Hybrid Data Remote Sensing Assimilation System (HyDRA)
Abstract/Contents
- Abstract
- Accurate and timely 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 uses primarily remotely sensed 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 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 ensemble models and triple collocation analyses. 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.
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | November 10, 2020 |
Creators/Contributors
| Author | Ahamed, Aakash |
|---|
Subjects
| Subject | Remote Sensing |
|---|---|
| Subject | Hydrology |
| Subject | Water Resources |
| Subject | Groundwater |
| Subject | Mass Balance |
| Subject | Geophysics |
| Subject | School of Earth |
| Subject | Energy |
| Subject | and Environmental Sciences |
Bibliographic information
| Related item |
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| Location | https://purl.stanford.edu/qf545cf4631 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under an Open Data Commons Attribution License v1.0.
Preferred citation
- Preferred Citation
- Ahamed, Aakash. (2020). Hybrid Data Remote Sensing Assimilation System (HyDRA). Stanford Digital Repository. Available at: https://purl.stanford.edu/qf545cf4631
Collection
Stanford Research Data
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- Contact
- aahamed@stanford.edu
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