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A model for estimating Ag-MAR flooding duration based on crop tolerance, root depth, and soil texture data

  • Author(s): Ganot, Y;
  • Dahlke, HE
  • et al.
Abstract

Agricultural Managed Aquifer Recharge (Ag-MAR) is an emerging MAR technique that uses agricultural fields as percolation basins to recharge the underlying aquifers. Ag-MAR can be a beneficial solution for storing excess surface water, however, if not managed properly it can potentially harm the soil and crops planted on the field at the time of recharge, ultimately leading to yield loss. Root zone residence time (RZRT), defined as the duration that the root-zone can remain saturated (or nearly saturated) during Ag-MAR without crop damage, is a key factor in Ag-MAR since extended periods of saturation in the root-zone can damage crops. Here we propose a simple RZRT model for estimating a safe Ag-MAR flooding duration based on hydraulic parameters deduced from soil texture, crop tolerance to saturation, effective root depth, and critical soil water content, which is the point where soil re-aeration occurs during drainage. We tested the model with different hydraulic parameter sets and compared the results to observed data and HYDRUS simulations. Using fitted and unfitted hydraulic parameters the average error of the predicted Ag-MAR flooding duration was less than 5 h, and up to a few days, respectively. Consequently, for crops with low flooding-tolerance, the model should be used with caution, but for more tolerant crops, the model provides reasonable predictions. The model also provides a first approximation of the possible amount of water that can be applied during an Ag-MAR event. Based on the RZRT model, we evaluated the Ag-MAR potential of various crops and effective root depths for each of the USDA soil texture classes. A spreadsheet containing the RZRT model including hydraulic parameters, and crop properties is publicly available and can be used as a learning tool or to estimate Ag-MAR flooding duration for different soils. The proposed model can be easily integrated into Ag-MAR assessment tools.

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