Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world’s breadbaskets for its large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model, for a long time period (13-year) and fine spatial scale (4-km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at point-scale, (2) applying a dynamic planting/harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the US Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting/harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications for estimating crop production, simulating the land-atmosphere interactions in croplands, and crop growth’s responses to the raising temperatures amid climate change.

Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world’s breadbaskets for its large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model, for a long time period (13-year) and fine spatial scale (4-km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at point-scale, (2) applying a dynamic planting/harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the US Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting/harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications for estimating crop production, simulating the land-atmosphere interactions in croplands, and crop growth’s responses to the raising temperatures amid climate change.

Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for their large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model for a long time period (13 years) and fine spatial scale (4 km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at a point scale, (2) applying a dynamic planting and harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the United States Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting and harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications in terms of estimating crop yields, modeling the land–atmosphere interactions in agricultural areas, and predicting crop growth responses to increasing temperatures amidst climate change.

Abstract. The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for their large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model for a long time period (13 years) and fine spatial scale (4 km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at a point scale, (2) applying a dynamic planting and harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the United States Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting and harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications in terms of estimating crop yields, modeling the land–atmosphere interactions in agricultural areas, and predicting crop growth responses to increasing temperatures amidst climate change.

Multi-attribute dataset visualizations are often designed based on attribute types, i.e., whether the attributes are categorical or numerical. Parallel Sets and Parallel Coordinates are two well-known techniques to visualize categorical and numerical data, respectively. A common strategy to visualize mixed data is to use multiple information linked view, e.g., Parallel Coordinates are often augmented with maps to explore spatial data with numeric attributes. In this paper, we design visualizations for mixed data, where the dataset may include numerical, categorical, and spatial attributes. The proposed solution SET-STAT-MAP is a harmonious combination of three interactive components: Parallel Sets (visualizes sets determined by the combination of categories or numeric ranges), statistics columns (visualizes numerical summaries of the sets), and a geospatial map view (visualizes the spatial information). We augment these components with colors and textures to enhance users' capability of analyzing distributions of pairs of attribute combinations. To improve scalability, we merge the sets to limit the number of possible combinations to be rendered on the display. We demonstrate the use of Set-stat-map using two different types of datasets: a meteorological dataset and an online vacation rental dataset (Airbnb). To examine the potential of the system, we collaborated with the meteorologists, which revealed both challenges and opportunities for Set-stat-map to be used for real-life visual analytics.

Wetlands are important ecosystems—they provide vital hydrological and ecological services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extents and hydrological processes is important for valuing wetlands' function. The purpose of this study is to dynamically represent the spatial extents and hydrological processes of wetlands and investigate their feedback to regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah-MP land surface model with two major modifications: (a) modifying the subgrid saturation fraction for spatial wetland extent and (b) incorporating a dynamic wetland storage to simulate hydrological processes. This scheme was evaluated at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13-year climate forcing produced by a high-resolution convection-permitting model. The differences between wetland and no-wetland simulations are significant, with increasing latent heat and evapotranspiration while suppressing sensible heat and runoff in the wetland scheme. Finally, the dynamic wetland scheme was applied in the Weather Research and Forecasting (WRF) model. The wetlands scheme not only modifies the surface energy balance but also interacts with the lower atmosphere, shallowing the planetary boundary layer height and promoting cloud formation. A cooling effect of 1–3°C in summer temperature is evident where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for >10 days over the summer of 2006, when a long-lasting heatwave occurred. This research has great implications for land surface/regional climate modeling and wetland conservation, especially in mitigating extreme heatwaves under climate change.

Abstract Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co‐creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high‐dimensional mapping, while remaining faithful to process‐based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.

Abstract Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km 2 , for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month -1 , respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

Abstract Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km 2 , for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month -1 , respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

• A methodological framework to combine multiple precipitation products is proposed. • Hybrid datasets based on hydrological evaluation improve hydrological modelling. • Considering seasonal characteristics of the river basin enhance model performance. Hydrologic-Land Surface Models (H-LSMs) are subject to input uncertainties arising from climate forcing data, especially precipitation. For better streamflow simulations and predictions, the generation of a hybrid dataset by combining existing precipitation products has attracted considerable interest in recent years. To assess the accuracy of the hybrid dataset, in-situ precipitation-gauge stations are used as a reference point. However, the robustness of the hybrid dataset in representing spatial details can be problematic when the evaluation uses only a sparse network of in-situ observations at regional or basin scales. This study aims to develop a methodological framework to generate hybrid precipitation datasets based on the model performance of streamflow simulations that are spatially representative across large river basins. The framework is illustrated using a Canadian H-LSM known as MESH (Modélisation Environmentale communautaire – Surface Hydrology) in the Saskatchewan River basin, Canada, for the period 2002–2010. Five regional and global precipitation products (Global Meteorological Forcing Dataset at Princeton University (Princeton); the WATCH Forcing Data methodology applied to the ERA-Interim (WFDEI) augmented by Climatic Research Unit (WFDEI [CRU]) and Global Precipitation Climatology Centre (WFDEI [GPCC]); North American Regional Reanalysis (NARR); and Canadian Precipitation Analysis (CaPA)) were included as candidates in this study. Results indicate that the generation of a hybrid dataset based on hydrological evaluation was useful for improving H-LSM modelling skills. Hybrid datasets showed a similar or better model performance compared to that of the best basin-wide precipitation product in the headwaters and gradually performed better downstream and at the basin outlet. When multiple products are combined model performance can be further enhanced by considering seasonality with respect to the hydrological regime of the river basin. This study demonstrates the usefulness of hybrid datasets in a large-scale river basin with low climate station network density.

• A methodological framework to combine multiple precipitation products is proposed. • Hybrid datasets based on hydrological evaluation improve hydrological modelling. • Considering seasonal characteristics of the river basin enhance model performance. Hydrologic-Land Surface Models (H-LSMs) are subject to input uncertainties arising from climate forcing data, especially precipitation. For better streamflow simulations and predictions, the generation of a hybrid dataset by combining existing precipitation products has attracted considerable interest in recent years. To assess the accuracy of the hybrid dataset, in-situ precipitation-gauge stations are used as a reference point. However, the robustness of the hybrid dataset in representing spatial details can be problematic when the evaluation uses only a sparse network of in-situ observations at regional or basin scales. This study aims to develop a methodological framework to generate hybrid precipitation datasets based on the model performance of streamflow simulations that are spatially representative across large river basins. The framework is illustrated using a Canadian H-LSM known as MESH (Modélisation Environmentale communautaire – Surface Hydrology) in the Saskatchewan River basin, Canada, for the period 2002–2010. Five regional and global precipitation products (Global Meteorological Forcing Dataset at Princeton University (Princeton); the WATCH Forcing Data methodology applied to the ERA-Interim (WFDEI) augmented by Climatic Research Unit (WFDEI [CRU]) and Global Precipitation Climatology Centre (WFDEI [GPCC]); North American Regional Reanalysis (NARR); and Canadian Precipitation Analysis (CaPA)) were included as candidates in this study. Results indicate that the generation of a hybrid dataset based on hydrological evaluation was useful for improving H-LSM modelling skills. Hybrid datasets showed a similar or better model performance compared to that of the best basin-wide precipitation product in the headwaters and gradually performed better downstream and at the basin outlet. When multiple products are combined model performance can be further enhanced by considering seasonality with respect to the hydrological regime of the river basin. This study demonstrates the usefulness of hybrid datasets in a large-scale river basin with low climate station network density.

Disappearing groundwater requires action to prevent widespread water scarcity

Disappearing groundwater requires action to prevent widespread water scarcity

Global groundwater volumes in the upper 2 km of the Earth's continental crust—critical for water security—are well estimated. Beyond these depths, a vast body of largely saline and non-potable groundwater exists down to at least 10 km—a volume that has not yet been quantified reliably at the global scale. Here, we estimate the amount of groundwater present in the upper 10 km of the Earth's continental crust by examining the distribution of sedimentary and crystalline rocks with depth and applying porosity-depth relationships. We demonstrate that groundwater in the 2–10 km zone (what we call “deep groundwater”) has a volume comparable to that of groundwater in the upper 2 km of the Earth's crust. These new estimates make groundwater the largest continental reservoir of water, ahead of ice sheets, provide a basis to quantify geochemical cycles, and constrain the potential for large-scale isolation of waste fluids.

Global groundwater volumes in the upper 2 km of the Earth's continental crust—critical for water security—are well estimated. Beyond these depths, a vast body of largely saline and non-potable groundwater exists down to at least 10 km—a volume that has not yet been quantified reliably at the global scale. Here, we estimate the amount of groundwater present in the upper 10 km of the Earth's continental crust by examining the distribution of sedimentary and crystalline rocks with depth and applying porosity-depth relationships. We demonstrate that groundwater in the 2–10 km zone (what we call “deep groundwater”) has a volume comparable to that of groundwater in the upper 2 km of the Earth's crust. These new estimates make groundwater the largest continental reservoir of water, ahead of ice sheets, provide a basis to quantify geochemical cycles, and constrain the potential for large-scale isolation of waste fluids.

Abstract Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015–2018) of SMAP datasets in combination with two approaches—Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process—and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at ~36 km resolution for the period from 2010 to 2018. We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04.4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics. Furthermore, we visually compare our global SM-based index maps with those produced by other organizations. Our Global SM Index Monitor (GSMIM) performed, in many tests, similarly to the CCI's product SM index but with the advantage of being a near-real-time tool, which has applications for identifying evolving drought for food security conditions, insurance, policymaking, and crop planning especially for the remote parts of the globe.

Abstract The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from −11.32 to 0.55 at observed gauges and from −12.24 to −1.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA’s Surface Water and Ocean Topography mission.

Fresh water – the bloodstream of the biosphere – is at the centre of the planetary drama of the Anthropocene. Water fluxes and stores regulate the Earth’s climate and are essential for thriving aquatic and terrestrial ecosystems, as well as water, food and energy security. But the water cycle is also being modified by humans at an unprecedented scale and rate. A holistic understanding of freshwater’s role for Earth System resilience and the detection and monitoring of anthropogenic water cycle modifications across scales is urgent, yet existing methods and frameworks are not well suited for this. In this paper we highlight four core Earth System functions of water (hydroclimatic regulation, hydroecological regulation, storage, and transport) and key related processes. Building on systems and resilience theory, we review the evidence of regional-scale regime shifts and disruptions of the Earth System functions of water. We then propose a framework for detecting, monitoring, and establishing safe limits to water cycle modifications, and identify four possible spatially explicit methods for their quantification. In sum, this paper presents an ambitious scientific and policy Grand Challenge that could substantially improve our understanding of the role of water in the Earth System and cross-scale management of water cycle modifications that would be a complementary approach to existing water management tools.

The planetary boundaries framework proposes quantified guardrails to human modification of global environmental processes that regulate the stability of the planet and has been considered in sustainability science, governance, and corporate management. However, the planetary boundary for human freshwater use has been critiqued as a singular measure that does not reflect all types of human interference with the complex global water cycle and Earth System. We suggest that the water planetary boundary will be more scientifically robust and more useful in decision-making frameworks if it is redesigned to consider more specifically how climate and living ecosystems respond to changes in the different forms of water on Earth: atmospheric water, frozen water, groundwater, soil moisture, and surface water. This paper provides an ambitious scientific road map to define a new water planetary boundary consisting of sub-boundaries that account for a variety of changes to the water cycle.

Abstract Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21 st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation; instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to divergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects; however, the contribution of pumping could easily far exceed the natural replenishment.

The growing concerns over urbanization and climate change have resulted in an exponential growth in publications on urban climatology in recent decades. However, an advanced synthesis that characterizes the existing studies is lacking. In this review, we used citation network analysis and a text mining approach to identify research trends and extract common research topics and the emerging domains in urban climatology. Based on the clustered networks, we found that aerosols and ozone, and urban heat island are the most popular topics. Together with other clusters, four emerging topical fields were identified: secondary organic aerosols, urban precipitation, flood risk and adaptation, and greenhouse gas emissions. The city case studies' geographical information was analyzed to explore the spatial–temporal patterns, especially in the emerging topical fields. Interdisciplinary research grew in recent years as the field of urban climatology expanded to interact with urban hydrology, health, energy issues, and social sciences. A few knowledge gaps were proposed: the lack of long‐term high‐temporal‐resolution observational data of organic aerosols for model validation and improvements, the need for predictions of urban effects on precipitation and extreme flooding events under climate change, and the lack of a framework for cooperation between physical sciences and social sciences under urban settings. To fill these gaps, we call for more observational data with high spatial and temporal resolution, using high‐resolution models that adequately represent urban processes to conduct scenario analyses for urban planning, and the development of intellectual frameworks for better integration of urban climatology and social‐economical systems in cities. This article is categorized under: Climate, History, Society, Culture > Disciplinary Perspectives

Emergence of global mean sea level (GMSL) from a ‘hiatus’ following a persistent La Niña highlights the need to understand the causes of interannual variability in GMSL. Several studies link interannual variability in GMSL to anomalous transport of water mass between land and ocean—and subsequent changes in water storage in these reservoirs—primarily driven by El Niño/Southern Oscillation (ENSO). Despite this, asymmetries in teleconnections between ENSO mode and land water storage have received less attention. We use historical simulations of natural climate variability to characterize asymmetries in the hydrological response to ENSO based on phase and duration. Findings indicate pronounced phase-specific and duration-specific asymmetries covering up to 93 and 50 million km2 land area, respectively. The asymmetries are seasonally dependent, and based on the mean regional climate are capable of influencing inherently bounded storage by pushing the storage-precipitation relationship towards nonlinearity. The nonlinearities are more pronounced in dry regions in the dry season, wet regions in the wet season, and during Year 2 of persistent ENSO events, limiting the magnitude of associated anomalies under persistent ENSO influence. The findings have implications for a range of stakeholders, including sea level researchers and water managers.

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.

Depletion of groundwater resources has been identified in numerous global aquifers, suggesting that extractions have exceeded natural recharge rates in critically important global freshwater supplies. Groundwater depletion has been ascribed to groundwater pumping, often ignoring influences of direct and indirect consequences of climate variability. Here, we explore relations between natural and human drivers and spatiotemporal changes in groundwater storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites using regression procedures and dominance analysis. Changes in groundwater storage are found to be influenced by direct climate variability, whereby groundwater recharge and precipitation exhibited greater influence as compared to groundwater pumping. Weak influence of groundwater pumping may be explained, in part, by quasi-equilibrium aquifer conditions that occur after “long-time” pumping, while precipitation and groundwater recharge records capture groundwater responses linked to climate-induced groundwater depletion. Evaluating groundwater response to climate variability is critical given the reliance of groundwater resources to satisfy water demands and impending changes in climate variability that may threaten future water availability.

The San Joaquin Valley and Tulare basins in California’s Central Valley have intensive agricultural activity and groundwater demand that has caused significant subsidence and depletion of water resources in the past. We measured groundwater pumping-induced land subsidence in the southern Central Valley from March 2015 to May 2017 using Sentinel-1 interferometric synthetic aperture radar (InSAR) data. The InSAR measurements provided fine spatial details of subsidence patterns and displayed a superposition of secular and seasonal variations that were coherent across our study region and correlated with precipitation variability and changes in freshwater demand. Combining InSAR and Global Positioning System (GPS) data, precipitation, and in situ well records showed a broad scale slowdown/cessation of long term subsidence in the wetter winter of 2017, likely reflecting the collective response of the Central Valley aquifer system to heavier-than-usual precipitation. We observed a very good temporal correlation between the Gravity Recovery and Climate Experiment (GRACE) satellite groundwater anomaly (GWA) variation and long-term subsidence records, regardless of local hydrogeology and mechanical properties. This indicates the subsidence from satellite geodesy is a very useful indicator for tracking groundwater storage change. With the continuing acquisition of Sentinel-1 and other satellites, we anticipate decadal-scale subsidence records with a spatial resolution of tens to hundreds of meters will be available in the near future to be combined with basin-averaged GRACE measurements to improve our estimate of time-varying groundwater change.

Abstract 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 Despite numerous advances in continental-scale hydrologic modeling and improvements in global Land Surface Models, an accurate representation of regional water table depth (WTD) remains a challenge. Data assimilation of observations from the Gravity Recovery and Climate Experiment (GRACE) mission leads to improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of lumped water storage. However, the usually shallow groundwater compartment of many models presents a problem with GRACE assimilation techniques, as these satellite observations also represent changes in deeper soils and aquifers. To improve the accuracy of modeled groundwater estimates and allow the representation of WTD at finer spatial scales, we implemented a simple, yet novel approach to integrate GRACE data, by augmenting the Variable Infiltration Capacity (VIC) hydrologic model. First, the subsurface model structural representation was modified by incorporating an additional (fourth) soil layer of varying depth (up to 1000 m) in VIC as the bottom ‘groundwater’ layer. This addition allows the model to reproduce water storage variability not only in shallow soils but also in deeper groundwater, in order to allow integration of the full GRACE-observed variability. Second, a Direct Insertion scheme was developed that integrates the high temporal (daily) and spatial (∼6.94 km) resolution model outputs to match the GRACE resolution, performs the integration, and then disaggregates the updated model state after the assimilation step. Simulations were performed with and without Direct Insertion over the three largest river basins in California and including the Central Valley, in order to test the augmented model's ability to capture seasonal and inter-annual trends in the water table. This is the first-ever fusion of GRACE total water storage change observations with hydrologic simulations aiming at the determination of water table depth dynamics, at spatial scales potentially useful for local water management.

Time-resolved satellite gravimetry has revolutionized understanding of mass transport in the Earth system. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has enabled monitoring of the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations and understanding responses to changes in the global climate system. Initially a pioneering experiment of geodesy, the time-variable observations have matured into reliable mass transport products, allowing assessment and forecast of a number of important climate trends and improve service applications such as the U.S. Drought Monitor. With the successful launch of the GRACE Follow-On mission, a multi decadal record of mass variability in the Earth system is within reach.

The flow of fresh groundwater to the ocean through the coast (fresh submarine groundwater discharge or fresh SGD) plays an important role in global biogeochemical cycles and coastal water quality. In addition to delivering dissolved elements from land to sea, fresh SGD forms a natural barrier against salinization of coastal aquifers. Here we estimate groundwater discharge rates through the near‐global coast (60°N to 60°S) at high resolution using a water budget approach. We find that tropical coasts export more than 56% of all fresh SGD, while midlatitude arid regions export only 10%. Fresh SGD rates from tectonically active margins (coastlines along tectonic plate boundaries) are also significantly greater than passive margins, where most field studies have been focused. Active margins combine rapid uplift and weathering with high rates of fresh SGD and may therefore host exceptionally large groundwater‐borne solute fluxes to the coast.

Earth‐orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real‐time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet the temporal requirements for access to satellite‐based river data remain uncharacterized for time‐sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low‐latency/near‐real‐time satellite products, with an emphasis on the forthcoming SWOT satellite mission. We apply a kinematic wave model to a global hydrography data set and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4, and 3 days to reach their basin terminus, the next downstream city, and the next downstream dam, respectively. Our findings suggest that a recently proposed ≤2‐day data latency for a low‐latency SWOT product is potentially useful for real‐time river applications.

Accurate and detailed knowledge of California's groundwater is of paramount importance for statewide water resources planning and management, and to sustain a multi-billion-dollar agriculture industry during prolonged droughts. In this study, we use water supply and demand information from California's Department of Water Resources to develop an aggregate groundwater storage model for California's Central Valley. The model is evaluated against 34 years of historic estimates of changes in groundwater storage derived from the United States Geological Survey's Central Valley Hydrologic Model (USGS CVHM) and NASA's Gravity Recovery and Climate Experiment (NASA GRACE) satellites. The calibrated model is then applied to predict future changes in groundwater storage for the years 2015-2050 under various precipitation scenarios from downscaled climate projections. We also discuss and project potential management strategies across different annual supply and demand variables and how they affect changes in groundwater storage. All simulations support the need for collective statewide management intervention to prevent continued depletion of groundwater availability.

NASA’s Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km2). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km2). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km2) dataset of groundwater storage changes over a portion of California’s Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model’s simulated maps have the potential for application to local groundwater management initiatives in the region.

In California, new groundwater legislation—the 2014 Sustainable Groundwater Management Act (SGMA)—mandates that groundwater sustainability agencies (GSAs) employ the concept of sustainable yield as their primary management goal. However, SGMA’s current definition of sustainable yield does not offer clear guidance for new agencies and lacks grounding in physics. This study presents a novel hydrologically based framework for quantifying sustainable yield under SGMA that is derived from a synthesis of scientific inquiry and analysis. We introduce a flexible three-step approach that basin managers can rely on to quantify sustainable yield values, incorporate the impact of “undesirable results”, and analyze groundwater sustainability over SGMA’s implementation horizon. Our framework is illustrated through a case study of the South San Joaquin Irrigation District, a proposed GSA in one of California’s critically overdrafted groundwater basins. We calculate sustainable yield for three different management scenarios and assess the impact of each scenario on future groundwater sustainability by performing an annual water groundwater balance through 2040. Our sustainable yield framework can be used as a basis for the development of SGMA’s groundwater management plans throughout California.

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-JPLSM 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.

Freshwater availability is changing worldwide. Here we quantify 34 trends in terrestrial water storage observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during 2002-2016 and categorize their drivers as natural interannual variability, unsustainable groundwater consumption, climate change or combinations thereof. Several of these trends had been lacking thorough investigation and attribution, including massive changes in northwestern China and the Okavango Delta. Others are consistent with climate model predictions. This observation-based assessment of how the world's water landscape is responding to human impacts and climate variations provides a blueprint for evaluating and predicting emerging threats to water and food security.

Endorheic (hydrologically landlocked) basins spatially concur with arid/semi-arid climates. Given limited precipitation but high potential evaporation, their water storage is vulnerable to subtle flux perturbations, which are exacerbated by global warming and human activities. Increasing regional evidence suggests a probably recent net decline in endorheic water storage, but this remains unquantified at a global scale. By integrating satellite observations and hydrological modelling, we reveal that during 2002–2016 the global endorheic system experienced a widespread water loss of about 106.3 Gt yr−1, attributed to comparable losses in surface water, soil moisture and groundwater. This decadal decline, disparate from water storage fluctuations in exorheic basins, appears less sensitive to El Nino–Southern Oscillation-driven climate variability, which implies a possible response to longer-term climate conditions and human water management. In the mass-conserved hydrosphere, such an endorheic water loss not only exacerbates local water stress, but also imposes excess water on exorheic basins, leading to a potential sea level rise that matches the contribution of nearly half of the land glacier retreat (excluding Greenland and Antarctica). Given these dual ramifications, we suggest the necessity for long-term monitoring of water storage variation in the global endorheic system and the inclusion of its net contribution to future sea level budgeting.

Abstract. 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.