. This gives a total of 486 single and 162 combined tests. The temperature-related indices pass about twice as many tests as the precipitation indices, and temporally more complex indices that involve consecutive days pass none of the combined tests. With respect to regions, there is some tendency of better performance at the coastal and mountaintop stations. With respect to methods, XDS performed best, on average, with 19% (48%) of passed combined (single) tests, followed by BCSD and QRNN with 10% (45%) and 10% (31%), respectively, ASD with 6% (23%), and TG with 4% (21%) of passed tests. Limitations of the testing approach and possible consequences for the downscaling of extremes in these regions are discussed.

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Abstract. Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.