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.

本研究针对森林治理后总地表碳含量进行案例研究，结合线性回归模型对总地表碳含量进行建模估计，该模型考虑了时间、气候变化和治理的三重交互作用。据此，在不同气候和治理条件下，对不同时间段的总地表碳含量进行了估算。森林模型的研究区域位于亚利桑那州弗拉格斯塔夫以南约55英里处的科科尼诺国家森林的摩戈隆山脉保护区，总面积为64,433英亩，其中37,667英亩被确定为疏伐区域，63,634英亩被确定为预定燃烧区域。美国森林服务局提供了220个森林样地的数据。这些样地最初于2014年在研究区域内采集，用于国家环境政策法案（NEPA）对拟议的森林治理方案的审查。气候扩展森林植被模拟程序被用于从2014年至2054年以10年为间隔模拟这些数据，考虑了有无治理措施以及不同的气候变化情景。在220个森林样地中，经过数据清理后，提供了189个样地的数据。使用Stata统计软件版本14.2对模型输出数据进行了线性回归拟合，以总地表碳含量（以每英亩吨为单位）作为因变量。回归方程为Y_i=β_0+β_rtc X+ε_i，其中因变量（Y│i）为总森林地表碳含量（以每英亩吨为单位），与包括年份（r）作为有序变量、森林治理（t）作为二元变量和气候变化情景（c）作为有序变量的三重交互作用进行回归，X为包含40种独立预测变量的向量。参数β_0为总地表碳含量截距，而模型中未解释的部分由残差（ε_i）捕捉，假设残差服从均值为零的正态分布。总共有40种治理、年份和气候变化情景的组合。对于每一种组合，都添加了系数以及95%置信区间的下限和上限到总地表碳含量截距。