Abstract. In Europe, water management is moving from flood defence to a risk management approach, which takes both the probability and the potential consequences of flooding into account. It is expected that climate change and socio-economic development will lead to an increase in flood risk in the Rhine basin. To optimize spatial planning and flood management measures, studies are needed that quantify future flood risks and estimate their uncertainties. In this paper, we estimated the current and future fluvial flood risk in 2030 for the entire Rhine basin in a scenario study. The change in value at risk is based on two land-use projections derived from a land-use model representing two different socio-economic scenarios. Potential damage was calculated by a damage model, and changes in flood probabilities were derived from two climate scenarios and hydrological modeling. We aggregated the results into seven sections along the Rhine. It was found that the annual expected damage in the Rhine basin may increase by between 54% and 230%, of which the major part (∼ three-quarters) can be accounted for by climate change. The highest current potential damage can be found in the Netherlands (110 billion C), compared with the second (80 billion C) and third (62 billion C) highest values in two areas in Germany. Results further show that the area with the highest fluvial flood risk is located in the Lower Rhine in NordrheinWestfalen in Germany, and not in the Netherlands, as is often perceived. This is mainly due to the higher flood protection standards in the Netherlands as compared to Germany.

[1] Climate change will increase winter precipitation, and in combination with earlier snowmelt it will cause a shift in peak discharge in the Rhine basin from spring to winter. This will probably lead to an increase in the frequency and magnitude of extreme floods. In this paper we aim to enhance the simulation of future low-probability flood peak events in the Rhine basin using different climate change scenarios, and downscaling methods. We use the output of a regional climate model (RCM) and a weather generator to create long, resampled time series (1000 years) of climate change scenarios as input for hydrological (daily) and hydrodynamic (hourly) modeling. We applied this approach to three parallel modeling chains, where the transformation method from different resampled RCM outputs to the hydrological model varied (delta change approach, direct output, and bias-corrected output). On the basis of numerous 1000 year model simulations, the results indicate a basin-wide increase in peak discharge in 2050 of 8%-17% for probabilities between 1/10 and 1/1250 years. Furthermore, the results show that increasing the length of the climate data series using a weather generator reduced the statistical uncertainty when estimating low-probability flood peak events from 13% to 3%. We further conclude that bias-corrected direct RCM output is to be preferred over the delta change approach because it provides insight into geographical differences in discharge projections under climate change. Also, bias-corrected RCM output can simulate changes in the variance of temperature and rainfall and in the number of precipitation days, as changes in temporal structure are expected under climate change. These added values are of major importance when identifying future problem areas due to climate change and when planning potential adaptation measures.Citation: te Linde, A. H., J. C. J. H. Aerts, A. M. R. Bakker, and J. C. J. Kwadijk (2010), Simulating low-probability peak discharges for the Rhine basin using resampled climate modeling data, Water Resour. Res., 46, W03512,

Abstract. Due to the growing wish and necessity to simulate the possible effects of climate change on the discharge regime on large rivers such as the Rhine in Europe, there is a need for well performing hydrological models that can be applied in climate change scenario studies. There exists large variety in available models and there is an ongoing debate in research on rainfall-runoff modelling on whether or not physically based distributed models better represent observed discharges than conceptual lumped model approaches do. In addition, it is argued that Land Surface Models (LSMs) carry the potential to accurately estimate hydrological partitioning, because they solve the coupled water and energy balance. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance in simulating discharge. Overall, the semi-distributed conceptual HBV model performed much better than the distributed land surface model VIC (E=0.62, r 2 =0.65 vs. E=0.31, r 2 =0.54 at Lobith). It is argued here that even for a well-documented river basin such as the Rhine, more complex modelling does not automatically lead to better results. Moreover, it is concluded that meteorological forcing data has a considerable influence on model performance, irrespectively to the type of model structure and the need for ground-based meteorological measurements is emphasized.