Inverse modeling of large‐scale spatially distributed vadose zone properties using global optimization

Abstract

[1] Computational capabilities have evolved to a point where it is possible to use multidimensional physically based hydrologic models to study spatial and temporal patterns of water flow in the vadose zone. However, models based on multidimensional governing equations have only received limited attention, in particular because of their computational, distributed input, and parameter estimation requirements. The aim of the present paper is to explore the usefulness and applicability of the inverse method to estimate vadose zone properties using the solution of a physically based, distributed three-dimensional model combined with spatially distributed measured tile drainage data from the 3880-ha Broadview Water District (BWD) in the San Joaquin Valley of California. The inverse problem is posed within a single-criterion Bayesian framework and solved by means of the computerized Shuffled Complex Evolution Metropolis global optimization algorithm. To study the benefits of using a spatially distributed three-dimensional vadose zone model, the results of the 3-D model were compared with those obtained using a simple storage-based bucket model and a spatially averaged one-dimensional unsaturated water flow model for a 2-year period. District-wide results demonstrate that measured spatially distributed patterns of drainage data contain only limited information for the identification of vadose zone model parameters and are particularly inadequate to identify the soil hydraulic properties. In contrast, the drain conductance and a soil matrix bypass coefficient were well determined, indicating that the dominant hydrology of the BWD was determined by drain system properties and preferential flow. Despite the significant CPU time needed for model calibration, results suggest that there are advantages in using physically based hydrologic models to study spatial and temporal patterns of water flow at the scale of a watershed. These models not only generate consistent forecasts of spatially distributed drainage data during the calibration and validation period but also possess unbiased predictive capabilities with respect to measured groundwater table depths not included in the calibration.

A review of model applications for structured soils: a) Water flow and tracer transport

“…The simulation results of MODHMS were compared with those obtained using a simple storage-based bucket model and a spatially averaged 1D flow model. The study concluded that the drain conductance and the bypass coefficient were determined well (while soil hydraulic properties were not) and that these parameters dominated the hydrology of the Broadview Water District (Vrugt et al, 2004). However, the conclusions about PF remain uncertain, since the simple bucket model gave similar results.…”

“…MODHMS (Panday and Huyakorn, 2004) was applied with global inverse parameter optimization to simulate spatially distributed tile drainage for a 2-year period in the Broadview Water District (38.8 km 2 ), San Joaquin Valley, California (Vrugt et al, 2004). The domain was horizontally discretized into 200 m × 200 m square cells, which were vertically subdivided into 14 layers of 0.3 m (top) to 0.9 m (bottom).…”

A comprehensive numerical analysis of the hydraulic behavior of a permeable pavement

“…One of the major reasons to apply inverse modeling is to estimate parameters that cannot be directly measured for various reasons. Numerous applications of inverse modeling for the estimation of soil hydraulic properties exist in the literature (Abbaspour et al, 2004;Hopmans et al, 2002;Vrugt et al, 2008Vrugt et al, , 2004. The gradient methods (Marquardt, 1963) have been used most widely among hydrologists and soil scientists.…”

On the use of surrogate-based modeling for the numerical analysis of Low Impact Development techniques

“…Numerous applications of inverse modeling for the estimation of soil hydraulic properties exist in the literature (Abbaspour et al, 2004;Hopmans et al, 2002;Vrugt et al, 2008;Vrugt et al, 2004). The gradient methods (Marquardt, 1963) have been most widely used among hydrologists and soil scientists.…”