Groundwater Storage Changes Research Articles

Estimation of groundwater level and storage changes using innovative trend analysis (ITA), GRACE data, and google earth engine (GEE)

Groundwater is the supreme source of fresh water and the third-largest water resource around the world. But in recent decades, groundwater-related issues in Manbhum Singhbhum Plateau have been significantly increasing day by day. In this plateau region, overuse of groundwater and inappropriate irrigation has greatly harmed the ecosystem, triggering salinity, increasing contamination of groundwater. Therefore, it is essential to estimate the magnitude of groundwater storage changes and water table fluctuation in both pre and post-monsoon seasons in this plateau fringe. The principal objectives of this study are i) to investigate groundwater level changes in the last two decades using observed water level data and ii) to estimate groundwater storage changes using satellite-derived datasets in a hard rock terrain region of India. The Innovative Trend Analysis (ITA) technique has been applied for its better visual interpretation and advantage in serial correlation than the traditional Mann-Kendell test. Gravity Recovery and Climate Experiment (GRACE) gridded data have been processed to obtain a time series trend of mean Equivalent Water Thickness (EWT) and soil moisture has been estimated in Google Earth Engine (GEE). Therefore, soil moisture is subtracted from terrestrial water storage (TWS) to estimate the changes in groundwater storage (GWS) in this plateau fringe. ITA result indicates that approximately 40% and 67.5% of monitoring sites had seriously dropping tendencies of groundwater table in pre and post-monsoon seasons respectively. Simultaneously, maximum groundwater storage (9.484 cm) occurred in Jul 2004 (wet months) whereas a minimum of −30.21 in March 2016 (dry months). Overall results indicate massive decline in groundwater table and groundwater storage. The main reasons for these alarming challenges are geological structure and excessive extraction of groundwater for drinking and irrigation purposes. These research findings will help to further investigations and perform crucial well-organized management of water resources in this part of Manbhum-Singhbhum Plateau.

Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning

ABSTRACT Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution. The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021. The factors influencing Groundwater Storage Anomalies (GWSA) were explored using Permutation Importance (PI) and other methods. The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy; the Root Mean Square Error (RMSE) can be reduced by up to 18.95%. Furthermore, Blender ensemble learning decreased the RMSE by 3.58%, achieving an R-Square (R2) value of 0.7924. Restricting the downscaling inversion to June–August data greatly enhanced the accuracy, as evidenced by a holdout dataset test with an R2 value of 0.8247. The overall GWSA variation from January to August exhibited ‘slow rise, slow fall, sharp fall, and sharp rise.’ Additionally, heavy rain exhibits a lag effect on the groundwater supply. Meteorological and topographical factors drive fluctuations in GWSA values and changes in spatial distribution. Human activities also have a significant impact.

Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model

The continuous ecological river supplement has enhanced the recharge of groundwater in the North China Plain, and alleviated the depletion of groundwater resources. In the study of ecological river supplement, efficient quantitative prediction of groundwater level (GWL) and groundwater storage (GWS) is a crucial issue. To predict GWL and estimate the variation of GWS in the North China Plain, this paper uses the attention-gated recurrent unit (Attention-GRU) model as a primary method and coefficient of determination (R2) and root mean square error (RMSE) as the indices to evaluate model performance. Compared to traditional recursive neural network (RNN) and long short-term memory (LSTM) model, Attention-GRU model demonstrated an outstanding performance, as the RMSE is respectively 0.5 m and 2.28 m in the training and testing periods, and the R2 is 0.87 and 0.68. With the usage of the convolution neural network (CNN) model, the specific yield field was continuously updated from 2018 to 2021. The average annual variation of GWS estimated by this method is respectively −15.19 × 104 m3, −13.29 × 104 m3, 2.55×104 m3 and 100.05×104 m3 from 2018 to 2021 in the North China Plain. Besides, the semiannual GWS variation, calculated using the updated specific yield, is −59.56 × 104 m3 in 2022. Furthermore, the influence of the reference time length and the driving factors were evaluated. Results show that the precipitation, evaporation and human activities are the most important temporal features influencing the changes in GWS, and the changes in different areas have varying local characteristics. In the piedmont plain, river ecological recharge played a crucial role for groundwater storage recovery. When it moved to the coastal plain, the contribution rate of recharge decreased from 23% to 11%. In addition, the optimal reference time length used in Attention-GRU model varies in different hydrogeology divisions, and the specific yield variation would result in substantial differences for the GWS estimation under different climate conditions and human activities. The systematic framework proposed in this paper is effective for evaluating and predicting GWL and GWS, and thereby able to support the management and development of groundwater, and provide a guidance for ecological river supplement.

Water Balance for the Euphrates Aquifer, South of Haditha District, Al-Anbar Governorate

Groundwater balance calculation was carried for the Euphrates aquifer extended in the southern parts of Haditha district within Al-Anbar Governorate, where the Euphrates aquifer in this area is considered as unconfined with a flow direction towards the Euphrates River. The yearly recharge quantity for the Western Desert catchment-basin (1664.19 ×106 m3/year); whilst the ground-water quantity that intervenes study area (1155926.53 m3/ year), as regenerated storativity. The quantity of consumed ground-water in the area through the present study (423567.36 m3/year) for both industrial and domestic uses. Therefore, the quantity of the ground-water storage change (ΔS) you will be (732359.17 m3/year). It is obviously that there is an increase in the continual storativity and the regenerated storage is sufficient to cover the water requirement.

Evaluating the Hydrological Components Contributions to Terrestrial Water Storage Changes in Inner Mongolia with Multiple Datasets.

In this study, multiple remote sensing data were used to quantitatively evaluate the contributions of surface water, soil moisture and groundwater to terrestrial water storage (TWS) changes in five groundwater resources zones of Inner Mongolia (GW_I, GW_II, GW_III, GW_IV and GW_V), China. The results showed that TWS increased at the rate of 2.14 mm/a for GW_I, while it decreased at the rate of 4.62 mm/a, 5.89 mm/a, 2.79 mm/a and 2.62 mm/a for GW_II, GW_III, GW_IV and GW_V during 2003-2021. Inner Mongolia experienced a widespread soil moisture increase with the rate of 4.17 mm/a, 2.13 mm/a, 1.20 mm/a, 0.25 mm/a and 1.36 mm/a for the five regions, respectively. Significant decreases were detected for regional groundwater storage (GWS) with the rate of 2.21 mm/a, 6.76 mm/a, 6.87 mm/a, 3.01 mm/a, and 4.14 mm/a, respectively. Soil moisture was the major contributor to TWS changes in GW_I, which accounted 58% of the total TWS changes. Groundwater was the greatest contributor to TWS changes in other four regions, especially GWS changes, which accounted for 76% TWS changes in GW_IV. In addition, this study found that the role of surface water was notable for calculating regional GWS changes.

Terrestrial and groundwater storage characteristics and their quantification in the Chitral (Pakistan) and Kabul (Afghanistan) river basins using GRACE/GRACE-FO satellite data

In Pakistan and Afghanistan, intensive groundwater abstraction has accelerated socioeconomic development, but it also endangers the long-term sustainability of groundwater resources. Sustainable water resource management throughout river basins requires a spatiotemporal analysis of groundwater storage changes, but continuous groundwater monitoring is critical while there are few observation wells in the Chitral Kabul River Basin (CKRB). Therefore, this study uses total water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on (GRACE-FO) and the Global Land Data Assimilation System (GLDAS) model water storage components, such as surface runoff (Qs), soil moisture content (SMS), and snow water equivalent (SWE) to determine the characteristics of groundwater storage (GWS) variations from 2003 to 2021. The seasonal decomposition LOESS method (STL) was used to assess the long-term trend, seasonal trend, and associated uncertainty of TWS and GWS time-series, while Mann-Kendall and Sen's slope estimator was used to detect and quantify the increasing and decreasing trend values of TWS and GWS time-series. The results showed that TWS indicated a general decreasing trend of − 3.67 ± 0.98 mm/month while GWS indicated a harsh declining trend of − 7.83 ± 0.52 mm/month from 2003 to 2021 in CKRB. The validation of GRACE-derived GWS results showed a correlation of R2 = 0.47 in comparison to in situ GWS. Spatially and temporally, according to sub-basins of CKRB, the more severe decline of TWS in Chitral-Kunar (− 2.66 mm/year) and GWS in Swat (− 4.34 mm/year) was observed. This research would be insightful to estimate the agroeconomic impact of the intensive groundwater in CKRB and its sub-basins.

Impact of Climate Change on the Spatio-Temporal Variation in Groundwater Storage in the Guangdong–Hong Kong–Macao Greater Bay Area

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is one of the world’s four major bay areas. Groundwater is indispensable in ensuring water supply for human production and living, as well as social and economic development. Studying the spatial–temporal variation in groundwater storage (GWS) and exploring the impact of climate change on GWS is of great significance for water resource management in the GBA. In this work, we conducted a simulation using the Community Land Model version 5.0 (CLM5.0) and combined it with Gravity Recovery and Climate Experiment (GRACE) data to calculate GWS in the GBA. In addition, based on the multiple linear regression model, we quantitatively assessed the effects of different climate factors on the change in GWS in the GBA. Comparisons with groundwater wells, automatic weather stations, and satellite observations demonstrated reasonable results. Our results showed that precipitation and evapotranspiration are the main factors affecting the change in GWS in the GBA. Precipitation dominates GWS anomaly changes in areas where wetting and precipitation vary drastically, such as the northern part of Foshan. GWS is closely related to evapotranspiration, in which water and heat changes are significant.

Spatiotemporal variability and controlling factors of groundwater depletion in endorheic basins of Northwest China

Recent global groundwater overpumping is threatening ecosystem stability and food security, particularly in arid basins. A solid investigation regarding the drivers of groundwater depletion is vital for groundwater restoration, hitherto, yet it remains largely unquantified. Here, a framework to quantify the contribution of natural forcing (NF) and anthropogenic perturbations (AP) to groundwater storage anomalies (GWSA) variability by separating the GWSA estimated by the Gravity Recovery and Climate Experiment (GRACE) satellite into natural- and human-induced GWSA was proposed in the northwest endorheic basin (NWEB) of China. Further, a multiple linear regression model was established for GWSA change prediction. Our results showed that, during the period 2003–2020, the GWSA depleted at a rate of 0.25 cm yr−1 in the entire NWEB. In addition, GWSA was found to decrease significantly (exceeding 1 cm yr−1) in the west of NWEB where there are heavily irrigated areas, and has become one of the regions with the most serious groundwater depletion in China. Whereas a significantly increasing trend (greater than 0.5 cm yr−1) was observed in the Qaidam basin and south part of the Tarim River basin, becoming a groundwater enrichment reservoir in NWEB. The negative contribution of AP to groundwater depletion has increased from 3% to 95% in the last decade, as determined by separating the effects of NF and AP on GWSA. The rapid expansion of the cropland area and the increase in water use due to population growth are investigated to be the main reasons for GWSA depletion, particularly in the North Tianshan Rivers, Turpan-Hami, and Tarim River basins. Therefore, we conclude that AP are dominating and accelerating groundwater depletion in the NWEB. The increase of GWSA in the Qaidam basin has been attributed to the increase in solid water melt and regional precipitation. The western route project of China’s south-north water diversion and water-saving irrigation are important ways to solve the problem of groundwater depletion in NWEB. Our results emphasize that a more feasible framework capable of reliably identifying the driving factors of groundwater storage change is a necessary tool for promoting the sustainable management of groundwater resources under both NF and AP in arid endorheic basins.

Reconstructing Groundwater Storage Changes in the North China Plain Using a Numerical Model and GRACE Data

Groundwater has been extensively exploited in the North China Plain (NCP) since the 1970s, leading to various environmental issues. Numerous studies have utilized Gravity Recovery and Climate Experiment (GRACE) satellite data to analyze changes in groundwater storage in the NCP and provide valuable insights. However, the low spatial resolution of GRACE data has posed challenges for its widespread application, and there have been limited studies focusing on refining groundwater storage changes in the NCP. In addition, the lack of data on the gap period between GRACE and GRACE-FO hinders in-depth research on regional groundwater storage anomalies (GWSA). This paper applied a groundwater storage model called NGFLOW-GRACE to construct a groundwater storage change model in the NCP at spatial resolutions of both 1° and 0.05°. The groundwater storage change model was calibrated and driven using gratis data, with hydrogeological parameter values estimated using the shuffled complex evolution algorithm (SCE-UA). The model exhibited favorable performance, with correlation coefficients greater than 0.85 during the calibration period and 55% of coefficients greater than 0.50 during the validation period. Interestingly, the results indicate that different combinations of remote sensing data do not significantly impact the outcomes, while the hydraulic gradient coefficient demonstrates the highest sensitivity. Appropriate reconstructed data were selected within the empty window period, and by downscaling the model to a resolution of 0.05°, a complete cycle (January 2003 to December 2020) of GWSA was derived. Through comprehensive comparisons with previous research findings on both temporal and spatial scales, it can be concluded that the downscaled groundwater storage changes obtained from the established model demonstrated high reliability.

Spatial-temporal variations of groundwater storage in China: A multiscale analysis based on GRACE data

Groundwater storage (GWS) plays a key role in the freshwater supply in China. Revealing the changes in GWS in China is critical for realizing sustainable utilization of groundwater resources. We used Gravity Recovery and Climate Experiment (GRACE) satellite data as well as HydroSHEDS datasets to quantify the variation of GWS in China from 2003 to 2020 at multiple scales. We found that GWS was decreasing at a rate of -2.09 mm/a in China. Three hot-spot areas with dramatic GWS decrease, located in Northwest, Southwest and North China, accounted for 55.52% of the total GWS decrease in China, and the reduction in GWS in these areas was driven primarily by human activities. Additionally, more than half of the urban agglomerations in China potentially impacted by the declining GWS. Therefore, it is suggested that targeted measures should be taken according to local conditions to strengthen the scientific management of groundwater.

Drift of Earth's Pole Confirms Groundwater Depletion as a Significant Contributor to Global Sea Level Rise 1993–2010

AbstractClimate model estimates show significant groundwater depletion during the 20th century, consistent with global mean sea level (GMSL) budget analysis. However, prior to the Argo float era, in the early 2000’s, there is little information about steric sea level contributions to GMSL, making the role of groundwater depletion in this period less certain. We show that a useful constraint is found in observed polar motion (PM). In the period 1993–2010, we find that predicted PM excitation trends estimated from various sources of surface mass loads and the estimated glacial isostatic adjustment agree very well with the observed. Among many contributors to the PM excitation trend, groundwater storage changes are estimated to be the second largest (4.36 cm/yr) toward 64.16°E. Neglecting groundwater effects, the predicted trend differs significantly from the observed. PM observations may also provide a tool for studying historical continental scale water storage variations.

A GRACE/GFO Empirical Low-Pass Filter to Extract the Mass Changes in Nicaragua

Among the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on temporal gravity products, the north–south stripe noise in the spherical harmonic coefficient (SHC) products contaminates the inversion of the Earth’s mass field. In this study, GRACE SHC products are adopted to estimate the mass changes in Nicaragua. To improve this estimation, we propose an empirical low-pass filter to suppress stripe noise. After only using our filter, the Nicaragua regional uncertainty diminishes from 123.26 mm to 69.11 mm, and the mean signal-to-noise ratio of all available months (2002–2021) improves from 1.67 to 1.8. Subsequently, our filter is employed to estimate the basin terrestrial water storage (TWS) change in Nicaragua. In the end, TWS change estimations are compared with various observations such as mascon products, hydrological models, and in situ groundwater observation. The main conclusions are as follows: (1) After using the wavelet coherent analysis, there is a negative resonance between TWS and the climate factor (El Nino–Southern Oscillation) with a period of 2~4 years; (2) The significant ~3.8-year periodic signal in groundwater storage change estimation is contributed by GRACE aliasing error. Our work can provide new knowledge and references for mass change in small areas.

Quantifying Multi-Source Uncertainties in GRACE-Based Estimates of Groundwater Storage Changes in Mainland China

The Gravity Recovery and Climate Experiment (GRACE) satellites have been widely used to estimate groundwater storage (GWS) changes, yet their uncertainties related to the multi-source datasets used are rarely investigated. This study focuses on quantifying the uncertainties of GRACE GWS estimates in mainland China during 2003–2015, by generating a total of 3456 solutions from the combinations of multiple GRACE products and auxiliary datasets. The Bayesian model averaging (BMA) approach is used to derive the optimal estimates of GWS changes under an uncertainty framework. Ten river basins are further identified to analyze the estimated annual GWS trends and uncertainty magnitudes. On average, our results show that the BMA-estimated annual GWS trend in mainland China is −1.93 mm/yr, whereas its uncertainty reaches 4.50 mm/yr. Albeit the estimated annual GWS trends and uncertainties vary across river basins, we found that the high uncertainties of annual GWS trends are tied to the large differences between multiple GRACE data and soil moisture products used in the GWS solutions. These findings highlight the importance of paying more attention to the existence of multi-source uncertainties when using GRACE data to estimate GWS changes.

Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion

Groundwater depletion occurs when the extraction exceeds its recharge and further impacts water resource management around the world, especially in developing countries. In India, most groundwater level observations are only available on a seasonal scale, i.e., January (late post-monsoon), May (pre-monsoon), August (monsoon), and November (early post-monsoon). The Gravity Recovery and Climate Experiment (GRACE) data are available to estimate the monthly variation in groundwater storage (GWS) by subtracting precipitation runoff, canopy water, soil moisture, and solid water (snow and ice) from the GLDAS model. Considering GRACE-based GWS data, the data fusion is further used to estimate monthly spatial maps of groundwater levels using time-varying spatial regression. Seasonal groundwater monitoring data are used in the training stage to identify spatial relations between groundwater level and GWS changes. Estimation of unknown groundwater levels through data fusion is accomplished by utilizing spatial coefficients that remain consistent with the nearest observed months. Monthly groundwater level maps show that the lowest groundwater level is 50 to 55 m below the earth’s surface in the state of Rajasthan. The accuracy of the estimated groundwater level is validated against observations, yielding an average RMSE of 2.37 m. The use of the GWS information enables identification of monthly spatial patterns of groundwater levels. The results will be employed to identify hotspots of groundwater depletion in India, facilitating efforts to mitigate the adverse effects of excessive groundwater extraction.

Measuring basin-scale aquifer storage change and mapping specific yield in Albuquerque, New Mexico, USA, with repeat microgravity data

Study RegionThe groundwater basin underlying the City of Albuquerque, New Mexico, USA. Study FocusThe study focuses on changes in groundwater storage and how those changes relate to groundwater-level changes. Groundwater storage change was measured using repeat microgravity at 35 stations from 2016 to 2021. Usually, storage is monitored by converting groundwater-level changes to storage changes using the aquifer storage coefficient, a difficult property to measure. With gravity, storage change can be measured directly. The storage coefficient, or specific yield in an unconfined aquifer, was estimated using the gravity method at individual sites, and a map created showing how this property varies over the region. New Hydrological Insights for the RegionFor the first time, a map of specific yield was produced based on interpolated maps of gravity-derived storage change and groundwater-level change, allowing inference of this property over a broad region even without collocated monitoring wells and gravity stations. Gravity data indicate aquifer drawdown and recovery mainly occurs in the central part of the basin where pumping is greatest. This spatial distribution is not captured by the more widely spaced monitoring well network. Because the aquifer is recovering from historically greater-magnitude pumping, net-neutral storage change (inflow=outflow) occurs when pumping from the central part of the well field is about 1.54 × 107 cubic meters (12,500 acre-feet) per year.

Quantifying basin-scale changes in groundwater storage using GRACE and one-way coupled hydrological and groundwater flow model in the data-scarce Bandung groundwater Basin, Indonesia

Assessing basin-scale groundwater storage changes is often difficult when groundwater table data are scarce. In this study, we quantify the groundwater storage changes by using the Wflow_sbm hydrological model coupled with the MODLOW groundwater model in the data-scarce area of the Bandung groundwater basin, Indonesia. The soil moisture storage change calculated by Wflow_sbm plus the groundwater storage change calculated by MODFLOW is compared to the water storage change estimated by the GRACE satellite between 2005 and 2015. The calculated cross-correlation coefficient is 0.502, and 62.1% of the simulated water storage change falls within GRACE's estimated uncertainty bounds. The important context in the water storage comparison are GRACE temporally local time-lags, data gaps in GRACE datasets, and differences in model seasonal performance and analyzed domain characteristics. The two latter factors highlight the importance of considering local groundwater-related information over large-scale global datasets in basin-scale groundwater storage change assessment. Based on the groundwater flow model, the current predicament of groundwater abstraction in the Bandung groundwater basin is highly unsustainable for future groundwater uses. On average, the groundwater storage in the study area is dwindling at the rate of 87 million m3/year between 2005 and 2018, dominantly a consequence of groundwater abstraction, whose effect is rippled to further impact the whole groundwater flow regime. Agreed by the situation shown by the limited data, the simulated groundwater table drawdown, spatially, is found to be locally and non-uniformly distributed. The capability of a one-way coupled hydrological and groundwater model to investigate basin-scale groundwater storage change, with comparable estimates to the GRACE dataset, unravel the opportunity of using such methods to estimate the behavior of future groundwater storage dynamics under the changing anthropogenic and climatic factors in catchment-scale studies.

Satellite-based estimates of groundwater storage depletion over Egypt

An arid climate accompanied by a freshwater shortage plagued Egypt. It has resorted to groundwater reserves to meet the increasing water demands. Fossil aquifers were lately adopted as the sole water source to provide the irrigation water requirements of the ongoing reclamation activities in barren areas. Yet, the scarcity of measurements regarding the changes in the aquifers’ storage poses a great challenge to such sustainable resource management. In this context, the Gravity Recovery and Climate Experiment (GRACE) mission enables a novel consistent approach to deriving aquifers’ storage changes. In this study, the GRACE monthly solutions during the period 2003–2021 were utilized to estimate alterations in terrestrial water storage (TWS) throughout Egypt. Changes in groundwater storage (GWS) were inferred by subtracting soil water content, derived from the GLDAS-NOAH hydrological model, from the retrieved TWS. The secular trends in TWS and GWS were obtained using the linear least square method, while the non-parametric technique (Mann–Kendall’s tau) was applied to check the trend significance. The derived changes in GWS showed that all aquifers are undergoing a significant loss rate in their storage. The average depletion rate over the Sinai Peninsula was estimated at 0.64 ± 0.03 cm/year, while the depletion rate over the Nile delta aquifer was 0.32 ± 0.03 cm/year. During the investigated period (2003–2021), the extracted groundwater quantity from the Nubian aquifer in the Western Desert is estimated at nearly 7.25 km3. The storage loss from the Moghra aquifer has significantly increased from 32 Mm3/year (2003–2009) to 262 Mm3/year (2015–2021). This reflects the aquifer exposure for extensive water pumping to irrigate newly cultivated lands. The derived findings on the aquifers’ storage losses provide a vital source of information for the decision-makers to be employed for short- and long-term groundwater management.

Remote Sensing-Based Estimates of Changes in Stored Groundwater at Local Scales: Case Study for Two Groundwater Subbasins in California’s Central Valley

Sustainable groundwater management requires high-quality and low-latency estimates of changes in groundwater storage (∆Sgw). However, estimates of ∆Sgw produced using traditional methods, including groundwater models and well-based measurements, typically lag years behind the present because collecting the required on-the-ground data is a time consuming, expensive, and labor-intensive task. Satellite remote sensing measurements provide potential pathways to overcome these limitations by quantifying ∆Sgw through closing the water balance. However, the range of spatial scales over which ∆Sgw can be accurately estimated using remote sensing products remains unclear. To bridge this knowledge gap, this study quantified ∆Sgw for the period of 2002 through to 2021 using the water balance method and multiple remote sensing products in two subbasins (~2700 km2–3500 km2) within California’s Central Valley: (1) the Kaweah–Tule Subbasin, a region where the pumping of groundwater to support agriculture has resulted in decades of decline in head levels, resulting in land subsidence, damage to infrastructure, and contamination of drinking water and (2) the Butte Subbasin, which receives considerably more rainfall and surface water and has not experienced precipitous drops in groundwater. The remote sensing datasets which we utilized included multiple sources for key hydrologic components in the study area: precipitation, evapotranspiration, and soil moisture. To assess the fidelity of the remote sensing-based model, we compared estimates of ∆Sgw to alternative estimates of ∆Sgw derived from independent sources of data: groundwater wells as well as a widely used groundwater flow model. The results showed strong agreement in the Kaweah–Tule Subbasin in long-term ∆Sgw trends and shorter-term trends during droughts, and modest agreement in the Butte Subbasin with remote sensing datasets suggesting more seasonal variability than validation datasets. Importantly, our analysis shows that the timely availability of remote sensing data can potentially enable ∆Sgw estimates at sub-annual latencies, which is timelier than estimates derived through alternate methods, and thus can support adaptive management and decision making. The models developed herein can aid in assessing aquifer dynamics, and can guide the development of sustainable groundwater management practices at spatial scales relevant for management and decision making.

Groundwater analysis using Gravity Recovery, Climate Experiment and Google Earth Engine: Bundelkhand region, India

India is one of the highest consumers of groundwater in the world. This precious resource is important for socio-economic development and it has been extremely rapidly depleting. Therefore, monitoring of groundwater storage (GWS) change is of great significance. Dependence on in-situ groundwater measurement for accurate assessment of regional-scale GWS changes is constrained, especially over extended durations, due to poor spatiotemporal coverage of in-situ measurement and uncertainty in the storage process. In present study, Gravity Recovery and Climate Experiment (GRACE), which offers quantitative measurement of terrestrial water storage (TWS) coupled with TerraClimate data, has been used to estimate the changes in GWS from 2002 to 2017 for the Bundelkhand region of Uttar Pradesh, India. Google Earth Engine and ArcGIS are the processing software employed to process and analyse the satellite data. It was observed that, on average GWS, has been depleted by 112 mm during the studied period. The highest GWS fluctuation was observed in 2012(-247 mm to –60 mm); the lowest fluctuation for year 2009, (−162 mm to −43 mm). Volumetric groundwater depletion in the study area was calculated using the average GWS depletion. There is a reduction of 1,12,000 m3 groundwater per unit square kilometre area over the period of 16 years, however, 3.29 × 109 m3 in the entire studied region. Because of the rugged topography and hard rock terrain, the recharge process is poor. The population of the area increased by 24%, or by a factor of two, throughout the research period.

Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier

The economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of in-situ groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an accurate assessment of these changes. Therefore, a new framework is implemented in this work for predicting the underground water level using remote sensing images. Generally, the water level is defined into five levels: Critical, Overexploited, Safe, Saline, and Semi-critical, based on water quantity. In this manuscript, the remote sensing images were acquired from remote sensing images. At first, Wiener filtering was employed for preprocessing. Secondly, the Vegetation Indexes (VI) (Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), Infrared index (IRI), Radar Vegetation Index (RVI)), and statistical features (entropy, Root Mean Square (RMS), Skewness, and Kurtosis) were extracted from the preprocessed remote sensing images. Then, the extracted features were combined as a novel hydro index, which was fed to the Ensemble Classifier (EC): Neural Networks (NN), Support Vector Machine (SVM), and improved Deep Convolutional Neural Network (DCNN) models for underground water level prediction in the remote sensing images. The obtained results prove the efficacy of the proposed framework by using different performance measures. The results shows that the False Positive Rate (FPR) of the proposed EC model is 0.0083, which is better than that of existing methods. On the other hand, the proposed EC model has a high accuracy of 0.90, which is superior to the existing traditional models: Long Short-Term Memory (LSTM) network, Naïve Bayes (NB), Random Forest (RF), Recurrent Neural Network (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU).