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The reanalysis products and derived products, ERA5 (Copernicus Climate Change Service, 2018) and W5E5 (WATCH Forcing Data (WFD) methodology applied to ERA5) (LANGE ET AL., 2021) have been recently published initiating a new phase of scientific research utilizing these datasets. ERA5 and W5E5 offer the possibility to reduce insecurities in model results through their improved quality compared to previous climate reanalyses (CUCCHI ET AL., 2020). The suitability of either climate forcing as input for the hydrological model WaterGAP and the influence of the models specific calibration routine has been evaluated with four model experiments. The model was validated by analysing the models ability to produce reasonable values for global water balance components and to reproduce observed discharge in 1427 basins as well as total water storage anomalies in 143 basins using well established efficiency metrics. Bias correction of W5E5 was found to lead to more global realistic mean precipitation and consequently discharge and AET values. In an uncalibrated model setup ERA5 results in better performances across all efficiency metrics. Model results produced with W5E5 as climate input were strongly improved through calibration ultimately leading to the best performances out of all four model experiments. However, model performances considerably improved through calibration with both climate forcings hence calibration was found to have the strongest effect on model performance. Furthermore, spatial differences in performance of either forcing were identified. Snow-dominated regions show an overall better performance with ERA5, while wetter and warmer regions are better represented with W5E5. Finally, it can be concluded that W5E5 should be preferred as climate input for impact modelling; however, depending on the spatial scale and region ERA5 should at least be considered, in particular for snow-dominated regions.
Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated simulations of hydrological droughts from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed hydrological droughts of at least one month duration, over 31 years (1971–2001). Hydrological drought events were identified from runoff-deficits and the Standardised Runoff Index (SRI). In all catchments, the CHMs performed relatively better than the GHMs, for simulating monthly runoff-deficits. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means, have limited abilities to accurately simulate drought events in all eight catchments, in terms of their occurrence and magnitude. Overall, there are opportunities to improve both CHMs and GHMs for better characterisation of hydrological droughts.