Evaluation of Groundwater Storage Variations in Northern China Using GRACE Data

Dynamic change of groundwater storage is one of the most important topics in the sustainable management of groundwater resources. Groundwater storage variations are firstly isolated from the terrestrial water storage change using the Global Land Data Assimilation System (GLDAS). Two datasets are used: (1) annual groundwater resources and (2) groundwater storage changes estimated from point-based groundwater level data in observation wells. Results show that the match between the GRACE-derived groundwater storage variations and annual water resources variation is not good in six river basins of Northern China. However, it is relatively good between yearly GRACE-derived groundwater storage data and groundwater storage change dataset in Huang-Huai-Hai Plain and the Song-Liao Plain. The mean annual depletion rate of groundwater storage in the Northern China was approximately 1.70 billion m3 yr−1 from 2003 to 2012. In terms of provinces, the yearly depletion rate is higher in Jing-Jin-Ji (Beijing, Tianjin, and Hebei province) and lowest in Henan province from 2003 to 2012, with the rate of 0.70 and 0.21 cm yr−1 Equivalent Water Height (EWH), respectively. Different land surface models suggest that the patterns from different models almost remain the same, and soil moisture variations are generally bigger than snow water equivalent variations.

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Estimating long-term groundwater storage and its controlling factors in Alberta, Canada

Hydrology and Earth System Sciences ◽

10.5194/hess-22-6241-2018 ◽

2018 ◽

Vol 22 (12) ◽

pp. 6241-6255 ◽

Cited By ~ 10

Author(s):

Soumendra N. Bhanja ◽

Xiaokun Zhang ◽

Junye Wang

Keyword(s):

Groundwater Recharge ◽

Environmental Sustainability ◽

Land Surface ◽

Water Withdrawal ◽

Groundwater Storage ◽

Budget Analysis ◽

Terrestrial Water ◽

The Individual ◽

Gravity Recovery

Abstract. Groundwater is one of the most important natural resources for economic development and environmental sustainability. In this study, we estimated groundwater storage in 11 major river basins across Alberta, Canada, using a combination of remote sensing (Gravity Recovery and Climate Experiment, GRACE), in situ surface water data, and land surface modeling estimates (GWSAsat). We applied separate calculations for unconfined and confined aquifers, for the first time, to represent their hydrogeological differences. Storage coefficients for the individual wells were incorporated to compute the monthly in situ groundwater storage (GWSAobs). The GWSAsat values from the two satellite-based products were compared with GWSAobs estimates. The estimates of GWSAsat were in good agreement with the GWSAobs in terms of pattern and magnitude (e.g., RMSE ranged from 2 to 14 cm). While comparing GWSAsat with GWSAobs, most of the statistical analyses provide mixed responses; however the Hodrick–Prescott trend analysis clearly showed a better performance of the GRACE-mascon estimate. The results showed trends of GWSAobs depletion in 5 of the 11 basins. Our results indicate that precipitation played an important role in influencing the GWSAobs variation in 4 of the 11 basins studied. A combination of rainfall and snowmelt positively influences the GWSAobs in six basins. Water budget analysis showed an availability of comparatively lower terrestrial water in 9 of the 11 basins in the study period. Historical groundwater recharge estimates indicate a reduction of groundwater recharge in eight basins during 1960–2009. The output of this study could be used to develop sustainable water withdrawal strategies in Alberta, Canada.

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The uncertainty of storage-based drought indices from GRACE

10.5194/egusphere-egu2020-3604 ◽

2020 ◽

Author(s):

Peyman Saemian ◽

Mohammad Javad Tourian ◽

Nico Sneeuw

Keyword(s):

Land Surface ◽

Water Storage ◽

Extended Period ◽

Drought Indices ◽

Terrestrial Water Storage ◽

Earth’S Gravity Field ◽

Grace Data ◽

Temporal And Spatial Variability ◽

Terrestrial Water ◽

Gravity Recovery

Climate change and the growing demand for freshwater have raised the frequency and intensity of extreme events like drought. Satellite observations have improved our understanding of the temporal and spatial variability of droughts. Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) have been observing variations in Earth's gravity field yielding valuable information about changes in terrestrial water storage anomaly (TWSA). The terrestrial water storage vertically integrates all forms of water on and beneath land surface including snow, surface water, soil moisture, and groundwater storage.Drought indices help to monitor drought by characterizing it in terms of their severity, location, duration and timing. Several drought indices have been developed based on GRACE water storage anomaly from a GRACE-based climatology, most of which suffer from the short record of GRACE, about 15 years, for their climatology. The limited duration of the GRACE observations necessitates the use of external datasets of TWSA with a more extended period for climatology. Drought characterization comes with its own uncertainties due to the inherent uncertainty in the GRACE data, the various post-processing approaches of GRACE data, and different options for external datasets on the other hand.This study offers a method to quantify uncertainties for the storage-based drought index. Moreover, we assess the sensitivity of major global river basins to the duration of the observations. The outcome of the study is invaluable in the sense that it allows for a more informative storage based drought, including uncertainty, thus enabling a more realistic risk assessment.

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Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

10.5194/hess-2020-665 ◽

2021 ◽

Author(s):

Natthachet Tangdamrongsub ◽

Michael F. Jasinski ◽

Peter Shellito

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Water Storage ◽

Land Surface Model ◽

Surface Model ◽

Model Parameters ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

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GROUNDWATER STORAGE CHANGE ESTIMATION USING GRACE SATELLITE DATA IN INDUS BASIN

Big Data In Water Resources Engineering (BDWRE) ◽

10.26480/bdwre.01.2020.10.15 ◽

2020 ◽

Vol 1 (1) ◽

pp. 10-15

Author(s):

Muhammad Salam ◽

Muhammad Jehanzeb Masud Cheema ◽

Wanchang Zhang ◽

Saddam Hussain ◽

Azeem Khan ◽

...

Keyword(s):

Water Table ◽

Land Surface ◽

Large Scale ◽

Water Storage ◽

Indus Basin ◽

Groundwater Storage ◽

Grace Satellite ◽

Over Exploitation ◽

Flooding Events ◽

Storage Change

Over exploitation of Ground Water (GW) has resulted in lowering of water table in the Indus Basin. While waterlogging, salinity and seawater intrusion has resulted in rising of water table in Indus Basin. The sparse piezometer network cannot provide sufficient data to map groundwater changes spatially. To estimate groundwater change in this region, data from Gravity Recovery and Climate Experiment (GRACE) satellite was used. GRACE measures (Total Water Storage) TWS and used to estimate groundwater storage change. Net change in storage of groundwater was estimated from the change in TWS by including the additional components such as Soil Moisture (SM), Surface water storage (Qs) and snowpack equivalent water (SWE). For the estimation of these components Global Land Data Assimilation system (GLDAS) Land Surface Models (LSMs) was used. Both GRACE and GLDAS produce results for the Indus Basin for the period of April 2010 to January 2017. The monitoring well water-level records from the Scarp Monitoring Organization (SMO) and the Punjab Irrigation and Drainage Authority (PIDA) from April 2009 to December 2016 were used. The groundwater results from different combinations of GRACE products GFZ (GeoforschungsZentrum Potsdam) CSR (Center for Space Research at University of Texas, Austin) JPL (Jet Propulsion Laboratory) and GLDAS LSMs (CLM, NOAH and VIC) are calibrated (April 2009-2014) and validated (April 2015-April 2016) with in-situ measurements. For yearly scale, their correlation coefficient reaches 0.71 with Nash-Sutcliffe Efficiency (NSE) 0.82. It was estimated that net loss in groundwater storage is at mean rate of 85.01 mm per year and 118,668.16 Km3 in the 7 year of study period (April 2010-Jan 2017). GRACE TWS data were also able to pick up the 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 the Indus Basin. Our study indicates that the GRACE based estimation of groundwater storage changes is skillful enough to provide monthly updates on the trend of the groundwater storage changes for resource managers and policy makers of Indus Basin.

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The Review of GRACE Data Applications in Terrestrial Hydrology Monitoring

Advances in Meteorology ◽

10.1155/2014/725131 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 17

Author(s):

Dong Jiang ◽

Jianhua Wang ◽

Yaohuan Huang ◽

Kang Zhou ◽

Xiangyi Ding ◽

...

Keyword(s):

Large Scale ◽

Water Cycle ◽

Grace Data ◽

Monitoring Result ◽

Terrestrial Water ◽

Terrestrial Hydrology ◽

Grace Satellite ◽

Gravity Recovery ◽

Storage Change ◽

Hydrological Monitoring

The Gravity Recovery and Climate Experiment (GRACE) satellite provides a new method for terrestrial hydrology research, which can be used for improving the monitoring result of the spatial and temporal changes of water cycle at large scale quickly. The paper presents a review of recent applications of GRACE data in terrestrial hydrology monitoring. Firstly, the scientific GRACE dataset is briefly introduced. Recently main applications of GRACE data in terrestrial hydrological monitoring at large scale, including terrestrial water storage change evaluation, hydrological components of groundwater and evapotranspiration (ET) retrieving, droughts analysis, and glacier response of global change, are described. Both advantages and limitations of GRACE data applications are then discussed. Recommendations for further research of the terrestrial water monitoring based on GRACE data are also proposed.

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Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

Remote Sensing ◽

10.3390/rs13061223 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1223

Author(s):

Manuela Girotto ◽

Rolf Reichle ◽

Matthew Rodell ◽

Viviana Maggioni

Keyword(s):

Data Assimilation ◽

Snow Water Equivalent ◽

Water Storage ◽

Synthetic Data ◽

Terrestrial Water Storage ◽

Grace Data ◽

Precipitation Estimates ◽

Terrestrial Water ◽

Snow Water ◽

Gravity Recovery

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

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CONVERSE TRENDS OF THE TERRESTRIAL AND GROUND WATER STORAGE CHANGES IN CANADA AND THE UNITED STATES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1793-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1793-1796

Author(s):

H. Wang ◽

L. Xiang ◽

H. Steffen ◽

P. Wu ◽

L. Jiang ◽

...

Keyword(s):

United States ◽

Snow Water Equivalent ◽

Water Storage ◽

High Water ◽

The United States ◽

Water Usage ◽

Grace Data ◽

Terrestrial Water ◽

Snow Water ◽

Satellite Altimetry Data

Abstract. We study the terrestrial water storage (TWS) and groundwater storage (GWS) changes in Canada and United States. We employ the separation approach from Wang et al. (2013) together with the improved GRACE data of Release 6 for a longer time span until December, 2016. The TWS signals from lake levels are derived from satellite altimetry data over the lakes while TWS signals due to soil moisture (SM) and snow water equivalent (SWE) changes from hydrology models. There are four significant trend anomalies in North America for both TWS and GWS changes. Two positive anomalies are found in Canada with their centers in the provinces of Saskatchewan and Quebec, respectively, due to increased precipitation and/or increased runoff in their surroundings. Two negative anomalies are shown in the United States with their centers in California and the northwest of Texas, respectively, which are due to decreased precipitation and, especially for California, high water usage for agriculture.

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Detect groundwater storage in an island watershed by GRACE gravimetry

10.5194/egusphere-egu2020-1827 ◽

2020 ◽

Author(s):

Chung-Chieh Huang ◽

Hong-Ru Lin ◽

Jyun-Lin Chen ◽

Shao-Yang Huang ◽

Jet-Chau Wen ◽

...

Keyword(s):

High Precision ◽

Large Scale ◽

Field Measurements ◽

Alluvial Fan ◽

Small Scale ◽

Groundwater Storage ◽

Satellite Gravimetry ◽

Grace Data ◽

Terrestrial Water ◽

Central Taiwan

&#160; &#160; &#160; &#160; &#160;Since the successful launch of the Gravity Recovery and Climate Experiment (GRACE) on March 17th, 2002, a number of scientists have adopted satellite gravimetry for the detection of variations on terrestrial water storage (TWS). Use of high-precision GRACE gravimetry presents advantages in hydrogeologic studies, such as providing accurate estimates of currents and gravity fields. Many studies have proven that the high-precision GRACE gravimetry can observe large-scale (over 50,000 km2) variations in groundwater storage (GWS). However, relatively few studies conducted using satellite gravimetry have focused on scales smaller than 5,000 km2.&#160;&#160;&#160;&#160;&#160;&#160;&#160; The purpose of this study is to investigate the potential for using GRACE gravimetry to observe small-scale variations in GWS specifically, this paper presents a case study of the Zhoushui River alluvial fan (~2,560 km2) in central Taiwan as an example of how well GRACE data compare to field-based data for ascertaining small-scale variations in GWS. Field measurements of groundwater level in 52 observation wells (2002-2017) were used to analyze variations in GWS. Results of this field-based analysis were compared to results obtained using the GWS data (2002-2017) obtained by GRACE gravimetry. This comparison allowed us to evaluate the similarities and differences in both methods as well as to prove the feasibility of using GRACE gravimetry in small-scale regions. Results of our comparative analysis indicate that water resources in small watershed can be successfully managed using gravimetric data collected by GRACE satellite.&#160;Keywords: Groundwater storage, GRACE, Watershed

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Hydroclimatological evaluation of CLM5 simulations using multiple data sources for land-atmosphere interaction studies over Scandinavia

10.5194/egusphere-egu2020-7247 ◽

2020 ◽

Author(s):

Yeliz A. Yılmaz ◽

Lena M. Tallaksen ◽

Frode Stordal

Keyword(s):

Observational Data ◽

Land Surface ◽

Snow Water Equivalent ◽

Surface Model ◽

Data Sets ◽

Earth System ◽

Data Set ◽

Cold Environments ◽

Community Land Model ◽

Terrestrial Water

Arctic amplification leads to rapid changes in the terrestrial water and energy balances at high northern latitudes. Advances in Earth System Models (ESMs) is improving our understanding of the underlying feedback mechanisms leading to these changes. The representation of the land surface in ESMs is essential to simulate and understand changes at the global and regional scales. The latest version of the land component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5), has received substantial new implementations to help simulate the land surface processes in cold environments. At the same time, the behaviour of offline CLM5 simulations and new observational data sets have not been systematically compared over Scandinavian regions. In this study, we run the CLM5 model at relatively high resolution (0.25 degrees) over Scandinavia (including Svalbard) for 15 years between 2002 and 2016. We evaluate the water and energy budget components of CLM5 using several reanalyses and satellite-based observational data sets. In particular, we use monthly model outputs and compare with the satellite retrievals from GRACE, MODIS, AMSR2, and AMSR-E, and reanalysis data sets from ERA5, GLDAS, and MERRA-2. As an additional data source, we use the local&#8208;scale measurements obtained from the Finse Eco-Hydrological Observatory (Finse EcHO) at 1200 m a.s.l, and the high-Arctic research site at Bayelva near Ny-&#197;lesund, Svalbard. Our investigation is focused on several variables including terrestrial water storage, snow water equivalent, turbulent fluxes, net radiation, and skin temperature. The results indicate that the perceived performance of the land surface model (CLM5) depends strongly on the reference observational data set. Regional discrepancies between data sets, particularly for Svalbard, prompts further investigation of the underlying sources of uncertainty. The results of this evaluation provide a valuable source of information for future studies in the region, particularly in the Land-ATmosphere Interactions in Cold Environments (LATICE) project, which focuses on cold region land surface dynamics, integrating across observational systems, laboratory experiments, field, and modeling efforts.Acknowledgement : This study is conducted under the LATICE strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo, and the project EMERALD (294948) funded by the Research Council of Norway.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0253.1 ◽

2019 ◽

Vol 20 (9) ◽

pp. 1981-1999 ◽

Cited By ~ 4

Author(s):

Yafeng Zhang ◽

Bin He ◽

Lanlan Guo ◽

Daochen Liu

Keyword(s):

Land Surface ◽

Snow Water Equivalent ◽

Time Lag ◽

Water Storage ◽

Correlation Coefficients ◽

Global Scale ◽

Time Lags ◽

Terrestrial Water Storage ◽

Global Pattern ◽

Terrestrial Water

Abstract A 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 > 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.

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