Development and Validation of an In Situ Groundwater Abstraction Sensor Network, Hydrologic Statistical Model, and Blockchain Trading Platform: A Demonstration in Solano County, California

[Image: see text] Megadrought in the western United States is jeopardizing water security. Groundwater regulations, such as California’s Sustainable Groundwater Management Act (SGMA), aim to preserve groundwater resources in overdrafted basins. Water agencies must establish sufficient monitoring systems to measure local groundwater abstraction and devise plans to moderate groundwater use. However, few technologies are available to monitor and regulate groundwater abstraction spatially and temporally. In this study, we deployed satellite-connected electrical current sensors on 11 agricultural groundwater pumps in Solano County, California over 2019–2022. A high correlation (R(2) = 0.706) was found between the in situ sensors and in-line flow meters. We then combine in situ sensor data with a land surface model to develop a multiple linear regression model of groundwater abstraction and groundwater level. Using a 10-fold cross-validation, it is found that our predictive groundwater abstraction model has approximately a 3.5% bias and a mean absolute error of 1.21 acre-feet, while our predictive groundwater level model has approximately 4.2% bias and about 5.9 acre-feet mean absolute error. Finally, we integrated these data with a blockchain-based groundwater credit trading platform to demonstrate how such a tool could be used for SGMA compliance.