Calibration of parameter fields consisting of multiple statistical populations.

If a parameter field to be calibrated consists of more than one statistical population, usually not only the parameter values are uncertain, but the spatial distributions of the populations are uncertain as well. In this study, we demonstrate the potential of the multimodal calibration method we proposed recently for the calibration of such fields, as applied to real-world ground water models with several additional stochastic parameter fields. Our method enables the calibration of the spatial distribution of the statistical populations, as well as their spatially correlated parameterization, while honoring the complete prior geostatistical definition of the multimodal parameter field. We illustrate the implications of the method in terms of the reliability of the posterior model by comparing its performance to that of a "conventional" calibration approach in which the positions of the statistical populations are not allowed to change. Information from synthetic calibration runs is used to show how ignoring the uncertainty involved in the positions of the statistical populations not only denies the modeler the opportunity to use the measurement information to improve these positions but also unduly influences the posterior intrapopulation distributions, causes unjustified adjustments to the cocalibrated parameter fields, and results in poorer observation reproduction. The proposed multimodal calibration allows a more complete treatment of the relevant uncertainties, which prevents the abovementioned adverse effects and renders a more trustworthy posterior model.

Predicting the bioavailability of copper and zinc in soils: modeling the partitioning of potentially bioavailable copper and zinc from soil solid to soil solution.

This research produced statistically based, semimechanistic models describing partitioning of Cu and Zn in 40 soils from the United States, Canada, the United Kingdom (UK), The Netherlands, and Chile with widely varying characteristics. Two different types of models were constructed, partitioning models and competitive adsorption models. Multiple linear regression (MLR) was employed to prioritize over 30 different soil characteristics. Multiple linear regression yielded equations predicting the partitioning of Cu and Zn. Equations were also created that estimated the potentially bioavailable fraction of Cu and/or Zn. Data from plant uptake studies (which are reported separately) governed the choice of a suitable chemical soil extraction that estimated bioavailable Cu (0.01 M HCl) and bioavailable Zn (0.01 M CaCl2). Soil pH (1:1 soil:deionized water [DI H2O]) and percent organic matter accounted for approximately 70% of the variability in Cu partitioning and 80% of the variability in bioavailable Cu in the 40 soils studied. For Zn, soil pH alone accounted for roughly 75% of the partitioning variability and 80% of the variability for the estimated bioavailable portion. The results presented here were used in conjunction with results from the plant uptake studies for the creation of models to assess the potential bioavailable metal associated with any given soil from a wide variety of locations.