Indian Rainwater Harvesting System (RHS) is an essential source of irrigation water in upstream agricultural areas. It is composed of hundreds of thousands of Small Reservoirs (SR) often disconnected from any perennial rivers. This study aims at quantifying the RHS Maximum Water Storage Capacity (MWSC) in the Telangana State, South India. The true bathymetries of 545 dry SR, located from Sentinel-2 (SENT-MWAE) and Landsat Maximal Water Area Extent (GSW-MWAE), are extracted from four Very High Resolution (VHR) Pleiades Digital Elevation Model (DEM). The average water depth at full capacity ranges from 22 cm to 4.6 m (average 1.3 m, std 0.6). The MWSC estimated within the Pleiades ground-coverage, for 62% of the total SR, accounts for 37.2 mm on average. The estimated capacity highly depends on the MWAE data source, varying from 5 to 30%. The Telangana RHS MWSC based on the RHS GSW-MWAE (1.6% of the Telangana area) is estimated at 29.7 mm +/-9 mm. This capacity seems small compared to the large dam capacity (113mm for 126 registered dams in Telangana), but matters in upstream areas, far from irrigated command areas, to complement local groundwater pumping (from 62 to 295 mm). These preliminary results show the high interest of VHR DEM to evaluate uncertainties derived from MWAE products and medium to high resolution DEM to map water storage in RHS.
Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on SMOS and SMAP surface soil moisture (SSM) data. This paper demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated area at the seasonal scale and at a 25 km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5-10%). More importantly, the amplitude of the second SSM peak is significantly correlated to the rice cover fraction within 25×25 km 2 pixels (R=0.81 for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information.
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 scales. 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 kilometres – 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 southern 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 the temporal Pearson correlation coefficient (R) and the 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 allows 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 in R is about +30 % or larger from August to March, including both the 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 for the first time a standardized and comprehensive framework to interpret spatially and temporally the quality and uncertainty of the downscaled GRACE-derived GWS products, supporting future efforts in GRACE downscaling methods in various hydrological contexts.
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