|Van Genuchten, Martinus -|
|Nicholson, Thomas -|
|Cady, Ralph -|
Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: September 20, 2009
Publication Date: December 14, 2009
Citation: Guber, A.K., Pachepsky, Y.A., Van Genuchten, M., Rowland, R.A., Nicholson, T.J., Cady, R.E. 2009. Uncertainty in Multimodel Water Flow Simulation Associated with Pedotransfer Functions and Weighing Methods. American Geophysical Union. EOS transaction AGU, 90(52) Fall Meeting Supliment, Abstract H41C-0898, 2009. Technical Abstract: Multimodeling (MM) has been developed during the last decade to improve prediction capability of hydrological models. The MM combined with the pedotransfer functions (PTFs) was successfully applied to soil water flow simulations. This study examined the uncertainty in water content simulations associated with the PTFs and MM weighting methods. Data on soil water contents were collected at the USDA-ARS Beltsville OPE3 field site from January to November 2007. Four locations were instrumented with Multisensor Capacitance Probes (SENTEK) to measure soil water content at depths from 10 to 100 cm with 10 cm increment. Standard meteorological data were measured in the vicinity of the site. Undisturbed soil samples were taken from the same depths to measure soil bulk density (BD), organic carbon content (OC) and soil texture in all locations. Fourteen PTFs, that had been developed from relatively large datasets (>200), were used to calculate soil hydraulic properties for each individual depth from measured BD, OC and soil texture. Thus, 14 sets of hydraulic parameters were obtained for each location. Then we solved the Richards equation with each set of hydraulic parameters for each location. Weighted predictions of those models were combined to obtain the multimodel predictions. The following weighting methods were compared in this study: (i) using only the best model; (ii) assigning equal weights to all models; (iii) using the superensemble; (iv) using the superensemble with the singular-value decomposition to find weights; (v) using Bayesian model averaging; and (vi) using information theory. The weighting methods were evaluated in terms of their accuracy and uncertainty (the average error and the standard deviation of errors in reproducing the test data). The multimodel training, i.e., weight determination, was done with daily water contents using moving windows that were from 30 to 150 day wide. All data outside the windows were used to test the model prediction. Generally, accuracy increased, while certainty decreased with an increase in the length of the training period. Weights of PTFs varied with depth for each location that meant these PTFs had different accuracy in predicting soil water content. Also different PTFs were used in models for the same depth at different locations, which implied that PTFs selection was depth- and site-specific. Accuracy and uncertainty of the multimodeling varied for six weighing methods. Best results were obtained with the singular value decomposition method; equal weighting resulted in the worst prediction. The highest uncertainty was associated with the singular value decomposition method. No statistically significant difference in the standard deviation of prediction errors was found between other weighting methods.