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Toward Improved Identifiability of Soil Hydraulic Parameters

On the Selection of a Suitable Parametric Model

Jasper A. Vrugt*,a, Willem Boutena, Hoshin V. Guptab and Jan W. Hopmansc

a Institute for Biodiversity and Ecosystem Dynamics, Section Physical Geography, University of Amsterdam, Nieuwe Achtergracht 166, Amsterdam, 1018 WV, The Netherlands
b Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, 85721, USA
c Hydrology Program, Department of Land, Air and Water Resources (LAWR), University of California, Davis, CA 95616, USA



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Fig. 1. Univariate posterior probability distributions for the soil hydraulic parameters (A) {theta}r, (B) {alpha}, (C) Ks, and (D) lvg in the Mualem–van Genuchten model (1980) using observed outflow dynamics during the a transient one-step outflow experiment. The symbol p(ß) along the y-axis in each of the figures denotes the posterior density.

 


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Fig. 2. Prediction uncertainty intervals of the Brooks and Corey , Mualem–van Genuchten, and Kosugi retention curves associated with the uncertainty in the posterior parameter estimates for the sandy and clayey soil. Measured retention points are indicated with the red symbols, whereas the dark line indicates the most likely parameter set.

 


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Fig. 3. Univariate posterior probability distributions of the parameters (A) {lambda}, (B) n, and (C) h0.5 in the Brooks and Corey , Mualem–van Genuchten, and Kosugi parametric models for the clayey soil. The symbol p(ß) along the y-axis in each of the figures denotes the posterior density.

 


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Fig. 4. Sensitivity of water content to the retention parameters (A) {theta}s, (B) {theta}r, (C) {alpha}, and (D) n in the parametric model of Mualem–van Genuchten for the sandy soil, sandy loam, and clayey soil.

 


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Fig. 5. Scatter plot of 4000 combinations of (A) {theta}rhb, (B) hb{lambda}, (C) {theta}r{lambda}, (D) {theta}r{alpha}, (E) {alpha}n, (F) {theta}rn, (G) {theta}rh0.5, (H) h0.5{sigma}, and (I) {theta}r{sigma} parameters sampled for the clayey soil using the SCEM-UA algorithm for the BC model (A–C), VG model (D–F), and KS model (G–I), respectively.

 


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Fig. 6. Evolution of the Gelman–Rubin convergence statistic for the parameter {theta}s in the (A) Mualem–van Genuchten and (B) Kosugi parametric model structures. Evolution of the current best RMSE value describing either observed outflow dynamics or matric head readings within the soil core for the (C) Mualem–van Genuchten and (D) Kosugi models.

 


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Fig. 7. Scatter plot of 1500 combinations of (A) {theta}rn, (B) Kslvg, (C) {theta}r{sigma}, and (D) Kslk parameters sampled for the one-step outflow experiment using the SCEM-UA algorithm in the case of Scenario II for the Mualem–van Genuchten model (A–B) and Kosugi model (C–D), respectively.

 


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Fig. 8. Prediction uncertainty intervals for the retention and hydraulic conductivity curves associated with the uncertainty in the parameter estimates for Scenarios I (purple) and II (blue) for the multistep (0–125–250–375–500 mbar) outflow experiment using the (A,C) Mualem–van Genuchten and (B,D) Kosugi parametric models. The red symbols correspond to the directly estimated retention and hydraulic conductivity points, whereas the dark line denotes the most likely parameter set for Scenario II.

 


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Fig. 9. (A–B) Outflow prediction uncertainty ranges of the HYDRUS-1D model associated with the uncertainty in the model (blue) and parameter estimates for Scenarios I (purple) and II (red) of the Mualem–van Genuchten and Kosugi parametric models for the multistep (0–125–250–375–500 mbar) outflow experiment (B–D). Uncertainties in the simulated outflow associated with the most probable parameter set of the Mualem–van Genuchten and Kosugi models. The blue region denotes model uncertainty, whereas the residual parameter uncertainty is indicated with the purple (Scenario I) and red regions (Scenario II). The black symbols correspond to the observed outflow data.

 





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