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Groundwater is a dynamic component of the global water cycle with important social, economic, ecological, and Earth system functions. We present a new global classification and mapping of groundwater systems, which we call groundwaterscapes, that represent predominant configurations of large‐scale groundwater system functions. We identify and map 15 groundwaterscapes which offer a new lens to conceptualize, study, model, and manage groundwater. Groundwaterscapes are derived using a novel application of sequenced self‐organizing maps that capture patterns in groundwater system functions at the grid cell level (∼10 km), including groundwater‐dependent ecosystem type and density, storage capacity, irrigation, safe drinking water access, and national governance. All large aquifer systems of the world are characterized by multiple groundwaterscapes, highlighting the pitfalls of treating these groundwater bodies as lumped systems in global assessments. We evaluate the distribution of Global Groundwater Monitoring Network wells across groundwaterscapes and find that industrial agricultural regions are disproportionately monitored, while several groundwaterscapes have next to no monitoring wells. This disparity undermines the ability to understand system dynamics across the full range of settings that characterize groundwater systems globally. We argue that groundwaterscapes offer a conceptual and spatial tool to guide model development, hypothesis testing, and future data collection initiatives to better understand groundwater's embeddedness within social‐ecological systems at the global scale. Key Points: Groundwaterscapes are presented as landscape units representing configurations of groundwater's social‐ecological and Earth system functionsA two‐stage self‐organizing map clustering method is implemented to derive 15 groundwaterscapes at the global scaleAll large aquifer systems of the world contain multiple groundwaterscapes [ABSTRACT FROM AUTHOR]

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future. [ABSTRACT FROM AUTHOR]

Evaporation (ET) is one of the crucial components of the water cycle, which serves as the nexus between global water, energy, and carbon cycles. Accurate quantification of ET is, therefore, pivotal in understanding various earth system processes and subsequent societal applications. The prevailing approaches for ET retrievals are either limited in spatiotemporal coverage or largely influenced by the choice of input data or simplified model physics, or a combination thereof. Here, using an independent mass conservation approach, we develop water-balance-based ET datasets (ET-WB) for the global land and the selected 168 major river basins. We generate 4669 probabilistic unique combinations of the ET-WB leveraging multi-source datasets (23 precipitation, 29 runoff, and 7 storage change datasets) from satellite products, in situ measurements, reanalysis, and hydrological simulations. We compare our results with the four auxiliary global ET datasets and previous regional studies, followed by a rigorous discussion of the uncertainties, their possible sources, and potential ways to constrain them. The seasonal cycle of global ET-WB possesses a unimodal distribution with the highest (median value: 65.61 mm per month) and lowest (median value: 36.11 mm per month) values in July and January, respectively, with the spread range of roughly ±10 mm per month from different subsets of the ensemble. Auxiliary ET products illustrate similar intra-annual characteristics with some over- or underestimation, which are completely within the range of the ET-WB ensemble. We found a gradual increase in global ET-WB from 2003 to 2010 and a subsequent decrease during 2010–2015, followed by a sharper reduction in the remaining years primarily attributed to the varying precipitation. Multiple statistical metrics show reasonably good accuracy of monthly ET-WB (e.g., a relative bias of ±20 %) in most river basins, which ameliorates at annual scales. The long-term mean annual ET-WB varies within 500–600 mm yr -1 and is consistent with the four auxiliary ET products (543–569 mm yr -1). Observed trend estimates, though regionally divergent, are evidence of the increasing ET in a warming climate. The current dataset will likely be useful for several scientific assessments centering around water resources management to benefit society at large. The dataset is publicly available in various formats (NetCDF, Mat, and Shapefile) at 10.5281/zenodo.8339655 (Xiong et al., 2023). [ABSTRACT FROM AUTHOR]

Evaporation (ET) is one of the crucial components of the water cycle, which serves as the nexus between global water, energy, and carbon cycles. Accurate quantification of ET is, therefore, pivotal in understanding various earth system processes and subsequent societal applications. The prevailing approaches for ET retrievals are either limited in spatiotemporal coverage or largely influenced by choice of input data or simplified model physics, or a combination thereof. Here, using an independent mass conservation approach, we develop water balance-based ET datasets (ET-WB) for the global land and the selected 168 major river basins. We generate 4669 probabilistic unique combinations of the ET-WB leveraging multi-source datasets (23 precipitation, 29 runoff, and 7 storage change datasets) from satellite products, in-situ measurements, reanalysis, and hydrological simulations. We compare our results with the four auxiliary global ET datasets and previous regional studies, followed by a rigorous discussion of the uncertainties, their possible sources, and potential ways to constrain them. The seasonal cycle of global ET-WB possesses a unimodal distribution with the highest (median value: 65.61 mm/month) and lowest (median value: 36.11 mm/month) values in July and January, respectively, with the spread range of roughly ±10 mm/month from different subsets of the ensemble. Auxiliary ET products illustrate similar intra-annual characteristics with some over/under-estimation, which are completely within the range of the ET-WB ensemble. We found a gradual increase in global ET-WB from 2003 to 2010 and a subsequent decrease during 2010–2015, followed by a sharper reduction in the remaining years primarily attributed to the varying precipitation. Multiple statistical metrics show reasonably good accuracy of monthly ET-WB (e.g., a relative bias of ±20 %) in most river basins, which ameliorates at annual scales. The long-term mean annual ET-WB varies within 500–600 mm/yr and is consistent with the for auxiliary ET products (543–569 mm/yr). Observed trend estimates, though regionally divergent, are evidence of the increasing ET in a warming climate. The current dataset will likely be useful for several scientific assessments centering around water resources management to benefit society at large. [ABSTRACT FROM AUTHOR]

Humans and ecosystems are deeply connected to, and through, the hydrological cycle. However, impacts of hydrological change on social and ecological systems are infrequently evaluated together at the global scale. Here, we focus on the potential for social and ecological impacts from freshwater stress and storage loss. We find basins with existing freshwater stress are drying (losing storage) disproportionately, exacerbating the challenges facing the water stressed versus non-stressed basins of the world. We map the global gradient in social-ecological vulnerability to freshwater stress and storage loss and identify hotspot basins for prioritization (n = 168). These most-vulnerable basins encompass over 1.5 billion people, 17% of global food crop production, 13% of global gross domestic product, and hundreds of significant wetlands. There are thus substantial social and ecological benefits to reducing vulnerability in hotspot basins, which can be achieved through hydro-diplomacy, social adaptive capacity building, and integrated water resources management practices. This work identifies the world's most vulnerable basins to social and ecological impacts from freshwater stress and storage loss: a set of 168 hotspot basins for global prioritization that encompass 1.5 billion people, 17% of global food crops, 13% of global GDP, and hundreds of significant wetlands. [ABSTRACT FROM AUTHOR]

The global water cycle is generally viewed as the cycling of water masses among the land, ocean, and atmosphere. This cycling predominantly occurs at the annual time scale and between land and ocean, constituting the seasonal global water cycle. NASA's Gravity Recovery and Climate Experiment (GRACE) and GRACE‐Follow On missions can directly observe the seasonal global water cycle and describe the changes in its intensity. We present seasonal amplitudes of the global land water and ocean mass anomalies between 04/2002 and 11/2020. We find that the average seasonal amplitude is 17.0 ± 0.6 mm sea level equivalent (SLE), and its interannual variability (σ = 1.1 mm SLE) is comparable to the long‐term trends in the land and ocean masses. We investigate two periods of rapid amplification and deamplification during which the amplitude changed as much as 29%, and find that changes in hydroclimatology in certain land regions modulate the seasonal global water cycle. Plain Language Summary: The global water cycle is generally viewed as the cycling of water among the land, ocean, and atmosphere. By observing the changes in the water mass, NASA's Gravity Recovery and Climate Experiment (GRACE) and GRACE‐Follow On missions are capable of measuring the global water cycle, and its changes, directly. The largest portion of the cycling occurs between global land and global ocean at the annual time scale, constituting the seasonal global water cycle. We present the amplitude of this cycling, its year‐to‐year changes that describe the changes in the intensity of the seasonal global water cycle, and the regions contributing to it. Key Points: The seasonal amplitude of global ocean and land water mass describes the seasonal global water cycle and is a metric of its strengthThe seasonal amplitude varies interannually with magnitude comparable to the long‐term trend in global ocean massHydrological changes in specific land regions are most responsible for these year‐to‐year variations [ABSTRACT FROM AUTHOR]

The transport of freshwater from continents to oceans through rivers has traditionally been estimated by routing runoff from land surface models within river models to obtain discharge. This paradigm imposes that errors are transferred from runoff to discharge, yet the analytical propagation of uncertainty from runoff to discharge has never been derived. Here we apply statistics to the continuity equation within a river network to derive two equations that propagate the mean and variance/covariance of runoff errors independently. We validate these equations in a case study of the rivers in the western United States and, for the first time, invert observed discharge errors for spatially distributed runoff errors. Our results suggest that the largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors. Our findings significantly advance the science of error quantification in model‐based estimates of river discharge. Plain Language Summary: Computer programs are routinely used to simulate the movement of water on Earth, including the flow of freshwater in rivers. A common approach for calculating river flow is to run a first computer program that estimates how much of the rain is partitioned into runoff, evaporation, or storage, then a second that transports the runoff through rivers. These computer programs are based on a simplified representation of nature and are imperfect. Until now, it has been unclear how the imperfections are transferred from one program to the next, and therefore challenging to understand the sources of errors. In this study, we present a methodology that can be used to backtrack errors in runoff, an otherwise unknown quantity, from observed errors in river flow. Our results reveal a surprising error source and can help diagnose limitations in simulations of the Earth's water cycle. Key Points: We present the first analytical equations for propagating runoff uncertainty into discharge uncertainty through a river networkOur methodology allows the inverse inference of runoff errors from observed discharge errors at continental scalesThe largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors [ABSTRACT FROM AUTHOR]

Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA's ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there. [ABSTRACT FROM AUTHOR]

Total continental freshwater discharge into the oceans is a key feature of the global water cycle, but it is currently impossible to observe using ground-based methods alone. To characterize the uncertainty across existing modeling and satellite approaches, the authors present ensembles of historic monthly global continental discharge estimates that enforce water mass balance over land and ocean. The authors combine independent measurements of ocean-landmass change from altimetry and GRACE with multiple estimates of evaporation minus precipitation ( E − P) from remote sensing and reanalysis data to compute 28 time series of global discharge. Results reveal agreement in mass budget across approaches but a large spread in global E − P estimates that propagates into the discharge estimates. It is found that discharges with reanalysis-based E − P provide a closer comparison with current observation-based estimates. After combining GRACE- and altimetry-based mass change estimates with moisture convergences from reanalysis, the total annual mean continental discharge into the oceans is 38 550 ± 4800 km3 yr−1. Last, the authors provide continent-wise discharge estimates from GRACE and moisture convergences over land, compare them to other studies, and discuss implications for ocean modeling. [ABSTRACT FROM AUTHOR]

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a 'grand challenge' to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort. [ABSTRACT FROM AUTHOR]

The article discusses how data from satellites may enable improved management of regional groundwater reserves. The authors note that not only is Earth's climate changing, but its water cycle as well. They add that the changes can be witnessed in rising rates of global precipitation, evaporation, and freshwater discharge, as well as the increasing frequency and intensity of flooding and drought in some regions. Topics include the decline in groundwater reserves worldwide, which have traditionally backed up water supplies during extended drought, a joint U.S.-German satellite mission that monitors climate variations, called GRACE (the Gravity Recovery and Climate Experiment), and the use of GRACE in water management.

Abstract Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPL SM model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle. Highlights • A soil moisture constraint is introduced to the widely-used PT-JPL ET model. • The new ET algorithm is applied globally using SMAP soil moisture observations. • SMAP data improves ET most in dry areas with large fractions of soil evaporation. • Soil moisture integration into soil evaporation reduces errors in ET by 23%. • Soil moisture integration into transpiration reduces errors in ET by 17%. [ABSTRACT FROM AUTHOR]