Soil carbon respiration rates are sensitive to changes in soil moisture content, and often feature a rapid rise to a peak value followed by an exponential decrease during a wetting event (i.e. the Birch effect). To simulate this behavior, previous studies have established a variety of models that encapsulate different environmental factors (e.g., temperature, soil moisture, etc.). However, the ability to predict the response of soil respiration to precipitation events across large-scale regions (e.g. watersheds, forests etc.) based on incubations of a limited set of soil samples is unknown. As such, we parse out the generic decreasing trend of soil respiration rate observed in a wetting event into three parameters based on a simple first order exponential decay equation: (1) peak respiration rate, (2) respiration pulse ‘half-life’, and (3) steady-state respiration rate. We interpret these parameters as encapsulating (1) the combined effects of bioavailable carbon and microbial activity at the beginning of a wetting event, (2) the consumption rate of the bioavailable carbon, and (3) the solubilization rate of carbon, respectively. To assess how different environmental factors (i.e. temperature, soil moisture, soil depth, and pre-incubation time) affect these pulse signal characteristics, we acquired 436 sets of parameters by applying the exponential function to over 30 studies of incubation time-series compiled in the Soil Incubation Database (SIDB). The dependency of the three parameters on the four environmental factors is evaluated for each individual study and the combined set are used to inform our application of model frameworks across diverse environments. Preliminary results show a lack of correlation between the ‘half-life’ of this pulse and temperature, soil moisture or soil depth across studies, indicating the consumption rate is insensitive to these three factors. In addition, the cumulative respired CO2 in an incubation experiment and the peak respiration rate show similar positive correlation with temperature, providing another way of estimating the amount of soil CO2 emission in situations where temperature is the primary variable (e.g. global warming). Further analysis will assess the rationality and conditions of upscaling using incubation data.