Groundwater Loss and Aquifer System Compaction in San Joaquin Valley During 2012-2015 Drought

Rapid groundwater decline and some cases of recovery in aquifers globally

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1-10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14-16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year-1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world's regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California

California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002-2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled groundwater level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical displacements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km³ from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of -5.7 ± 1.2 km³ yr^-1 and -9.8 ± 1.7 km³ yr^-1 occurred during the extended drought periods of January 2007-December 2009, and October 2011-September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater depletion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal.

Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations

The monthly high-resolution terrestrial water storage anomalies (TWSA) during the 11-months of gap between GRACE (Gravity Recovery And Climate Experiment) and its successor GRACE-FO (-Follow On) missions are missing. The continuity of the GRACE-like TWSA series with commensurate accuracy is of great importance for the improvement of hydrologic models both at global and regional scales. While previous efforts to bridge this gap, though without achieving GRACE-like spatial resolutions and/or accuracy have been performed, high-quality TWSA simulations at global scale are still lacking. Here, we use a suite of deep learning (DL) architectures, convolutional neural networks (CNN), deep convolutional autoencoders (DCAE), and Bayesian convolutional neural networks (BCNN), with training datasets including GRACE/-FO mascon and Swarm gravimetry, ECMWF Reanalysis-5 data, normalized time tag information to reconstruct global land TWSA maps, at a much higher resolution (100 km full wavelength) than that of GRACE/-FO, and effectively bridge the 11-month data gap globally. Contrary to previous studies, we applied no prior de-trending or de-seasoning to avoid biasing/aliasing the simulations induced by interannual or longer climate signals and extreme weather episodes. We show the contribution of Swarm and time inputs which significantly improved the TWSA simulations in particular for correct prediction of the trend component. Our results also show that external validation with independent data when filling large data gaps within spatio-temporal time series of geophysical signals is mandatory to maintain the robustness of the simulation results. The results and comparisons with previous studies and the adopted DL methods demonstrate the superior performance of DCAE. Validations of our DCAE-based TWSA simulations with independent datasets, including in situ groundwater level, Interferometric Synthetic Aperture Radar measured land subsidence rate (e.g. Central Valley), occurrence/timing of severe flash flood (e.g. South Asian Floods) and drought (e.g. Northern Great Plain, North America) events occurred within the gap, reveal excellent agreements.

Hydrological drought characterization based on GNSS imaging of vertical crustal deformation across the contiguous United States

Continuous Global Navigation Satellite System (GNSS) measurements allow us to track subtle elastic crustal deformation in the response to hydrological mass variations and provide an additional tool to independently characterize hydrological extremes (e.g., droughts and floods). In this study, we develop a time-varying GNSS imaging strategy that depends on the principal component analysis of GNSS-sensed vertical crustal displacement (VCD) in 2006-2020 and the monthly images of hydrology-induced deformation are generated for drought characterization across the contiguous United States. The first 12 principal components are selected in our time-varying imaging system, which account for 85% of the data variance. Considering that surface water loads are inversely correlated with the induced elastic vertical motions, we reverse the signs of the GNSS-imaged time series in all grids in subsequent studies (referred to as negative VCD (NVCD)). The GNSS-NVCD data generally correlate well with the water estimates from the Gravity Recovery and Climate Experiment (GRACE) and North American Land Data Assimilation System (NLDAS). Using the GNSS-imaged gridded NVCD products, we produce a GNSS-based drought severity index (GNSS-DSI) based on the climatological methodology, which is implemented by standardizing the GNSS NVCD anomalies that deviate from climatological normal. In most regions, strong linear correlations are accessible for GNSS-DSI relative to GRACE-DSI and the self-calibrating Palmer Drought Severity Index (scPDSI). The new drought monitoring tool, which is based solely on GNSS-measured vertical positions, is used for hydrological drought characterization (onset, end, duration, magnitude, intensity, and recovery); it succeeds in identifying well-documented historical droughts from the US drought monitor (USDM). Our study presents a new drought characterization framework using solely GNSS-measured hydrological loading displacements from a dense GNSS network, which has great potential to strengthen operational drought monitoring and assessment.

Using Sentinel-1 and GRACE satellite data to monitor the hydrological variations within the Tulare Basin, California

Subsidence induced by groundwater depletion is a grave problem in many regions around the world, leading to a permanent loss of groundwater storage within an aquifer and even producing structural damage at the Earth’s surface. California’s Tulare Basin is no exception, experiencing about a meter of subsidence between 2015 and 2020. However, understanding the relationship between changes in groundwater volumes and ground deformation has proven difficult. We employ surface displacement measurements from Interferometric Synthetic Aperture Radar (InSAR) and gravimetric estimates of terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) satellite pair to characterize the hydrological dynamics within the Tulare basin. The removal of the long-term aquifer compaction from the InSAR time series reveals coherent short-term variations that correlate with hydrological features. For example, in the winter of 2018–2019 uplift is observed at the confluence of several rivers and streams that drain into the southeastern edge of the basin. These observations, combined with estimates of mass changes obtained from the orbiting GRACE satellites, form the basis for imaging the monthly spatial variations in water volumes. This approach facilitates the quick and effective synthesis of InSAR and gravimetric datasets and will aid efforts to improve our understanding and management of groundwater resources around the world.

Joint Inversion of GNSS and GRACE for Terrestrial Water Storage Change in California

Global Navigation Satellite System (GNSS) vertical displacements measuring the elastic response of Earth's crust to changes in hydrologic mass have been used to produce terrestrial water storage change (∆TWS) estimates for studying both annual ∆TWS as well as multi-year trends. However, these estimates require a high observation station density and minimal contamination by nonhydrologic deformation sources. The Gravity Recovery and Climate Experiment (GRACE) is another satellite-based measurement system that can be used to measure regional TWS fluctuations. The satellites provide highly accurate ∆TWS estimates with global coverage but have a low spatial resolution of ∼400 km. Here, we put forward the mathematical framework for a joint inversion of GNSS vertical displacement time series with GRACE ∆TWS to produce more accurate spatiotemporal maps of ∆TWS, accounting for the observation errors, data gaps, and nonhydrologic signals. We aim to utilize the regional sensitivity to ∆TWS provided by GRACE mascon solutions with higher spatial resolution provided by GNSS observations. Our approach utilizes a continuous wavelet transform to decompose signals into their building blocks and separately invert for long-term and short-term mass variations. This allows us to preserve trends, annual, interannual, and multi-year changes in TWS that were previously challenging to capture by satellite-based measurement systems or hydrological models, alone. We focus our study in California, USA, which has a dense GNSS network and where recurrent, intense droughts put pressure on freshwater supplies. We highlight the advantages of our joint inversion results for a tectonically active study region by comparing them against inversion results that use only GNSS vertical deformation as well as with maps of ∆TWS from hydrological models and other GRACE solutions. We find that our joint inversion framework results in a solution that is regionally consistent with the GRACE ∆TWS solutions at different temporal scales but has an increased spatial resolution that allows us to differentiate between regions of high and low mass change better than using GRACE alone.

Repeating Nontectonic Seasonal Stress Changes and a Possible Triggering Mechanism of the 2019 Ridgecrest Earthquake Sequence in California

Here we characterize the 13-year history of nontectonic horizontal strain anomalies across the regions surrounding Ridgecrest, CA, using cGPS data from January 2007. This time-dependent model reveals a seasonality in the nontectonic strain anomalies and the associated Coulomb stress changes of ∼±0.5-2 kPa. In the area surrounding the epicenters of the 2019 Ridgecrest earthquake sequence of July, we find that the seasonal preseismic Coulomb stress changes peaked every early summer (May and June) during the last 13 years including during June 2019, a month prior to the large events. In addition, our statistical tests confirm that more strike-slip earthquakes (M_w ≥ 2) occur during times when seasonal stress changes are increasing on right-lateral faults in comparison with times when stresses are decreasing. These results suggest that the timing of the 2019 Ridgecrest earthquakes may have been modulated by nontectonic seasonal stress changes. The dynamic source of the seasonal nontectonic strain/stress anomalies, however, remains enigmatic. We discuss a possible combination of driving forces that may be attributable for the seasonal variations in nontectonic strain/stress anomalies, which captured in cGPS measurements.

Characterization of Groundwater Recharge and Flow in California's San Joaquin Valley From InSAR-Observed Surface Deformation

Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel-1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two-thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean -42.0 mm/yr; peak -345 mm/yr) are twice that in 2017 (mean -20.4 mm/yr; peak -177 mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface-water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface-water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA).

Poromechanical controls on spontaneous imbibition in earth materials

Over the last century, the state of stress in the earth's upper crust has undergone rapid changes because of human activities associated with fluid withdrawal and injection in subsurface formations. The stress dependency of multiphase flow mechanisms in earth materials is a substantial challenge to understand, quantify, and model for many applications in groundwater hydrology, applied geophysics, CO₂ subsurface storage, and the wider geoenergy field (e.g., geothermal energy, hydrogen storage, hydrocarbon recovery). Here, we conduct core-scale experiments using N₂/water phases to study primary drainage followed by spontaneous imbibition in a carbonate specimen under increasing isotropic effective stress and isothermal conditions. Using X-ray computed micro-tomography images of the unconfined specimen, we introduce a novel coupling approach to reconstruct pore-deformation and simulate multiphase flow inside the deformed pore-space followed by a semi-analytical calculation of spontaneous imbibition. We show that the irreducible water saturation increases while the normalized volume of spontaneously imbibed water into the specimen decreases (46-25%) in response to an increase in effective stress (0-30 MPa), leading to higher residual gas saturations. Furthermore, the imbibition rate decreases with effective stress, which is also predicted by a numerical model, due to a decrease in water relative permeability as the pore-space becomes more confined and tortuous. This fundamental study provides new insights into the physics of multiphase fluid transport, CO₂ storage capacity, and recovery of subsurface resources incorporating the impact of poromechanics.

Spatially coherent regional changes in seasonal extreme streamflow events in the United States and Canada since 1950

Complex hydroclimate in the United States and Canada has limited identification of possible ongoing changes in streamflow. We address this challenge by classifying 541 stations in the United States and Canada into 15 "hydro-regions," each with similar seasonal streamflow characteristics. Analysis of seasonal streamflow records at these stations from 1910 to present indicates regionally coherent changes in the frequency of extreme high- and low-flow events. Where changes are significant, these events have, on average, doubled in frequency relative to 1950 to 1969. In hydro-regions influenced by snowmelt runoff, extreme high-flow event frequency has increased despite snowpack depletion by warming winter temperatures. In drought-prone hydro-regions of the western United States and Southeast, extreme low-flow event frequency has increased, particularly during summer and fall. The magnitude and regional consistency of these hydrologic changes warrant attention by watershed stakeholders. The hydro-region framework facilitates quantification and further analyses of these changes to extreme streamflow.

Understanding of Contemporary Regional Sea-Level Change and the Implications for the Future

Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea-level observing system, the knowledge of regional sea-level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea-level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea-level change. Here we review the individual processes which lead to sea-level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea-level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea-level observation network-particularly as related to satellite observations-in the improved scientific understanding of the contributors to regional sea-level change.

Subsidence-Derived Volumetric Strain Models for Mapping Extensional Fissures and Constraining Rock Mechanical Properties in the San Joaquin Valley, California

Large-scale subsidence due to aquifer-overdraft is an ongoing hazard in the San Joaquin Valley. Subsidence continues to cause damage to infrastructure and increases the risk of extensional fissures.Here, we use InSAR-derived vertical land motion (VLM) to model the volumetric strain rate due to groundwater storage change during the 2007-2010 drought in the San Joaquin Valley, Central Valley, California. We then use this volumetric strain rate model to calculate surface tensile stress in order to predict regions that are at the highest risk for hazardous tensile surface fissures. We find a maximum volumetric strain rate of -232 microstrain/yr at a depth of 0 to 200 m in Tulare and Kings County, California. The highest risk of tensile fissure development occurs at the periphery of the largest subsiding zones, particularly in Tulare County and Merced County. Finally, we assume that subsidence is likely due to aquifer pressure change, which is calculated using groundwater level changes observed at 300 wells during this drought. We combine pressure data from selected wells with our volumetric strain maps to estimate the quasi-static bulk modulus, K, a poroelastic parameter applicable when pressure change within the aquifer is inducing volumetric strain. This parameter is reflective of a slow deformation process and is one to two orders of magnitude lower than typical values for the bulk modulus found using seismic velocity data. The results of this study highlight the importance of large-scale, high-resolution VLM measurements in evaluating aquifer system dynamics, hazards associated with overdraft, and in estimating important poroelastic parameters.

Seasonal and Long-Term Groundwater Unloading in the Central Valley Modifies Crustal Stress

Changes in terrestrial water content cause elastic deformation of the Earth's crust. This deformation is thought to play a role in modulating crustal stress and seismicity in regions where large water storage fluctuations occur. Groundwater is an important component of total water storage change in California, helping to drive annual water storage fluctuations and loss during periods of drought. Here we use direct estimates of groundwater volume loss during the 2007-2010 drought in California's Central Valley obtained from high resolution Interferometric Synthetic Aperture Radar-based vertical land motion data to investigate the effect of groundwater volume change on the evolution of the stress field. We show that GPS-derived elastic load models may not capture the contribution of groundwater to terrestrial water loading, resulting in an underestimation of nontectonic crustal stress change. We find that groundwater unloading during the drought causes Coulomb stress change of up to 5.5 kPa and seasonal fluctuations of up to 2.6 kPa at seismogenic depth. We find that faults near the Valley show the largest stress change and the San Andreas fault experiences only ~40 Pa of Coulomb stress change over the course of a year from groundwater storage change. Annual Coulomb stress change peaks dominantly in the fall, when the groundwater level is low; however, some faults experience peak stress in the spring when groundwater levels are higher. Additionally, we find that periods of increased stress correlate with higher than average seismic moment release but are not correlated with an increase in the number of earthquakes. This indicates groundwater loading likely contributes to nontectonic loading of faults, especially near the Valley edge, but is not a dominant factor in modulation of seismicity in California because the amplitude of stress change declines rapidly with distance from the Valley. By carefully quantifying and spatially locating groundwater fluctuations, we will improve our understanding of what drives nontectonic stress and forces that modulate seismicity in California.

Satellite-based monitoring of groundwater depletion in California's Central Valley

Range change data, obtained from Synthetic Aperture Radar satellites, form the basis for estimates of aquifer volume change in California’s Central Valley. The estimation algorithm incorporates a function penalizing changes far from known well locations, linking the aquifer volume changes to agricultural, industrial, and municipal pumping within the Tulare basin. We show that the range changes are compatible with the hypothesis that the source of aquifer volume changes are variations in effective pressure around documented wells. Specifically, inclusion of the well distance penalty does not degrade the fit to the observations, inversions with and without it both give variance reductions of 99.6%. The patterns of aquifer volume change vary significantly from the drought year, between October 2015 and October 2016, to a wet year in 2017, and into 2018, a year with near average rainfall. The 2.3 million acre-feet of estimated volume reduction, a lower bound on the amount of water extracted from the basin between October 2015 and 2016, agrees with independent estimates of 1.8 and 2.3 million acre-feet. The aquifer volume reduction is also compatible with a loss of 3.1 km³ (2.5 million acre-feet) in groundwater volume derived from Gravity Recovery and Climate Experiment (GRACE) satellite data.