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The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5∘ spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data – allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020).

When assessing global water resources with hydrological models, it is essential to know about methodological uncertainties. The values of simulated water balance components may vary due to different spatial and temporal aggregations, reference periods, and applied climate forcings, as well as due to the consideration of human water use, or the lack thereof. We analyzed these variations over the period 1901–2010 by forcing the global hydrological model WaterGAP 2.2 (ISIMIP2a) with five state-of-the-art climate data sets, including a homogenized version of the concatenated WFD/WFDEI data set. Absolute values and temporal variations of global water balance components are strongly affected by the uncertainty in the climate forcing, and no temporal trends of the global water balance components are detected for the four homogeneous climate forcings considered (except for human water abstractions). The calibration of WaterGAP against observed long-term average river discharge Q significantly reduces the impact of climate forcing uncertainty on estimated Q and renewable water resources. For the homogeneous forcings, Q of the calibrated and non-calibrated regions of the globe varies by 1.6 and 18.5 %, respectively, for 1971–2000. On the continental scale, most differences for long-term average precipitation P and Q estimates occur in Africa and, due to snow undercatch of rain gauges, also in the data-rich continents Europe and North America. Variations of Q at the grid-cell scale are large, except in a few grid cells upstream and downstream of calibration stations, with an average variation of 37 and 74 % among the four homogeneous forcings in calibrated and non-calibrated regions, respectively. Considering only the forcings GSWP3 and WFDEI_hom, i.e., excluding the forcing without undercatch correction (PGFv2.1) and the one with a much lower shortwave downward radiation SWD than the others (WFD), Q variations are reduced to 16 and 31 % in calibrated and non-calibrated regions, respectively. These simulation results support the need for extended Q measurements and data sharing for better constraining global water balance assessments. Over the 20th century, the human footprint on natural water resources has become larger. For 11–18% of the global land area, the change of Q between 1941–1970 and 1971–2000 was driven more strongly by change of human water use including dam construction than by change in precipitation, while this was true for only 9–13 % of the land area from 1911–1940 to 1941–1970.

Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation.Projections of terrestrial water storage (TWS)—the sum of all continental water—are key to water resource and drought estimates. A hydrological model ensemble predicts climate warming will more than double the land area and population exposed to extreme TWS drought by the late twenty-first century.

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002–2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH,MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤−0.5 km³/y) and increasing (≥0.5 km³/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km³/y, whereas most models estimate decreasing trends (−71 to 11 km³/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km³/y) but negative for models (−450 to −12 km³/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and humaninduced water storage changes may be underestimated.

WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model (WGHM). Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be achieved with WaterGAP 2.2d model output.

Over recent decades, the global population has been rapidly increasing and human activities have altered terrestrial water fluxes to an unprecedented extent. The phenomenal growth of the human footprint has significantly modified hydrological processes in various ways (e.g. irrigation, artificial dams, and water diversion) and at various scales (from a watershed to the globe). During the early 1990s, awareness of the potential for increased water scarcity led to the first detailed global water resource assessments. Shortly thereafter, in order to analyse the human perturbation on terrestrial water resources, the first generation of large-scale hydrological models (LHMs) was produced. However, at this early stage few models considered the interaction between terrestrial water fluxes and human activities, including water use and reservoir regulation, and even fewer models distinguished water use from surface water and groundwater resources. Since the early 2000s, a growing number of LHMs have incorporated human impacts on the hydrological cycle, yet the representation of human activities in hydrological models remains challenging. In this paper we provide a synthesis of progress in the development and application of human impact modelling in LHMs. We highlight a number of key challenges and discuss possible improvements in order to better represent the human–water interface in hydrological models.

To support implementation of the Paris Agreement, the new HAPPI ensemble of 20 bias-corrected simulations of four climate models was used to drive two global hydrological models, WaterGAP and LPJmL, for assessing freshwater-related hazards and risks in worlds approximately 1.5 °C and 2 °C warmer than pre-industrial. Quasi-stationary HAPPI simulations are better suited than transient CMIP-like simulations for assessing hazards at the two targeted long-term global warming (GW) levels. We analyzed seven hydrological hazard indicators that characterize freshwater-related hazards for humans, freshwater biota and vegetation. Using a strict definition for significant differences, we identified for all but one indicator that areas with either significantly wetter or drier conditions (calculated as percent changes from 2006-2015) are smaller in the 1.5 °C world. For example, 7 day high flow is projected to increase significantly on 11% and 21% of the global land area at 1.5 °C and 2 °C, respectively. However, differences between hydrological hazards at the two GW levels are significant on less than 12% of the area. GW affects a larger area and more people by increases-rather than by decreases-of mean annual and 1-in-10 dry year streamflow, 7 day high flow, and groundwater recharge. The opposite is true for 7 day low flow, maximum snow storage, and soil moisture in the driest month of the growing period. Mean annual streamflow shows the lowest projected percent changes of all indicators. Among country groups, low income countries and lower middle income countries are most affected by decreased low flows and increased high flows, respectively, while high income countries are least affected by such changes. The incremental impact between 1.5 °C and 2 °C on high flows would be felt most by low income and lower middle income countries, the effect on soil moisture and low flows most by high income countries.

Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.

Land surface and hydrologic models (LSMs/HMs) are used at diverse spatial resolutions ranging from catchment-scale (1–10 km) to global-scale (over 50 km) applications. Applying the same model structure at different spatial scales requires that the model estimates similar fluxes independent of the chosen resolution, i.e., fulfills a flux-matching condition across scales. An analysis of state-of-the-art LSMs and HMs reveals that most do not have consistent hydrologic parameter fields. Multiple experiments with the mHM, Noah-MP, PCR-GLOBWB, and WaterGAP models demonstrate the pitfalls of deficient parameterization practices currently used in most operational models, which are insufficient to satisfy the flux-matching condition. These examples demonstrate that J. Dooge's 1982 statement on the unsolved problem of parameterization in these models remains true. Based on a review of existing parameter regionalization techniques, we postulate that the multiscale parameter regionalization (MPR) technique offers a practical and robust method that provides consistent (seamless) parameter and flux fields across scales. Herein, we develop a general model protocol to describe how MPR can be applied to a particular model and present an example application using the PCR-GLOBWB model. Finally, we discuss potential advantages and limitations of MPR in obtaining the seamless prediction of hydrological fluxes and states across spatial scales.

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models' ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model-a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.