Gravity Recovery And Climate Experiment Satellite Data Research Articles

Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations

Terrestrial water storage (TWS) is pivotal in understanding environmental dynamics, climate change, and human impacts. Despite the utility of land surface models, uncertainties persist in their parameterization schemes. This study employs GRACE (Gravity Recovery and Climate Experiment) satellite data to optimize the runoff parameterization scheme within the Common Land Model by a data assimilation and parameter optimization method. The optimization algorithm sets an adjustment factor that varies with time and space for runoff simulation and updates it along with the running of the land surface model. The evaluation reveals that there are improved correlation coefficients and reduced root mean square errors compared to GRACE observations. Independent assessments by using in situ river discharge observations demonstrate enhanced model performance, particularly in mountainous regions such as western North America. This study underscores the efficacy of integrating GRACE data to improve land surface model parameterization, offering more accurate predictions of TWS changes.

Evaluating groundwater resources trends through multiple conceptual models and GRACE satellite data.

In this research, three numerical groundwater flow models, developed and calibrated from three equally plausible conceptual models over the Nasia Basin, have been used to assess groundwater resources variations over a transient period. The use of multiple numerical models reduces the effect of uncertainties in conceptual model formulation. All the three calibrated numerical models indicate an increasing trend of groundwater recharge and storage over the period of the groundwater level monitoring. This suggests that the prevailing erratic climatic conditions in the area are conducive for increasing groundwater recharge and storage in the terrain. The high-intensity, short duration rainfall patterns, attending climate change in the basin, enhance high levels of infiltration and percolation, leading to steadily increasing groundwater recharge. Groundwater recharge estimates from each of the models over the transient period appear to reflect the pattern of seasonal variations in rainfall in the region. Data from the models indicates a significant role of baseflow in sustaining perennial streamflow in the area. This presents a significant development in terms of groundwater-based adaptation projects, especially in agriculture. The trend of groundwater recharge in the Nasia Basin is in sync with regional groundwater storage variations estimated from the Gravity Recovery and Climate Experiment (GRACE) satellite data collected and processed over the Volta Basin. At the Volta Basin level, groundwater storage variations indicate a strong positive trend of increasing groundwater recharge from 2002 (beginning of the GRACE mission) to 2022 (end point of the data used for this research). Analysis of the GRACE data suggests that there is a cumulative increase in groundwater storage by 30cm, representing approximately 120 km3 of groundwater over the period in the basin. This translates into approximately 15mm/year of groundwater storage increase. Thus, at both the regional and local levels, groundwater appears to be responding positively to the impacts of erratic rainfall patterns observed in the area recently. The high-intensity, short duration rainfall patterns appear to favor significant groundwater recharge, resulting in a strong positive groundwater storage signal. The high positive groundwater storage signal suggests increasing groundwater resources potential in the area, indicating promising opportunities for groundwater-based climate change adaptation interventions.

Assessing the interannual and subseasonal variabilities in water storage using multi-source soil moisture products and GRACE/GRACE-FO satellites and its applications

Since 2002, the terrestrial water storage anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-on (GRACE-FO), have been widely used in water storage studies. Interannual and subseasonal variabilities in terrestrial water storage are critical for understanding the change of water cycle and interpretation of GRACE error. In this study, we evaluate the interannual plus subseasonal terms of global TWSA from GRACE satellite data and soil moisture storage estimated from multi-source soil moisture data. Results show that soil moisture change dominated total water storage change in most regions, especially in relatively arid regions. We further reconstruct the continuous TWSA by combing the trend and seasonal terms from the GRACE(-FO) TWSA data with the interannual and subseasonal variabilities of multi-source soil moisture products on a global scale from April 2002 to December 2021. Our reconstruction result is further compared with the TWSA from GRACE(-FO) and two recent reconstruction products. The comparisons show that the reconstruction result has great consistency with GRACE(-FO), with 82% of the grids showing Nash-Sutcliffe efficiency coefficient values greater than 0.6 based on JPL Mascon solution. This study emphasizes that the information from soil moisture can provide an efficient approach for tracking the interannual and subseasonal variabilities of long-term terrestrial water storage.

Global analysis of the correlation and propagation among meteorological, agricultural, surface water, and groundwater droughts

Groundwater drought monitoring relies on ground observation data, which cannot be used to reflect large-scale droughts in groundwater resources. The Gravity Recovery and Climate Experiment Satellite (GRACE) improved the situation and provided a new solution for groundwater drought research. However, the propagation relationship among global different drought types has not been fully explored. We employed CRU precipitation data, MERR2 reanalysis soil moisture data, GLDAS and GRACE data to calculate SPI (Standardized precipitation index), SSI (Standardized soil moisture index), SRI (Standardized runoff index), and GDI (Groundwater drought index), characterizing meteorological, agricultural, surface water and groundwater droughts, respectively. The Pearson correlation coefficient was adopted to study the propagation time of these four types of droughts. The results showed that the average propagation times for the different drought types are meteorological drought to surface water drought (3.5 months), meteorological drought to agricultural drought (5.7 months), agricultural drought to groundwater drought (12.97 months), surface water drought to groundwater drought (13.78 months), and meteorological drought to groundwater drought (14.47 months) from longest to shortest. (2) Climate conditions had a significant impact on the propagation time of different drought types. Low temperatures in cold climates resulted in the longest drought propagation time, while dry summer climates in temperate climates reduced drought propagation time. There were weaker propagation relationships in arid climates. In tropical climates, precipitation may not be the main driving factor for drought propagation. (3) Different land cover types show significant differences in the propagation of groundwater droughts, with forests having a longer propagation time from meteorological drought to agricultural drought or surface water drought than grassland and cropland, and forests having the shortest propagation time when meteorological drought, agricultural drought, and surface water drought is propagated to groundwater drought. Woody plants have deeper root systems than herbaceous plants and can draw up deeper groundwater. Forests have greater water storage capacity and weaker groundwater recharge than grasslands and croplands, resulting in forests being more resistant to agricultural and surface water droughts and less resistant to groundwater droughts during meteorological droughts. This study can help to clarify the propagation laws among different drought types and understand the internal mechanisms that affect the development of drought during the water cycle.

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Abstract. Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellite provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor flood in various regions at monthly intervals. However, the coarse temporal resolution of GRACE and GRACE-FO satellite data has been limiting their applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then temporally downscaled into daily values based on three different learning-based models, namely a multi-layer perceptron (MLP) model, a long-short term memory (LSTM) model and a multiple linear regression (MLR) model. Furthermore, a new index incorporating temporally downscaled TWSA estimates combined with daily average precipitation anomalies is proposed to monitor the severe flood events at sub-monthly timescales for the Yangtze River basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing the monthly TWSA with root mean square error (RMSE) = 10.9 mm per month and Nash–Sutcliffe efficiency (NSE) = 0.89 during the validation period; (2) the MLP model can be useful in temporally downscaling monthly TWSA estimates into daily values; (3) the proposed normalized daily flood potential index (NDFPI) facilitates robust and reliable characterization of severe flood events at sub-monthly timescales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual subbasins. All these findings can provide new opportunities for applying GRACE and GRACE-FO satellite data to investigations of sub-monthly signals and have important implications for flood hazard prevention and mitigation in the study region.

Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin

The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25° from 1°. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and self-calibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely, NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions.

Oasis sustainability assessment in arid areas using GRACE satellite data.

An oasis is an important habitat for humans, plants, and wildlife in arid desert areas. The sustainability of an oasis is crucial for a smooth regional ecological functioning and healthy economic development. However, the overexploitation of groundwater will result in unsustainable oasis development. Due to the lack of long-term groundwater monitoring data, the impact of groundwater level changes on the sustainability of an oasis has not been studied extensively. In the present study, we used the ground water storage anomaly (GWSA) in combination with the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS) for the rapid identification of oasis sustainability, which has been tested and evaluated in Hotan and Qira oasis located in arid areas. The results showed that (1) the GWSA is a suitable and reliable indicator for trend change analysis in small-scale oasis and, (2) additionally, M-K test results for long-term trend change of GWSA showed a positive correlation with water resource carrying capacity (WRCC). These results suggest that GWSA can be used as a reliable index for the rapid assessment of oasis sustainability status in arid areas. Moreover, the potential applicability of GRACE satellite data in evaluating the groundwater sustainability in arid areas lacking proper data has also been proved in this study. These findings have provided a foundation to evaluate the sustainability status of an oasis and set a reference point to formulate future policies for the oasis.

Using Sentinel-1 and GRACE satellite data to monitor the hydrological variations within the Tulare Basin, California

Subsidence induced by groundwater depletion is a grave problem in many regions around the world, leading to a permanent loss of groundwater storage within an aquifer and even producing structural damage at the Earth’s surface. California’s Tulare Basin is no exception, experiencing about a meter of subsidence between 2015 and 2020. However, understanding the relationship between changes in groundwater volumes and ground deformation has proven difficult. We employ surface displacement measurements from Interferometric Synthetic Aperture Radar (InSAR) and gravimetric estimates of terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) satellite pair to characterize the hydrological dynamics within the Tulare basin. The removal of the long-term aquifer compaction from the InSAR time series reveals coherent short-term variations that correlate with hydrological features. For example, in the winter of 2018–2019 uplift is observed at the confluence of several rivers and streams that drain into the southeastern edge of the basin. These observations, combined with estimates of mass changes obtained from the orbiting GRACE satellites, form the basis for imaging the monthly spatial variations in water volumes. This approach facilitates the quick and effective synthesis of InSAR and gravimetric datasets and will aid efforts to improve our understanding and management of groundwater resources around the world.

Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data

Groundwater (GW) is a vital resource for human consumption, agriculture, environmental sustainability, and socio-economic development in different parts of the world. Besides, GW plays a crucial role in minimizing the impacts during extreme drought events. However, there is a fast decline in groundwater resources due to increased sectoral water demand compounded by reduced rainfall and rising temperature. Therefore, it is essential to develop appropriate tools to study spatio-temporal dynamics and predict groundwater levels to improve water resource management, especially during drought events. In this study, we applied machine learning algorithms based on support vector machines (SVMs), combined with data assimilation (DA) technique to predict the change in groundwater levels (CGWLs) at 1 to 3-month time scales for 46 GW wells located at the northeast United States. The in-situ climate variables and the Gravity Recovery and Climate Experiment (GRACE) mission-informed groundwater anomalies data (GWA) are used to develop the models. The results suggest that SVMs (SVM-DA) models forced with limited climate variables (i.e., precipitation, solar radiation, air temperature, infrared surface temperature) can forecast the CGWLs up to 3-month lead times at most of the locations. The addition of GRACE data as a forcing variable can improve the performance of SVMs at most of the stations, where a strong relationship exists between the CGWLs and the GWA. The SVM-DA model comparatively performed better than SVMs at most of the stations.

Effects of climate and irrigation on GRACE-based estimates of water storage changes in major US aquifers

Understanding climate and human impacts on water storage is critical for sustainable water-resources management. Here we assessed climate and human drivers of total water storage (TWS) variability from Gravity Recovery and Climate Experiment (GRACE) satellites compared with drought severity and irrigation water use in 14 major aquifers in the United States. Results show that long-term variability in TWS tracked by GRACE satellites is dominated by interannual variability in most of the 14 major US aquifers. Low TWS trends in the humid eastern U.S. are linked to low drought intensity. Although irrigation pumpage in the humid Mississippi Embayment aquifer exceeded that in the semi-arid California Central Valley, a surprising lack of TWS depletion in the Mississippi Embayment aquifer is attributed to extensive streamflow capture. Marked storage depletion in the semi-arid southwestern Central Valley and south-central High Plains totaled ∼90 km3, about three times greater than the capacity of Lake Mead, the largest U.S. reservoir. Depletion in the Central Valley was driven by long-term droughts (⩽5 yr) amplified by switching from mostly surface water to groundwater irrigation. Low or slightly rising TWS trends in the northwestern (Columbia and Snake Basins) US are attributed to dampening drought impacts by mostly surface water irrigation. GRACE satellite data highlight synergies between climate and irrigation, resulting in little impact on TWS in the humid east, amplified TWS depletion in the semi-arid southwest and southcentral US, and dampened TWS deletion in the northwest and north central US Sustainable groundwater management benefits from conjunctive use of surface water and groundwater, inefficient surface water irrigation promoting groundwater recharge, efficient groundwater irrigation minimizing depletion, and increasing managed aquifer recharge. This study has important implications for sustainable water development in many regions globally.

Identifying and separating climate- and human-driven water storage anomalies using GRACE satellite data

Hydrological changes, which are affected by climate variability and human activities, pose challenges to the sustainable management and conservation of water resources. As such, it is important to improve our understanding of how climate and anthropogenic activities impact land water storage. Previous studies using Gravity Recovery and Climate Experiment (GRACE) satellite data have struggled to effectively separate the impacts of climate variability and human activities on water storage. Here, we propose a statistical model that simulates natural dynamically driven changes in water storage to reconstruct climate-driven water storage anomalies at basin scales and discuss the possibility of differentiating climate- and human-driven water storage anomalies. The prediction and reconstruction performance of this method shows optimal robustness. Compared with water storage anomalies calculated using land surface models, our reconstruction results are closer to GRACE observations, especially in humid basins with few human interventions. Mismatches are found in basins with intensive human interventions. Isolated human-driven water storage anomalies in four representative basins correlate well with anthropogenic water usage in each basin. The results of this study provide a method to effectively separate and monitor climate- and human-driven water storage anomalies, thereby providing information for better protection and utilization of water resources.

Effects of Extreme Climate Change in 2016 and Its Correlation with EWH Variability in Indonesia

Gravity Recovery and Climate Experiment (GRACE) satellite mission, which is dedicated specifically to the Earth’s gravity change monitoring, also can be used to observe water mass variations. GRACE data is provided through temporal mass variations, primarily water volume variations such as surface water and groundwater changes. GRACE satellite data can be obtained in monthly spherical harmonics coefficients depicting the change of surface mass density. This study investigates the effects of extreme climate change during 2016 in Indonesia by utilizing the GRACE data. Here, the surface mass density change is converted into equivalent water height (EWH). Since the amount of rainfall in Indonesia indirectly dominates the water mass variability, the EWH is then compared with the precipitation over the same period of this study. The result showed that during 2016, the highest and lowest EWH was presented in August with the highest value of 23.527 cm over the Aceh region and the lowest EWH of 0.665 cm over the Nusa Tenggara region. The most increased precipitation during 2016 is in the Kalimantan region, with a value of 0.610 mm/hr in February. The lowest one is in the Nusa Tenggara region, with a value of 0.009 mm/hr in August. For comparison with the EWH, the precipitation rates were converted into monthly precipitation in cm. The correlations between EWH and precipitations variabilities showed different results in different study zones, with the highest correlation in zone 6, which covers North Kalimantan. In comparison, the lowest correlation occurred in zone 25, which covers Yamdena and Tragan islands_ area in the Moluccas

Two Severe Prolonged Hydrological Droughts Analysis over Mainland Australia Using GRACE Satellite Data

In recent years, many droughts have happened over mainland Australia, especially the two severe prolonged droughts, from 2006 to 2009 and 2018 to 2020, resulting in serious water scarcity. Therefore, using the Total Storage Deficit Index (TSDI) from the Gravity Recovery and Climate Experiment (GRACE), we analyzed the two severe prolonged droughts from the perspective of the affected area, spatial evolution, frequency, severity and drought driving factors. The results show that the affected area of Drought 2006–2009 ranged from 57% to 95%, and that of Drought 2018–2020 ranged from 45% to 95%. Drought 2006–2009 took its rise in southeastern Australia and gradually spread to the central part. Drought 2018–2020 originated in the southwest corner of the Northern Territory and northern New South Wales, and gradually expanded to Western Australia and the whole New South Wales respectively. During Drought 2006–2009, Victoria suffered drought all months, including 59% mild drought and 41% moderate drought, North Territory had the highest drought severity of 44.26 and Victoria ranked the second high with the severity of 35.51 (cm months). For Drought 2018–2020, Northern Territory was also dominated by drought all months, including 92% mild drought and 8% moderate drought, the drought severities were in North Territory and Western Australia with 52.19 and 31.44 (cm months), respectively. Finally, the correlation coefficients between the two droughts and Indo-Pacific climate variability including El Niño-Southern Oscillation and Indian Ocean Dipole (IOD) are computed. By comparing the correlation coefficients of Drought 2018–2020 with Drought 2006–2009, we find that the impact of the El Niño on the hydrological drought becomes weaker while IOD is stronger, and the role of Southern Oscillation on droughts is diverse with the quite different spatial patterns. The results from Fourier analysis confirm that the two hydrological droughts are all related to Indo-Pacific climate variability but with slightly different driving mechanisms.

Satellite Monitoring of Mass Changes and Ground Subsidence in Sudan’s Oil Fields Using GRACE and Sentinel-1 Data

Monitoring environmental hazards, owing to natural and anthropogenic causes, is an important issue, which requires proper data, models, and cross-validation of the results. The geodetic satellite missions, for example, the Gravity Recovery and Climate Experiment (GRACE) and Sentinel-1, are very useful in this respect. GRACE missions are dedicated to modeling the temporal variations of the Earth’s gravity field and mass transportation in the Earth’s surface, whereas Sentinel-1 collects synthetic aperture radar (SAR) data, which enables us to measure the ground movements accurately. Extraction of large volumes of water and oil decreases the reservoir pressure and form compaction and, consequently, land subsidence occurs, which can be analyzed by both GRACE and Sentinel-1 data. In this paper, large-scale groundwater storage (GWS) changes are studied using the GRACE monthly gravity field models together with different hydrological models over the major oil reservoirs in Sudan, that is, Heglig, Bamboo, Neem, Diffra, and Unity-area oil fields. Then, we correlate the results with the available oil wells production data for the period of 2003–2012. In addition, using the only freely available Sentinel-1 data, collected between November 2015 and April 2019, the ground surface deformation associated with this oil and water depletion is studied. Owing to the lack of terrestrial geodetic monitoring data in Sudan, the use of GRACE and Sentinel-1 satellite data is very valuable to monitor water and oil storage changes and their associated land subsidence over our region of interest. Our results show that there is a significant correlation between the GRACE-based GWS anomalies (ΔGWS) and extracted oil and water volumes. The trend of ΔGWS changes due to water and oil depletion ranged from –18.5 ± 6.3 to –6.2 ± 1.3 mm/year using the CSR GRACE monthly solutions and the best tested hydrological model in this study. Moreover, our Sentinel-1 SAR data analysis using the persistent scatterer interferometry (PSI) method shows a high rate of subsidence, that is, –24.5 ± 0.85, –23.8 ± 0.96, –14.2 ± 0.85, and –6 ± 0.88 mm/year over Heglig, Neem, Diffra, and Unity-area oil fields, respectively. The results of this study can help us to control the integrity and safety of operations and infrastructure in that region, as well as to study the groundwater/oil storage behavior.

Daily GRACE satellite data evaluate short-term hydro-meteorological fluxes from global atmospheric reanalyses

Changes in terrestrial water storage as observed by the satellite gravity mission GRACE (Gravity Recovery and Climate Experiment) represent a new and completely independent way to constrain the net flux imbalance in atmospheric reanalyses. In this study daily GRACE gravity field changes are used for the first time to investigate high-frequency hydro-meteorological fluxes over the continents. Band-pass filtered water fluxes are derived from GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days corresponding to typical time-scales of weather system persistence at moderate latitudes. By comparison with the latest atmospheric reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) we show that daily GRACE gravity field models contain realistic high-frequency water flux information. Furthermore, GRACE-derived water fluxes can clearly identify improvements realized within ERA5 over its direct predecessor ERA-Interim particularly in equatorial and temperate climate zones. The documented improvements are in good agreement with rain gauge validation, but GRACE also identifies three distinct regions (Sahel Zone, Okavango Catchment, Kimberley Plateau) with a slight degradation of net-fluxes in ERA5 with respect to ERA-Interim, thereby highlighting the potentially added value of non-standard daily GRACE gravity series for hydro-meteorological monitoring purposes.

GRACE satellite monitoring and driving factors analysis of groundwater storage under high-intensity coal mining conditions: a case study of Ordos, northern Shaanxi and Shanxi, China

Coal mining in northwestern China is an important industry. For the traditional monitoring of water resources in coal-rich regions, a single monitoring well or remote-sensing image is often used to obtain the groundwater level or water body area. The process is restricted by the spatial distribution of monitoring wells and the quality of remote sensing images. The regions of Ordos, Northern Shaanxi (including Yan’an and Yulin cities), herein collectively referred to as OYY, and Shanxi (SX) were studied. Here, groundwater storage anomalies (GWSA) were derived using the gravity recovery and climate experiment (GRACE) satellite data and WaterGAP global hydrology model, and the change trend of groundwater storage (GWS) was explored. Using time series analysis and grey slope relational analysis, the potential driving factors of regional GWSA were derived and considered independent variables. In combination with GWSA, the quantitative relationship between the variables was established by partial least squares regression. Results showed that: (1) the decreasing rate of GWS in OYY and SX reached –0.65 and –1.16 cm/year, respectively, from 2003 to 2014; (2) the main driving factors leading to the reduction of GWS included coal-mining water consumption for OYY and water consumption by coal mining and agricultural irrigation for SX, and the weights of water consumption by coal mining and agricultural irrigation for SX were both 50%. Therefore, GRACE satellite data show good application in groundwater monitoring of coal-mining concentrated areas, providing an important basis for the formulation of water resource management measures.

Entropy Analysis of Seasonal Groundwater Level Fluctuations for Delhi and Haridwar RegionsofIndia UsingGrace Satellite Data

Groundwater monitoring and its spatio-temporal study require installation and management of ground-based observation wells on a large scale. The cost associated with such a study is generally high. An alternative to it is to use remote sensing data to manage groundwater resources in the least cost. There are only a few satellites which can provide gravity-based groundwater data. Gravity Recovery and Climate Experiment (GRACE) is a satellite which measures the change in gravity and is further used to study groundwater fluctuations. In the present study, groundwater fluctuations data (Product of GRACE satellite data) for Haridwar and Delhi region of India has been used to study the temporal and spatial variability using entropy theory. The temporal data from 2003 to 2016 has been used for both regions. The results suggested that the groundwater fluctuations are increasing in both regions of the study area. Results suggested that fluctuation of groundwater was high for the winter season of all years, but in the post-monsoon season, the fluctuation in between Delhi and Hardwar has been detected just about same Seasonal fluctuation in water level for both regions showed a maximum rise of 60 cm in water level and also maximum fall in the same range

Estimates of groundwater depletion under extreme drought in the Brazilian semi-arid region using GRACE satellite data: application for a small-scale aquifer

The temporal and spatial monitoring of groundwater levels is among the most widely used techniques for understanding groundwater reserves, which is essential for the management of regions with drought-related issues. Between 2010 and 2017, the Brazilian semi-arid region suffered a severe drought, presenting intensity and societal impacts undetected in decades. This research aimed to understand how Gravity Recovery and Climate Experiment (GRACE) data can be used as a tool for monitoring groundwater reserves in one of the most important aquifers in the Araripe Sedimentary Basin (Middle Aquifer System), located in a developing region with scarce amounts of data, and where 84,000,000 m3 of groundwater is abstracted annually through pumping. Groundwater storage (GWS) in-situ data were related to GWS estimates based on a combination of GRACE-based terrestrial water storage (TWS with both mascon and spherical harmonic solutions) and Global Land Data Assimilation System (GLDAS) soil moisture (CLM, MOS, NOAH and VIC models were evaluated). Results were analyzed with Nash-Sutcliffe (NS) and Pearson correlation coefficient metrics, and showed that the GWS GRACE-based estimate using the Community Land Model (CLM) land-surface model was more suitable for representing aquifer storage variations. Seven wells (58%) demonstrated a NS > 0.50 for both GWS GRACE-based solutions. In conclusion, GWS GRACE-based methodology has potential for monitoring the 1,394-km2 outcrop area of the Middle Aquifer System.

Assessment of Physical Water Scarcity in Africa Using GRACE and TRMM Satellite Data

The critical role of water in enabling or constraining human well-being and socioeconomic activities has led to an interest in quantitatively establishing the status of water (in)sufficiency over space and time. Falkenmark introduced the first widely accepted measure of water status, the Water Scarcity Index (WSI), which expressed the status of the availability of water resources in terms of vulnerability, stress, and scarcity. Since then, numerous indicators have been introduced, but nearly all adopt the same basic formulation; water status is a function of “available water” resource—by the demand or use. However, the accurate assessment of “available water” is difficult, especially in data-scarce regions, such as Africa. In this paper, therefore, we introduce a satellite-based Potential Available Water Storage indicator, PAWS. The method integrates GRACE (Gravity Recovery and Climate Experiment) satellite Total Water Storage (TWS) measurements with the Tropical Rainfall Measuring Mission (TRMM) precipitation estimates between 2002 and 2016. First, we derived the countries’ Internal Water Storage (IWS) using GRACE and TRMM precipitation data. Then, the IWS was divided by the population density to derive the PAWS per capita. Following the Falkenmark thresholds, 54% of countries are classified in the same water vulnerability status as the AQUASTAT Internal Renewable Water Resources (IRWR) method. Of the remaining countries, PAWS index leads to one or two categories shift (left or right) of water status. The PAWS index shows that 14% (~160 million people) of Africa’s population currently live under water scarcity status. With respect to future projections, PAWS index suggests that a 10% decrease in future water resources would affect ~37% of Africa’s 2025 population (~600 million people), and 57% for 2050 projections (~1.4-billion people). The proposed approach largely overcomes the constraints related to the data needed to rapidly and robustly estimate available water resources by incorporating all stocks of water within the country, as well as underscores the recent water storage dynamics. However, the estimates obtained concern potential available water resources, which may not be utilizable for practical, economic, and technological issues.

Gravity Changes Due to Large Earthquakes Detected in GRACE Satellite Data via Empirical Orthogonal Function Analysis

AbstractWe report on the seven largest earthquakes for which we can detect unequivocal coseismic signals in the time‐variable gravity (TVG) data of the GRACE (Gravity Recovery and Climate Experiment) satellite mission during the GRACE era of 2002–2017. After reducing the land‐hydrological effect according to model Global Land Data Assimilation System, we employ the empirical orthogonal function (EOF) analysis to solve for the spatial pattern and time history of the coherent (standing‐oscillation) modes, which provide in an objective manner an overall view of the spatiotemporal scenario. The solved EOF mode for an earthquake‐induced TVG typically features a spatial pattern (along with polarity) for the coseismic jump, in conjunction with the time series showing the jump (plus possible preseismic and postseismic variability). In all cases the earthquake EOF mode solutions match well with the least squares coseismic jump in both spatial pattern and amplitude and agree generally with previous reports in the literature. We conclude according to the examined cases that the lowest earthquake magnitude threshold that can be detected by GRACE is the Mw 8.3 of the 2013 deep‐focus Okhotsk event, which in fact provides a testable case where the TVG we observe versus the seismologically derived source models may reflect different aspects of the source mechanism under different timescales.