Abstract
This study presents an original method to assess the effectiveness and robustness of measures aimed at reducing the gap between water resources and demand under climate and water use changes. A modeling framework integrating human and hydro-climatic dynamics and accounting for interactions between resource and demand was developed and applied in two basins of different scales and with contrasted water uses: the Herault (2 500 km\textlesssup\textgreater2\textlesssup/\textgreater, France) and the Ebro (85 000 km\textlesssup\textgreater2\textlesssup/\textgreater, Spain) basins. Natural streamflow was evaluated using a conceptual hydrological model. A demand-driven reservoir management model was designed to account for streamflow regulations from the main dams. Municipal water demand was estimated from population and unit water consumption data, and irrigation water demand was computed from crop and soil data, agricultural practices (irrigated areas and irrigation efficiency) and climate forcings. Environmental flows were accounted for by defining streamflow thresholds under which water withdrawals were strictly limited. Climate simulations from the CMIP5 exercise were used to generate 18 climate scenarios at the 2050 horizon, and a trend water use scenario was built based on demographic and local socio-economic trends by the mid-century. Indicators comparing water availability to demand were computed. To assess the efficiency of potential adaptation measures under climate change scenarios, the sensitivity of water stress to variations in the main drivers of water demand was tested. The effectiveness and robustness of individual measures varied between the basins and inside each basin, depending on the demand nodes. Finally an adaptation scenario combining different measures was tested in both basins, using the integrative modeling framework. This scenario led to encouraging results regarding the decrease of water stress. However, the effectiveness of the combined adaptation strategies was shown to be insufficiently robust to climate change uncertainties.