The appraisal of groundwater storage dwindling effect, by applying high resolution downscaling GRACE data in and around Mehsana district, Gujarat, India

2021 ◽  
Vol 13 ◽  
pp. 100559
Author(s):  
Anuradha Karunakalage ◽  
Tandrila Sarkar ◽  
Suresh Kannaujiya ◽  
Prakash Chauhan ◽  
Pranshu Pranjal ◽  
...  
Keyword(s):  
High Resolution ◽  
Groundwater Storage ◽  
Grace Data

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

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 63 ◽  
Author(s):  
Md Rahaman ◽  
Balbhadra Thakur ◽  
Ajay Kalra ◽  
Ruopu Li ◽  
Pankaj Maheshwari
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Land Surface ◽  
Human Impacts ◽  
Pearson Correlation ◽  
Land Surface Model ◽  
High Plains ◽  
Surface Model ◽  
Groundwater Storage ◽  
Grace Data

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.

Groundwater storage in Africa

2017 ◽  
Author(s):  
Gabin Archambault
Keyword(s):  
High Resolution ◽  
Effective Porosity ◽  
Data Sources ◽  
Groundwater Storage ◽  
British Geological Survey ◽  
Full List ◽  
The World ◽  
Storage Type ◽  
Porosity Range ◽  
Aquifer Flow

This 5 km resolution grid presents groundwater storage in Africa (in mm). This parameter was estimated by combining the saturated aquifer thickness and effective porosity of aquifers across Africa. For each aquifer flow/storage type an effective porosity range was assigned based on a series of studies across Africa and surrogates in other parts of the world. Groundwater storage is given in millimeters. Detailed description of the methodology, and a full list of data sources used to develop the layer can be found in the peer-reviewed paper available here: http://iopscience.iop.org/article/10.1088/1748-9326/7/2/024009/pdf The raster and a high resolution PDF file are available for download on the website of British Geological Survey (BGS): http://www.bgs.ac.uk/research/groundwater/international/africanGroundwater/mapsDownload.html Groundwater Storage

scholarly journals Evaluation of Groundwater Storage Variations in Northern China Using GRACE Data

Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjie Yin ◽  
Litang Hu ◽  
Jiu Jimmy Jiao
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Groundwater Resources ◽  
Dynamic Change ◽  
Northern China ◽  
Groundwater Storage ◽  
Grace Data ◽  
Depletion Rate ◽  
Terrestrial Water ◽  
Storage Change

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.

scholarly journals A high resolution model of linear trend in mass variations from DMT-2: Added value of accounting for coloured noise in GRACE data

2017 ◽  
Vol 103 ◽  
pp. 12-25 ◽  
Author(s):  
Hassan H. Farahani ◽  
Pavel Ditmar ◽  
Pedro Inácio ◽  
Olga Didova ◽  
Brian Gunter ◽  
...  
Keyword(s):  
High Resolution ◽  
Linear Trend ◽  
Added Value ◽  
Coloured Noise ◽  
Grace Data ◽  
Resolution Model ◽  
High Resolution Model

Remote sensing of groundwater storage change - past, present and future

2020 ◽  
Author(s):  
Susanna Werth ◽  
Manoochehr Shirzaei ◽  
Grace Carlson ◽  
Chandrakanta Ojha
Keyword(s):  
High Resolution ◽  
Surface Deformation ◽  
Central Valley ◽  
Groundwater Depletion ◽  
High Temporal Resolution ◽  
Hydrological Models ◽  
Groundwater Storage ◽  
Aquifer System ◽  
Vertical Land Motion ◽  
The Impact

<p>Groundwater remains one of the least comprehensively monitored storage components in the hydrological cycle, because it's flow and storage processes are strongly linked to geology of the underground and because direct observations from well sites provide only point observations of complex and partly deep aquifer systems.</p><p>In recent years, geodetic methods have become increasingly available to complement ground-based observations and to expand investigations of the impact of climate extremes or human water use on groundwater storage variability. Satellite gravimetry from the Gravity Recovery And Climate Experiment (GRACE/FO) has been shown to be sensitive to groundwater depletion at large spatial scales (> 300km) and relatively high temporal resolution (monthly). These data provide a valuable boundary condition for regional studies, and they have been applied widely to improve parameter and structure of hydrological models.</p><p>Moreover, changes in groundwater stocks cause surface deformation associated with regional elastic loading of the Earth’s crust and localized poroelastic compaction of the aquifer skeleton, which are detectable by GPS and InSAR. The loading signal is typically much smaller than the land subsidence due to poroelastic compaction and thus masks out the loading signal adjacent to the aquifer system. However, the poroelastic signal can be used to estimate groundwater volume change in confined aquifer units and provides insight into the mechanical properties of the aquifer system. Also, the deformation sensors provide spatial resolutions of tens of meters (e.g., InSAR) to several kilometers (e.g., GPS) that can be used to solve for the volume of fluid removed from the aquifer system.</p><p>In this presentation, we demonstrate and discuss the applicability of poroelastic modeling, by applying GPS and InSAR based observations of vertical land motion, to quantify groundwater storage changes. Using the Central Valley in California as an example, we will show when this approach is applicable and when it is not, depending on the type of aquifer and observed deformation compared to water level changes. Using a 1-D poroelastic calculation based on deformation data, we find a groundwater loss of 21.3±7.2 km<sup>3</sup> for the entire Central Valley during 2007-2010 and of 29.3±8.7 km<sup>3</sup> for the San Joaquin Valley during 2012-2015. These loss estimates during drought are consistent with that of GRACE-based estimates considering uncertainty ranges.</p><p>Finally, we will discuss the increased availability of high-resolution radar data from Sentinel 1A/B as well as the upcoming radar mission NASA-ISRO SAR Mission (NISAR), to be launched in 2022, and how this will allow for high-resolution monitoring of vertical land motion and with that of compaction in confined aquifers around the world. The availability of these datasets increases the capability of geodetic methods for groundwater monitoring at higher spatial resolution than GRACE data, hence, providing the potential to apply these datasets to further improve parameterization and formulation of groundwater routines in regional to large-scale hydrological models.</p>

Assessing Impacts of Climate Extremes and Human Water Use on GRACE Total Water Storage Trends in Major U.S. Aquifers

2020 ◽  
Author(s):  
Bridget Scanlon ◽  
Ashraf Rateb ◽  
Alexander Sun ◽  
Himanshu Save
Keyword(s):  
Surface Water ◽  
Water Use ◽  
Groundwater Level ◽  
Water Storage ◽  
Climate Extremes ◽  
High Plains ◽  
Total Water ◽  
Groundwater Storage ◽  
Grace Data ◽  
The U.S

<p>There is considerable concern about water depletion caused by climate extremes (e.g., drought) and human water use in the U.S. and globally. Major U.S. aquifers provide an ideal laboratory to assess water storage changes from GRACE satellites because the aquifers are intensively monitored and modeled. The objective of this study was to assess the relative importance of climate extremes and human water use on GRACE Total Water Storage Anomalies in 14 major U.S. aquifers and to evaluate the reliability of the GRACE data by comparing with groundwater level monitoring (~-23,000 wells) and regional and global models. We quantified total water and groundwater storage anomalies over 2002 – 2017 from GRACE satellites and compared GRACE data with groundwater level monitoring and regional and global modeling results.  </p> <p>The results show that water storage changes were controlled primarily by climate extremes and amplified or dampened by human water use, primarily irrigation. The results were somewhat surprising, with stable or rising long-term trends in the majority of aquifers with large scale depletion limited to agricultural areas in the semi-arid southwest and southcentral U.S. GRACE total water storage in the California Central Valley and Central/Southern High Plains aquifers was depleted by drought and amplified by groundwater irrigation, totaling ~70 km<sup>3</sup> (2002–2017), about 2× the capacity of Lake Mead, the largest surface reservoir in the U.S. In the Pacific Northwest and Northern High Plains aquifers, lower drought intensities were partially dampened by conjunctive use of surface water and groundwater for irrigation and managed aquifer recharge, increasing water storage by up to 22 km<sup>3</sup> in the Northern High Plains over the 15 yr period. GRACE-derived total water storage changes in the remaining aquifers were stable or slightly rising throughout the rest of the U.S.</p> <p>GRACE data compared favorably with composite groundwater level hydrographs for most aquifers except for those with very low signals, indicating that GRACE tracks groundwater storage dynamics. Comparison with regional models was restricted to the limited overlap periods but showed good correspondence for modeled aquifers with the exception of the Mississippi Embayment, where the modeled trend is 4x the GRACE trend. The discrepancy is attributed to uncertainties in model storage parameters and groundwater/surface water interactions. Global hydrologic models (WGHM-2d and PCR-GLOBWB-5.0 overestimated trends in groundwater storage in heavily exploited aquifers in the southwestern and southcentral U.S. Land surface models (CLSM-F2.5 and NOAH-MP) seem to track GRACE TWSAs better than global hydrologic models but underestimated TWS trends in aquifers dominated by irrigation.</p> <p>Intercomparing GRACE, traditional hydrologic monitoring, and modeling data underscore the importance of considering all data sources to constrain water storage changes.  GRACE satellite data have critical implications for many nationally important aquifers, highlighting the importance of conjunctively using surface-water and groundwater and managed aquifer recharge to enhance sustainable development.</p>

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