When and where to measure soil gases: an optimization approach for time series and spatial information

A fundamental question for any sampling design is identifying where and when to measure. Technological advances allow us to measure multiple greenhouse gases (GHGs) simultaneously, and now it is possible to provide complete GHG budgets from soils (i.e., CO₂, CH₄, and N₂O fluxes). We present a data-driven method for identifying optimized samples from times series (1D approach) or spatial arrays (2D approach) of information on soil gases. The autocorrelated conditioned Latin Hypercube Sampling (acLHS) combines a conditioned Latin Hypercube (cLHS) to obtain a representative sample of the joint probability distribution function and an autocorrelation model to ensure a reproducible spatial or temporal dependency function (i.e., temporal or spatial variability). The results show a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixed time intervals (e.g., once or twice per month) and the intrinsic temporal variability in and patterns of different GHG fluxes. Furthermore, the acLHS is more efficient than other sampling methods (i.e., fixed sampling, cLHS) as it can better reproduce the joint probability distribution and the temporal or spatial variability of the variables of interest. We test this approach using time series and spatial arrays to evaluate the relationship between soil CO₂ efflux and temperature. These results have implications for assessing gas fluxes from soils and consequently reduce uncertainty in the role of soils in biogeochemical cycles.