| Summary: | With the goal of balancing demand for groundwater supply and demand, many water managers are intentionally recharging water into aquifers for later use by flooding agricultural fields, a practice called Ag-MAR. A major challenge with Ag-MAR implementation is understanding subsurface properties which may influence the quantity and quality of water recharged. We acquired data across a 138-acre orchard near Modesto, California, with a towed electromagnetic system, tTEM, from which we derived images of subsurface electrical resistivity to a depth of 42 m. We built on a previously developed workflow to that uses collocated resistivity values and sediment-type logs to obtain a subsurface model representing variation in the fraction of coarse-grained material (coarse fraction). We modified the workflow to reduce the uncertainty in resistivity and sediment type by averaging both of these values over larger depth intervals in determining the resistivity distribution that corresponded to each sediment type. We selected resistivity values to represent each sediment type to reduce the bias caused by the differences in sampled volumes in the input data. We compared our coarse-fraction model to sediment-type logs and observations of water table fluctuations during flooding experiments. Using the coarse-fraction model, we estimated the length of the potential recharge pathway from each 20 m x 20 m grid point at the ground surface to the water table, and the depth to the shallowest barrier to flow. We conclude that tTEM data captures the spatial variation in coarse fraction at a scale relevant to Ag-MAR.
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