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

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.

Nitrogen Fate During Agricultural Managed Aquifer Recharge: Linking Plant Response, Hydrologic, and Geochemical Processes

“…However, flooding fields for long periods of time might negatively impact crop performance, as shown for V1, which needs to be considered and balanced (Levintal et al, 2022). A possible solution is to use a new model that predicts crop damage as a function of the duration of saturated conditions in the soil root-zone, soil texture, and crop tolerance to waterlogged conditions (Ganot and Dahlke, 2021a).…”

“…In California, for example, Ag-MAR is implemented as one of the methods to overcome ongoing groundwater depletion (Kocis and Dahlke, 2017). However, using farmland as spreading grounds bears the risk to leach contaminants from the water or soil to groundwater which can impact drinking water quality (Bachand et al, 2014), waterlogging of the root zone for long periods that can reduce crop health (Ganot and Dahlke, 2021a), and ecosystem service tradeoffs, such as short and long-term effects on in-stream flows (Levintal et al, 2022). Out of the above, leaching of legacy nitrogen (N), mainly in the form of nitrate (NO 3 -), is possibly the most widespread environmental risk of Ag-MAR.…”

The probabilistic behavior of AquaCrop parameters: a Monte-Carlo study

“…Crop models represent the mathematics of agricultural processes based on theory and empirical research; thus, the representation entails different assumptions and simpli cations of reality that make the output variables uncertain and inaccurate (Han et al, 2019;Andrea Saltelli et al, 2008). On the other hand, too many parameters (up to hundreds) must be speci ed to describe the properties of the soil-crop-atmosphere system (Ganot & Dahlke, 2021;Thorp et al, 2020). Estimating every parameter in the model needs signi cant eld measurements, which is costly and time-consuming.…”

Optimizing Managed Aquifer Recharge Locations in California's Central Valley Using an Evolutionary Multi‐Objective Genetic Algorithm Coupled With a Hydrological Simulation Model

“…Likewise, Zaidi et al (2015) identified suitable MAR locations in Saudi Arabia by overlaying maps of soil texture, vadose zone thickness, land use, and slope. A common alternative approach to these MCDA analyses conducted in Geographic Information Systems is the identification of MAR locations and their benefits and risks through numerical modeling (Bachtouli & Comte, 2019;Ganot & Dahlke, 2021;Kacimov et al, 2016;Tzoraki et al, 2018;Wurl & Imaz-Lamadrid, 2018;Zlotnik et al, 2017). Scherberg et al (2014), for example, used the Integrated Water Flow Model (IWFM) (Dogrul, 2013) to quantify the impact of selected MAR scenarios in the Walla River basin, USA.…”