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Climate change is expected to alter the hydrological cycle resulting in large-scale impacts on water availability. However, future climate change impact assessments are highly uncertain. For the first time, multiple global climate (three) and hydrological models (eight) were used to systematically assess the hydrological response to climate change and project the future state of global water resources. This multi-model ensemble allows us to investigate how the hydrology models contribute to the uncertainty in projected hydrological changes compared to the climate models. Due to their systematic biases, GCM outputs cannot be used directly in hydrological impact studies, so a statistical bias correction has been applied. The results show a large spread in projected changes in water resources within the climate- hydrology modelling chain for some regions. They clearly demonstrate that climate models are not the only source of uncertainty for hydrological change, and that the spread resulting from the choice of the hydrology model is larger than the spread originating from the climate models over many areas. But there are also areas showing a robust change signal, such as at high latitudes and in some midlatitude regions, where the models agree on the sign of projected hydrological changes, indicative of higher confidence in this ensemble mean signal. In many catchments an increase of available water resources is expected but there are some severe decreases in Central and Southern Europe, the Middle East, the Mississippi River basin, southern Africa, southern China and south-eastern Australia. [ABSTRACT FROM AUTHOR]

Abstract: During the last decades the Aral Sea basin has suffered an enormous depletion of water resources within its lakes and rivers with consequences for society, economy, and nature. Within this model study, Global Change impacts on the Amu Darya and Syr Darya rivers, as well as on the Aral Sea itself, are being analysed for the period 1958–2002. In a first step, a multi-annual data base on crop specific irrigated areas has been set-up, which has then been integrated in the hydrology and water use model WaterGAP3. As a second step, anthropogenic water abstractions have been calculated, which were then assimilated in the simulation of river runoff of the Amu Darya and Syr Darya. The last step includes the simulation of the water balance of the Aral Sea, by taking into account modelled river inflow. Within WaterGAP3, the water use module has been switched on and off to separate the impacts of Climate and Global Change (i.e. water abstractions). Irrigation water abstractions are very well represented by WaterGAP3 and lie within the range of reported values. Modelled river discharge also shows a good fit to observed data, whereas phases are in sync but volumes are slightly overestimated. Simulated volumes of the Aral Sea itself are well reflected by the model, though results for the period 1990–2002 are too high. In this study, the Climate Change impacts are much smaller (14%) than the water use impacts (86%) on the shrinkage of the Aral Sea. Finally, an outlook on potential scenario model studies is given, which could analyse the different strategies of mitigation and adaptation of Global Change in the Aral Sea basin. [Copyright &y& Elsevier]

Multi-objective optimization algorithms are widely used for the calibration of conceptual hydrological models. Such algorithms yield a set of Pareto-optimal solutions, reflecting the model structure uncertainty. In this study, a multi-objective optimization strategy is suggested, which aims at reducing the model structure uncertainty by considering parameter interaction within Pareto-optimal solutions. The approach has been used to develop a nested setup of a rainfall-runoff model, which is integrated in a probabilistic meso-/macroscale flood forecasting system. The optimization strategy aided in determining the best combination of a lumped (computationally efficient in operational real time forecasting) and a semi-distributed parameterization of the hydrological model. First results are shown for two sub-basins of the Mulde catchment in Germany. The different phenomena of parameter interaction were analysed in this case study to reduce the model structure uncertainties. [ABSTRACT FROM AUTHOR]