Copula-Based Cosimulation for Simulating Temporal or Spatial Data in Biogeosciences

Accurate modeling of dependencies between variables of interest is imperative for understanding biophysical processes and mechanisms relevant to biogeosciences research. This study presents copula-based cosimulation (CopCoSim) as an approach to model the temporal or spatial joint distributions of multiple variables by capturing their dependencies and correlations. We compared CopCoSim with the traditional Sequential Gaussian CoSimulation (SGCoSim) technique through two applications, representing one (i.e., time) and two dimensions (i.e., space) on a topic relevant to biogeosciences. Specifically, we present an application for soil CO₂ efflux, which is a major flux in the global carbon budget, using two case studies: (a) temporal distribution of soil CO₂ efflux and temperature and (b) spatial distribution of soil CO₂ efflux and temperature across the conterminous United States (CONUS). The methodology involves three steps: selecting a representative training data set, applying stochastic simulation methods, and evaluating model performance. The results indicate that CopCoSim provides a more accurate model with higher precision for representing variables of interest. CopCoSim better reproduces the univariate probability distribution, temporal or spatial autocorrelation, and dependency relationships between the predictor and response variables. Because CopCoSim does not rely on linear correlation structures and normality assumptions, it captures complex dependence structures and behaviors among variables. We propose that CopCoSim is useful for research in biogeosciences, where variables of interest (e.g., soil CO₂ efflux and temperature) are often interdependent and exhibit complex temporal or spatial patterns.