Bayesian hierarchical models for soil CO2 flux and leak detection at geologic sequestration sites

Proper characterizations of background soil CO2 respiration rates are critical for interpreting CO2 leakage monitoring results at geologic sequestration sites. In this paper, a method is developed for determining temperature-dependent critical values of soil CO2 flux for preliminary leak detection inference. The method is illustrated using surface CO2 flux measurements obtained from the AmeriFlux network fit with alternative models for the soil CO2 flux versus soil temperature relationship. The models are fit first to determine pooled parameter estimates across the sites, then using a Bayesian hierarchical method to obtain both global and site-specific parameter estimates. Model comparisons are made using the deviance information criterion (DIC), which considers both goodness of fit and model complexity. The hierarchical models consistently outperform the corresponding pooled models, demonstrating the need for site-specific data and estimates when determining relationships for background soil respiration. A hierarchical model that relates the square root of the CO2 flux to a quadratic function of soil temperature is found to provide the best fit for the AmeriFlux sites among the models tested. This model also yields effective prediction intervals, consistent with the upper envelope of the flux data across the modeled sites and temperature ranges. Calculation of upper prediction intervals using the proposed method can provide a basis for setting critical values in CO2 leak detection monitoring at sequestration sites.