HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Schoups, G. Articles by Hopmans, J. W. Search for Related Content PubMed Articles by Schoups, G. Articles by Hopmans, J. W. GeoRef GeoRef Citation Agricola Articles by Schoups, G. Articles by Hopmans, J. W. Related Collections Spatial Variability Uncertainty Analysis Coupled Flow/Transport Models

SPECIAL SECTION: PARAMETER IDENTIFICATION AND UNCERTAINTY ASSESSMENT IN THE UNSATURATED ZONE

Evaluation of Model Complexity and Input Uncertainty of Field-Scale Water Flow and Salt Transport

Hydrology Program, Department of Land, Air and Water Resources (LAWR), University of California, Davis, CA 95616, USA; G. Schoups, Currently at Department of Geological and Environmental Sciences, Stanford University, Stanford, CA 94305, USA
^* Corresponding author (jwhopmans{at}ucdavis.edu)

Received 14 November 2005.

Prediction of large-scale vadose zone water flow and salt transport is affected by errors due to uncertainties in model structure, that is, complexity of representation of hydrologic processes and model input uncertainties such as parameter values, boundary conditions, and initial conditions. Selection of an appropriate level of model complexity must consider these two sources of uncertainty. We illustrate this selection process for the prediction of field-scale crop transpiration and salt drainage in irrigated agriculture given a limited number of point-scale measurements. Various levels of model input uncertainty in hydraulic properties and infiltration rates are considered, representative of a range of spatial heterogeneities. Model complexity is defined relative to a "true" model, in terms of the level of spatial and temporal averaging used in the approximate model. This set-up allows for the separation of the total model error into different terms representing the model input error and the structural model error. Results show that the relative contribution of structural model error to the total model error decreases as spatial heterogeneity or uncertainty in the model input increases. Model input uncertainty may be reduced by taking a larger number of point-scale measurements. Our analysis further illustrates that there exists an optimal trade-off between model complexity and model input uncertainty. It suggests that complex models may be replaced by more simple ones as long as the resulting structural model error is smaller than the prediction errors due to input uncertainty. The methodology provides a straightforward and objective approach to identifying an optimal level of model complexity as a function of the degree of field-scale heterogeneity and data availability.

Abbreviations: ADE, advection–dispersion equation • pdf, probability density function • RE, Richards equation

This article has been cited by other articles:

				J. A. Vrugt and S. P. Neuman Introduction to the Special Section in Vadose Zone Journal: Parameter Identification and Uncertainty Assessment in the Unsaturated Zone Vadose Zone J., August 24, 2006; 5(3): 915 - 916. [Full Text] [PDF]