GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas

International audienceTexas experienced the most extreme one-year drought on record in 2011 with precipitation at 40% of long-term mean and agricultural losses of ~$7.6 billion. We assess the value of Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage (TWS) change as an alternative remote sensing-based drought indicator, independent of traditional drought indicators based on in situ monitoring. GRACE shows depletion in TWS of 62.3 ± 17.7 km3 during the 2011 drought. Large uncertainties in simulated soil moisture storage depletion (14-83 km3) from six land surface models indicate that GRACE TWS is a more reliable drought indicator than disaggregated soil moisture or groundwater storage. Groundwater use and groundwater level data indicate that depletion is dominated by changes in soil moisture storage, consistent with high correlation between GRACE TWS and the Palmer Drought Severity Index. GRACE provides a valuable tool for monitoring statewide water storage depletion, linking meteorological and hydrological droughts

Return to rapid ice loss in Greenland and record loss in 2019 detected by the GRACE-FO satellites

Between 2003-2016, the Greenland ice sheet (GrIS) was one of the largest contributors to sea level rise, as it lost about 255 Gt of ice per year. This mass loss slowed in 2017 and 2018 to about 100 Gt yr−1. Here we examine further changes in rate of GrIS mass loss, by analyzing data from the GRACE-FO (Gravity Recovery and Climate Experiment – Follow On) satellite mission, launched in May 2018. Using simulations with regional climate models we show that the mass losses observed in 2017 and 2018 by the GRACE and GRACE-FO missions are lower than in any other two year period between 2003 and 2019, the combined period of the two missions. We find that this reduced ice loss results from two anomalous cold summers in western Greenland, compounded by snow-rich autumn and winter conditions in the east. For 2019, GRACE-FO reveals a return to high melt rates leading to a mass loss of 223 ± 12 Gt month−1 during the month of July alone, and a record annual mass loss of 532 ± 58 Gt yr−1

Mass balance of the Greenland Ice Sheet from 1992 to 2018

In recent decades, the Greenland Ice Sheet has been a major contributor to global sea-level rise1,2, and it is expected to be so in the future3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the degree and trajectory of today’s imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. Although the ice sheet was close to a state of balance in the 1990s, annual losses have risen since then, peaking at 335 ± 62 billion tonnes per year in 2011. In all, Greenland lost 3,800 ± 339 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.6 ± 0.9 millimetres. Using three regional climate models, we show that reduced surface mass balance has driven 1,971 ± 555 billion tonnes (52%) of the ice loss owing to increased meltwater runoff. The remaining 1,827 ± 538 billion tonnes (48%) of ice loss was due to increased glacier discharge, which rose from 41 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. Between 2013 and 2017, the total rate of ice loss slowed to 217 ± 32 billion tonnes per year, on average, as atmospheric circulation favoured cooler conditions15 and as ocean temperatures fell at the terminus of Jakobshavn Isbræ16. Cumulative ice losses from Greenland as a whole have been close to the IPCC’s predicted rates for their high-end climate warming scenario17, which forecast an additional 50 to 120 millimetres of global sea-level rise by 2100 when compared to their central estimate

Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020

Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9g€¯mm to global mean sea level, with the rate of mass loss rising from 105g€¯Gtg€¯yr-1 between 1992 and 1996 to 372g€¯Gtg€¯yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9g€¯Gtg€¯yr-1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86g€¯Gtg€¯yr-1 in 2017 to 444g€¯Gtg€¯yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9g€¯Gtg€¯yr-1) and, to a lesser extent, from the Antarctic Peninsula (13±5g€¯Gtg€¯yr-1). East Antarctica remains close to a state of balance, with a small gain of 3±15g€¯Gtg€¯yr-1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at 10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021)

Using regularization for error reduction in GRACE gravity estimation

textThe Gravity Recovery and Climate Experiment (GRACE) is a joint National Aeronautics and Space Administration / Deutsches Zentrum für Luftund Raumfahrt (NASA/DLR) mission to map the time-variable and mean gravity field of the Earth, and was launched on March 17, 2002. The nature of the gravity field inverse problem amplifies the noise in the data that creeps into the mid and high degree and order harmonic coefficients of the earth's gravity fields for monthly variability, making the GRACE estimation problem ill-posed. These errors, due to the use of imperfect models and data noise, are manifested as peculiar errors in the gravity estimates as north-south striping in the monthly global maps of equivalent water heights. In order to reduce these errors, this study develops a methodology based on Tikhonov regularization technique using the L-curve method in combination with orthogonal transformation method. L-curve is a popular aid for determining a suitable value of the regularization parameter when solving linear discrete ill-posed problems using Tikhonov regularization. However, the computational effort required to determine the L-curve can be prohibitive for a large scale problem like GRACE. This study implements a parameter-choice method, using Lanczos bidiagonalization that is a computationally inexpensive approximation to L-curve called L-ribbon. This method projects a large estimation problem on a problem of size of about two orders of magnitude smaller. Using the knowledge of the characteristics of the systematic errors in the GRACE solutions, this study designs a new regularization matrix that reduces the systematic errors without attenuating the signal. The regularization matrix provides a constraint on the geopotential coefficients as a function of its degree and order. The regularization algorithms are implemented in a parallel computing environment for this study. A five year time-series of the candidate regularized solutions show markedly reduced systematic errors without any reduction in the variability signal compared to the unconstrained solutions. The variability signals in the regularized series show good agreement with the hydrological models in the small and medium sized river basins and also show non-seasonal signals in the oceans without the need for post-processing.Aerospace Engineering and Engineering Mechanic

CSR GRACE RL06 Mascon Solutions

GRACE RL06 Monthly mascon solutions from the Center for Space Researc

CSR RL05 GRACE Daily Swath Mass Anomaly Estimates over the Ocean

CSR GRACE Daily Swath Mass Anomaly Estimates over the Ocea

Quantifying temporal variations in water resources of a vulnerable middle eastern transboundary aquifer system

Freshwater resources in the arid Arabian Peninsula, especially transboundary aquifers shared by Saudi Arabia, Jordan, and Iraq, are of critical environmental and geopolitical significance. Monthly Gravity Recovery and Climate Experiment (GRACE) satellite-derived gravity field solutions acquired over the expansive Saq transboundary aquifer system were analysed and spatiotemporally correlated with relevant land surface model outputs, remote sensing observations, and field data to quantify temporal variations in regional water resources and to identify the controlling factors affecting these resources. Our results show substantial GRACE-derived terrestrial water storage (TWS) and groundwater storage (GWS) depletion rates of −9.05 ± 0.25 mm/year (−4.84 ± 0.13 km3/year) and −6.52 ± 0.29 mm/year (−3.49 ± 0.15 km3/year), respectively. The rapid decline is attributed to both climatic and anthropogenic factors; observed TWS depletion is partially related to a decline in regional rainfall, while GWS depletions are highly correlated with increasing groundwater extraction for irrigation and observed water level declines in regional supply wells

Reconstruction of GRACE Mass Change Time Series Using a Bayesian Framework

Abstract Gravity Recovery and Climate Experiment and its Follow On (GRACE (‐FO)) missions have resulted in a paradigm shift in understanding the temporal changes in the Earth's gravity field and its drivers. To provide continuous observations to the user community, missing monthly solutions within and between GRACE (‐FO) missions (33 solutions) need to be imputed. Here, we modeled GRACE (‐FO) data (196 solutions) between 04/2002–04/2021 to infer missing solutions and derive uncertainties in the existing and missing observations using Bayesian inference. First, we parametrized the GRACE (‐FO) time series using an additive generative model comprising long‐term variability (secular trend + interannual to decadal variations), annual, and semi‐annual cycles. Informative priors for each component were used and Markov Chain Monte Carlo (MCMC) was applied to generate 2,000 samples for each component to quantify the posterior distributions. Second, we reconstructed the new data (229 solutions) by joining medians of posterior distributions of all components and adding back the residuals to secure the variability of the original data. Results show that the reconstructed solutions explain 99% of the variability of the original data at the basin scale and 78% at the one‐degree grid scale. The results outperform other reconstructed data in terms of accuracy relative to land surface modeling. Our data‐driven approach relies only on GRACE (‐FO) observations and provides a total uncertainty over GRACE (‐FO) data from the data‐generation process perspective. Moreover, the predictive posterior distribution can be potentially used for “nowcasting” in GRACE (‐FO) near‐real‐time applications (e.g., data assimilations), which minimize the current mission data latency (40–60 days)

Assessing modern rates of river sediment discharge to the ocean using satellite gravimetry

International audienceWorldwide rivers annually export about 19 Gigatons of sediments to the ocean that mostly accumulate in the coastalzones and on the continental shelves. This sediment discharge testifies of the intensity of continental erosionand records changes in climate, tectonics and human activity. However, natural and instrumental uncertaintiesinherent to the in-situ measurements of sediment discharge prevent from conclusive estimates to better understandthese linkages. Here we develop a new method, using the Gravity Recovery and Climate Experiment (GRACE)satellite data, to infer mass-integrative estimates of sediment discharge of large rivers to the ocean. GRACE satelliteprovides global gravity time series that have proven useful for quantifying mass transport, including continentalwater redistribution at the Earth surface (ice sheets and glaciers melting, groundwater storage variations) but hasbeen seldom used for monitoring sediment mass transfers so far. Here we pair the analysis of regularized GRACEsolutions at high spatial resolution corrected from all known contributions (hydrology, ocean, atmosphere) to aparticle tracking model that predicts the location of the sediment sinks for 13 rivers with the highest sedimentsloads in the world. We find that the resulting GRACE-derived sediment discharges off the mouth of the Amazon,Ganges-Brahmaputra, Changjiang (Yangtze), Indus, Magdalena, Godavari and Mekong rivers are consistent within-situ measurements. Our results suggest that the lack of time continuity and of global coverage in terrestrialsediment discharge measurements could be reduced by using GRACE, which provides global and continuous datasince 2002. GRACE solutions are regularly improved and new satellite gravity missions are being prepared hencemaking our approach even more relevant in a near future. The accumulation of sediments over time will keepincreasing the signal to noise ratio of the gravity time series, which will improve the precision of the GRACE-derived sediment discharges values