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United States Department of Agriculture

Agricultural Research Service

Title: Inverse Estimation of Soil Hydraulic and Solute Transport Parameters from Transient Field Experiments: Heterogeneous Soil

Authors
item Abbasi, Fariborz - LEUVEN, BELGIUM
item Jacques, Deiderik - SCK-CEN, BELGIUM
item Simunek, Jiri - UC, RIVERSIDE, CA
item Feyen, Jan - LEUVEN, BELGIUM
item Van Genuchten, Martinus

Submitted to: American Society of Agricultural Engineers
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 18, 2003
Publication Date: July 1, 2003
Citation: Abbasi, F., Jacques, D., Simunek, J., Feyen, J., Van Genuchten, M.T. 2003. Inverse estimation of soil hydraulic and solute transport parameters from transient field experiments: heterogeneous soil. American Society of Agricultural Engineers. Transactions of the ASAE Vol 46(4):1097-1111

Interpretive Summary: Understanding solute transport processes in the vadose zone between the soil surface and the groundwater table is essential for limiting or effectively managing soil and groundwater pollution. Unfortunately, solute transport, particularly at the field scale, can be very complex because of the presence of several mutually interactive soil physical, chemical, and biological processes that may vary substantially over space and time. Previous studies have shown that water flow and solute transport processes are influenced by soil type, flow rate, flow regime, irrigation method, surface flow depth, preferential flow, soil heterogeneity, initial conditions, and/or boundary conditions. Several field studies suggest that solute transport in heterogeneous soils can be described with a relatively traditional transport model (called the CDE convection-dispersion equation), while other studies have shown considerable deviations between model predictions and field data. In this study we used the standard two-dimensional CDE model to analyze several field-scale furrow irrigation experiments. Soil water flow and solute transport parameters in the model were inversely estimated from five different transient experiments on blocked-end furrows assuming the presence of four soil horizons. First, the saturated hydraulic conductivity (permeability) and the transport parameters were simultaneously estimated for the different soil horizons and compared with those previously obtained from the same experiments using the assumption of profile homogeneity. Results showed only minor improvements in the model predictions. To improve the predictions, the most sensitive unknown soil hydraulic and solute transport parameters were estimated in two steps by means of sequential estimation of the water flow parameters followed by the estimation of the transport parameters. This two-step method somewhat improved the predicted cumulative infiltration rate during the first irrigation event, and more significantly the soil water contents, particularly of the surface horizons, while predictions of the deep percolation rates of water did not improve. The different parameter estimation techniques predicted the solute concentrations in the soil profiles reasonably well, considering the temporal and spatial heterogeneity of the soil profile. Considering temporal variability in the soil hydraulic (water flow) parameters between the first and second irrigations, presumably because of physical deterioration of the furrow surfaces, could have further improved the model predictions. Results of this study are important to better understand and predict the effects of alternative water management practices on water and agrochemical transport in irrigated soils.

Technical Abstract: While inverse parameter estimation techniques for determining key parameters affecting water flow and solute transport are becoming increasingly common in saturated and unsaturated zone studies, their application to practical problems, such as irrigation, have received relatively little attention. In this article, we used the Levenberg-Marquardt optimization algorithm in combination with the HYDRUS-2D numerical code to estimate soil hydraulic and solute transport parameters of several soil horizons below experimental furrows. Three experiments were carried out, each of the same duration but with different amounts of water and solutes resulting from 6, 10, and 14 cm water depths in the furrows. Two more experiments were performed with the same amounts of applied water and solute and, consequently, for different durations, on furrows with depths of 6 and 10 cm of water. We first used a scaling method to characterize spatial variability in the soil hydraulic properties, and then simultaneously estimated the saturated hydraulic conductivity (Ks) and the longitudinal dispersivity (DL) for the different horizons. Model predictions showed only minor improvements over those previously obtained assuming homogeneous soil profiles. In an effort to improve the predictions, we also carried out a two-step, sequential optimization in which we first estimated the soil hydraulic parameters followed by estimation of the solute transport parameters. This approach allowed us to include additional parameters in the optimization process. A sensitivity analysis was performed to determine the most sensitive hydraulic and solute transport parameters. Soil water contents were found to be most sensitive to the n parameter in van Genuchten's soil hydraulic model, followed by the saturated water content (WCs), while solute concentrations were most affected by WCs and DL. For these reasons, we estimated WCs and n for the various soil horizons during the first step of the sequential optimization process, and only DL during the second step. Sequential estimation somewhat improved predictions of the cumulative infiltration rates during the first irrigation event. It also significantly improved descriptions of the soil water content, particularly of the upper horizons, as compared to those obtained using simultaneous estimation, whereas deep percolation rates of water did not improve. Solute concentrations in the soil profiles were predicted equally well with both optimization approaches.

Last Modified: 12/19/2014
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