SFR Methodology Case Study Data
Forest model output data for a case study of total surface carbon after forest treatment combined with output from fitting a linear regression to modeled estimates of total surface carbon with a three-way-interaction of time, climate change, and treatment, resulting in estimations of total surface carbon at different time periods under different climatic and treatment conditions. The study area for the forest model is 64,433 acres, located approximately 55 miles south of Flagstaff, Arizona on the Mogollon Rim Ranger District of the Coconino National Forest, with 37,667 acres identified for thinning and 63,634 acres identified for prescribed burning. Data on 220 forest plots was provided by the USFS. These plots were originally sampled in 2014 within the study area for the purpose of a National Environmental Policy Act (NEPA) review of proposed forest treatments. The Climate Extension to the Forest Vegetation Simulator modelling program was utilized to model these data at 10-year intervals from 2014 through 2054 with and without treatment under different climate change scenarios. Out of 220 forest plots, data for 189 plots are provided here after data cleaning. A linear regression was fitted to model output data, with total surface carbon (measured in tons per acre) as the dependent variable, using Stata statistical software version 14.2. The equation for this regression is Y_i=β_0+β_rtc X+ε_i where the dependent variable (Y│i) is total forest surface carbon (measured in tons per acre), which was regressed against a three-way-interaction where β_rtc X is a vector that covers year as an ordinal variable (r), forest treatment as a binary variable (t), and climate change scenario as an ordinal variable (c), with forty total combinations as independent predictor variables (X). The parameter β_0 is the total surface carbon intercept, while the unexplained portion of the model is captured by the residuals (ε_i), which are assumed to be normally distributed with a mean of zero. In total, there were forty combinations of treatment, year, and climate change scenario. For each of these combinations, coefficients and the lower and upper bounds of 95% confidence intervals were added to the total surface carbon intercept.