Motivated by the availability of 20,000 line-km of airborne electromagnetic (AEM) data covering the Central Valley of California, we developed a workflow that uses resistivity profiles from electromagnetic (EM) data to assess the suitability of areas for groundwater recharge. We defined a suitable area as one where “fastpaths” of coarse-grained material could efficiently move water from the ground surface to the water table. We defined recharge metrics and generated the corresponding maps by integrating resistivity profiles from AEM data, sediment type (from driller’s logs), water level measurements, and water quality measurements. The workflow is publicly available through a web-based application, fastpath (https://fastpath.stanford.edu). We produced maps displaying recharge metrics on a 400 m x 400 m grid covering the Central Valley, with 80% of the cells sufficiently close to an AEM resistivity profile (within ~ 3 km) to be assessed for recharge. Various decisions are made in the workflow that result in a range of values for determined metrics at any given location. The maps summarizing all metrics show that between 19% (2,000,000 acres) and 56% (7,000,000 acres) of the total area in the valley is land suitable for recharge. The landcover with the largest total area of land classified as suitable is cultivated crops. We estimated the total space available for recharge water to be ~170 km3 which is two orders of magnitude greater than an estimate of the total volume of water likely to be available for recharge.

A document by Seogi Kang. Click on the document to view its contents.

Airborne electromagnetic (AEM) data can be inverted to recover models of the electrical resistivity of the subsurface; these, in turn, can be transformed to obtain models of sediment type. AEM data were acquired in Butte and Glenn Counties, California, U.S.A. to improve the understanding of the aquifer system. Around 800 line-kilometers of high-quality data were acquired, imaging to a depth of ~300 m. We developed a workflow designed to obtain, from the AEM data, information about the large-scale structure and heterogeneity of the aquifer system to better understand the vertical connectivity. Using six different forms of inversion and posterior sampling of the recovered resistivity models, we produced 6006 resistivity models. These models were transformed to models of sediment type and estimates of percentage of sand/gravel. Exploring the model space, containing the resistivity models and the derived models, allowed us to delineate the large-scale structure of the aquifer system in a way that captures and communicates the uncertainty in the identified sediment type. The uncertainty increased, as expected, with depth, but also served to indicate, as areas of high uncertainty in sediment type, the location of both large-scale and small-scale interfaces between sediment type. A plan view map of the integrated percentage of sand/gravel, when compared to existing hydrographs, revealed the extent of lateral changes in vertical connectivity within the aquifer system throughout the study area.

Working with airborne electromagnetic (AEM) data acquired in the Kaweah Subbasin in the Central Valley of California, U.S.A., we developed a new approach for imaging the top of the bedrock and the confining Corcoran Clay layer. Our approach included multiple L2-norm and Lp-norm inversions as well as an interpolation process. The major improvement in imaging the two targets was made in the Lp-norm inversion step by incorporating prior knowledge. For the Corcoran Clay, pairs of resistivity and driller’s logs at two wells guided the selection of the best resistivity model and were used to increase the accuracy of the estimated Clay thickness. The bedrock surface was poorly constrained by well data in the existing groundwater model, appearing as a flat surface. We had good AEM data coverage in the area so had higher confidence in the obtained map of the bedrock surface at depths ranging from 15 m to 160 m. There was relatively good agreement between the location of the Corcoran Clay in the AEM data (depth ranging from 50 to 130 m and thickness ranging from 3 to 25 m) and the existing groundwater model, with both depth and thickness showing ~15% relative difference. The AEM data provided information about the continuity of the Corcoran Clay that is challenging to capture in the well data. The locations of the bedrock and Corcoran Clay were used in a structurally-constrained inversion to improve the imaging of the smaller-scale resistivity structure.