Rapid mapping of global flood precursors and impacts using novel five-day GRACE solutions.

Floods affect communities and ecosystems worldwide, emphasizing the importance of identifying their precursors and enhancing resilience to these events. Here, we calculated Antecedent Total Water Storage (ATWS) anomalies from the new 5-day (5D) Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) satellite solutions to enhance the detection of pre-flood and active flood conditions and to map post-flood storage anomalies. The GRACE data were compared with ~ 3300 flood events reported by the Dartmouth Flood Observatory (2002-2021), revealing distinct ATWS precursor signals in 5D solutions, in contrast to the monthly solutions. Specifically, floods caused by saturation-excess runoff-triggered by persistent rainfall, monsoonal patterns, snowmelt, or rain-on-snow events-show detectable ATWS increases 15 to 50 days before and during floods, providing a valuable opportunity to improve flood monitoring. These 5D solutions also facilitate a more rapid mapping of post-flood storage changes to assess flood recovery from tropical cyclones and sub-monthly weather extremes. Our findings show the promising potential of 5D GRACE solutions, which are still in the development phase, for future integration into operational frameworks to enhance flood detection and recovery, facilitating the rapid analysis of storage changes relative to monthly solutions.

Reconstruction of GRACE Mass Change Time Series Using a Bayesian Framework.

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).

Multi-decadal assessment of water budget and hydrological extremes in the Tigris-Euphrates Basin using satellites, modeling, and in-situ data.

The transboundary Tigris-Euphrates Basin (TEB) is prone to water-scarcity disputes. Water scarcity is related to aridity, climate extremes, limited supplies, upstream reservoir storage, rising water demand, and population growth. Understanding the water budget and storage changes in the basin in relation to hydrological extremes is fundamental to mitigate the drought and flood impacts and the key to efficient water resources management. This study evaluated the water budget related to drought occurrences in the TEB over four decades (1979-2020) based on GRACE/GRACE-FO, and altimetry satellites data, in situ observations, and hydrological modeling using a Bayesian model averaging (BMA) approach. Results show that severe droughts occurred at about decadal timescales with increasing recovery times. Severe and exceptional droughts dominated from (1998 to 2000, 2007 to 2009). Mild to moderate droughts occurred in 1983-1984, 1989-1992, 2011-2013, and 2018. The most severe drought occurred in 2007-2009, with the largest decline (-80 km3) in GRACE total water storage (TWS). Depletion in TWS was dominated by depletion in reservoir storage. In contrast, groundwater (GW) depletion accounted for only 25-30% of TWS decline. Storage depletion was amplified by human intervention (e.g., irrigation and GW abstraction) by at least 50% during drought. Marked recovery in TWS occurred in 2019 and 2020 (totaling ~144 km3 by July 2020, representing ~2× total depletion between 2007 and 2018) in response to regional flooding. Applying the BMA approach to the estimates of water cycle fluxes improved the accuracy and similarity of storage change, but not variability relative to GRACE. In summary, prolonged droughts are the norm rather than the exception in the TEB over the past four decades. The frequency and severity of droughts have substantial implications for water scarcity for countries sharing the TEB and underscore riparian countries' needs to expand their water management portfolio to mitigate drought impacts.

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

Climate variability and change along with anthropogenic water use have affected the (re)distribution of water storage and fluxes across the Contiguous United States (CONUS). Available hydrological models, however, do not represent recent changes in the water cycle. Therefore, in this study, a novel Bayesian Markov Chain Monte Carlo-based Data Assimilation (MCMC-DA) approach is formulated to integrate Terrestrial Water Storage changes (TWSC) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission into the W3RA water balance model. The benefit of this integration is its dynamic solution that uses GRACE TWSC to update W3RA's individual water storage estimates while rigorously accounting for uncertainties. It also down-scales GRACE data and provides groundwater and soil water storage changes at ~12.5 km resolution across the CONUS covering 2003-2017. Independent validations are performed against in-situ groundwater data (from USGS) and Climate Change Initiative (CCI) soil moisture products from the European Space Agency (ESA). Our results indicate that MCMC-DA introduces trends, which exist in GRACE TWSC, mostly to the groundwater storage and to a lesser extent to the soil water storage. Higher similarity is found between groundwater estimation of MCMC-DA and those of USGS in the southeastern CONUS. We also show a stronger linear trend in MCMC-DA soil water storage across the CONUS, compared to W3RA (changing from ±0.5 mm/yr to ±2 mm/yr), which is closer to independent estimates from the ESA CCI. MCMC-DA also improves the estimation of soil water storage in regions with high forest intensity, where ESA CCI and hydrological models have difficulties in capturing the soil-vegetation-atmosphere continuum. The representation of El Niño Southern Oscillation (ENSO)-related variability in groundwater and soil water storage are found to be considerably improved after integrating GRACE TWSC with W3RA. This new hybrid approach shows promise for understanding the links between climate and the water balance over broad regions.

Inferencing the land subsidence in the Nile Delta using Sentinel-1 satellites and GPS between 2015 and 2019.

The Nile Delta is home to half of Egypt's population and has ample agricultural, industrial, and cultural resources, yet the land subsides in response to many natural and anthropogenic impacts. We report the recent subsidence rate and patterns in the Nile Delta using the synthetic aperture radar Sentinel-1 data of 144 images obtained between 2015 and 2019, based on coherence small baseline subset interferometry of ~2900 interferograms. We distinguished three patterns of deformation due to three different physical mechanisms: 1) The land subsides with rates ranging from -12 to -20 mm/year in major cities (such as Zagazig, Mit Ghamr, Tanta, Mansoura and Mahla) due to urban-induced loading; 2) A subsidence rate ranges between -3 and -8 mm/year along the coastal margins due to natural sediment dewatering and compaction. This rate is consistent with the global positioning system rate of -3.5 mm/year and 3) A subsidence rate ranges from -20 to -16 mm/year and -6 to -12 mm/year in newly reclaimed lands on the west and east of the delta's flood plains, respectively, due to groundwater overexploitation. Our findings, in contrast with results from previous studies of regional deformation mainly felt to be controlled by natural processes, demonstrate a localized subsidence and predominant anthropogenic control on the land deformation and call for revisiting sea level rise-related flooding models in the Nile Delta. In light of the new findings, the authorities should take necessary measures to reduce the ongoing land subsidence through enforcement of urban planning policies in the delta's flood plain and development of a sustainable management strategy for groundwater extraction.

Terrestrial Water Storage in African Hydrological Regimes Derived from GRACE Mission Data: Intercomparison of Spherical Harmonics, Mass Concentration, and Scalar Slepian Methods.

Spherical harmonics (SH) and mascon solutions are the two most common types of solutions for Gravity Recovery and Climate Experiment (GRACE) mass flux observations. However, SH signals are degraded by measurement and leakage errors. Mascon solutions (the Jet Propulsion Laboratory (JPL) release, herein) exhibit weakened signals at submascon resolutions. Both solutions require a scale factor examined by the CLM4.0 model to obtain the actual water storage signal. The Slepian localization method can avoid the SH leakage errors when applied to the basin scale. In this study, we estimate SH errors and scale factors for African hydrological regimes. Then, terrestrial water storage (TWS) in Africa is determined based on Slepian localization and compared with JPL-mascon and SH solutions. The three TWS estimates show good agreement for the TWS of large-sized and humid regimes but present discrepancies for the TWS of medium and small-sized regimes. Slepian localization is an effective method for deriving the TWS of arid zones. The TWS behavior in African regimes and its spatiotemporal variations are then examined. The negative TWS trends in the lower Nile and Sahara at -1.08 and -6.92 Gt/year, respectively, are higher than those previously reported.