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

Abstract

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.

Shortfalls and Challenges in Groundwater Regime Monitoring

“…Shukla et al opinioned GRACE mission, widely used for monitoring groundwater storage change, could be utilized to get information on the exact amount of water above or below the surface of the Earth that may be used to counteract such situations of the water crisis [20]. Claire Pasca et al suggested advanced methods to improve the data quality close to the measured values [21]. The downscaling performance is evaluated by comparing the downscaled versus in situ GWS data over 38 pixels at 0.50 resolution.…”