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Title: Optimizing soil hydraulic parameters in in RZWQM2 under fallow conditions

Author
item FANG, Q - Chinese Academy Of Sciences
item Green, Timothy
item Ma, Liwang
item Malone, Robert - Rob
item Erskine, Robert - Rob
item Ahuja, Lajpat

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/26/2010
Publication Date: 9/15/2010
Citation: Fang, Q.X., Green, T.R., Ma, L., Malone, R.W., Erskine, R.H., Ahuja, L.R. 2010. Optimizing soil hydraulic parameters in in RZWQM2 under fallow conditions. Soil Science Society of America Journal. 74(6):1897-1913. doi:10.2136/sssaj2009.0380.

Interpretive Summary: Effective estimation of soil hydraulic parameters is essential for predicting soil water dynamics and related biochemical processes in agricultural systems. However, high uncertainty in estimated parameter values due to variations in space and time limit a model’s skill for prediction and application. In this study, a global search analysis method (Latin Hypercube Sampling, LHS) and a local optimization method (Parameter ESTimation software, PEST) were explored to calibrate soil hydraulic parameters in the Root Zone water Quality Model (RZWQM2). Six methods of estimating parameters of the soil water retention curve and saturated hydraulic conductivity were evaluated to simulate soil water dynamics under fallow conditions in eastern Colorado, USA. The PEST optimization based on soil type values as initial estimates resulted in relatively good calibration results but with some unrealistic soil hydraulic parameters and soil evaporation. Such problems can be avoided by combining LHS and PEST. The calibrated soil hydraulic parameters showed similar trends with soil depth for the four parameter estimation methods, but resulted in large differences between simulated and observed soil water data. The goodness of model parameterization depends on the methods of estimating soil hydraulic properties, and also on the calibration strategy and available data sets. These results demonstrated the challenge in calibrating soil hydraulic parameters, and showed that different calibration procedures with cross-validation can reduce parameter uncertainties.

Technical Abstract: Effective estimation of soil hydraulic parameters is essential for predicting soil water dynamics and related biochemical processes in agricultural systems. However, high uncertainties in estimated parameter values limit a model’s skill for prediction and application. In this study, a global search analysis (Latin Hypercube Sampling, LHS) and gradient-based optimization (Parameter ESTimation software, PEST) were explored to calibrate soil hydraulic parameters in the Root Zone water Quality Model (RZWQM2). Six hierarchical methods of estimating Brook-Corey parameters of the soil water retention curve and saturated hydraulic conductivity (Ksat) were evaluated to simulate daily soil water dynamics under fallow conditions in eastern Colorado, USA. The initial PEST optimization based on soil type values as initial estimates resulted in good model responses, but with some unrealistic soil hydraulic parameters and soil evaporation. PEST or combined LHS+PEST optimization results were better than the LHS search analysis alone. The calibrated soil hydraulic parameters showed similar trends with soil depth for the hierarchical estimation methods in RZWQM2, but resulted in large differences between simulated and observed soil water data. Model parameterization depends on the combined methods of estimating soil hydraulic properties, optimization procedure, and available data. Calibration results using water content measurements at four depths (30, 60, 90 and 150 cm) were similar to results using all ten depths. This study demonstrated challenges in calibrating soil hydraulic parameters, and showed that different calibration procedures with cross-validation can reduce parameter uncertainties.