Groundwater Storage Anomalies Research Articles

Application of GRACE to the estimation of groundwater storage change in a data‐poor region: A case study of Ngadda catchment in the Lake Chad Basin

AbstractThe present study is to explore the feasibility of GRACE‐based estimation of a groundwater storage change in a data‐poor region using a case study of the Ngadda catchment in the Lake Chad Basin. Although the Ngadda catchment has only one set of in situ time series data of groundwater from 2006 to 2009 and a limited number of groundwater measurements in 2005 and 2009, GRACE‐based groundwater storage change can be evaluated against the in situ groundwater measurements combined with specific yield data. The cross‐correlation analysis in the Ngadda catchment shows that maximum rainfall reached in July and August, whereas both the maximum total water storage anomaly and the maximum groundwater storage anomaly occurred 2months later. Whereas the mean annual amplitude of total water storage anomaly is about 17cm from both the average total water storage anomaly from three mascon products and the one from three spherical harmonic products, the mean annual amplitude of soil moisture storage anomaly is substantially varied from 5.58cm for CLM to about 14cm for NOAH and Mosaic. The goodness‐of‐fit tests show that CLM soil moisture produces the closest estimation of groundwater storage anomaly to the in situ groundwater measurements. The present study shows that GRACE‐based estimation for groundwater storage anomaly can be a cost‐effective and alternative tool to observe how groundwater changes in a basin scale under the limitation of modelling and in situ data availability.

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.

Observational Evidence of Irrigation Influence on Vegetation Health and Land Surface Temperature in India

AbstractIrrigated area in India has significantly increased after the green revolution. However, quantification of irrigation influence on vegetation health and land surface temperature (LST) remains limited. Here, we develop a high‐resolution (250 m) remotely sensed data of enhanced vegetation index (EVI) and LST from Moderate Resolution Imaging Spectroradiometer for 2000–2017 at the 8‐day temporal resolution for India to quantify the role of irrigation in the modulation of EVI, LST, and vegetation health. We show that irrigation‐dominated regions have significantly (p value < 0.05) higher EVI and cooler (1–2 K) LST during the crop‐growing season in the Indo‐Gangetic Plain. Vegetation health in highly irrigated areas is poorly correlated with meteorological drought primarily due to irrigation. Moreover, vegetation health is negatively correlated with groundwater storage anomalies in the highly irrigated Indo‐Gangetic Plain. Irrigation in Indo‐Gangetic Plain modulated EVI, LST, and vegetation health and resulted in groundwater depletion during 2002–2016.

Propagation dynamics from meteorological to groundwater drought and their possible influence factors

The propagation of meteorological drought in a complete water cycle is not limited to hydrological and agricultural droughts, but also involves groundwater drought. Moreover, the intensification of water cycle under the background of global warming may also affect the time of drought propagation. Therefore, studying the dynamic propagation and possible influence factors from meteorological to groundwater drought is helpful to monitor and assess the risk of groundwater drought. Here we use terrestrial water storage anomalies observations from the Gravity Recovery and Climate Experiment satellites and simulated soil moisture and runoff variations from the Global Land Data Assimilation System to show that the groundwater storage anomalies in the Pearl River Basin (PRB). The standardized precipitation index and drought severity index were used to characterize meteorological and groundwater drought, respectively. Results indicated that: (1) the propagation time of meteorological to groundwater drought in the PRB during 2002–2015 was 8 months, and that in spring and summer was shorter than that in autumn and winter; (2) the time of drought propagation has a significant deceasing trend (p < 0.01), indicating that the water cycle in the PRB was accelerating; (3) increasing soil moisture accelerates the response of groundwater to precipitation in the surplus period due to the stored-full runoff mechanism, whilst intensifying evapotranspiration rate and heat wave facilitate the drought propagation in the deficit period; (4) compared with Arctic Oscillation and El-Niño Southern Oscillation, Pacific Decadal Oscillation is the main driving force to accelerate drought propagation in the PRB.

Differences in Response of Terrestrial Water Storage Components to Precipitation over 168 Global River Basins

AbstractA time lag exists between precipitation P falling and being converted into terrestrial water. The responses of terrestrial water storage (TWS) and its individual components to P over the global scale, which are vital for understanding the interactions and mechanisms between climatic variables and hydrological components, are not well constrained. In this study, relying on land surface models, we isolate five component storage anomalies from TWS anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment mission (GRACE): canopy water storage anomalies (CWSA), surface water storage anomalies (SWSA), snow water equivalent anomalies (SWEA), soil moisture storage anomalies (SMSA), and groundwater storage anomalies (GWSA). The responses of TWSA and of the individual components of TWSA to P are then evaluated over 168 global basins. The lag between TWSA and P is quantified by calculating the correlation coefficients between GRACE-based TWSA and P for different time lags, then identifying the lag (measured in months) corresponding to the maximum correlation coefficient. A multivariate regression model is used to explore the relationship between climatic and basin characteristics and the lag between TWSA and P. Results show that the spatial distribution of TWSA trend presents a similar global pattern to that of P for the period January 2004–December 2013. TWSA is positively related to P over basins but with lags of variable duration. The lags are shorter in the low- and midlatitude basins (1–2 months) than those in the high-latitude basins (6–9 months). The spatial patterns of the maximum correlations and the corresponding lags between individual components of the TWSA and P are consistent with those of the GRACE-based analysis, except for SWEA (3–8 months) and CWSA (0 months). The lags between GWSA, SMSA, and SWSA to P can be arranged as GWSA &amp;gt; SMSA ≥ SWSA. Regression analysis results show that the lags between TWSA and P are related to the mean temperature, mean precipitation, mean latitude, mean longitude, mean elevation, and mean slope.

Groundwater Depletion Estimated from GRACE: A Challenge of Sustainable Development in an Arid Region of Central Asia

Under climate change and increasing water demands, groundwater depletion has become regional and global threats for water security, which is an indispensable target to achieving sustainable developments of human society and ecosystems, especially in arid and semiarid regions where groundwater is a major water source. In this study, groundwater depletion of 2003–2016 over Xinjiang in China, a typical arid region of Central Asia, is assessed using the gravity recovery and climate experiment (GRACE) satellite and the global land data assimilation system (GLDAS) datasets. In the transition of a warm-dry to a warm-wet climate in Xinjiang, increases in precipitation, soil moisture and snow water equivalent are detected, while GRACE-based groundwater storage anomalies (GWSA) exhibit significant decreasing trends with rates between-3.61 ± 0.85 mm/a of CSR-GWSA and −3.10 ± 0.91 mm/a of JPL-GWSA. Groundwater depletion is more severe in autumn and winter. The decreases in GRACE-based GWSA are in a good agreement with the groundwater statistics collected from local authorities. However, at the same time, groundwater abstraction in Xinjiang doubled, and the water supplies get more dependent on groundwater. The magnitude of groundwater depletion is about that of annual groundwater abstraction, suggesting that scientific exploitation of groundwater is the key to ensure the sustainability of freshwater withdrawals and supplies. Furthermore, GWSA changes can be well estimated by the partial least square regression (PLSR) method based on inputs of climate data. Therefore, GRACE observations provide a feasible approach for local policy makers to monitor and forecast groundwater changes to control groundwater depletion.

Estimating High-Resolution Groundwater Storage from GRACE: A Random Forest Approach

Gravity Recovery and Climate Experiment (GRACE) data have become a widely used global dataset for evaluating the variability in groundwater storage for the different major aquifers. Moreover, the application of GRACE has been constrained to the local scale due to lower spatial resolution. The current study proposes Random Forest (RF), a recently developed unsupervised machine learning method, to downscale a GRACE-derived groundwater storage anomaly (GWSA) from 1° × 1° to 0.25° × 0.25° in the Northern High Plains aquifer. The RF algorithm integrated GRACE to other satellite-based geospatial and hydro-climatological variables, obtained from the Noah land surface model, to generate a high-resolution GWSA map for the period 2009 to 2016. This RF approach replicates local groundwater variability (the combined effect of climatic and human impacts) with acceptable Pearson correlation (0.58 ~ 0.84), percentage bias (−14.67 ~ 2.85), root mean square error (15.53 ~ 46.69 mm), and Nash-Sutcliffe efficiency (0.58 ~ 0.84). This developed RF model has significant potential to generate finer scale GWSA maps for managing groundwater at both local and regional scales, especially for areas with sparse groundwater monitoring wells.

Detection of large-scale groundwater storage variability over the karstic regions in Southwest China

The 2003–2013 monthly groundwater storage (GWS) anomalies in the highly karstic region (HKR) and low karstic region (LKR) in the Southwest China are estimated from the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) data by using the ancillary data of surface water storage (SWS) and soil moisture storage (SMS) from the WaterGAP model simulations. The leakage errors in the estimated GWS anomalies are corrected through using the iterative forward modelling approach. The estimated GWS anomalies compare well with in situ groundwater-level observations with the correlation coefficient r = 0.71 and the root-mean-square-error (RMSE) = 42 mm. For both HKR and LKR, ∼60% of temporal variability of TWS is contributed by GWS variability, while SMS contributes to 18% (HKR) and 28% (LKR), and SWS contributes to 22% (HKR) and 14% (LKR) of the TWS variability. Due to the higher permeability of the epi-karst zones and their better connection with the subsurface aquifers, GWS anomalies in HKR show larger correlations with SWS (r = 0.73, RMSE = 51 mm) and SMS (r = 0.68, RMSE = 47 mm) and a shorter lag to precipitation than that in LKR (SWS: r = 0.56, RMSE = 50 mm, and SMS: r = 0.48, RMSE = 49 mm). During the extreme drought in 2009, GWS loss in HKR (LKR) was 74.3 mm/yr (42.7 mm/yr), accounting for 66% (62%) of total TWS loss. The severe GWS loss was mainly due to larger discharges through the well-developed subsurface drainage system rather than human depletion, since groundwater resources are still under-exploited in Southwest China (∼4 km3/yr, 12% of the potentially exploitable amounts). A quicker recovery of GWS from 2009 drought can be observed in HKR than LKR due to the larger and earlier (approximately one month) precipitation and infiltration, and quicker response of groundwater to precipitation in HKR.

Statistical Downscaling of GRACE‐Derived Groundwater Storage Using ET Data in the North China Plain

AbstractDue to coarse spatial resolution, the application of Gravity Recovery and Climate Experiment (GRACE) data in local groundwater resource management has been limited. To overcome this issue, a downscaling approach is presented to improve the spatial resolution of GRACE‐derived groundwater storage anomalies using evapotranspiration (ET) data. The statistical downscaling method is only applied in areas where there is a strong relationship between GRACE‐derived groundwater storage (GWS) and ET, and the relationship can be established under different spatial resolutions. In this study, GWS anomalies are downscaled from 110 km to 2 km in the North China Plain, capturing subgrid heterogeneity in groundwater storage changes. The downscaled results are further validated using data from 111 observation wells from 2003 to 2014 in the Beijing Plain. The results show that the in situ groundwater levels agree with the Gravity Recovery and Climate Experiment (GRACE) downscaled results for the Beijing Plain in both interannual and monthly scales, with the correlation coefficient of 0.94 and 0.80, respectively. The correlation coefficients vary from 0.63 to 0.78, in 10 of the 13 Beijing Plain administrative regions. The regional downscaling approach employed in this study may be readily integrated into local water resources planning activities.

Long-term groundwater storage changes and land subsidence development in the North China Plain (1971\u20132015)

The North China Plain (NCP) has been suffering from groundwater storage (GWS) depletion and land subsidence for a long period. This paper collects data on GWS changes and land subsidence from in situ groundwater-level measurements, literature, and satellite observations to provide an overview of the evolution of the aquifer system during 1971–2015 with a focus on the sub-regional variations. It is found that the GWS showed a prolonged declining rate of −17.8 ± 0.1 mm/yr during 1971–2015, with a negative correlation to groundwater abstraction before year ~2000 and a positive correlation after ~2000. Statistical correlations between subsidence rate and the GWS anomaly (GWSA), groundwater abstraction, and annual precipitation show that the land subsidence in three sub-regions (Beijing, Tianjin, and Hebei) represents different temporal variations due to varying driver factors. Continuous drought caused intensive GWS depletion (−76.1 ± 6.5 mm/yr) and land subsidence in Beijing during 1999–2012. Negative correlations between total groundwater abstraction and land subsidence exhibited after the 1980s indicate that it may be questionable to infer subsidence from regional abstraction data. Instead, the GWSA generally provides a reliable correlation with subsidence. This study highlights the spatio-temporal variabilities of GWS depletion and land subsidence in the NCP under natural and anthropogenic impacts, and the importance of GWS changes for understanding land subsidence development.

Vertical Displacements Driven by Groundwater Storage Changes in the North China Plain Detected by GPS Observations

The North China Plain (NCP) has been experiencing the most severe groundwater depletion in China, leading to a broad region of vertical motions of the Earth’s surface. This paper explores the seasonal and linear trend variations of surface vertical displacements caused by the groundwater changes in NCP from 2009 to 2013 using Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE) techniques. Results show that the peak-to-peak amplitude of GPS-derived annual variation is about 3.7~6.0 mm and is highly correlated (R &gt; 0.6 for most selected GPS stations) with results from GRACE, which would confirm that the vertical displacements of continuous GPS (CGPS) stations are mainly caused by groundwater storage (GWS) changes in NCP, since GWS is the dominant component of total water storage (TWS) anomalies in this area. The linear trends of selected bedrock-located IGS CGPS stations reveal the distinct GWS changes in period of 2009–2010 (decrease) and 2011–2013 (rebound), which are consistent with results from GRACE-derived GWS anomalies and in situ GWS observations. This result implies that the rate of groundwater depletion in NCP has slowed in recent years. The impacts of geological condition (bedrock or sediment) of CGPS stations to their results are also investigated in this study. Contrasted with the slight linear rates (−0.69~1.5 mm/a) of bedrock-located CGPS stations, the linear rates of sediment-located CGPS stations are between −44 mm/a and −17 mm/a. It is due to the opposite vertical displacements induced by the Earth surface’s porous and elastic response to groundwater depletion. Besides, the distinct renewal characteristics of shallow and deep groundwater in NCP are discussed. The GPS-based vertical displacement time series, to some extent, can reflect the quicker recovery of shallow unconfined groundwater than the deep confined groundwater in NCP; through one month earlier to attain the maximum height for CGPS stations nearby shallow groundwater depression cones than those nearby deep groundwater depression cones.

Sensitivity of the Gravity Recovery and Climate Experiment (GRACE) to the complexity of aquifer systems for monitoring of groundwater

The Gravity Recovery and Climate Experiment (GRACE) satellite mission is aimed at assessment of groundwater storage under different terrestrial conditions. The main objective of the presented study is to highlight the significance of aquifer complexity to improve the performance of GRACE in monitoring groundwater. Vidarbha region of Maharashtra, central India, was selected as the study area for analysis, since the region comprises a simple aquifer system in the western region and a complex aquifer system in the eastern region. Groundwater-level-trend analyses of the different aquifer systems and spatial and temporal variation of the terrestrial water storage anomaly were studied to understand the groundwater scenario. GRACE and its field application involve selecting four pixels from the GRACE output with different aquifer systems, where each GRACE pixel encompasses 50–90 monitoring wells. Groundwater storage anomalies (GWSA) are derived for each pixel for the period 2002 to 2015 using the Release 05 (RL05) monthly GRACE gravity models and the Global Land Data Assimilation System (GLDAS) land-surface models (GWSAGRACE) as well as the actual field data (GWSAActual). Correlation analysis between GWSAGRACE and GWSAActual was performed using linear regression. The Pearson and Spearman methods show that the performance of GRACE is good in the region with simple aquifers; however, performance is poorer in the region with multiple aquifer systems. The study highlights the importance of incorporating the sensitivity of GRACE in estimation of groundwater storage in complex aquifer systems in future studies.

Improved methods for estimating local terrestrial water dynamics from GRACE in the Northern High Plains

Abstract Investigating terrestrial water cycle dynamics is vital for understanding the recent climatic variability and human impacts in the hydrologic cycle. In this study, a downscaling approach was developed and tested, to improve the applicability of terrestrial water storage (TWS) anomaly data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission for understanding local terrestrial water cycle dynamics in the Northern High Plains region. A non-parametric, artificial neural network (ANN)–based model, was utilized to downscale GRACE data by integrating it with hydrological variables (e.g. soil moisture) derived from satellite and land surface model data. The downscaling model, constructed through calibration and sensitivity analysis, was used to estimate TWS anomaly for watersheds ranging from 5000 to 20,000 km 2 in the study area. The downscaled water storage anomaly data were evaluated using water storage data derived from an (1) integrated hydrologic model, (2) land surface model (e.g. Noah), and (3) storage anomalies calculated from in-situ groundwater level measurements. Results demonstrate the ANN predicts monthly TWS anomaly within the uncertainty (conservative error estimate = 34 mm) for most of the watersheds. Seasonal derived groundwater storage anomaly (GWSA) from the ANN correlated well ( r = ∼0.85) with GWSAs calculated from in-situ groundwater level measurements for a watershed size as small as 6000 km 2 . ANN downscaled TWSA matches closely with Noah-based TWSA compared to standard GRACE extracted TWSA at a local scale. Moreover, the ANN-downscaled change in TWS replicated the water storage variability resulting from the combined effect of climatic and human impacts (e.g. abstraction). The implications of utilizing finer resolution GRACE data for improving local and regional water resources management decisions and applications are clear, particularly in areas lacking in-situ hydrologic monitoring networks.

Spatio-temporal variability of groundwater storage in India

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Groundwater storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent). In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Validation of GRACE based groundwater storage anomaly using in-situ groundwater level measurements in India

In this study, we tried to validate groundwater storage (GWS) anomaly obtained from a combination of GRACE and land-surface model based estimates, for the first time, with GWS anomaly obtained from a dense network of in-situ groundwater observation wells within 12 major river basins in India. We used seasonal data from >15,000 groundwater observation wells between 2005 and 2013, distributed all over the country. Two recently released GRACE products, RL05 spherical harmonics (SH) and RL05 mascon (MS) products are used for comparison with in-situ data. To our knowledge, this is the first study of comparing the performance of two independent GRACE products at a sub-continental scale. Also for the first time, we have created a high resolution (0.10×0.10) map of specific yield for the entire country that was used for calculating GWS. Observed GWS anomalies have been computed using water level anomalies and specific yield information for the locale of individual observation wells that are up-scaled to basin-scale in order to compare with GRACE-based estimates. In general GRACE-based estimates match well (on the basis of the statistical analyses performed in the study) with observed estimates in most of the river basins. On comparing with observed GWS anomaly, GRACE-SH estimates match well in terms of RMSE, while GRACE-MS estimates show better association in terms of correlation, while the output of skewness, kurtosis, coefficient of variation (CV) and scatter analyses remain inconclusive for inter-comparison between two GRACE estimates. We used a non-parametric trend estimation approach, the Hodrick-Prescott (HP) filter, to further assess the performance of the two GRACE estimates. GRACE-MS estimates clearly outperform GRACE-SH estimates for reproducing observed GWS anomaly trends with significantly (>95% confidence level) strong association in 10 out of 12 basins for GRACE-MS estimates, on the other hand, GRACE-SH estimates show significantly (>95% confidence level) strong association in 6 out of 12 basins. On the basis of the study output, we recommend using GRACE-MS estimates for groundwater studies over the region and other regions of the globe with similar climatic, hydrogeologic or groundwater withdrawal conditions.

Satellite Gravimetric Estimation of Groundwater Storage Variations Over Indus Basin in Pakistan

Like other agrarian countries, Pakistan is now heavily dependent on its groundwater resources to meet the irrigated agricultural water demand. Groundwater has emerged as a major source with more than 60% contribution in total water supplies. In the absence of groundwater regulation, the uneven and overexploitation of groundwater resource in Indus Basin has caused several problems of water table decline, groundwater mining, and deterioration of groundwater quality. This study evaluates the potential of Gravity Recovery and Climate Experiment Satellite (GRACE)-based estimation of changes in groundwater storage (GWS) as a cost-effective approach for groundwater monitoring and policy recommendations for sustainable water management in the Indus basin. The GRACE monthly gravity anomalies from 2003 to 2010 were analyzed as total water storage (TWS) variations. The variable infiltration capacity hydrological model-generated soil moisture and surface runoff were used for the separation of TWS into GWS anomalies. The GRACE-based GWS anomalies are found to favorably agree with trends inferred from in situ piezometric data. A general depletion trend is observed in Upper Indus Plain (UIP) where groundwater is found to be declining at a mean rate of about 13.5 mm per year in equivalent height of water during 2003–2010. A total loss of about 11.82 km3 per year fresh groundwater stock is inferred for UIP. Based on TWS variations and ground knowledge, the two southern river plains, Bari and Rechna are found to be under threat of extensive groundwater depletion. GRACE TWS data were also able to pick up signals from the large-scale flooding events observed in 2010 and 2014. These flooding events played a significant role in the replenishment of the groundwater system in Indus Basin. Our study indicates that the GRACE-based estimation of GWS changes is skillful enough to provide monthly updates on the trend of the GWS changes for resource managers and policy makers of Indus basin.

Monitoring and comparison of terrestrial water storage changes in the northern high plains using GRACE and in-situ based integrated hydrologic model estimates

Enhanced measurement of the variation of the terrestrial water cycle are imperative to better understand the dynamics, water availability, and evaluate impacts of global changes on the water cycle. This study quantified storage in the various terrestrial water compartments using an integrated hydrologic model (IHM) – MIKE SHE that simulates the entire terrestrial water cycle and the Gravity Recovery and Climate Experiment (GRACE) satellite data in the intensively irrigated Northern High Plains (area ∼ 250,000 km2). The IHM, mainly constructed using in-situ data, was evaluated using field measured groundwater level, stream flow, and soil moisture data. The model was first used to calculate the incremental water storage for each water balance component (e.g. storage in the saturated zone) and then the GRACE equivalent terrestrial water storage anomaly. In the study area, storage in the saturated zone is the major component of the terrestrial water storage (TWS) anomaly. The GRACE-derived TWS anomaly and the anomaly simulated from the model are generally in agreement on a monthly scale with few discrepancies. Generally, both GRACE and the IHM results displayed a statistically significant increasing trend in the total TWS and groundwater storage anomalies from 2002–2013 over the Northern High Plains. This study demonstrates the applicability of an integrated hydrologic model to monitor TWS variations in a large area, and GRACE data and IHMs are capable of reproducing observed trends in TWS.

Diagnosing Land Water Storage Variations in Major Indian River Basins using GRACE observations

Scarcity of freshwater is one of the most critical resource issue the world is facing today. Due to its finite nature, renewable freshwater reserves are under relentless pressure due to population growth, economic development and rapid industrialization. Assessment of Terrestrial Water Storage (TWS), as an unified measure of freshwater reserve, is vital to understand hydrologic and climatic processes controlling its availability. In this study, TWS variations from Gravity Recovery and Climate Experiment (GRACE) satellites are analyzed in conjuction with multi-platform hydrologic observations for the period of 2003–2012. Here, the primary objective is to quantify and attribute the observed short-term variability of TWS and groundwater storage in the largest river basins of India (Ganga, Godavari, Krishna and Mahanadi). Alongside commendable agreement between TWS variations obtained from GRACE and water balance computation, results highlight some of the important deficiencies between the two. While monthly changes in TWS are highly correlated with precipitation, monthly TWS anomalies reveal a 1–2 month lag in their concurrence. Analysis of groundwater storage estimates demonstrate significant decline in the Ganga basin (−1.28±0.20mm/month) but practically no change in the Mahanadi basin. On the contrary, groundwater storage in Godavari and Krishna basins reveal notable increase at the rate of 0.74±0.21mm/month and 0.97±0.21mm/month respectively. Subsequently, in order to assess the influence of quasi-periodic, planetary scale, variations in the Earth’s climate system, groundwater storage anomalies are evaluated with reference to ENSO variability. Results manifest that in all the basins, with the exception of Ganga, groundwater storage is dominantly influenced by ENSO, with large decrease (increase) during El Niño (La Niña) events. In the Ganga basin, groundwater storage variations refer to possible amalgamation of human intervention and natural climate variability.

Calibration of a large-scale groundwater flow model using GRACE data: a case study in the Qaidam Basin, China

Traditional numerical models usually use extensive observed hydraulic-head data as calibration targets. However, this calibration process is not applicable in remote areas with limited or no monitoring data. This study presents an approach to calibrate a large-scale groundwater flow model using the monthly Gravity Recovery and Climate Experiment (GRACE) satellite data, which have been available globally on a spatial grid of 1° in the geographic coordinate system since 2002. A groundwater storage anomaly isolated from the terrestrial water storage (TWS) anomaly is converted into hydraulic head at the center of the grid, which is then used as observed data to calibrate a numerical model to estimate aquifer hydraulic conductivity. The aquifer system in the remote and hyperarid Qaidam Basin, China, is used as a case study to demonstrate the applicability of this approach. A groundwater model using FEFLOW is constructed for the Qaidam Basin and the GRACE-derived groundwater storage anomaly over the period 2003–2012 is included to calibrate the model, which is done using an automatic estimation method (PEST). The calibrated model is then run to output hydraulic heads at three sites where long-term hydraulic head data are available. The reasonably good fit between the calculated and observed hydraulic heads, together with the very similar groundwater storage anomalies from the numerical model and GRACE data, demonstrate that this approach is generally applicable in regions of groundwater data scarcity.

Groundwater variability across temporal and spatial scales in the central and northeastern U.S.

Depth-to-water measurements from 181 monitoring wells in unconfined or semi-confined aquifers in nine regions of the central and northeastern U.S. were analyzed. Groundwater storage exhibited strong seasonal variations in all regions, with peaks in spring and lows in autumn, and its interannual variability was nearly unbounded, such that the impacts of droughts, floods, and excessive pumping could persist for many years. We found that the spatial variability of groundwater storage anomalies (deviations from the long term mean) increases as a power function of extent scale (square root of area). That relationship, which is linear on a log–log graph, is common to other hydrological variables but had never before been shown with groundwater data. We describe how the derived power function can be used to determine the number of wells needed to estimate regional mean groundwater storage anomalies with a desired level of accuracy, or to assess uncertainty in regional mean estimates from a set number of observations. We found that the spatial variability of groundwater storage anomalies within a region often increases with the absolute value of the regional mean anomaly, the opposite of the relationship between soil moisture spatial variability and mean. Recharge (drainage from the lowest model soil layer) simulated by the Variable Infiltration Capacity (VIC) model was compatible with observed monthly groundwater storage anomalies and month-to-month changes in groundwater storage.