Seasonal water storage fluctuations are critical for evaluating water scarcity linked to climate forcing and human intervention. Here we compare seasonal changes in land total water storage anomalies using seven global hydrologic and land surface models (WGHM, PCR‐GLOBWB, and five GLDAS models) to GRACE satellite data in 183 river basins globally. This work builds on previous analysis that focused on total water storage anomaly trends. Results show that most models underestimate seasonal water storage amplitudes in tropical and (semi)arid basins and land surface models generally overestimate amplitudes in northern basins. Some models (CLM‐5.0 and PCR‐GLOBWB) agree better with GRACE than others. Causes of model‐GRACE discrepancies are attributed to missing storage compartments (e.g., surface water and/or groundwater) and underestimation of modeled storage capacities in tropical basins and to variations in modeled fluxes in northern basins. This study underscores the importance of considering water storage, in addition to water fluxes, to improve global models.
Water is a critical resource, but ensuring it is available 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. Gravity Recovery And Climate Experiment (GRACE) satellite data show declining, stable, and rising trends in total water storage over the past two decades in various regions globally. Groundwater monitoring provide longer term context over the past century, showing rising water storage in Northwest India, Central Pakistan, and 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. Waterresource resilience can be increased by diversifying management strategies. These approaches include green solutions, such as forest and wetland preservation, and gray solutions, including 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.Water is a critical resource, but ensuring it is available 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 varied among regions over the past century. Some areas, including Northwest India, Central Pakistan, and Northwest United States, have seen rises in water storage over the past century. Others, including the US High Plains and Central Valley, have experienced net declines. Climate variability causes some changes in water storage but human intervention, particularly irrigation, is a major driver. Waterresource resilience can be increased by diversifying management strategies. These approaches include green solutions, such as forest and wetland preservation, and gray solutions, including 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.
GRACE satellite data are widely used to estimate groundwater storage (GWS) changes in aquifers globally; however, comparisons with GW monitoring and modeling data are limited. Here we compared GWS changes from GRACE over 15 yr (2002-2017) in 14 major U.S. aquifers with groundwater-level (GWL) monitoring data in~23,000 wells and with regional and global hydrologic and land surface models. Results show declining GWS trends from GRACE data in the six southwestern and south-central U.S. aquifers, totaling −90 km 3 over 15 yr, related to long-term (5-15 yr) droughts, and exceeding Lake Mead volume by~2.5×. GWS trends in most remaining aquifers were stable or slightly rising. GRACE-derived GWS changes agree with GWL monitoring data in most aquifers (correlation coefficients, R = 0.52-0.95), showing that GRACE satellites capture groundwater (GW) dynamics. Regional GW models (eight models) generally show similar or greater GWS trends than those from GRACE. Large discrepancies in the Mississippi Embayment aquifer, with modeled GWS decline approximately four times that of GRACE, may reflect uncertainties in model storage parameters, stream capture, pumpage, and/or recharge rates. Global hydrologic models (2003-2014), which include GW pumping, generally overestimate GRACE GWS depletion (total: approximately −172 to −186 km 3) in heavily exploited aquifers in southwestern and south-central U.S. by~2.4× (GRACE: −74 km 3), underscoring needed modeling improvements relative to anthropogenic impacts. Global land surface models tend to track GRACE GWS dynamics better than global hydrologic models. Intercomparing remote sensing, monitoring, and modeling data underscores the importance of considering all data sources to constrain GWS uncertainties. Plain Language Summary The major U.S. aquifers provide an ideal system to assess GRACE (Gravity Recovery and Climate Experiment) satellite data. We compared GRACE groundwater storage anomalies (GWSAs) with groundwater level anomalies (GWLAs) from~23,000 wells and with groundwater storage (GWS) from regional and global models in 14 major U.S. aquifers. Results show large GWS declines from GRACE in southwestern (Central Valley and Arizona Alluvial Basins) and south-central (Central and Southern High Plains and Texas) aquifers from multiyear droughts (5-15 yr). In contrast, GWS trends in aquifers throughout the rest of the U.S. showed mostly stable or rising values. Time series of GRACE GWSAs compared favorably with GWLAs from most aquifers, suggesting that GRACE data track groundwater (GW) dynamics. Regional GW models show similar or greater declines in GWS compared with GRACE data, with the largest discrepancy of a factor of four times greater modeled depletion in the ©2020. American Geophysical Union. All Rights Reserved.
Understanding climate and human impacts on water storage is critical for sustainable water-resources management. Here we assessed climate and human drivers of total water storage (TWS) variability from Gravity Recovery and Climate Experiment (GRACE) satellites compared with drought severity and irrigation water use in 14 major aquifers in the United States. Results show that long-term variability in TWS tracked by GRACE satellites is dominated by interannual variability in most of the 14 major US aquifers. Low TWS trends in the humid eastern U.S. are linked to low drought intensity. Although irrigation pumpage in the humid Mississippi Embayment aquifer exceeded that in the semi-arid California Central Valley, a surprising lack of TWS depletion in the Mississippi Embayment aquifer is attributed to extensive streamflow capture. Marked storage depletion in the semi-arid southwestern Central Valley and south-central High Plains totaled ∼90 km3, about three times greater than the capacity of Lake Mead, the largest U.S. reservoir. Depletion in the Central Valley was driven by long-term droughts (⩽5 yr) amplified by switching from mostly surface water to groundwater irrigation. Low or slightly rising TWS trends in the northwestern (Columbia and Snake Basins) US are attributed to dampening drought impacts by mostly surface water irrigation. GRACE satellite data highlight synergies between climate and irrigation, resulting in little impact on TWS in the humid east, amplified TWS depletion in the semi-arid southwest and southcentral US, and dampened TWS deletion in the northwest and north central US Sustainable groundwater management benefits from conjunctive use of surface water and groundwater, inefficient surface water irrigation promoting groundwater recharge, efficient groundwater irrigation minimizing depletion, and increasing managed aquifer recharge. This study has important implications for sustainable water development in many regions globally.
The Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow‐on, GRACE‐FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The ∼1‐year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. In this study, we applied an automated machine learning (AutoML) workflow to perform gridwise GRACE‐like data reconstruction. AutoML represents a new paradigm for optimal algorithm selection, model structure selection, and hyperparameter tuning, addressing some of the most challenging issues in machine learning applications. We demonstrated the workflow over the conterminous U.S. (CONUS) using six types of machine learning models and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML‐assisted gap filling achieved satisfactory performance over the CONUS. On the testing data, the mean gridwise Nash‐Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root‐mean‐square‐error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE‐FO periods (after June 2017). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end‐to‐end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large‐scale hydrological prediction systems and when predictor importance exhibiting strong spatial variability.
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