Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into W3RA

A holistic overview of the applications of GRACE-observed terrestrial water storage in hydrology and climate science

Terrestrial Water Storage (TWS) represents a vital element of the hydrological cycle, with its fluctuations significantly impacting the climate of the Earth and its ecological balance. Since its launch in 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has revolutionized the ability to observe and analyze large-scale mass changes within Earth's system components. This paper offers a comprehensive and current overview of GRACE satellite gravimetry, highlighting its relevance to hydrological and climate-related studies. It outlines the fundamental measurement principles of the GRACE mission, provides an in-depth explanation of GRACE data products (including spherical harmonic and mascon solutions), examines emerging trends in GRACE-based research, and reviews key applications in hydrology and climate science. Additionally, it addresses the major challenges in utilizing GRACE data and explores promising avenues for future research and applications.

A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins

The coarse spatial resolution of about 300 km in Total Water Storage Anomalies (TWSA) data from the Gravity Recovery And Climate Experiment (GRACE) and its follow-on (GRACE-FO, hereafter GRACE) missions presents significant challenges for local water resource management. Previous approaches to addressing this issue through statistical downscaling have been limited by the reliance on the scale-invariance assumption, residual correction, hydrological models, and a lack of consideration for spatial correlations among the TWSA grids. This study introduces the DownGAN generative adversarial network, which downscales GRACE TWSA to 25 km, as exemplified in the Yangtze River Basin (YRB) and the Nile River Basin (NRB). Additionally, we propose a novel downscaling scheme to address the above limitations. DownGAN receives static and dynamic variables as inputs while considering their potential time-delay effects. The downscaled TWSA is validated using a synthetic example, in-situ runoff, groundwater levels, and two hydrological models. The potential benefits of the downscaled TWSA in closing the water balance budget and monitoring hydrological droughts in the YRB and NRB are explored. The synthetic example indicates that DownGAN trained using the proposed downscaling scheme can downscale the YRB and NRB's TWSA from 1° to 0.5° and 0.25°, respectively. DownGAN outperforms RecNet, a fully convolutional neural network, producing continuous, consistent, and realistic downscaled TWSA. The downscaled TWSA exhibits high correlations with the runoff and groundwater levels in the YRB and NRB, respectively. In addition, DownGAN demonstrates better performance in closing the water balance budget and monitoring drought events in both the YRB and NRB than HR GRACE mascon products, as evidenced by its higher correlations with the total water storage changes derived from the water balance equation and two drought indices, respectively. DownGAN is adaptable to other downscaling tasks and regions, offering a flexible downscaling factor, minimal assumptions, cost-effectiveness, and realistic predictions.

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.

Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the Irrigated Indus Basin (IIB)

The growth of agricultural production systems is a major driver of groundwater depletion worldwide. Balancing groundwater supply and food production requires localized understanding of groundwater storage and depletion variations in response to diverse cropping systems and surface water availability for irrigation. While advances through Gravity Recovery and Climate Experiment (GRACE) have facilitated estimating the groundwater storage (GWS) changes in recent years, the coarse resolution of GRACE data hinders the characterization of GWS variation hotspots. Herein, we present a novel spatial water balance approach to improve the distributed estimation of groundwater storage and depletion changes at a spatial scale that can detect the hotspots of GWS variation. We used a mixed geographically weighted regression (MGWR) model to downscale GRACE Level-3 data from coarse resolution (1° × 1°) to fine scale (1 km × 1 km) based on high resolution environmental variables. We then combined the downscaled GRACE-based GWS variations with results from a calibrated Soil and Water Assessment Tool (SWAT) model. We demonstrate an application of the approach in the Irrigated Indus Basin (IIB). Between 2002 and 2019, total loss of groundwater reserves varied in the IIB's 55 canal command areas with the highest loss observed in Dehli Doab by >50 km³ followed by 7.8-49 km³ in the upstream, and 0.77-7.77 km³ in the downstream canal command areas. GWS declined by -325.55 mm/year at Dehli Doab, followed by -186.86 mm/year at BIST Doab, -119.20 mm/year at BARI Doab, and -100.82 mm/year at JECH Doab. The rate of groundwater depletion is increasing in the canal command areas of Delhi Doab and BIST Doab by 0.21-0.35 m/year. Larger groundwater depletion in some canal command areas (e.g., RACHNA, BIST Doab, and Delhi Doab) is associated with the rice-wheat cropping system, low rainfall, and low flows from tributaries.

Bridging the gap between GRACE and GRACE-FO using a hydrological model

Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), two successive satellite-based missions starting in 2002, have provided an unprecedented way of measuring global terrestrial water storage anomalies (TWSA). However, a temporal gap exists between GRACE and GRACE-FO products from July 2017 to May 2018, which introduces bias and uncertainties in TWSA calculations and modeling. Previous studies have incorporated hydroclimatic factors as predictors for filling the gap, but most of them utilized artificial intelligence or pure statistical models that generally de-trended TWSA and had no physical foundation. Thus, a physically-based reconstruction is required for increasing robustness. In this study, we bridge the temporal gap by developing an empirical hydrological model. The "abcd" model, a T-based snow component, and linear correction are utilized to represent runoff generation, snow dynamics, and long-term trends. The testing results indicate that our hydrological model can successfully reconstruct TWSA in tropical, temperature, and continental climates, although further improvement is needed for arid climates. Our reconstruction for the gap achieves high accuracy and robustness as shown by the evaluations against sea-level budget and GLDAS-derived TWSA. Compared to previous studies using artificial intelligence or statistical techniques, our hydrological model performs similarly in the gap filling but does not involve de-trended or de-seasonalized transformations, which will facilitate the combination of GRACE and GRACE-FO products and improve the physical understanding of global TWSA.

Evaluating Groundwater Storage Change and Recharge Using GRACE Data: A Case Study of Aquifers in Niger, West Africa

Accurately assessing groundwater storage changes in Niger is critical for long-term water resource management but is difficult due to sparse field data. We present a study of groundwater storage changes and recharge in Southern Niger, computed using data from NASA Gravity Recovery and Climate Experiment (GRACE) mission. We compute a groundwater storage anomaly estimate by subtracting the surface water anomaly provided by the Global Land Data Assimilation System (GLDAS) model from the GRACE total water storage anomaly. We use a statistical model to fill gaps in the GRACE data. We analyze the time period from 2002 to 2021, which corresponds to the life span of the GRACE mission, and show that there is little change in groundwater storage from 2002–2010, but a steep rise in storage from 2010–2021, which can partially be explained by a period of increased precipitation. We use the Water Table Fluctuation method to estimate recharge rates over this period and compare these values with previous estimates. We show that for the time range analyzed, groundwater resources in Niger are not being overutilized and could be further developed for beneficial use. Our estimated recharge rates compare favorably to previous estimates and provide managers with the data required to understand how much additional water could be extracted in a sustainable manner.