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NASA NTRS2019

GRAIL-Identified Gravity Anomalies in Oceanus Procellarum: Insight into Subsurface Impact and Magmatic Structures on the Moon

Four, quasi-circular, positive Bouguer gravity anomalies (PBGAs) that are similar in diameter (~90-190 km) and gravitational amplitude (> 140 mGal contrast) are identified within the central Oceanus Procellarum region of the Moon. These spatially associated PBGAs are located south of Aristarchus Plateau, north of Flamsteed crater, and two are within the Marius Hills volcanic complex (north and south). Each is characterized by distinct surface geologic features suggestive of ancient impact craters and/or volcanic/plutonic activity. Here, we combine geologic analyses with forward modeling of high-resolution gravity data from the Gravity Recovery and Interior Laboratory (GRAIL) mission in order to constrain the subsurface structures that contribute to these four PBGAs. The GRAIL data presented here, at spherical harmonic degrees 6–660, permit higher resolution analyses of these anomalies than previously reported, and reveal new information about subsurface structures. Specifically, we find that the amplitudes of the four PBGAs cannot be explained solely by mare-flooded craters, as suggested in previous work; an additional density contrast is required to explain the high-amplitude of the PBGAs. For Northern Flamsteed (190 km diameter), the additional density contrast may be provided by impact-related mantle uplift. If the local crust has a density ~2800 kg/cu.m, then ~7 km of uplift is required for this anomaly, although less uplift is required if the local crust has a lower mean density of ~2500 kg/cu.m. For the Northern and Southern Marius Hills anomalies, the additional density contrast is consistent with the presence of a crustal complex of vertical dikes that occupies up to ~50% of the regionally thin crust. The structure of Southern Aristarchus Plateau (90 km diameter), an anomaly with crater-related topographic structures, remains ambiguous. Based on the relatively small size of the anomaly, we do not favor mantle uplift; however, understanding mantle response in a region of especially thin crust needs to be better resolved. It is more likely that this anomaly is due to subsurface magmatic material given the abundance of volcanic material in the surrounding region. Overall, the four PBGAs analyzed here are important in understanding the impact and volcanic/plutonic history of the Moon, specifically in a region of thin crust and elevated temperatures characteristic of the Procellarum KREEP Terrane.

Deutsch, Ariel N.↗

NASA NTRS2017

International Challenges of GRACE Follow-On

The Gravity Recovery and Climate Experiment - Follow-On (GRACE-FO) Mission is a NASA directed mission to continue the goals of the original GRACE mission and provide continuity for the GRACE data set. The GRACE-FO mission is the result of an international cooperation to develop a concept and approach that minimizes cost and risk and maximizes the probability of success through limited changes to the original GRACE system design. The result is a system architecture in which maintenance of heritage is paramount, including heritage derived through the partnership with the German Research Centre for Geosciences (GFZ) in Germany. As a secondary goal, GRACE-FO will carry a Laser Ranging Interferometer (LRI) as a technology demonstration, which will provide laser interferometry measurements of inter-satellite range, complementary to the K/Ka-Band microwave link to demonstrate laser-ranging technology in support of future GRACE-like missions. Another secondary objective is the continuation of GRACE radio occultation measurements.

Kayali, Sammy↗

NASA NTRS2018

The Sensitivity of US Wildfire Occurrence to Pre-Season Soil Moisture Conditions Across Ecosystems

It is well accepted that drought and low moisture conditions are linked with increased wildfire occurrence. However, quantifying the sensitivity of wildfire to surface moisture state has been challenging due to a lack of soil moisture observations at an appropriate spatial scale. Here we apply model simulations of surface soil moisture that numerically assimilate observations from NASA’s Gravity Recovery and Climate Experiment (GRACE) mission, combined in a predictive algorithm with the US Forest Service’s Fire-Occurrence Database. We estimate a relationship between historic surface moisture and wildfire occurrence to produce annual probable wildfire occurrence and burned area at 0.25-degree resolution for the contiguous United States by land-cover classification. Cross-validation indicates increased frequency of smaller fires when the months preceding fire season are wet, while larger fires are more frequent when soils are dry. This demonstrates that assimilated GRACE data holds information that could aid national-scale fire potential assessments for early decision-support.

Fire-Occurrence database↗

NASA NTRS2019

Long-Term, Non-Anthropogenic Groundwater Storage Changes Simulated by Three Global-Scale Hydrological Models

This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. Evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. Simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5-7 decades with modeled rates ranging from 0.01 to 2.18 mm year-1. This large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.

Groundwater storage↗

NASA NTRS2019

Terrestrial Water Storage

Terrestrial water storage can be defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow,ice, water stored in the vegetation, river and lake water. Terrestrial water storage (TWS) changes have been observed by the Gravity Recovery and Climate Experiment (GRACE) mission since 2002. GRACE has provided an unprecedented view of the terrestrial water storage variations at large scales. Extremes in water storage often are associated with droughts and flooding events because they are driven by the surplus or deficit of water. Few hydrologic observing networks yield sufficient data for comprehensive monitoring of changes in the total amount of water stored in a region. GRACE observations have helped to fill this gap. This book chapter is divided into 4 sections.Section 1 provides an overview of the GRACE mission. Section 2 reviews the terrestrial water storage solutions that are available. Section 3 describes regional to global applications of GRACE for monitoring extremes in the terrestrial water storage, including droughts and flooding events. Conclusions and future directions are reported in Section 4.

Terrestrial Water Storage↗

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 NTRS2019

Satellite Orbital Drag During Magnetic Storms

We investigate satellite orbital drag effects at lowEarth orbit associated with thermosphere heating during magnetic storms caused by coronal mass ejections. CHAllenge Minisatellite Payload (CHAMP) and Gravity Recovery And Climate Experiment (GRACE) neutral density data are used to compute orbital drag. Stormtoquiet density comparisons are performed with background densities obtained by the JacchiaBowman 2008 (JB2008) empirical model. Our storms are grouped in different categories regarding their intensities as indicated by minimum values of the SYMH index. We then perform superposed epoch analyses with storm main phase onset as zero epoch time. In general, we find that orbital drag effects are larger for CHAMP (lower altitudes) in comparison to GRACE (higher altitudes). Results show that storm time drag effects manifest first at high latitudes, but for extreme storms, particularly observed by GRACE, stronger orbital drag effects occur during early main phase at low/equatorial latitudes, probably due to heating propagation from high latitudes. We find that storm time orbital decay along the satellites' path generally increases with storm intensity, being stronger and faster for the most extreme events. For these events, orbital drag effects decrease faster probably due to elevated cooling effects caused by nitric oxide, which introduce modeled density uncertainties during storm recovery phase. Errors associated with total orbit decay introduced by JB2008 are generally the largest for the strongest storms and increase during storm times, particular during recovery phases. We discuss the implication of these uncertainties for the prediction of collision between space objects at lowEarth orbit during magnetic storms.

Oliveira, D. M.↗

NASA NTRS2019

Thermospheric Heating and Cooling Times During Geomagnetic Storms, Including Extreme Events

We present the first quantitative calculations of thermospheric heating and cooling times for geomagnetic storms of different intensity, including extreme events. We utilize the neutral mass density database of the CHAllenging Mini‐satellite Payload and Gravity Recovery And Climate Experiment missions to produce thermospheric global system response to geomagnetic storms caused by coronal mass ejections via superposed epoch analysis during May 2001 to December 2015. Storm events are grouped in five different categories based on the minimum value of the SYM‐H index. We calculate the time from storm onset for the thermosphere to reach maximum intensification (heating time) and the time from onset for the thermosphere to recover (cooling time). We find that heating and cooling times decrease as storm intensity increases and the effect is more pronounced for the cooling times. For extreme storms, the thermospheric heating time is 9.5 hr, while the cooling time is 22 hr.

Zesta, Eftyhia↗

NASA NTRS2019

Regularization and Error Characterization of GRACE Mascons

We present a new global time-variable gravity mascon solution derived from Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The new product from the NASA Goddard Space Flight Center (GSFC) results from a novel approach that combines an iterative solution strategy with geographical binning of inter-satellite range-acceleration residuals in the construction of time-dependent regularization matrices applied in the inversion of mascon parameters. This estimation strategy is intentionally conservative as it seeks to maximize the role of the GRACE measurements on the final solution while minimizing the influence of the regularization design process. We fully reprocess the Level 1B data in the presence of the final mascon solution to generate true post-fit inter-satellite residuals, which are utilized to confirm solution convergence and to validate the mascon noise uncertainties. We also present the mathematical case that regularized mascon solutions are biased, and that this bias, or leakage, must be combined with the estimated noise variance to accurately assess total mascon uncertainties. The estimated leakage errors are determined from the monthly resolution operators. We present a simple approach to compute the total uncertainty for both individual mascon and regional analysis of the GSFC mascon product, and validate the results in comparison with independent mascon solutions and calibrated Stokes uncertainties. Lastly, we present the new solution and uncertainties with global analyses of the mass trends and annual amplitudes, and compute updated trends for the global ocean, and the respective contributions of the Greenland Ice Sheet, Antarctic Ice Sheet, Gulf of Alaska, and terrestrial water storage. This analysis highlights the successful closure of the global mean sea level budget, that is, the sum of global ocean mass from the GSFC mascons and the steric component from Argo floats agrees well with the total determined from sea surface altimetry.

Loomis, B. D.↗

NASA NTRS2020

Central Valley Water Resources: Improving California Groundwater Assessments using GRACE and InSAR Datasets for Water Resource Management

California’s Central Valley is one of the most productive agricultural areas in the world, producing approximately $20 billion in crops annually. The recent California droughts of 2007-2010 and 2012-2019 resulted in increased groundwater pumping in the Central Valley to adequately irrigate farmland. Overdrafting of the Central Valley aquifer results in groundwater depletion, land subsidence, and permanent loss of groundwater storage. In 2014, depletion of groundwater led the state of California to enact the Sustainable Groundwater Management Act (SGMA),requiring critically overdrafted, high, and medium priority sub-basins to reach sustainable levels of groundwater pumping and recharge by 2042. SGMA allows local Groundwater Sustainability Agencies (GSAs) the authority to create Groundwater Sustainability Plans (GSPs) at the sub-basin level. To assist California’s Department of Water Resources (DWR), this project quantified groundwater change and land subsidence in Central Valley sub-basins with sparse or unreliable well and Geographic Positioning Systems (GPS) data. This was done using NASA’s Gravity Recovery and Climate Experiment (GRACE), GRACE FollowOn (GRACE-FO), and interferograms derived from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Advanced Land Observing Satellite 2 (ALOS-2)Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2). Time series of the GRACE and InSAR data were compared with well and GPS data in data-dense sub-basins to determine the feasibility of these datasets for groundwater storage and subsidence monitoring. We found that GRACE and InSAR data are effective tools for determining groundwater change and land subsidence and can be used on their own to monitor sub-basins in the absence of well and GPS data

Water Resources↗

NASA NTRS2020

Central Valley Water Resources: Improving California Groundwater Assessments using GRACE and InSAR Datasets for Water Resource Management

California’s Central Valley is one of the most productive agricultural areas in the world, producing approximately $20 billion in crops annually. The recent California droughts of 2007-2010 and 2011-2017 resulted in increased groundwater pumping in the Central Valley to adequately irrigate farmland. Overdrafting of the Central Valley aquifer results in groundwater depletion, land subsidence, and permanent loss of groundwater storage. In 2014, depletion of groundwater led the state of California to enact the Sustainable Groundwater Management Act (SGMA), requiring critically overdrafted, high, and medium priority sub-basins to reach sustainable levels of groundwater pumping and recharge by 2042. SGMA allows local Groundwater Sustainability Agencies the authority to create Groundwater Sustainability Plans at the sub-basin level. To assist California’s Department of Water Resources, this project quantified groundwater change and land subsidence in Central Valley sub-basins with sparse or unreliable well and GPS data. This was done using NASA’s Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and interferograms derived from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2). Time series of the GRACE and InSAR data were compared with well and GPS data in data-dense sub-basins to determine the feasibility of these datasets for groundwater storage and subsidence monitoring. We found thatGRACE and InSAR data are effective tools for determining groundwater change and land subsidence and can be used on their own to monitor sub-basins in the absence of well and GPS data.

Water Resources↗

NASA NTRS2021

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

This global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically-triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) and Gravity Recovery and Climate Experiment (GRACE) remote sensing data, Catchment Land Surface Model (CLSM) simulations and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300-km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011-2016, unless the CLSM model-only soil water content is already high (≥ 50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015-2019) more generally updates the soil water conditions towards higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Anne Felsberg↗

NASA NTRS2020

Basin-Scale River Runoff Estimation from GRACE Gravity Satellites, Climate Models and In Situ Observations: a Case Study in the Amazon Basin

River runoff is estimated as a water budget residual using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage time series, ERA5 reanalysis data, and precipitation observations for January 2003 through December 2015 for the Obidos upstream drainage basin and for the entire Amazon basin. Estimated runoff based on the water budget agrees remarkably well with in situgauge observations at Obidos, especially at seasonal time scales, with nearly perfect phase agreementbut slightly larger seasonal amplitude. The discrepancy in the seasonal amplitude maybe attributed to underestimation of river gauge runoff during the wet season when water overflows the riverbanks. The ERA5 model appears to overestimate long-term mean evapotranspiration in the Amazon by ~2 cm/monthb ased on comparisons with precipitation and runoff observations. Using precipitation data based on satellites and gauge observations relative to gauge observations alone improved agreement between water budget runoff estimates and in situ runoff observations. Seasonal variations in ERA5 simulated runoff are about twice as large as those from in situobservations and show a large phase lag as well. Water budget based runoff for the entire Amazon (~ 7,200 km3averaged 29over the 13 yr period) is significantly larger than observed runoff (~5,700 km3) at notably larger than previous estimates for the entire Amazon. These differences may be partly related to submarine runoff from the Amazon basin that cannot be captured by surface gauges.

Jianli Chen↗

NASA NTRS2020

Groundwater Drought: Environmental Controls and Monitoring

Arising from a long-term deficit of precipitation, groundwater drought often lags meteorological drought by months to years, depending on climate and subsurface hydrogeological conditions. Due to the paucity of in situ groundwater data in many parts of the world, global monitoring of groundwater storage variations and drought may be best achieved using satellite observations and/or groundwater time series simulated by hydrological models. The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions have greatly benefitted the modeling and monitoring of groundwater storage changes and drought at the global scale. In this chapter, we first review environmental controls on the temporal variability of groundwater using in situ data. We then describe an approach that infuses GRACE and GRACE-FO observations into a land surface model for assessing groundwater storage changes and drought globally. We also discuss characteristics of simulated groundwater drought and the limitations of current groundwater drought monitoring approaches.

Bailing Li↗

NASA NTRS2021

An Evaluation of Remotely Sensed and In-Situ Data Sufficiency for SGMA-Scale Groundwater Studies in the Central Valley, California

California’s Central Valley aquifer is a critical freshwater resource for the state, providing drinking water to 6.5 million residents and irrigation water for more than half of the nation’s produce. Increasing demands have led to a steady decline of aquifer water levels, leading to irreversible compaction and land subsidence. The Sustainable Groundwater Management Act of California (SGMA), instituted in 2014, requires high-priority regions to establish sustainability plans by 2020-2022 that mitigate groundwater depletion and land subsidence. Many regions within the Central Valley lack groundwater monitoring wells or continuous well data, rendering in-situ monitoring challenging. When combined with other data, NASA’s Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions provide valuable information about groundwater storage changes at a sub-basin scale. Interferometric Surface Aperture Radar (InSAR) measurements can map land subsidence, and Global Positioning System (GPS) can be used to estimate crustal uplift. Yet, all of these measurements have differences in resolution, coverage, discretization, and record length. Here we assess the various datasets that are potentially useful for Central Valley sub-basin groundwater monitoring and provide an analysis of gaps and other issues in regard to their suitability for SGMA-related analysis. Finally, we offer next steps and recommendations on data treatment and integration.

Kyra H Kim↗

NASA NTRS2020

Central Valley Water Resources II: Groundwater Sustainability Management Support in the California Central Valley using GRACE and InSAR Datasets

California’s Central Valley is one of the most productive agricultural regions in the United States, producing a fourth of the nation’s food supply. The water demand in this region is heavily dependent on groundwater resources, resulting in over pumping of aquifers at unsustainable rates during recent periods of severe drought. Over the past century, Central Valley aquifers have experienced a significant decline in groundwater levels, resulting in land subsidence and irreversible loss in groundwater storage. In 2014, the state enacted the Sustainable Groundwater Management Act, requiring high and medium priority subbasins to suspend overdraft and achieve sustainable levels of pumping and recharge by 2042.The California Department of Water Resources (DWR) oversees subbasin groundwater management; however, monitoring remains challenging due to sparse and inconsistent in situ data. To assist the DWR, this project developed a user-friendly executable application and an interactive visualization tool to quantify groundwater storage and land subsidence trends using remotely sensed and in situ data. The team utilized NASA’s Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) interferograms, and Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) interferograms in conjunction with well and GPS measurements to analyze groundwater and subsidence trends. GRACE and well data returns produced a strong Pearson correlation of .84, while Sentinel-1 and GPS data returns produced a Pearson correlation of .41 over the entire Central Valley. These findings suggest remotely sensed GRACE and interferometric SAR data can be used in the absence of in situ data.

DEVELOP Project Summary↗

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 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↗