Preprints
https://doi.org/10.5194/hess-2022-39
https://doi.org/10.5194/hess-2022-39
 
08 Mar 2022
08 Mar 2022
Status: this preprint is currently under review for the journal HESS.

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

Claire Pascal1, Sylvain Ferrant2, Adrien Selles2, Jean-Christophe Maréchal2, Abhilash Paswan3, and Olivier Merlin1 Claire Pascal et al.
  • 1Centre d’Étude Spatiale de la BIOsphère, (CESBIO-UPS-CNRS-IRD-CNES-INRAE), 18 av. Ed. Belin, Toulouse CEDEX 9, 31401, France
  • 2Bureau de Recherches Géologiques et Minières (BRGM), Université de Montpellier, 1039 rue de Pinville, Montpellier, 34000, France
  • 3National Geophysical Research Institute, CSIR, Hyderabad, India

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.

Claire Pascal et al.

Status: open (until 27 Jun 2022)

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Claire Pascal et al.

Claire Pascal et al.

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Short summary
This paper presents a new validation method for the downscaling of GRACE (Gravity Recovery and Climate Experiment) data. It measures the improvement of the downscaled data against the low-resolution data in both temporal, and for the first time, spatial domains. This validation method offers a standardized and comprehensive framework to interpret spatially and temporally the quality of the downscaled products, supporting the future efforts on GRACE downscaling methods.