Vapor movement is often an important part in the total water flux in the vadose zone of arid or semiarid regions because the soil moisture is relatively low. The two major objectives of this study were to develop a numerical model in the HYDRUS‐1D code that (i) solves the coupled equations governing liquid water, water vapor, and heat transport, together with the surface water and energy balance, and (ii) provides flexibility in accommodating various types of meteorological information to solve the surface energy balance. The code considers the movement of liquid water and water vapor in the subsurface to be driven by both pressure head and temperature gradients. The heat transport module considers movement of soil heat by conduction, convection of sensible heat by liquid water flow, transfer of latent heat by diffusion of water vapor, and transfer of sensible heat by diffusion of water vapor. The modifications allow a very flexible way of using various types of meteorological information at the soil–atmosphere interface for evaluating the surface water and energy balance. The coupled model was evaluated using field soil temperature and water content data collected at a field site. We demonstrate the use of standard daily meteorological variables in generating diurnal changes in these variables and their subsequent use for calculating continuous changes in water contents and temperatures in the soil profile. Simulated temperatures and water contents were in good agreement with measured values. Analyses of the distributions of the liquid and vapor fluxes vs. depth showed that soil water dynamics are strongly associated with the soil temperature regime.

A correct delineation of hazardous areas in a contaminated site relies first on accurate predictions of pollutant concentrations, a task usually complicated by the presence of censored data (observations below the detection limit) and highly positively skewed histograms. This paper compares the prediction performances of four geostatistical algorithms (ordinary kriging, log-normal kriging, multi-Gaussian kriging, and indicator kriging) through the cross validation of a set of 600 dioxin concentrations. Despite its theoretical limitations, log-normal kriging consistently yields the best results (smallest prediction errors, least false positives, and lowest total costs). The cross validation has been repeated 100 times for a series of sampling intensities, which reduces the risk that these results simply reflect sampling fluctuations. Indicator kriging (IK), in the simplified implementation of median IK, produces good predictions except for a moderate bias caused by the underestimation of high dioxin concentrations. Ordinary kriging is the most affected by data sparsity, leading to a large proportion of remediation units wrongly declared contaminated when less than 100 observations were used. Last, decisions based on multi-Gaussian kriging estimates are the most costly and create a large proportion of false positives that cannot be reduced by the collection of additional samples.

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Data collected along transects are becoming more common in environmental studies as indirect measurement devices, such as geophysical sensors, that can be attached to mobile platforms become more prevalent. Because exhaustive sampling is not always possible under constraints of time and costs, geostatistical interpolation techniques are used to estimate unknown values at unsampled locations from transect data. It is known that outlying observations can receive significantly greater ordinary kriging weights than centrally located observations when the data are contiguously aligned along a transect within a finite search window. Deutsch (1994) proposed a kriging algorithm, finite domain kriging, that uses a redundancy measure in place of the covariance function in the data-to-data kriging matrix to address the problem of overweighting the outlying observations. This paper compares the performances of two kriging techniques, ordinary kriging (OK) and finite domain kriging (FDK), on examining unexploded ordnance (UXO) densities by comparing prediction errors at unsampled locations. The impact of sampling design on object count prediction is also investigated using data collected from transects and at random locations. The Poisson process is used to model the spatial distribution of UXO for three 5000 Â 5000 m fields; one of which does not have any ordnance target (homogeneous field), while the other two sites have an ordnance target in the center of the site (isotropic and anisotropic fields). In general, for a given sampling transects width, the differences between OK and FDK in terms of the mean error and the mean square error are not significant regardless of the sampled area and the choice of the field. When 20% or more of the site is sampled, the estimation of object counts is unbiased on average for all three fields regardless of the choice of the transect width and the choice of the kriging algorithm. However, for non-homogeneous fields (isotropic and anisotropic fields), the mean error fluctuates considerably when a small number of transects are sampled. The difference between the transect sampling and the random sampling in terms of prediction errors becomes almost negligible if more than 20% of the site is sampled. Overall, FDK is no better than OK in terms of the prediction performances when the transect sampling procedure is used.

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