Hybrid Data Remote Sensing Assimilation (HyDRA) System
- Accurate estimates of groundwater storage changes are critical to the sustainable management of worldwide aquifers, but few methods exist that can assess both local and regional aquifer dynamics, especially in regions where groundwater monitoring wells are sparse. In this study, we developed a water mass balance methodology that integrates remotely sensed and in situ hydrologic data and models to estimate changes in groundwater storage at multiple spatial scales. To demonstrate this method, we quantified monthly groundwater storage changes in California’s Central Valley Alluvial Aquifer and the surrounding watershed from 2001 to 2019. Groundwater storage changes are calculated as the residual of inflows (precipitation, streamflow, runoff) minus outflows (evapotranspiration, streamflow), corrected for fluctuations in surface water storage (reservoirs, snow) and soil moisture. Trends, timing, and magnitude of our results agree with independent estimates of groundwater storage change calculated from (a) water levels measured in groundwater wells, (b) the Gravity Recovery and Climate Experiment (GRACE) (c) regional groundwater flow models, and (d) estimates derived from GPS. The results reproduce periods of drought (2011-2015) and the exceptionally wet 2016-2017 California winter. Uncertainty in estimates of groundwater storage changes corresponding to (a-d) was quantified through a triple collocation analysis. The use of remote sensing data to monitor sub-regional to regional changes in groundwater storage can contribute significantly to the sustainable management of aquifers, particularly in parts of the world with limited access to monitoring well data, hydrogeologic information, and groundwater flow models.
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- Preferred Citation
- Ahamed, Aakash. (2020). Hybrid Data Remote Sensing Assimilation (HyDRA) System. Stanford Digital Repository. Available at: https://purl.stanford.edu/pr455kv2009
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