Gravity Recovery And Climate Experiment Terrestrial Water Storage Research Articles

Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.

Reinterpreting Global GRACE Trends Based on Century‐Long GRACE‐REC Data

AbstractAssessing changes in freshwater availability accurately is crucial for societal development. Previous studies have examined long‐term variations in basin‐scale terrestrial water storage (TWS) using Gravity Recovery and Climate Experiment (GRACE) mission data. However, different basins exhibit distinct spatial and temporal TWS variation patterns. To better interpret the TWS trends in each basin during the GRACE era (2003–2016), this study proposes a novel criterion based on a century‐long GRACE‐REC data set. This criterion assesses the trends in GRACE TWS (TrendG), precipitation‐induced trends (TrendPI), and non‐precipitation‐induced trends (TrendNPI) over the GRACE period. By calculating upper and lower bound values for long‐term climate‐driven TWS trends using GRACE‐REC data, an indicator is provided to evaluate the range of TWS trend variations in a basin under natural conditions. Results reveal that among the 266 global basins analyzed in this study, the trends (TrendG, TrendPI, or TrendNPI) in 115 basins exceed the maximum or minimum water storage trends associated with natural climate variability. This includes 20 large basins, 34 medium basins, and 61 small basins, indicating significant TWS changes during the GRACE period. Furthermore, we analyze the driving mechanisms of TWS trends in the 20 large basins using multi‐source data. The mechanisms identified through this method align well with both our analysis and previous studies, confirming the reliability of this approach for assessing TWS trends.

Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model

Terrestrial water storage (TWS) encompasses various components such as snow water, surface water, canopy water, soil water, and groundwater. Therefore, TWS is strongly affected by several factors including climate change, floods, drought, and anthropogenic activities. Predicting and quantifying TWS is critical in understanding terrestrial water cycles, as well as for managing water resources at the regional and global scale. TWS can be monitored with the Gravity Recovery and Climate Experiment (GRACE) satellites and estimated using land surface models (LSMs) on various spatial scales. The present study demonstrated how large-scale TWS data assimilation affects model performance by assessing the variations in TWS and related hydrological variables. Moreover, unlike traditional physical-based models, the assimilation of TWS data into an LSM enabled the assessment of anthropogenic impacts. TWS data from GRACE was assimilated into the Community Land Model version 5 (CLM5) with the CLM5-BGC biogeochemistry module covering the East Asia region. The model was forced using 40-ensemble meteorological forcing data from 2003 to 2010 and GRACE TWS data were assimilated using the ensemble adjustment Kalman filter (EAKF). Data assimilation markedly improved the performance of the model, as demonstrated by a decrease in the root mean square error from 0.71 mm/month in the free run (FR) without data assimilation to 0.48 mm/month in the data assimilation (DA) run, in addition to an increase in the correlation coefficient from 0.55 in FR to 0.69 in DA. Additionally, unlike the FR model, the DA model effectively captured the observed negative trends of the TWS, soil moisture, and evapotranspiration in Northern India and the Northern China Plain. Finally, our findings also demonstrated that the anthropogenic contribution to the negative trend of the TWS over Northern India and Northern China Plain was marginally increased in the DA model compared to the FR model. Collectively, our findings demonstrated that TWS data assimilation enables a more realistic evaluation of the long-term and large-scale changes in hydrologic variables.

Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.

Global evaluation of model agreement and uncertainty in terrestrial water storage simulations from ISIMIP 2b framework

Terrestrial water storage (TWS) is a vital component in global hydrologic cycle with direct linkage to water resources availability and hydrologic extremes. Assessing the uncertainty in TWS projections are fundamentally important for developing counter-measures against impacts of climate change on water resources and disasters. This study presents a global-scale evaluation of model agreement and uncertainty in TWS anomaly (TWSA) simulations within the ISIMIP 2b framework by comparison with the 2006–2016 Gravity Recovery and Climate Experiment (GRACE) data. The total 24 members of ISIMIP2b ensembles considered here include the permutations of 6 global water models (GWMs: CLM4.5, H08, LPLmL, MATSIRO, PCR-GLOBWB, WaterGAP2) simulations forced by 4 global climate models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) under RCP2.6 and RCP6.0 scenarios. Results show that the fraction of global land area with the same sign between the ISIMIP2b ensemble mean and GRACE TWSA is 47.5 % and 47.3 % under RCP2.6 and RCP6.0, respectively. The ensemble mean reproduces the negative (positive) TWSA in southern North America, southwestern Asia, northern and central Africa (central South America and northern Europe) under both scenarios. More than 50 % of ensemble members agree with GRACE TWS better in the sign of drying trend than wetting trend over the global land. The interannual TWS variations among ensemble members show better agreement in North America and Europe than other continents. More than 80 % of ensemble members simulate larger (smaller) TWS seasonal amplitude than GRACE in North America, Europe, and Asia (South America, Africa, and Australia). GWM is found to be the main source of uncertainty in TWS simulations in North America, Europe, and Asia (>55 %), while GCM uncertainty is the dominant uncertainty source in South America, Africa, and Australia (>45 %).

Reconstruction of GRACE terrestrial water storage anomalies using Multi-Layer Perceptrons for South Indian River basins.

The Gravity Recovery and Climate Experiment (GRACE) satellite mission began in 2002 and ended in June 2017. GRACE applications are limited in their ability to study long-term water cycle behavior because the data is limited to a short period, i.e., from 2002 to 2017. In this study, we aim to reconstruct (1960-2002) GRACE total water storage anomalies (TWSA) to obtain a continuous TWS time series from 1960 to 2016 over four river basins of South India, namely the Godavari, Krishna, Cauvery and Pennar River basins, using Multilayer Perceptrons (MLP). The Seasonal Trend Decomposition using Loess procedure (STL) method is used to decompose GRACE TWSA and forcing datasets into linear trend, interannual, seasonal, and residual parts. Only the de-seasoned (i.e., interannual and residual) components are reconstructed using the MLP method after the linear trend and seasonal components are removed. Seasonal component is added back after reconstruction of de-seasoned GRACE TWSA to obtain complete TWSA series from 1960 to 2016. The reconstructed GRACE TWSA are converted to groundwater storage anomalies (GWSA) and compared with nearly 2000 groundwater observation well networks. The results conclude that the MLP model performed well in reconstructing GRACE TWSA at basin scale across four river basins. Godavari (GRB) experienced the highest correlation (r = 0.96) between the modelled TWSA and GRACE TWSA, followed by Krishna (KRB) with r = 0.93, Cauvery (CRB) with r = 0.91, and Pennar (PCRB) with r = 0.92. The seasonal GWSA from GRACE (GWSAGRACE) correlated well with the GWSA from groundwater observation wells (GWSAOBS) from 2003 to 2016. KRB exhibited the highest correlation (r=0.85) followed by GRB (r=0.81), PCRB (r=0.81) and CRB (r=0.78). The established MPL technique could be used to reconstruct long-term TWSA. The reconstructed TWSA data could be useful for understanding long-term trends, as well as monitoring and forecasting droughts and floods over the study regions.

Non-linear interactions between vegetation and terrestrial water storage in Australia

• Revisit analysis shows that vegetation growth is water-limited in 60% of Australia. • Non-linear interactions between NDVI and TWS occur in 58% of Australia. • The area where high temperatures limit vegetation growth has nearly tripled recently. Vegetation conditions are deeply dependent on water availability. Conversely, vegetation can also affect terrestrial water conditions by modifying evapotranspiration and other hydrological processes. Knowledge of the interactions between vegetation and land water conditions are required for understanding and simulating terrestrial water and carbon budgets in response to climate change. For the first time, in this study, a non-linear Granger causality approach is applied to investigate the water-vegetation interactions over Australia. They are characterized by respectively the normalized difference vegetation index (NDVI) and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS). The results indicate that non-linear interactions between TWS and NDVI occur in over half (58.4%) of the continent, which are more likely to take place for grasslands, followed by shrublands, agricultural lands, savannas, and forests. A revisit-analysis of the dominant driver among water, temperature, and radiation for vegetation conditions confirms that Australia is mostly water limited. Although the total area of primarily water-limited regions has slightly decreased from 67.0% in the earlier sub-period (1985–1999) to 65.2% in the more recent sub-period (2000–2015), it accounts for a much larger proportion of the continent than that of temperature- and radiation-limited regions in both sub-periods. The spatial coverage of regions where temperature is the dominant factor has nearly tripled for the recent sub-period. This could most likely be due to the warmer climate that exacerbates the limitation of high temperature on vegetation growth and functioning. These results contribute to an improved understanding of Australia’s terrestrial water and carbon cycles. The GRACE TWS shows to be highly relevant data for such investigations at a continental scale.

Jointly using the GLDAS 2.2 model and GRACE to study the severe Yangtze flooding of 2020

A severe flood occurred in the middle and lower reaches of the Yangtze River (MLYR) in 2020 and caused an obvious terrestrial water storage (TWS) increase. However, the process and cause of this flood have not been fully revealed. To address this problem, we use Global Land Data Assimilation System (GLDAS) −2.2 daily TWS data that assimilate Gravity Recovery and Climate Experiment (GRACE) observations to thoroughly investigate this flood event. Evaluation results show that the GLDAS TWS agrees well with GRACE TWS and water budget results around the MLYR mainstream, which provides confidence in using GLDAS TWS data to monitor floods. The daily GLDAS TWS anomalies and hydrological observations around the MLYR mainstream coincide well in terms of their temporal patterns. These two independent indicators together identify this severe flood event and its process. GLDAS and hydrologic results suggest that this flood event started south of the MLYR mainstream basin in early June and then moved northward, and in July it covered the whole MLYR mainstream basin and intensified along the MLYR mainstream and its north. This flood event reached its peak around the MLYR mainstream basin and north of it around late July, and then retreated progressively till September. GLDAS results show that the largest regional positive TWS anomaly was ∼ 130.8 mm and happened in late July around the MLYR mainstream, influencing a vast area of ∼ 9.2 × 105 km2 in the MLYR basin. The flood center first appeared along the MLYR mainstream, then moved northward, and finally stayed north of the MLYR mainstream. These results provide us a quantitative understanding of it. The water budget analysis demonstrates that the above-normal precipitation around the MLYR mainstream during June-July 2020 is the main cause of this flood. The weather analysis demonstrates that the precipitation increase is mainly due to anomalies in monsoon circulation systems, including the anomalous extension of the Western Pacific subtropical high and South Asia high, anomalous low-level southwesterly wind and upper-level westerly jet. And the precipitation increase is also closely associated with the anomalous blocking high and cold air activities in the middle and high latitudes.

Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial Water Storage Records: An Innovative Approach

Temporal gaps within the Gravity Recovery and Climate Experiment (GRACE) (gap: 20 months), between GRACE and GRACE Follow-On (GRACE-FO) missions (gap: 11 months), and within GRACE-FO record (gap: 2 months) make it difficult to analyze and interpret spatiotemporal variability in GRACE- and GRACE-FO-derived terrestrial water storage (TWSGRACE) time series. In this study, an overview of data and approaches used to fill these gaps and reconstruct the TWSGRACE record at the global scale is provided. In addition, the study provides an innovative approach that integrates three machine learning techniques (deep-learning neural networks [DNN], generalized linear model [GLM], and gradient boosting machine [GBM]) and eight climatic and hydrological input variables to fill these gaps and reconstruct the TWSGRACE data record at both global grid and basin scales. For each basin and grid cell, the model performance was assessed using Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (CC), and normalized root-mean-square error (NRMSE), a leader model was selected based on the model performance, and variables that significantly control leader model outputs were defined. Results indicate that (1) the leader model reconstructed the TWSGRACE with high accuracy over both grid and local scales, particularly in wet and low anthropogenically active regions (grid scale: NSE = 0.65 ± 0.20, CC = 0.81 ± 0.13, and NSE = 0.56 ± 0.16; basin scale: NSE = 0.78 ± 0.14, CC = 0.89 ± 0.07, and NRMSE = 0.43 ± 0.14); (2) no single model was flawless in reconstructing the TWSGRACE over all grids or basins, so a combination of models is necessary; (3) basin-scale models outperform grid-scale models; (4) the DNN model outperforms both GLM and GBM at the basin scale, whereas the GBM outperforms at the grid scale; (5) among other inputs, the Global Land Data Assimilation System (GLDAS)-derived TWS controls the model performance on both basin and grid scales; and (6) the reconstructed TWSGRACE data captured extreme climatic events over the investigated basins and grid cells. The developed approach is robust, effective, and could be used to accurately reconstruct TWSGRACE for any hydrologic system across the globe.

Reconstructing the data gap between GRACE and GRACE follow-on at the basin scale using artificial neural network

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) observations, have been used to monitor the terrestrial water storage (TWS) change for almost 20 years. But the nearly 1-year gap between GRACE and GRACE-FO breaks the continuity of the observations, which influences the study on short-term TWS change and may introduce biases in GRACE (FO)-based data analysis. In this study, we propose to combine multichannel singular spectrum analysis (MSSA) and back propagation neural network (BPNN) to reconstruct this data gap. We use the MSSA first to initially interpolate the missing GRACE TWS data and second to decompose the hydroclimatic driving data and the target GRACE TWS data into partially reconstructed components (RC) and then use the BPNN to establish the relationships between each target RC and driving RCs. To reasonably test the model performance, we customize a sliding window test method that uses a 1-year window to determine the training and testing data so that we can approximate the real case. Using the proposed methods, we reconstruct the TWS data gaps in 28 hot areas that suffered severe TWS changes with a mean root mean square error (RMSE) of 2.7 cm and in 26 major river basins with a mean RMSE of 2.2 cm. This combined method outperforms the MSSA-based methods and most artificial neural network-based methods. Given the fact that the nominal accuracy of GRACE is ~2 cm and the TWS changes were large in the hot areas, the reconstruction accuracy is impressive. This study is expected to provide an advanced method for gap filling, data reconstruction, and data fusion as well as provide high-quality continuous TWS data for hydrological and climatic studies, especially in the 28 hot areas where no reconstructed data are available.

Assimilation of SBAS-InSAR Based Vertical Deformation Into Land Surface Model to Improve the Estimation of Terrestrial Water Storage

The gravity recovery and climate experiment (GRACE) provides an unprecedented opportunity to detect the spatial and temporal variation of the terrestrial water storage (TWS) for regional to continental scales. However, the GRACE system's coarse temporal resolution (∼monthly) and data discontinuity missing perplexed the TWS research during the operation. In this article, the data assimilation (DA) method was employed to integrate the vertical deformation obtained from the small baseline subset (SBAS) InSAR processing into the NASA catchment land surface model (CLSM), which improved the estimation of the TWS. First, we used a one-dimensional ensemble Kalman filter for DA research to estimate the TWS in Dali Prefecture, southwestern China. Finally, we compared the estimated TWS with the GRACE-based TWS from December 2, 2018 to January 21, 2021. The unbiased root-mean-square of the open loop (OL; without DA) method and the SBAS-InSAR DA method are 61 mm and 30 mm in Dali Prefecture, respectively. Results revealed that the numerical difference between the estimated TWS and the GRACE TWS retrievals was significantly decreased by the SBAS-InSAR DA method than the OL method. In addition, the temporal resolution of the SBAS-InSAR DA-based TWS was improved to 12 days compared with GRACE-based TWS. Furthermore, we recovered the discontinuous deletion and blank of GRACE-based TWS from 2015 to 2018 by the SBAS-InSAR DA method.

Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment

Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.

Effects of climate change on terrestrial water storage and basin discharge in the lancang River Basin

Study RegionLancang River Basin (upper reaches of the Mekong River basin within China). Study FocusComplex terranes and diverse climates are a bottleneck for understanding the hydrology of rivers originating from the Tibetan Plateau. This study deals with the impact of climate change on water storage in the Lancang River Basin, which is governed by the South Asian monsoon system. We evaluated the spatiotemporal distribution of multi-source precipitation, evapotranspiration, and terrestrial water storage (TWS) to understand the hydrological system in the region. We provide evidence of climate change impacts on TWS and basin discharge over an upstream region of the transboundary river system. New Hydrological Insights for the RegionThe Five Gravity Recovery and Climate Experiment (GRACE) products and Global Land Data Assimilation System (GLDAS) TWS display analogous seasonal distribution, even though the amounts differ between them. The GRACE and GLDAS TWS exhibited a significant negative trend in the basin from 2002 to 2016. However, the Center for Space Research (CSR-M) at the University of Texas and the Jet Propulsion Laboratory (JPL-M) mascon solutions concede more severe and much wider TWS reduction than the three spherical harmonic (SH) solutions. In addition, a downward trend was observed for basin discharge over 15 years as a response to climate change (decreased precipitation and increased evapotranspiration). Furthermore, we identified a 2-month time lag between precipitation and TWS, which could be a response to climatic factors along with aquifer properties in a karst dominated region.

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

AbstractThis study explores multi‐sensor data assimilation (DA) using synthetic Advanced Microwave Scanning Radiometer (AMSR‐E) passive microwave brightness temperature spectral differences () 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 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 observations and GRACE/GRACE‐FO TWS retrievals to better characterize terrestrial freshwater storage across regional scales.

An enhanced water storage deficit index (EWSDI) for drought detection using GRACE gravity estimates

Accurate detection and monitoring of drought events are important particularly in arid and semi-arid regions of the world. Gravity Recovery and Climate Experiment (GRACE) gravity estimates have been used widely for this purpose and a number of indices have been developed using the GRACE Terrestrial Water Storage Anomalies (TWSA) values. In the current study, a new approach is proposed to enhance the performance of the GRACE-based Water Storage Deficit Index (WSDI). The proposed Enhanced Water Storage Deficit Index (EWSDI) was developed based on the grid-based standardization of the Water Storage Deficit (WSD) values. The decomposed time series of the TWSA were computed in an attempt to evaluate the performance of the approach based on different components of the TWSA time series. Standardized Precipitation Index (SPI) and modelled Soil Moisture Storage (SMS) were also used to validate the functionality of this new GRACE-derived index. The applicability of the EWSDI index was tested in the semi-arid climatic conditions of Turkey and the results showed that the detrended EWSDI better correlated with SPI-09 and annual SPI with correlation coefficient values of 0.70 and 0.76, respectively. The findings also suggested an approximate enhancement of 13% over the existing WSDI when applied on the detrended TWSA. The findings of this study reveal that the proposed approach is effective in improving the performance of the existing WSDI to detect drought events in terms of monthly and annual correlation coefficients achieved.

Deep Learning Approaches to Spatial Downscaling of GRACE Terrestrial Water Storage Products Using EALCO Model Over Canada

Estimating terrestrial water storage (TWS) with high spatial resolution is crucial for hydrological and water resource management. Comparing to traditional in-situ data measurement, observation from space borne sensor such as Gravity Recovery and Climate Experiment (GRACE) satellites is quite effective to obtain a large-scale TWS data. However, the coarse resolution of the GRACE data restricts its application at a local scale. This paper presents three novel convolutional neural network (CNN) based approaches including the Super-Resolution CNN (SRCNN), the Very Deep Super-Resolution (VDSR), and the Residual Channel Attention Networks (RCAN) to spatial downscaling of the monthly GRACE TWS products using the outputs of the Ecological Assimilation of Land and Climate Observations (EALCO) model over Canada. We also compare the performance of CNN-based methods with the empirical linear regression-based downscaling method. All comparison results were evaluated by root mean square error (RMSE) between the reconstructed GRACE TWS and the original one. RMSEs over the matched pixels are 22.3, 14.4, 18.4 and 71.6 mm of SRCNN, VDSR, RCAN and linear regression-based method respectively. Obviously, VDSR shows the best accuracy among all methods. The result shows all CNN-based super resolution methods preform much better than traditional method in spatial downscaling.

Reconstructing GRACE-like TWS anomalies for the Canadian landmass using deep learning and land surface model

Terrestrial water storage (TWS) is an essential part of the global water cycle. Long-term information of observed and modeled TWS is fundamental to analyze water resources, meteorological extreme events (e.g., droughts and floods), and the climate change impacts. Over the past several decades, hydrologists have been applying physically-based hydrological model (GHM) and land surface model (LSM) to simulate TWS and its components (e.g., groundwater storage). However, the reliability of these physically-based models is often affected by uncertainties in climatic forcing data, model parameters, model structure, and mechanisms for physical process representations. Launched in March 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission exclusively applies remote sensing techniques to measure the variations in TWS on a global scale. The mission length of GRACE, however, is too short to meet the requirements for analyzing long-term TWS. Therefore, lots of effort have been devoted to the reconstruction of GRACE-like TWS data for the pre-GRACE era. Data-driven methods, such as multilinear regression and machine learning, exhibit a great potential to reconstruct TWS data by integrating GRACE observations and physically-based model simulations. The advances in artificial intelligence enable adaptive learning of correlations between variables in complex spatiotemporal systems. However, the applicability of various deep learning techniques has not been adequately studied for GRACE TWS reconstruction. In this study, three deep learning-based models are developed to reconstruct the historical TWS using LSM outputs for the Canadian landmass from 1979 to 2002. The performance of the models is evaluated against the GRACE-observed TWS in 2002–2004 and 2014–2016. The trained models achieve a mean correlation coefficient of 0.96, with a mean RMSE of 53 mm. The results show that the LSM-based deep learning models significantly improve the correlations between original LSM simulations and GRACE observations.

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

AbstractThis 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 2011–16, 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 2015–19) more generally updates the soil water conditions toward 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.

A time series assessment of terrestrial water storage and its relationship with hydro-meteorological factors in Gilgit-Baltistan region using GRACE observation and GLDAS-Noah model

Mountains regions like Gilgit-Baltistan (GB) province of Pakistan are solely dependent on seasonal snow and glacier melt. In Indus basin which forms in GB, there is a need to manage water in a sustainable way for the livelihood and economic activities of the downstream population. It is important to monitor water resources that include glaciers, snow-covered area, lakes, etc., besides traditional hydrological (point-based measurements by using the gauging station) and remote sensing-based studies (traditional satellite-based observations provide terrestrial water storage (TWS) change within few centimeters from the earth’s surface); the TWS anomalies (TWSA) for the GB region are not investigated. In this study, the TWSA in GB region is considered for the period of 13 years (from January 2003 to December 2016). Gravity Recovery and Climate Experiment (GRACE) level 2 monthly data from three processing centers, namely Centre for Space Research (CSR), German Research Center for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL), System Global Land Data Assimilation System (GLDAS)-driven Noah model, and in situ precipitation data from weather stations, were used for the study investigation. GRACE can help to forecast the possible trends of increasing or decreasing TWS with high accuracy as compared to the past studies, which do not use satellite gravity data. Our results indicate that TWS shows a decreasing trend estimated by GRACE (CSR, GFZ, and JPL) and GLDAS-Noah model, but the trend is not significant statistically. The annual amplitude of GLDAS-Noah is greater than GRACE signal. Mean monthly analysis of TWSA indicates that TWS reaches its maximum in April, while it reaches its minimum in October. Furthermore, Spearman’s rank correlation is determined between GRACE estimated TWS with precipitation, soil moisture (SM) and snow water equivalent (SWE). We also assess the factors, SM and SWE which are the most efficient parameters producing GRACE TWS signal in the study area. In future, our results with the support of more in situ data can be helpful for conservation of natural resources and to manage flood hazards, droughts, and water distribution for the mountain regions.

Assimilation of Ground‐Based GPS Observations of Vertical Displacement into a Land Surface Model to Improve Terrestrial Water Storage Estimates

AbstractGround‐based Global Positioning System (GPS) observations of vertical surface displacement can be used to study terrestrial water storage (TWS) change after properly accounting for nonhydrological loading effects. This study systematically merged ground‐based GPS observations of vertical displacement into a land surface model in order to better estimate TWS. Assimilation was conducted across two snow‐dominated watersheds in the western United States using a one‐dimensional ensemble Kalman filter (EnkF). Modeled estimates were compared against TWS retrievals derived from the Gravity Recovery and Climate Experiment (GRACE) mission and in situ measurements of snow water equivalent (SWE), soil moisture, and runoff. The GPS data assimilation (GPS DA) technique improves prediction skill of TWS anomalies relative to the Open Loop (OL; model run without assimilation) when compared against GRACE TWS retrievals, especially during an extended drought period post‐2011 (e.g., correlation coefficient ROL = 0.46 and RGPSDA = 0.82 in the Great Basin). Furthermore, GPS DA improves SWE estimation with improved R values found over 76% and 69% of all pixels collocated with in situ stations in the Great Basin and Upper Colorado watersheds, respectively. GPS DA estimates of surface soil moisture and runoff, however, exhibit degraded performance relative to the OL due to the limited sensitivity of GPS observations to surface soil moisture variations and the lack of a dynamic surface routing scheme as well as other missing model physics (e.g., river and dam regulation, irrigation‐related water withdrawals, surface water impoundments) that are not included in the Catchment Land Surface Model.