Abstract
Groundwater dependent ecosystems (GDEs) are increasingly threatened worldwide, but the shallow groundwater resources that they are reliant upon are seldom monitored. In this study, we used satellite-based remote sensing to model groundwater levels under groundwater dependent ecosystems across California, USA. Depth to groundwater was modelled for a 35-year period (1985-2019) within all groundwater dependent ecosystems across the state (n = 95,135). Our model was developed within Google Earth Engine using Landsat satellite imagery, climate data, and field-based groundwater data (n = 627 shallow (<30 m) monitoring wells) as predictors in a Random Forest model. Our findings show that (1) 44% of groundwater dependent ecosystems have experienced a significant long-term decline in groundwater levels compared to 28% with a significant increase; (2) groundwater level declines have intensified during the most recent two decades, with 39% of groundwater dependent ecosystems experiencing declines in the 2003-2019 period compared to 27% in the 1985-2002 period; and (3) groundwater declines are most prevalent within GDEs existing in areas of the state where sustainable groundwater management is absent. Our results indicate that declining shallow groundwater levels may be adversely impacting California’s groundwater dependent ecosystems. Particularly where groundwater levels have fallen beneath plant roots or streams thereby affecting key life processes, such as forest recruitment/succession, or hydrological processes, such as streamflow that affects aquatic habitat. In the absence of groundwater monitoring well data, our model and findings can be used to help state and local water agencies fill in data gaps of shallow groundwater conditions, evaluate potential effects on GDEs, and improve sustainable groundwater management policy in California.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.