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AbstractAccording to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. These changes are expected to have severe direct impacts on the management of water resources, agricultural productivity and drinking water supply. The different regions of the Mediterranean landscape are already experiencing and expecting a broad range of natural and man-made threats to water security. Current projections of future hydrological change, based on regional climate model results and subsequent hydrological modeling schemes, are very uncertain and poorly validated. The Rio Mannu di San Sperate Basin, located in Sardinia, Italy, is one test site of the CLIMB project. The catchment has a size of 472.5 km2. The catchment was already affected by multi-drought periods (1990-2000) (Piras et al. 2014). The process-based Water Simulation Model (WaSiM) was set up to model current and future hydrological conditions. The availability of measured meteorological and hydro-logical data is poor as it is common for many Mediterranean catchments. The lack of available measured input data hampers the calibration of the model setup and the validation of model outputs. A soil sampling campaign was conducted together with the department of Geography of the University of Kiel to assess more precisely the physical properties of the top soil (30cm depth) at 239 locations in the Rio Mannu catchment. Different deterministic and hybrid geostatistical regionalization methods like Multi-Linear Regression, Inverse Distance Weighting, Ordinary Kriging and Regression Kriging (Odeh et al. 1995) were used to calculate spatially distributed maps of particular lab-analyzed soil information. The applied regionalization methods were then tested on the prediction performance. The best performing prediction method was used to calculate a new classified soil texture map for the catchment. Soil hydrological properties were assigned to the soil texture classes by pedo-transfer functions. WaSiM was then parameterized in 2 different settings. One setting (WASiM-ARU) used the standard available soil information of Aru et al. (1990) and the other (WASiM-RKS) the improved new soil information. The WaSiM-ARU setting was used for calibration and validation. WaSiM-ARU was calibrated and validated with spatially distributed evapotranspiration rates derived with the triangle method (Jiang and Islam, 1999) and soil moisture records, due to missing adequate gauging information in the catchment. The modeled evapotranspiration result girds using WaSiM-RKS setup with the improved soil model setup show a better fit especially for the growing season to those derived from remote sensing without further calibration. Both WaSiM setups were driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. The climate change impact was assessed based on differences between reference and future time series and with climate change indices like the standardized difference precipitation index, the evapotranspiration index and by the number of consecutive flow conditions. Furthermore long-term annual and monthly mean changes were analyzed. The simulated results show a reduction of all hydrological quantities in the future. Furthermore simulation results reveal an earlier onset of dry conditions in the catch-ment. The comparison of modeling results shows that the quality of the soil model setup has a major impact on the spatial distribution of modeling outputs. Finally runoff modeling results of both WaSiM setups were compared to other modeling results which were processed with other hydrological models in the test site within CLIMB. Those models used a very similar setup as WaSiM-ARU. The comparison shows a significant uncertainty in the processed results based on to the applied hydrological model. Especially in ungauged catchments like the Rio Mannu those uncertainties need to be considered in the climate change impact assessment analysis, the resulting adaption strategies and for the policy decision making. However, findings also show that the quality of soil input and parametrization creates uncertainties when using WaSiM that are in the same range as the uncertainties produced by the different applied hydrological models. The study shows that the combination of sophisticated climate model downscaling and bias correction techniques, improved hydrological model parametrization with improved soil information, and validation with in-situ and remote sensing measurements, has a high potential to improve environmental impact assessment in data scarce regions.
Meyer, Swen (2016): Climate change impact assessment under data scarcity: improved hydrological model parametrization using field monitoring techniques and geostatistics. Dissertation, LMU München: Fakultät für Geowissenschaften