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Human actions compromise the many life-supporting functions provided by the freshwater cycle. Yet, scientific understanding of anthropogenic freshwater change and its long-term evolution is limited. Using a multi-model ensemble of global hydrological models, we estimate how, over a 145-year industrial period, streamflow and soil moisture have deviated from pre-industrial baseline conditions (defined by 5th–95th percentiles, at 0.5° grid level and monthly time step). We find increased frequency of local deviations on ~45% of land area, mainly in regions under heavy direct or indirect human pressures. To estimate humanity’s aggregate impact on the freshwater cycle, we present the evolution of deviation occurrence at regional to global scales. Currently, local streamflow and soil moisture deviations occur on 18.2% and 15.8% of global land area, respectively, which is 8.0 and 4.7 percentage points beyond the ~3 percentage point wide pre-industrial variability envelope. Finally, we discuss applying our approach to define a planetary boundary for freshwater change. Our results indicate a substantial shift from stable pre-industrial streamflow and soil moisture conditions to persistently increasing change and a transgression of the freshwater change planetary boundary, calling for urgent actions to reduce human disturbance of the freshwater cycle.

The availability of freshwater resources is of essential importance for humans, freshwater biota, and ecosystem functions. In this scope, global hydrological models (GHMs) are developed to improve the understanding of the global freshwater situation in a globalized world, by filling gaps in observational coverage and assessing scenarios of the future under consideration of different socioeconomic developments and climate change. The Water Global Assessment and Prognosis (WaterGAP) model calculates water use and availability and is in development since 25 years. It consists of five water use models (for irrigation, domestic, cooling of thermal power plants, manufacturing, and livestock sectors) and the WaterGAP Global Hydrology Model (WGHM). Recently, the latest model version, WaterGAP 2.2e, was finalized, containing a number of enhancements and revisions such as integrating new reservoirs, improving naturalized simulations and updating the calibration data base. Furthermore, WaterGAP2.2e is applied in the Inter-Sectoral Impact Model Intercomparison Project ISIMIP3.This presentation provides an overview of the WaterGAP 2.2e scheme and features, assesses streamflow and total water storage anomalies against reference data, shows water balance components, and provides examples of application within ISIMIP3 with a focus on climate forcing uncertainty and selected indicators of climate change hazards.

Water scarcity is a growing concern in many regions worldwide, as demand for clean water increases and supply becomes increasingly uncertain under climate change. Already today, more than 4 billion people experience water scarcity at least one month per year (Mekonnen and Hoekstra, 2016). Developing socio-economic conditions and growing population increase water demands, while climate change leads to changes in freshwater availability. Most studies quantify water scarcity in discrete time windows, with fixed population and climate change signals (e.g., 30 years or long-term averages). Recently, however, Thiery et al. (2021) proposed a novel approach, in which climate change impacts are integrated over a person's lifetime. In this cohort perspective, lifetime impact values are comparable across generations and regions. Evaluating this perspective for natural hazards, they showed, for example, that a newborn will experience a sixfold increase in drought exposure compared to a 60-year-old (Thiery et al., 2021). In this study, we use this cohort perspective to study how much water scarcity a person experiences during their lifetime. Based on monthly fluctuations in water demand and availability, we estimate the total amount of water demand not met and refer to it as 'lifetime water deficit'. To this end, we use an ensemble of four global hydrological models (MATSIRO, CWatM, LPJmL and H08), each forced by four GCMs and two RCP scenarios from the InterSectoral Impact Model Intercomparison Project (ISIMIP2b). The simulations account for varying population and socio-economic conditions in the historical and future period, following the SSP2 scenario. Combined with country-based population, cohort distribution and life expectancies, lifetime water deficits are quantified for different generations on a country level. Our findings reveal high water lifetime deficit values for regions that are already water scarce today, such as the Mediterranean, North Africa and the Middle East. In these regions, more than 70% of the lifetime water demand is not met when needed. Further comparison reveals differences in spatial, intergenerational and climate change scenarios, and provides information on different scenarios. Overall, this study provides a new perspective on quantifying water scarcity and the climate and population impacts. References:Mekonnen, M. M., & Hoekstra, A. Y. (2016). Four billion people facing severe water scarcity. Science Advances, 2(2). https://doi.org/10.1126/sciadv.1500323Thiery, W., Lange, S., Rogelj, J., Schleussner, C. F., Gudmundsson, L., Seneviratne, S. I., Andrijevic, M., Frieler, K., Emanuel, K., Geiger, T., Bresch, D. N., Zhao, F., Willner, S. N., Büchner, M., Volkholz, J., Bauer, N., Chang, J., Ciais, P., Dury, M., … Wada, Y. (2021). Intergenerational inequities in exposure to climate extremes. Science, 374(6564), 158–160. https://doi.org/10.1126/science.abi7339

Global hydrological models enhance our understanding of the Earth system and support the sustainable management of water, food and energy in a globalized world. They integrate process knowledge with a multitude of model input data (e.g., precipitation, land cover and soil properties and location and extent of surface water bodies) that describe the state of the Earth. However, they do not fully utilize observations of model output variables (e.g., streamflow and water storage) to decrease model output uncertainty by, e.g., parameter estimation. For the pilot region Mississippi River basin, we assessed the suitability of three ensemble-based multi-variable calibration approaches for identifying both optimal and behavioral parameter sets for the global hydrological model WaterGAP, utilizing observations of streamflow (Q) and total water storage anomaly (TWSA). The common first steps in all approaches are 1) the definition of spatial units for which calibration parameters are uniformly adjusted (CDA units), combined with the selection of observation data, 2) the identification of potential calibration parameters and their a-priori probability distributions and 3) sensitivity analyses to select the most influential model parameters per CDA unit that will be adjusted by calibration. In the estimation of model output uncertainty, we considered the uncertainties of the Q and TWSA observations. We found that the Pareto-optimal calibration (POC) approach, which utilizes the Borg multi-objective evolutionary search algorithm to find Pareto-optimal parameter sets, is best suited for identifying a single “optimal” parameter set for each CDA unit. This parameter set leads to an improved fit to the monthly time series of both Q and TWSA as compared to the standard WaterGAP variant, which is only calibrated against mean annual Q, and can be used to compute the best estimate of WaterGAP output. The Generalized Likelihood Uncertainty Estimation (GLUE) approach is less suitable than POC to identify the optimal parameter set but enables the estimation of model output uncertainties that are due to the equifinality of parameter sets and the observation uncertainty. The potential advantages of the ensemble Kalman filter calibration and data assimilation (EnCDA) approach, in which both parameter sets and water storages are updated, could not be realized, likely due to the high computational burden of this approach, This limited the EnCDA ensemble size to 32, while 20,000 ensemble members could be evaluated in the case of POC and GLUE. Partitioning the whole Mississippi River basin into five CDA units (sub-basins) instead of only one improved model performance during the calibration and validation periods. Very diverse parameter sets were found to lead to similarly good fits to observations, but the range of values of three parameters could be narrowed by calibration. Model structure uncertainties, in particular regarding the operation of man-made reservoirs, the location and extent of small wetlands, and the (lacking) representation of losing river conditions in WaterGAP, are suspected to be the main reasons for the low coverage of the observation uncertainty bands by the GLUE-derived model output uncertainty bands. Model structure uncertainties are also the likely reason for major trade-offs between optimal fit to Q and TWSA. Calibration against GRACE TWSA only, in regions without Q observations, may worsen the Q simulation as compared to the uncalibrated model variant. We plan to add additional remotely-sensed observations in the multi-variable calibration of WaterGAP and suggest considering parameter uncertainty in multi-model ensemble studies of the global freshwater system.

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Water is a critical resource, but ensuring its availability faces challenges from climate extremes and human intervention. In this Review, we evaluate the current and historical evolution of water resources, considering surface water and groundwater as a single, interconnected resource. Total water storage trends have varied across regions over the past century. Satellite data from the Gravity Recovery and Climate Experiment (GRACE) show declining, stable and rising trends in total water storage over the past two decades in various regions globally. Groundwater monitoring provides longer-term context over the past century, showing rising water storage in northwest India, central Pakistan and the northwest United States, and declining water storage in the US High Plains and Central Valley. Climate variability causes some changes in water storage, but human intervention, particularly irrigation, is a major driver. Water-resource resilience can be increased by diversifying management strategies. These approaches include green solutions, such as forest and wetland preservation, and grey solutions, such as increasing supplies (desalination, wastewater reuse), enhancing storage in surface reservoirs and depleted aquifers, and transporting water. A diverse portfolio of these solutions, in tandem with managing groundwater and surface water as a single resource, can address human and ecosystem needs while building a resilient water system.

In the version of this article originally published, reference 9 was incorrectly cited in the last sentence of the second paragraph under ‘Introduction’ and in the first sentence of the second paragraph under the ‘Water scarcity’ subsection. Scanlon et al. (Environ. Res. Lett. https://doi.org/ 10.1088/1748-9326/ac3bfc, 2022) was incorrectly cited in the last sentence under ‘Drivers of water-resource variability’ but is now replaced with reference 38, and Figure 3 was wrongly stated to be adapted from reference 19 instead of reference 36. Reference 40 was mistakenly cited in the last sentence of the second paragraph under the ‘Increasing water access and supplies’ subsection, and reference 37 was inadvertently duplicated in the reference list. References 28 (now reading ‘Winter, T. C., Harvey, J. W., Franke, O. L. and Alley, W. M. Ground Water and Surface Water: A Single Resource. Circular 1139 (United States Geological Survey, 1998)’) and 94 (now reading ‘Scanlon, B. R., Reedy, R. C., Faunt, C. C., Pool, D. and Uhlman, K. Enhancing drought resilience with conjunctive use and managed aquifer recharge in California and Arizona. Environ. Res. Lett. 11, 035013 (2016)’) initially referred to incorrect sources. Lastly, the name of author Hannes Müller Schmied was incorrectly spelled Hannes Mueller Schmied, and an affiliation for him was missing: Senckenberg Leibniz Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany. The errors have been corrected in the HTML and PDF versions of the article.

We describe the new Global Land Water Storage data set (GLWS2.0), which contains total water storage anomalies (TWSA) over the global land with a spatial resolution of 0.5°, covering the time frame 2003 to 2019 without gaps, and including an uncertainty quantification. GLWS is produced by assimilating 4° gridded GRACE and GRACE-FO-derived TWSA into the WaterGAP global hydrological model using the Parallel Data Assimilation Framework (PDAF). The resulting data set represents thus an optimal synthesis of GRACE data, the hydrological model and implicitly all data sets that went into the model. This synthesis seeks to fit GRACE and GRACE-FO TWSA grids within error bars (from propagating full level-2 error variance covariance matrices), and at the same time it solves the horizontal and vertical water balances as represented in the hydrological model, again within error bars. To this end, the uncertainty of the hydrological model simulation is represented via a 32-member ensemble, where we account for the uncertainty of the precipitation and temperature data and for the uncertainty of some model calibration parameters. As a result, when no GRACE (-FO) data is available, GLWS represents the mean of an ensemble where each member is dynamically consistent with the model. It is important to understand that this mean depends on the ensemble and the previous GRACE estimates, and thus differs from published WaterGAP standard runs even if there is no GRACE data within a particular month. Due to the dynamical constraints, the assimilation-derived GLWS data set does not represent a simple downscaling of the GRACE data, i.e. spatial smoothing of GLWS does not necessarily correspond to GRACE (-FO) TWSA. GLWS indeed contains all water storages that are represented in WaterGAP (e.g. groundwater), but here we will focus only on TWSA. The main updates with respect to the release 1 were the use of an updated version of WaterGAP as well as minor bug fixes in the assimilation. GLWS1.0 and GLWS2.0 have already been provided to several research groups within the DFG GlobalCDA research unit and beyond for evaluation purposes. In this presentation we describe the methods and data sets that went into GLWS2.0, and the validation of the resulting 0.5° TWSA grids from a geodesy applications perspective, including comparisons to GRACE and GRACE-FO data and to GNSS-derived site displacements. We will also show some extended experiments with jointly assimilating river discharge data, model parameter estimation, and the integration of machine-learning based model prediction in our assimilation approach.

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., 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. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

Global hydrological models simulate water storages and fluxes of the water cycle, motivated to assess water problems such as water scarcity, high flows and more generally the impact of anthropogenic change on the global water system. However, the models include many uncertainties due to the model inputs (e.g. climate forcing data), model parameters, and model structure which can lead to disagreements when simulation results are compared to observations. To reduce and quantify these uncertainties, some of the models are calibrated against in-situ streamflow observations or compared against total water storage anomalies (TWSA) derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. In recent years, TWSA data are integrated into some models via data assimilation to directly improve the realism of the models.In this study, we present our framework for jointly assimilating satellite and in-situ observations into the WaterGAP Global Hydrological Model (WGHM). In addition to GRACE TWSA maps, for the first time here we experimentally jointly assimilate in-situ streamflow observations from gauge stations. This is in preparation for the Surface Water and Ocean Topography (SWOT) satellite, which will be launched this year and is expected to allow the derivation of streamflow observations globally for rivers wider than 50-100m. GRACE assimilation strongly improves the TWSA simulations in the Mississippi River Basin, e.g. the correlation increases to 91%, with which our results are consistent with previous studies. However, we find in this case that the streamflow simulation deteriorates, for example, correlation reduces from 92% to 61% at the most downstream gauge station. In contrast, jointly assimilating GRACE data and streamflow observations from GRDC gauge stations improves the streamflow observations by up to 33% in terms of e.g. RMSE and correlation while maintaining the good TWSA simulations. In view of the upcoming SWOT mission, our data suggest that the SWOT data will help to further improve the structure and simulations of global hydrological models.

Process-based impact models are frequently used for a range of applications and are valuable for simulating fundamental processes in a changing world. Model Intercomparison Projects like the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, www.isimip.org) act as an umbrella for various sectors (e.g. water, agriculture, health) and numerous modelling teams that are following a common modeling protocol that enables model intercomparison and (cross-) sectoral multi-model impact assessments. However, such assessments require reliable model outputs which can be checked from two perspectives.First, a quality control (QC) check ensures that simulated files follow the standards defined in the modelling protocol and includes plausibility checks. For example, structural inconsistencies and correct metadata entries can be assessed, but also in cases where the range of a specific variable exceeds plausibility limits (e.g. negative precipitation values), such a tool can facilitate error checking which is very helpful especially in the case of high data volume simulation outputs (e.g., errors stemming from an erroneous unit conversion).Second, a quality assessment (QA) tool compares model output to observation data or benchmark models. This is particularly important for model development and improvement as it can highlight benefits and limitations of models for e.g., specific model configurations, but it also informs the identification of models that are best suited for specific regions and research questions.Within the EU COST-Action “Process-based models for climate impact attribution across sectors“ (PROCLIAS), the aim is to establish a QC/QA workflow for the ISIMIP models. A QC tool is already developed and in operation which checks the data format and, exemplarily for the global water sector, each variable for plausibility ranges. An operational QA tool does not yet exist within PROCLIAS and ISIMIP but some experiences have been gained with existing evaluation frameworks such as ILAMB and the ESMValTool.This presentation provides experiences gained with the QC tool and the application of ISIMIP data to existing QA frameworks and outlines the next milestones. It is planned to extend the plausibility ranges to all ISIMIP sectors by a survey within the modelling teams. For the QA tool, specific developments are required to integrate sector-specific evaluation methods (e.g., basin outlines into ILAMB). To use ESMValTool, the model output data needs to be restructured to a CF-compliant format. With the ISIMIP global water sector as a pilot sector, experiences are gained that will then be transferred to other sectors. This activity also calls for an exchange of ideas and experiences from other modeling communities.

Water scarcity (WS) is projected to increase as a result of future climate and population changes, and its impacts will be hazardous to vulnerable groups of people in South Asia (SA). We present the first assessment of WS in SA under the Paris Agreement, using two global hydrological models (GHM) forced by three global climate models (GCM) under the RCP 6.0. Results show that a significant alteration in the hydrological fluxes contributes to the decline in water availability, along with an intense increase in water consumption that augments WS. The seasonal shifting in WS significantly increases the population affected by approximately 16% in June to September (JJAS) to 42% in December to February (DJF) under 1.5 ºC warming. We also highlight spatial hotspots of WS in SA, including North-central, Northwestern, and Southern India, Eastern Pakistan, Northern, and Northwestern Bangladesh. We observe a 1.5 ºC temperature increase as a critical point for WS in SA with a profound impact on 875 million people and suggest the Paris Agreement temperature target of even 1.5 ºC will not ubiquitously alleviate future warming-driven WS. Hence, immediate mitigations and legislations are required to curtail WS in SA.

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.

International audience; Declines in terrestrial water storage (TWS) exacerbate regional water scarcity and global sea level rise. Increasing evidence has shown that recent TWS declines are substantial in ecologically fragile drylands, but the mechanism remains unclear. Here, by synergizing satellite observations and model simulations, we quantitatively attribute TWS trends during 2002-2016 in major climate zones to three mechanistic drivers: climate variability, climate change, and direct human activities. We reveal that climate variability had transitory and limited impacts (

Droughts are anticipated to intensify in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought indices, makes it difficult to compare drought assessments. This issue is widely known, but its relative importance has never been quantitatively evaluated in comparison to other sources of uncertainty. Here, encompassing three drought categories (meteorological, agricultural, and hydrological droughts) with four temporal scales of interest, we evaluated changes in the drought frequency using multi-model and multi-scenario simulations to identify areas where the definition issue could result in pronounced uncertainties and to what extent. We investigated the disagreement in the signs of changes between drought definitions and decomposed the variance into four main factors: drought definitions, greenhouse gas concentration scenarios, global climate models, and global water models, as well as their interactions. The results show that models were the primary sources of variance over 82% of the global land area. On the other hand, the drought definition was the dominant source of variance in the remaining 17%, especially in parts of northern high-latitudes. Our results highlight specific regions where differences in drought definitions result in a large spread among projections, including areas showing opposite signs of significant changes. At a global scale, 7% of the variance resulted independently from the definition issue, and that value increased to 44% when 1st and 2nd order interactions were considered. The quantitative results suggest that by clarifying hydrological processes or sectors of interest, one could avoid these uncertainties in drought assessments to obtain a clearer picture of future drought change.

Human actions and climate change have drastically altered river flows across the world, resulting in adverse effects on riverine ecosystems. Environmental flows (EFs) have emerged as a prominent tool for safeguarding riverine ecosystems. However, at the global scale, the assessment of EFs is associated with significant uncertainty. Here, we present a novel method to determine EFs by Environmental Flow Envelopes (EFE), which is an envelope of variability bounded by discharge limits within which riverine ecosystems are not seriously compromised. The EFE is defined globally in approximately 4,400 sub–basins at monthly time resolution, considering also the methodological uncertainties related with global EF studies. In addition to a lower bound of discharge, the EFE introduces an upper bound of discharge, identifying areas where streamflow has increased substantially. Further, instead of only showing whether EFs are violated, as commonly done, we quantify, for the first time, the frequency, severity, and trends of EFE violations, which can be considered as potential threats to riverine ecosystems. We use pre–industrial (1801–1860) quasi-natural discharge and a suite of hydrological EFR methods and global hydrological models to estimate EFE, applying data from the ISIMIP 2b ensemble. We then compare the EFEs to recent past (1976–2005) discharge to assess the violations of the EFE. We found that the EFE violations most commonly manifest themselves by insufficient streamflow during the low flow season, with less violations during intermediate flow season, and only few violations during high flow season. These violations are widespread: discharge in half of the sub–basins of the world has violated the EFE during more than 5 % of the months between 1976 and 2005. The trends in EFE violations have mainly been increasing during the past decades and will likely remain problematic with projected increases in anthropogenic water use and hydroclimatic changes. Indications of excessive streamflow through EFE upper bound violations are relatively scarce and spatially distributed, although signs of increasing trends can be identified and potentially attributed to climate change. While the EFE provides a quick and globally robust way of determining environmental flow allocations at the sub–basin scale, local fine–tuning is necessary for practical applications and further research on the coupling between quantitative discharge and riverine ecosystem responses is required.

Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributions. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling, include global mean sea-level (GMSL) anomalies from satellite altimetry; the global mean steric component from Argo drifter data with incorporation of sea surface temperature data; the ocean mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry; the contribution from global glacier mass changes assessed by a global glacier model; the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes, assessed from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend) and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period Jan 2003–Aug 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions (now about 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. The SLB of linear trends is closed for P1 and P2, that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.

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 runoff simulations 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 monthly runoff and hydrological droughts over 31 years (1971–2001). Observed and simulated hydrological drought events were identified using the Standardised Runoff Index (SRI) and were classified based on intensity. Our results show that for all eight catchments, CHMs out-performed GHMs in monthly runoff estimation showing a better representation of observed runoff than GHMs. 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 present limited ability to accurately simulate severe drought events in all eight catchments, in terms of their timing and intensity. By analysing the monthly runoff time-series for several extreme droughts over the historical period, we identify room for improvement in the models so that extreme droughts may ultimately be better represented by both CHMs and GHMs.

Global hydrological models simulate water storages and fluxes of the water cycle which is important for e.g. water management decisions and drought/flood predictions. However, the models include many uncertainties due to the model inputs (e.g. climate forcing data), model parameters, and model structure resulting in disagreements with observations. To reduce these uncertainties, the models are typically calibrated against in-situ discharge observations or GRACE-derived total water storage anomalies (TWSA) are integrated into the model by data assimilation.In this study, we introduce a framework for jointly assimilating multiple observations into the WaterGAP 2.2d model over the Mississippi River Basin for 2003-2018. We do not only assimilate GRACE-derived TWSA but also in-situ discharge observations from gauge stations. In addition, we vary the number as well as the location of the considered discharge stations to derive information about e.g. the influence of assimilating down- or upstream stations.Our results show a strong influence of the GRACE data and that the assimilation of multiple discharge stations resembles the results of a traditional calibration approach. We expect the most downstream stations to have a larger impact on the assimilation results than the more upstream stations (as the downstream stations already include the information of the upstream stations). The gained insights of this study show a great potential to better assess and understand the global freshwater system and become even more relevant in view of the Surface Water and Ocean Topography (SWOT) satellite. SWOT will be launched in 2022 and is expected to allow the derivation of discharge observations globally for rivers wider than 50-100m.

Droughts are anticipated to intensify or become more frequent in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought definition, makes it difficult to compare drought projections and hampers overviewing future changes in drought. This issue is widely known and underscored in recent reports of the Intergovernmental Panel on Climate Change, but the relative importance of the issue and its spatial distribution have never been quantitatively evaluated compared to other sources of uncertainty.Here, using a multi-scenario and multi-model dataset with combinations of three climate change scenarios, four global climate models and seven global water models, we evaluated changes in the frequency of three categories of drought (meteorological, agricultural, and hydrological droughts) by a consistent standardized approach with four different temporal scales of accumulation periods to show how differences among the drought definitions could result in critical uncertainties. For simplicity, this study focuses on one drought index per drought category. Firstly we investigated the disagreement in the sign of changes between definitions, and then we decomposed the overall uncertainty to estimate the relative importance of each source of uncertainty. By a multifactorial ANOVA, uncertainty was decomposed into four main factors, namely drought definitions, climate change scenarios, global climate models and global water impact models, and their interactions.Our results highlight specific regions where the sign of change disagrees between drought definitions. Importantly, changes in drought frequency in such regions tended to be statistically insignificant with low ensemble member agreement. Drought definition attributed to18% of the main factor uncertainty at the global scale, and the definition was the dominant uncertainty source over 11% of the global land area. The contribution of difference in the drought category showed a higher contribution to overall uncertainty than the difference in scales. The contribution of scenario uncertainty was the least among the main factors in general, though it is a dominant factor in the far-future in a couple of hotspot regions such as the Mediterranean region. Overall, model uncertainties were the primary source of uncertainty, and the definition issue was less important over large areas. However, definition uncertainty was the primal uncertainty source with significant changes in particular regions, such as parts of high-latitude areas in the northern hemisphere. One needs to pay attention to these regions in overviewing future drought change. Nonetheless, what this study quantified is the relative importance of uncertainty stemming from drought definition that should be avoidable or reducible if one treats drought specifically. Our results indicate that we can reduce uncertainty in drought projections to some extent and get a clearer picture by clarifying hydrological processes or sectors of interest.

The frequency and severity of drought increase in many regions of the world, which emphasizes the need for sufficient research to better monitor and trigger management plans. An important role hereby plays hydrological drought, because it affects water supply and crop yields that are necessary to ensure food security. Typically, hydrological drought detection is based on in-situ observations of fluxes or storages at the surface. However, this neglects the fact that drought might occur in multiple storages with different timing and severity. The use of subsurface storage, e.g. groundwater, is rare because the available in-situ well level monitoring is irregularly distributed in space and time and access might be restricted, for example due to national security reasons or problems in converting them to storage estimates.The satellite mission Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO offer a great possibility to observe the total water storage, i.e. the sum of surface and subsurface storages, on a global scale from space. However, GRACE is restricted to monthly data on a spatial resolution of about 300 km and the vertical sum of the storages. Hydrological models present another possibility to derive global storage information with a finer spatial (~50km), temporal and vertical resolution than GRACE but they do not perfectly represent the reality because they are underlying assumptions and are affected by uncertainty of forcing data. Therefore, to enable downscaling of GRACE while improving the models realism, the GRACE measurements are assimilated into a hydrological model.In previous works we used a framework that assimilates GRACE into the WaterGAP Global Hydrological Model (WGHM) regionally or basin-wise. In this work we present a new framework that globally assimilates GRACE on a 4 degree grid with full uncertainty information from 2003 to 2018. The framework enables to assimilate about 95% of the global WGHM land surface except Greenland. With regard to vertical and spatial resolution the performance of model, observation and assimilation is compared. Global GRACE based drought indicators are applied and its development in the different compartments of surface water, soil and groundwater is analyzed to identify new insights into the propagation of drought. We expect that by including GRACE we derive new information especially for groundwater droughts, which might reveal time lags and a different severity as compared to surface water droughts for some regions.

Assessing freshwater availability in the Middle East (ME) and North Africa (MENA) is crucial to sustaining the life of about ~0.5 billion people who live in this region. Rapid population growth along with climate change imposes additional stresses and limiting the reserve of freshwater resources. The Gravity Recovery and Climate Experiment (GRACE) mission its Follow On (FO) provide an essential tool for studying terrestrial total water storages (TWS) that can be linked to different key drivers. One approach to assessing water depletion is estimating the trend in TWS. Nevertheless, the reliability in the trend is compromised by natural variability (e.g., interannual variations). In this study, we evaluated decadal trends of the GRACE TWS for the period (2002-2020) in the MENA region, including 26 countries. We also analyzed the historical variability of climate-driven TWS (excluding human intervention) for 116 years (yr) (1901-2016) based on the WaterGAP global hydrology model (WGHM) using the cyclostationary empirical orthogonal function approach. Natural variability in TWS includes the modulated seasonal cycle, interannual, decadal, and interdecadal variation. We compared the historical variability of TWS based on the WGHM model with the decadal trends in GRACE and GRACE-FO satellites (18.4 yr) based on two mascons (CSR and JPL) GRACE solutions.Results show that the variability (e.g., standard deviation) in the climate-driven TWS from WGHM is ≤ 10 mm (1901 – 2016) throughout most of the region. Variability is higher in northern Iran, southern Turkey, western coast of the Persian Gulf, Nile River, northwestern Africa (coastal), and south of Sahara (e.g., Chad, Mali, and Sudan). Such regions with higher variability receive substantial annual precipitation or include a major surface water body (e.g., Nile river).Decadal TWS trends are more highly negative throughout most ME, particularly most of Iran and Saudi Arabia, than in N Africa, except for Tunisia. Less severe or stable GRACE TWS trends are found in parts of the ME (Iraq, west Iran, southern Saudi Arabia, Yemen, Oman) and most of N Africa. In contrast, increasing GRACE TWS trends are dominant south of the Sahara (Chad, Sudan, Niger, and Mali) and in parts of the ME (Kuwait, W Yemen). The declining GRACE trends throughout much of the ME (Iran, Iraq, Syria, Arabian Peninsula) and parts of N Africa (Egypt, Libya, Tunisia, and Algeria) are considered reliable because they highly exceed the historical simulated variability of climate-driven TWS (1901 – 2016). Trends in some other localized regions are insignificant relative to historical variability (ratio < 2) in west Iran, Nile river, northwest Egypt, Morocco, and Mauritania. The total loss of water in MENA based on the GRACE period (2002-2020) is about 760 Gt, with an annual trend of -41 Gt/year and R2 0.72. MENA's total loss represent~3.5% of the annual rate of global sea-level rise with a total of ~2 mm between 2002 and 2020. Combining GRACE data with long-term simulations of TWS helps interpret recent GRACE data within the context of long-term variability and allows us to isolate the human drive contribution to TWS trends.

The Global Runoff Data Centre (GRDC) deploys the data product Global Freshwater Fluxes into the World Oceans since 1996. The quantification of freshwater fluxes into the ocean is an important link to oceanography and climatology, as the salinity of sea water influences ocean currents and is a driver of evaporation and therefore interconnects to the general circulation of the atmosphere.The first versions of the data product were based on runoff coefficient estimates for ungauged basins. Since 2004, results of the global hydrological model WaterGAP (AG Hydrologie, Goethe-Universität Frankfurt), calibrated with GRDC station data, were used to calculate the freshwater fluxes.On the basis of the latest WaterGAP 2.2d model, we could now derive the freshwater fluxes in a refined temporal resolution and for a considerably extended time period (1901-2016).We present a statistical analysis of monthly and annual freshwater input to the oceans within 5° and 10° latitude zones, from 5° cells along the coastlines and from the Global International Water Assessment regions (GIWA).Beyond that, the GRDC has revised its GIS product Major River Basins of the World, which is now consistent with the WMO Regions and Subregions. The freshwater fluxes have been determined likewise for these catchments.The provision of integrative data products is one of the objectives of the Global Terrestrial Network Hydrology (GTN-H) corresponding with the commitments of the WMO members at the eighteenth session of the World Meteorological Congress.

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 done with WaterGAP2.2d model output.

Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.

Summarization: The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. Presented on: Earth's Future