A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk

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 predict groundwater levels under groundwater dependent ecosystems across California, USA. Depth to groundwater was modelled for a 35-years 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 (1985–2019) 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.

Establishing 3D hydrogeological solid model and database for sustainable groundwater management in the Vietnam Mekong delta

The Vietnam Mekong delta (VMD) is a tide-dominated delta formed by the Mekong river system. The sediments are dominantly fine grained and were deposited in the receiving basin with slight inclination of pre-existing deposits in the East sea and gulf of Thailand. The VMD is homeland to about 18 million people that exploit about 4-6 million m3/day of groundwater mainly for domestic use. In recent years, significant groundwater depletion has been occurring in many parts of the VMD due to excessive pumping. Consequently, the VMD has become increasingly faced with serious land subsidence, salt groundwater intrusion, and contamination. Establishing a 3D hydrogeological solid model and database are sorely needed to achieve sustainable groundwater management, and to serve as a basis for further in-depth analyses to quantify contributions from the above-mentioned hazards to current hydrogeological conditions. Therefore, a 3D hydrogeological solid model and database were built based on more than 1000 well logs available from the VMD. An areal distribution of the Holocene, Pleistocene, Pliocene, and Late Miocene subsurfaces from this 3D hydrogeological solid model and database showed zones of tectonic depression and uplift from Early Miocene - Quaternary. Also, the resulting areal distribution aquitards and aquifers thicknesses gave hints of ground saltwater intrusion and contamination.

Challenges to the Use of a Base of Fresh Water in Groundwater Management: Total Dissolved Solids vs. Depth Across California

According to the California Sustainable Groundwater Management Act, the bottom of a basin, and subsequently the depth to which groundwater is managed, can be defined through physical or geochemical qualities of the aquifer. Total dissolved solids (TDS) concentrations are most frequently used to define the basin bottom. However, upper limits in TDS concentrations for “fresh” and “useable” groundwater can range from 1,000 to 10,000 mg/L. To evaluate the applicability of using TDS concentrations to delineate depths subject to sustainable groundwater management, we analyze 216,754 TDS measurements throughout the state of California. We find major challenges to reasonably estimating the BFW with our dataset in 73% of California due to data insufficiencies or complexity introduced by non-montonic TDS-depth relationships. We estimate the BFW in 22% of the Central Valley, a key agricultural region with large groundwater demands and many critically overdrafted groundwater subbasins. Using a TDS limit of 3,000 mg/L, where possible, we estimate the shallowest BFW in the Central Valley to be 155 m below ground surface and the deepest BFW to be 589 m below ground surface. We find that the base of brackish water (TDS < 10,000 mg/L) can extend more than 500 m deeper than the BFW in 78% of the Central Valley where we are able to estimate the BFW, which corresponds to 2% of California. There is a need to evaluate alternative strategies for defining groundwater subject to sustainable management, which likely involves additional measurements and analysis to better characterize groundwater quality with depth throughout California.

Assessing groundwater management sustainability of coastal cities by utilizing the City Blueprint Approach

<p>    Groundwater is the largest freshwater resource available on Earth, and many coastal regions are depending on groundwater as a primary freshwater source. For example, in Busan and Incheon, two of the largest coastal cities in South Korea, 5.7% and 7.0% of freshwater uses are from groundwater while only 1.8% is from groundwater in Seoul, the capital of the country. Globally, groundwater availability is diminishing primarily by population increase, and especially in coastal regions, this problem is exacerbated by overexploitation and seawater intrusion, which causes groundwater contamination and further reduces its availability. Here, we view the groundwater system and its management for sustainability as a complex problem that is associated with various social, economic, and environmental factors. By adopting the City Blueprint Approach (CBA), which has been used extensively for assessing the sustainability of integrated water management of numerous cities on the globe, we identify water management factors that potentially have direct and indirect links and feedbacks with groundwater variables. We selected Busan and Incheon as case studies for coastal cities that are facing the risk of groundwater salinization by seawater intrusion. This study aims to 1) assess City Blueprint (CB) of selected coastal cities, 2) identify major factors for coastal groundwater management through correlation analysis, and 3) suggest management options regarding identified factors for sustainable groundwater management of the study areas. Our results on CB indicate that the groundwater quality and quantity of the selected cities are currently in ‘good’ status. Also, from the correlation analysis, we identified heat risk and freshwater scarcity as the major factors that potentially can affect groundwater quantity. For groundwater quality, the factors of voice and accountability, regulatory quality, and rule of law and control of corruption, most of which had not been explicitly considered for groundwater management, were identified as the major factors. Some of these factors were assessed from ‘little concern’ to ‘very concern’ for both cities. These results indicate that, regarding the linkages between groundwater variables and other factors in concern, more actions beyond environmental factors should be taken for sustainable groundwater management. This study helps to understand how non-conventional factors could contribute to coastal groundwater, and can provide extensive options for sustainable groundwater management.</p><p> </p><p><strong>Acknowledgement</strong>: This research was supported by the Development program of Minimizing of Climate Change Impact Technology through the National Research Foundation of Korea (NRF), funded by the Korean government (Ministry of Science and ICT) (NRF-2020M3H5A1080775).</p>