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Exploring Hydraulic Connectivity in a Groundwater System with Airborne EM Method

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ButteResistivity
Figure 1. A three-dimensional view of the resistivity model in Butte and Glenn Counties, California.

The Place

The study area spans portions of Butte and Glenn Counties in the Northern Sacramento Valley portion of the Central Valley in California. Land use in the area is dominated by irrigated agriculture supported by surface water diversions and groundwater pumping.

The Problem

The existing groundwater model in the area, developed using well data, lacks sufficient detail to understand the connection between various depths within the aquifer system since stratigraphic units represented in the model include both aquifer and aquitard materials. Vertical head data from multi-completion wells provide useful information about the vertical connectivity of the aquifer system but are only available at a limited number of locations. Installing many of these wells is very expensive. The airborne electromagnetic (AEM) method provides a cost-effective mapping capability to cover a large aquifer system fast. Yet the method is not directly imaging the vertical connectivity of the system, but the electrical resistivity distribution of the system, which has a close linkage with the vertical connectivity. Further, the resolution of the AEM data generally decreases with depth creating a large uncertainty in the resulting resistivity model propagated through the derived interpretation of the vertical connectivity.

Our Approach

We viewed sediment type as an important linkage between electrical resistivity and vertical connectivity based on the fact that coarse-grained materials (e.g., sand, gravel) provide a conduit of the vertical flow, and fine-grained materials (e.g., clay) act as a barrier of the vertical flow. The AEM survey was designed to fly over the drillers’ logs containing in-situ sediment-type information to maximize the number of co-located driller’s logs and AEM soundings (i.e., AEM data at a location). Obtained co-located data were used to construct a relationship between resistivity and sediment type. To address the limited resolution, we incorporated available ancillary data (e.g., resistivity logs) when obtaining resistivity information from the AEM data. Further, by obtaining many resistivity models fitting the data, we captured the associated uncertainty.

Results to Date

The link between resistivity and sediment type was estimated and used to transform the resistivity to sediment type. The resulting sediment-type information was used to interpret the large-scale structure of the aquifer system.

Connectivity though resistivity
Figure 2. A three-dimensional view of the probability model of the coarse-dominated unit; three interfaces indicate boundaries of the large-scale structure.

For addressing the vertical connectivity, we transformed the resistivity to coarse fraction and calculated the vertically integrated coarse-fraction map, which integrated values of coarse fraction from base to top of the system. This map was closely connected with the vertical connectivity of the aquifer system. From the resistivity models, many of these maps were generated capturing the associated uncertainty. A comparison of the map and measured vertical hydraulic gradients at 12 monitoring wells showed a strong correlation. This demonstrated the value of the problem-focused approach for estimating the vertical connectivity of the system.

Butte Connectivity map view
Figure 3. Comparison of the vertically integrated coarse fraction map from AEM and the vertical hydraulic gradient from in-situ monitoring wells.

Project Sponsors

California Department of Water Resources

Ministry of Environment and Food of Denmark, the Ecoinnovation Programme

Gordon and Betty Moore Foundation

Project Publications and Presentations

Kang, S., Knight, R., Greene, T., Christina, B., & Fogg, G. (2021). Exploring the Model Space of Airborne Electromagnetic Data to Delineate Large-Scale Structure and Heterogeneity Within an Aquifer System. Water Resources Research, 57(10), e2021WR029699. https://doi.org/https://doi.org/10.1029/2021WR029699

Project Leads / Contacts

Seogi Kang

Rosemary Knight

Noah Dewar