Abstract. CO 2 production and transport from forest floors is an important component of the carbon cycle and is closely related to the global atmosphere CO 2 concentration. If we are to understand the feedback between soil processes and atmospheric CO 2 , we need to know more about the spatiotemporal variability of this soil respiration under different environmental conditions. In this study, long-term measurements were conducted in a spruce-dominated forest ecosystem in western Germany. Multivariate analysis-based similarities between different measurement sites led to the detection of site clusters along two CO 2 emission axes: (1) mainly controlled by soil temperature and moisture condition, and (2) mainly controlled by root biomass and the forest floor litter. The combined effects of soil temperature and soil moisture were used as a time-dependent rating factor affecting the optimal CO 2 production and transport at cluster level. High/moderate/weak time-dependent rating factors were associated with the different clusters. The process-based, most distant clusters were identified using specified pattern characteristics: the reaction rates in the soil layers, the activation energy for bio-chemical reactions, the soil moisture dependency parameter, the root biomass factor, the litter layer factor and the organic matter factor. A HYDRUS-1D model system was inversely used to compute soil hydraulic parameters from soil moisture measurements. Heat transport parameters were calibrated based on observed soil temperatures. The results were used to adjust CO 2 productions by soil microorganisms and plant roots under optimal conditions for each cluster. Although the uncertainty associated with the HYDRUS-1D simulations is higher, the results were consistent with both the multivariate clustering and the timedependent rating of site production.Finally, four clusters with significantly different environmental conditions (i.e. permanent high soil moisture condition, accumulated litter amount, high variability in soil moisture content, and dominant temperature dependence) were found to be relevant in explaining the spatio-temporal variability of CO 2 efflux and providing reference-specific characteristic values for the investigated area.