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

Karunakalage, Anuradha; Sarkar, Tandrila; Kannaujiya, Suresh; Chauhan, Prakash; Pranjal, Pranshu; Taloor, Ajay Kumar; Kumar, Sunil

doi:10.1016/j.gsd.2021.100559

Cited by 26 publications

(18 citation statements)

References 37 publications

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“…This potential reservoir of 143 mm represents 20% of GRACE TWS amplitude in this area during the 2002-2021 time period (710 mm), yet the reservoirs are rarely simultaneously full and, most of the time, surface water storage can be neglected. Most studies use model outputs of SM to deconvolute GRACE TWS, principally GLDAS (Ali et al, 2021;Chen et al, 2019;Jyolsna et al, 2021;Karunakalage et al, 2021;Yin et al, 2018;Zhang et al, 2021b;Zuo et al, 2021) but also other models like NLDAS (Sahour et al, 2020;Seyoum and Milewski, 2017) or EALCO (Zhong et al, 2021), although (Ning et al, 2014) directly compares downscaled TWS to in situ measurements. We used the Global Land Evaporation Amsterdam Model (GLEAM) v3.5b monthly root zone soil moisture (RZSM) dataset to simulate SM storage which we transform into…”

Section: Deconvolution Of Grace Tws With Gleammentioning

confidence: 99%

“…Here we used a horizontally and vertically homogeneous Sy, obtained with a linear adjustment between LR GWL and GWS-LRref. Some authors avoid this issue by directly comparing GRACE-downscaled GWS with GWL measurements (Karunakalage et al, 2021;Ning et al, 2014;Tian et al, 2019Tian et al, , 2017Yin et al, 2018;Zhang et al, 2021b, a;Zuo et al, 2021). Another issue is that instantaneous GWL can be impacted by short or long-term effects such as the neighbourhood pumping intensity at the moment of piezometer measurement, which cannot be detected nor rectified with the monthly temporal frequency of acquisition.…”

Section: Other Uncertainty Sources In the Validation Exercisementioning

confidence: 99%

“…The data-based downscaling approach consists in (i) deriving a statistical model of TWS from ancillary data available at high resolution (HR), (ii) calibrating it at low resolution (LR), (iii) applying it at HR, and (iv) removing the contribution of surface and soil moisture water stocks to isolate GWS. This data-driven approach rests on the hypothesis that the hydrological/physical processes that link those variables are identical at all resolutions (Ali et al, 2021;Jyolsna et al, 2021;Karunakalage et al, 2021;Sahour et al, 2020;Seyoum and Milewski, 2017;Vishwakarma et al, 2021;Zhang et al, 2021a, b). In the literature, data-driven methods have been used to downscale GRACE data at various scales, either at the watershed scale for a thematic approach as in Seyoum and Milewski (2017) (5,000 km 2 to 20,000 km 2 ), or grid-based.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the GWS simulated by models was occasionally used as reference (Houborg et al, 2012;Seyoum and Milewski, 2017). In most studies, the quality of the downscaled GWS data set is evaluated by comparing its time series with that of GWL or GWS derived from in situ measurements for each HR unit (spatialized -HR pixel -or localized -observation well) with one or several metrics including coefficient of determination (R 2 ) or Nash-Sutcliffe efficiency coefficient (NSE), Pearson correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) (Ali et al, 2021;Jyolsna et al, 2021;Karunakalage et al, 2021;Sahour et al, 2020;Yin et al, 2018;Zhang et al, 2021b, a;Zuo et al, 2021). In those studies, the downscaling procedure is considered efficient if those metrics fall within an acceptable range, or if the downscaled product qualitatively restitutes the long-term trends of in situ data.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, scaling factors are not expected to efficiently downscale GRACE TWS data as neighbouring pixels are highly dependent (Landerer and Swenson, 2012). Yet, many studies directly use GRACE harmonics solutions at 1 • resolution (Ali et al, 2021;Jyolsna et al, 2021;Karunakalage et al, 2021;Ning et al, 2014;Seyoum et al, 2019;Yin et al, 2018;Zhang et al, 2021b, a;Zuo et al, 2021) or mascons solution at 0.5 • resolution (Karunakalage et al, 2021;Nie et al, 2019;Tian et al, 2017) as LR input data, which is way finer than their actual resolution. There is no study evaluating the uncertainty in downscaled GRACE data associated with the above assumption i.e.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating downscaling methods of GRACE data: a case study over a fractured crystalline aquifer in South India

Pascal

Ferrant²,

Selles³

et al. 2022

Preprint

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Abstract. GRACE (Gravity Recovery and Climate Experiment) and its follow-on mission have provided since 2002 monthly anomalies of total water storage (TWS), which are very relevant to assess the evolution of groundwater storage (GWS) at global and regional scale. However, the use of GRACE data for groundwater irrigation management is limited by their coarse (≃ 300 km) resolution. The last decade has thus seen numerous attempts to downscale GRACE data at higher – typically several tens of km – resolution and to compare the downscaled GWS data with in situ measurements. Such comparison has been classically made in time, offering an estimate of the static performance of downscaling (classic validation). The point is that the performance of GWS downscaling methods may vary in time due to changes in the dominant hydrological processes through the seasons. To fill the gap, this study investigates the dynamic performance of GWS downscaling by developing a new metric for estimating the downscaling gain (new validation) against non-downscaled GWS. The new validation approach is tested over a 113,000 km2 fractured granitic aquifer in South India. GRACE TWS data are downscaled at 0.5° (≃ 50 km) resolution with a data-driven method based on random forest. The downscaling performance is evaluated by comparing the downscaled versus in situ GWS data over a total of 38 pixels at 0.5° resolution. The spatial mean of temporal Pearson correlation coefficient (R) and root mean square error (RMSE) are 0.79 and 7.9 cm, respectively (classic validation). Confronting the downscaled results with the non-downscaling case indicates that the downscaling method allow a general improvement in terms of temporal agreement with in situ measurements (R = 0.76 and RMSE = 8.2 cm for the non-downscaling case). However, the downscaling gain (new validation) is not static. The mean downscaling gain on R is about +30 % or larger from August to March including both wet and dry (irrigated) agricultural seasons and falls to about +10 % from April to July, during a transition period including the driest months (April–May) and the beginning of monsoon (June–July). The new validation approach hence offers a standardized and comprehensive framework to interpret spatially and temporally the quality and uncertainty of the downscaled GRACE-derived GWS products, supporting the future efforts on GRACE downscaling methods in various hydrological contexts.

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Section: Deconvolution Of Grace Tws With Gleammentioning

confidence: 99%

Section: Other Uncertainty Sources In the Validation Exercisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating downscaling methods of GRACE data: a case study over a fractured crystalline aquifer in South India

Pascal

Ferrant²,

Selles³

et al. 2022

Preprint

View full text Add to dashboard Cite

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GPS and InSAR derived evidences of intra‐basin stress and strike‐slip tectonics in the vicinity of 2001 (M7.7) earthquake, Kachchh, western India

et al. 2022

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The current geodetic investigation is based on nearly a decade of continuous Global Positioning System (GPS) data towards the western part of the Indian Plate (2009–2019). This research focused on the Kachchh Rift Basin (KRB), the seismically most active intra‐plate region of the Indian Plate, which has seen three ≥ M 7.0 earthquakes in the last two centuries. The GPS stations on the northern and southern margins show SSW and NNE directed motion, indicating that the KRB is currently under the influence of regional compressive stress on both flanks and the same is reflected in the form of earthquake activity in the center portion. Furthermore, the existence of intra‐basin stress, in addition to regional stress, increases strain accumulation and results in the creation of a Principal Deformation Zone (PDZ) in the central part of the KRB. The average annual deformation in the middle of KRB remains 1.0 ± 0.5 mm/year, while sites near the South Wagad Fault (SWF) experience 1.2 mm/year of fault parallel motion. The dominant fault normal motion along the flanks with a maximum fault parallel motion in the middle intimates the existence of strike‐slip tectonics in the central Kachchh. The geometric relationship of strike‐slip faults in a compressive margin and strain accumulation in pre‐existing faults (PDZ) is responsible for current strike‐slip and thrust motion in the Kachchh, including the 2001 Bhuj earthquake (M 7.7). Our geodetic model is well‐corroborated with the geological observations of the wrench fault model of strike‐slip faults. PS‐InSAR results in the northern part of the region between Khadir and Bela Islands show ≤6.0 mm of annual LOS displacement. GPS results indicate ≈ 1.3 to 1.4 ± 0.5 mm/year of fault parallel motion along the transverse Ekal Amrapar Fault (EAF). The non‐significant variation in GRACE‐derived TWS values (from 2009 to 2018) rules out non‐tectonic deformation in the vicinity of EAF and thus points to tectonic activity as the cause of the derived deformation in the region.

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Source apportionment of groundwater quality in agriculture-dominated semiarid region, India—using an integrated approach of hydrochemistry, stable isotopes and land use/land cover change

Gupta

Kumari

2023

Environ Dev Sustain

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

Cited by 26 publications

References 37 publications

Evaluating downscaling methods of GRACE data: a case study over a fractured crystalline aquifer in South India

Evaluating downscaling methods of GRACE data: a case study over a fractured crystalline aquifer in South India

GPS and InSAR derived evidences of intra‐basin stress and strike‐slip tectonics in the vicinity of 2001 (M7.7) earthquake, Kachchh, western India

Source apportionment of groundwater quality in agriculture-dominated semiarid region, India—using an integrated approach of hydrochemistry, stable isotopes and land use/land cover change

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