Geophysical site characterization, with the tTEM system, for studies of nitrate mobilization during recharge in the Central Valley of California

In California’s Central Valley, the forecast from climate models is that future precipitation events will be less frequent, more extreme, and deliver a higher proportion of the precipitation as rain. The combination of these effects will challenge the state’s ability to capture and store this critical freshwater resource and will also threaten downstream communities with flooding. A water management strategy gaining popularity in California is managed aquifer recharge (MAR), where excess surface water is captured and directed to selected sites to recharge the underlying groundwater systems. AgMAR is a form of MAR where water is spread over agricultural land to recharge the underlying aquifer over short periods during the wet winter months. One concern with this strategy is the decades of intensive agriculture in the Central Valley. The intense usage of fertilizers such as nitrogen threatens to contaminate the groundwater systems on which municipalities and homeowners rely. Research into nitrate mobilization has shown that the stratigraphic characterization of a site is the dominant factor in determining the infiltration rate and mobilization of contaminants. Therefore, in order to develop a model of nitrate mobilization, detailed information is needed about surface stratigraphy. In this study, a towed-transient electromagnetic geophysical method (tTEM) was used to image the subsurface, in combination with sediment type logs, to characterize the subsurface sediments. tTEM data were acquired on a 56-ha commercial almond farm in the early spring of 2022. The tTEM data were inverted to recover a resistivity model of the site, exhibiting a high degree of spatial variability. Regions of high resistivity typically suggest coarser grained material that is more permeable and hydraulically conductive, whereas regions of lower resistivity tend to be composed of finer grained material and are less hydraulically conductive. We created a site-specific resistivity-to-sediment-type transform to extract sediment-type data from the tTEM data using data from the 1D resistivity models along with twenty co-located sediment type logs and water table measurements. Using the maximum likelihood model, we transformed the recovered resistivity model into a sediment-type model. The integration of tTEM data and well-derived information about sediment types to construct a sediment-type model can provide information about connected pathways for recharge and help inform nitrate mobilization models. This study is allowing us to develop a methodology that can be applied elsewhere for the assessment of a site for agMAR when there are concerns about nitrate mobilization. This work is in support of a larger project on groundwater sustainability in agricultural systems in the southwestern United States.