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NASA NTRS2012

Drought Indicators Based on Model Assimilated GRACE Terrestrial Water Storage Observations

The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (greater than 150,000 square kilometers) and temporal (greater than 10 day) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (GRACE-DAS), based on the Catchment Land Surface Model (CLSM) enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture 39 components (i.e. groundwater, soil moisture, snow), which individually are more useful for scientific applications. In this study, GRACE-DAS was applied to North America and GRACE-based drought indicators were developed as part of a larger effort that investigates the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the Drought Monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive datasets of groundwater storage from USGS monitoring wells and soil moisture from the Soil Climate Analysis Network (SCAN) were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of GRACE assimilated water storage field for improving drought detection.

Houborg, Rasmus↗
NASA NTRS2020

Groundwater and Terrestrial Water Storage

Groundwater, soil moisture, surface water, snow, and ice are the components of terrestrial water storage (TWS). On multi-annual timescales groundwater typically controls TWS variability, except in permanently frozen regions (Li et al. 2015). Even on an annual basis TWS changes are a reasonable proxy for groundwater storage changes, the latter being insufficiently observed in most of the world. From 2002 to 2017 the Gravity Recovery and Climate Experiment (GRACE; Tapley et al. 2004) and since 2018 the GRACE Follow On (GRACE-FO) satellite missions have enabled estimation of TWS anomalies (departures from the long-term mean) based on precise observations of variations in Earth’s gravity field. To bridge the 11-month gap between GRACE and GRACE-FO we make use of output from a land surface model that assimilates data from both missions (Li et al. 2019)

Matthew Rodell↗
NASA NTRS2021

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

Data Assimilation↗
NASA NTRS2015

Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA's Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE's coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.

flood↗
NASA NTRS2026

Spatially Refined Satellite Gravimetry Captures Human Signatures in Global Terrestrial Water Storage Trends

Human activities have directly altered the water cycle through water management, aquifer pumping, agricultural irrigation, and land use change. Although satellite gravimetry has transformed global hydrological research, its coarse resolution limits attribution of freshwater change to human activities at many management-relevant scales. Here we assessed global terrestrial water storage (TWS) trends from April 2002 to November 2025 using “stacked” regression of Level-1B intersatellite ranging data, which leverages temporal information and variability to dramatically improve effective spatial resolution relative to standard approaches. We combined this refined product with rigorous uncertainty analysis, autocorrelation-robust geostatistical methods, and literature assessment to evaluate TWS trend associations with land and water use, climate variability, and glacial mass loss. We identified TWS trend hotspots exhibiting significant spatial associations with anthropogenic 40 drivers, including groundwater and surface-water irrigation, rainfed agriculture, deforestation, and reservoir impoundment. Across these regions, cumulative TWS losses (3,122 Gt) substantially exceeded gains (2,432 Gt). Compared with traditional regression of monthly mascons, our approach yielded regional trend magnitudes that are on average 33% larger, revealing that global freshwater depletion, particularly from groundwater pumping, is considerably more acute than previously estimated. Multivariate regression models show that humans account for a significant share of the spatial variability in TWS trends on every non-polar continent except Australia. We detected localized TWS gains linked to rainfed agriculture, surface water irrigation, and reservoir filling that were unresolved in earlier gravimetric studies. The methodology provides a foundation for future gravity missions to independently track decadal freshwater change with unprecedented spatial fidelity.

groundwater↗
NASA NTRS2021

Estimating Terrestrial Snow Mass via Multi-sensor Assimilation of Synthetic AMSR-E Brightness Temperature Spectral Differences and Synthetic GRACE Terrestrial Water Storage Retrievals

This study explores multi-sensor data assimilation (DA) using synthetic Advanced Microwave Scanning Radiometer (AMSR-E) passive microwave brightness temperature spectral differences (∆T(B)) and synthetic Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) retrievals to improve estimates of snow water equivalent (SWE), subsurface water storage, and TWS across snow-covered terrain. Results show that multi-sensor DA improves SWE estimates by reducing the RMSE by 14.1% relative to a model-only simulation. Multi-sensor assimilation also yields the smallest TWS RMSE (reduced by 13.0% relative to a model-only simulation). However, multi-sensor DA does not always yield complementary updates, and can sometimes lead to conflicting changes to SWE, where the assimilation of synthetic ∆T(b) generates positive SWE increments while the assimilation of synthetic TWS removes SWE, which can ultimately degrade the posterior SWE estimates. This synthetic experiment provides useful insight for future DA experiments using real-world AMSR-E/AMSR-2 ∆T(b) observations and GRACE/GRACE-FO TWS retrievals to better characterize terrestrial freshwater storage across regional scales.

Jing Wang↗
NASA NTRS2019

Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

Terrestrial water storage↗
NASA NTRS2010

Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

Housborg, Rasmus↗
NASA NTRS2015

Terrestrial Water Storage

During 2014 dryness continued in the Northern Hemisphere and relative wetness continued in the Southern Hemisphere (Fig. 2.21; Plate 2.1g). These largely canceled out such that the global land surface began and ended the year with a terrestrial water storage (TWS) anomaly slightly below 0 cm (equivalent height of water; Fig. 2.22). TWS is the sum of groundwater, soil moisture, surface water, snow, and ice. Groundwater responds more slowly to meteorological phenomena than the other components because the overlying soil acts as a low pass filter, but often it has a larger range of variability on multiannual timescales (Rodell and Famiglietti 2001; Alley et al. 2002).In situ groundwater data are only archived and made and Tanzania. The rest of the continent experienced mixed to dry conditions. Significant reductions in TWS in Greenland, Antarctica, and southern coastal Alaska reflect ongoing ice sheet and glacier ablation, not groundwater depletion.

Northern Hemisphere↗
NASA NTRS2012

Assimilation of GRACE Terrestrial Water Storage into a Land Surface Model: Evaluation 1 and Potential Value for Drought Monitoring in Western and Central Europe

A land surface model s ability to simulate states (e.g., soil moisture) and fluxes (e.g., runoff) is limited by uncertainties in meteorological forcing and parameter inputs as well as inadequacies in model physics. In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother. GRACE data assimilation led to improved runoff correlations with gauge data in 17 out of 18 hydrological basins, even in basins smaller than the effective resolution of GRACE. Improvements in root zone soil moisture were less conclusive, partly due to the shortness of the in situ data record. In addition to improving temporal correlations, GRACE data assimilation also reduced increasing trends in simulated monthly TWS and runoff associated with increasing rates of precipitation. GRACE assimilated root zone soil moisture and TWS fields exhibited significant changes in their dryness rankings relative to those without data assimilation, suggesting that GRACE data assimilation could have a substantial impact on drought monitoring. Signals of drought in GRACE TWS correlated well with MODIS Normalized Difference Vegetation Index (NDVI) data in most areas. Although they detected the same droughts during warm seasons, drought signatures in GRACE derived TWS exhibited greater persistence than those in NDVI throughout all seasons, in part due to limitations associated with the seasonality of vegetation.

Li, Bailing↗
NASA NTRS2011

Groundwater and Terrestrial Water Storage

Most people think of groundwater as a resource, but it is also a useful indicator of climate variability and human impacts on the environment. Groundwater storage varies slowly relative to other non-frozen components of the water cycle, encapsulating long period variations and trends in surface meteorology. On seasonal to interannual timescales, groundwater is as dynamic as soil moisture, and it has been shown that groundwater storage changes have contributed to sea level variations. Groundwater monitoring well measurements are too sporadic and poorly assembled outside of the United States and a few other nations to permit direct global assessment of groundwater variability. However, observational estimates of terrestrial water storage (TWS) variations from the GRACE satellites largely represent groundwater storage variations on an interannual basis, save for high latitude/altitude (dominated by snow and ice) and wet tropical (surface water) regions. A figure maps changes in mean annual TWS from 2009 to 2010, based on GRACE, reflecting hydroclimatic conditions in 2010. Severe droughts impacted Russia and the Amazon, and drier than normal weather also affected the Indochinese peninsula, parts of central and southern Africa, and western Australia. Groundwater depletion continued in northern India, while heavy rains in California helped to replenish aquifers that have been depleted by drought and withdrawals for irrigation, though they are still below normal levels. Droughts in northern Argentina and western China similarly abated. Wet weather raised aquifer levels broadly across western Europe. Rains in eastern Australia caused flooding to the north and helped to mitigate a decade long drought in the south. Significant reductions in TWS seen in the coast of Alaska and the Patagonian Andes represent ongoing glacier melt, not groundwater depletion. Figures plot time series of zonal mean and global GRACE derived non-seasonal TWS anomalies (deviation from the mean of each month of the year) excluding Greenland and Antarctica. The two figures show that 2010 was the driest year since 2003. The drought in the Amazon was largely responsible, but an excess of water in 2009 seems to have buffered that drought to some extent. The drying trend in the 25-55 deg S zone is a combination of Patagonian glacier melt and drought in parts of Australia.

Rodell, Matthew↗
NASA NTRS2016

Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

Kumar, Sujay V.↗
NASA NTRS1991

Terrestrial water storage and polar motion

This study compares observed polar motion for the period 1900-1985 with meteorologic and hydrologic data for the world over the same period, in an effort to determine whether water storage, in combination with air mass redistribution, can account for the observed variance of polar motion. Monthly time series of estimated continental water storage and air mass excitation functions have been compared at the annual frequency and at the Chandler frequency using power, coherence, multiple coherence, and phase spectra. There is a discrepancy in accounting for more than half the variance of polar motion across a broad range of frequencies. Similar results have been obtained in recent studies of polar motion at frequencies above 1 cycle per year using modem space geodetic determinations of polar motion. The persistence of the discrepancey at the annual frequency and its broadband nature suggest a source of polar motion excitation due to air and water motion which has either not been correctly estimated or not yet identified.

Kuehne, John↗
NASA NTRS2007

Comparison of Seasonal Terrestrial Water Storage Variations from GRACE with Groundwater-level Measurements from the High Plains Aquifer (USA)

This study presents the first direct comparison of variations in seasonal GWS derived from GRACE TWS and simulated SM with GW-level measurements in a semiarid region. Results showed that variations in GWS and SM are the main sources controlling TWS changes over the High Plains, with negligible storage changes from surface water, snow, and biomass. Seasonal variations in GRACE TWS compare favorably with combined GWS from GW-level measurements (total 2,700 wells, average 1,050 GW-level measurements per season) and simulated SM from the Noah land surface model (R = 0.82, RMSD = 33 mm). Estimated uncertainty in seasonal GRACE-derived TWS is 8 mm, and estimated uncertainty in TWS changes is 11 mm. Estimated uncertainty in SM changes is 11 mm and combined uncertainty for TWS-SM changes is 15 mm. Seasonal TWS changes are detectable in 7 out of 9 monitored periods and maximum changes within a year (e.g. between winter and summer) are detectable in all 5 monitored periods. Grace-derived GWS calculated from TWS-SM generally agrees with estimates based on GW-level measurements (R = 0.58, RMSD = 33 mm). Seasonal TWS-SM changes are detectable in 5 out of the 9 monitored periods and maximum changes are detectable in all 5 monitored periods. Good correspondence between GRACE data and GW-level measurements from the intensively monitored High Plains aquifer validates the potential for using GRACE TWS and simulated SM to monitor GWS changes and aquifer depletion in semiarid regions subjected to intensive irrigation pumpage. This method can be used to monitor regions where large-scale aquifer depletion is ongoing, and in situ measurements are limited, such as the North China Plain or western India. This potential should be enhanced by future advances in GRACE processing, which will improve the spatial and temporal resolution of TWS changes, and will further increase applicability of GRACE data for monitoring GWS.

Strassberg, Gil↗
NASA NTRS2007

Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin

NASA's GRACE mission has the potential to be extremely valuable for water resources applications and global water cycle research. What makes GRACE unique among Earth Science satellite systems is that it is able to monitor variations in water stored in all forms, from snow and surface water to soil moisture to groundwater in the deepest aquifers. However, the space and time resolutions of GRACE observations are coarse. GRACE typically resolves water storage changes over regions the size of Nebraska on a monthly basis, while city-scale, daily observations would be more useful for water management, agriculture, and weather prediction. High resolution numerical (computer) hydrology models have been developed, which predict the fates of water and energy after they strike the land surface as precipitation and sunlight. These are similar to weather and climate forecast models, which simulate atmospheric processes. We integrated the GRACE observations into a hydrology model using an advanced technique called data assimilation. The results were new estimates of groundwater, soil moisture, and snow variations, which combined the veracity of GRACE with the high resolution of the model. We tested the technique over the Mississippi River basin, but it will be even more valuable in parts of the world which lack reliable data on water availability.

Zaitchik, Benjamin F.↗
NASA NTRS2011

Remote Sensing of Terrestrial Water Storage with GRACE and Future Gravimetry Missions

The Gravity Recovery and Climate Experiment (GRACE) has demonstrated that satellite gravimetry can be a valuable tool for regional to global water cycle observation. Studies of ice sheet and glacier mass losses, ocean bottom pressure and circulation, and variability of water stored on and in the land including groundwater all have benefited from GRACE observations, and the list of applications and discoveries continues to grow. As the mission approaches its tenth anniversary of launch on March 12,2012, it has nearly doubled its proposed lifetime but is showing some signs of age. In particular, degraded battery capacity limits the availability of power in certain orbital configurations, so that the accelerometers must be turned off for approximately one month out of six. The mission managers have decided to operate the spacecrafts in a manner that maximizes the remaining lifetime, so that the longest possible climate data record is available from GRACE. Nevertheless, it is not unlikely that there will be a data gap between GRACE and the GRACE Follow On mission, currently proposed for launch in 2016. In this presentation we will describe recent GRACE enabled science, GRACE mission health, and plans for GRACE Follow On and other future satellite gravimetry missions.

Rodell, Matt↗