Central and Southern Europe is undergoing a drying trend driven by increased evapotranspiration and rising air temperatures, even though precipitation levels remain stable. In the Bug River Basin, GRACE observations indicate that total water storage (TWS) declined at a rate of 8.8 ± 5.2 mm/year between 2012 and 2023. To validate this trend, we analysed spatial and temporal discrepancies between TWS-GRACE and water budget-based estimates (TWS-WB). Using ensemble data assimilation techniques, we integrated hydrometeorological data with TWS-GRACE. Regression models developed for TWS simulation were employed to adjust TWS-GRACE estimates. The results demonstrate that TWS fusion effectively mitigates uncertainties in TWS-GRACE caused by its low spatial and temporal resolution. Correlation analysis between TWS-fusion and TWS-GRACE identified errors in GRACE solutions and commonly used autoregressive methods for filling data gaps. Our findings show that model developed in this study significantly improved alignment between TWS-GRACE and TWS-WB, reducing RMSE from 34.7 to 14.9 mm/month. The proposed data fusion approach based on combining GRACE observations with precipitation, evapotranspiration, and runoff data, offers a viable alternative for extending TWS-GRACE time series beyond the GRACE observational period. Additionally, our research provides valuable insights for downscaling GRACE data and addressing challenges in spatial and temporal interpolation, which remain critical in water resource studies.
GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2grid scale. The downscaled data at 1 km2resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities.
more » « less- Award ID(s):
- 2114701
- PAR ID:
- 10523669
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 60
- Issue:
- 7
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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