Modeling climate-smart forest management and wood use for climate mitigation potential in Maryland and Pennsylvania

State and local governments are increasingly interested in understanding the role forests and harvested wood products play in regional carbon sinks and storage, their potential contributions to state-level greenhouse gas (GHG) reductions, and the interactions between GHG reduction goals and potential economic opportunities. We used empirically driven process-based forest carbon dynamics and harvested wood product models in a systems-based approach to project the carbon impacts of various forest management and wood utilization activities in Maryland and Pennsylvania from 2007–2100. To quantify state-wide forest carbon dynamics, we integrated forest inventory data, harvest and management activity data, and remotely-sensed metrics of landuse change and natural forest disturbances within a participatory modeling approach. We accounted for net GHG emissions across (1) forest ecosystems (2) harvested wood products, (3) substitution benefits from wood product utilization, and (4) leakage assoc..., Primary data inputs to CBM-CFS3 included a detailed forest inventory, growth-yield relations to estimate forest productivity, and estimates of harvest yields and intensity, land-use change, and natural disturbances. Inventory data are categorized by a series of forest classifiers defining relevant characteristics such as spatially referenced boundaries, ownership, forest type, site productivity, or reserve status. Allometric equations are used to predict tree volume-to-biomass relationships (Boudewyn et al., 2007). For this study, forest inventory, growth-yield curves, and harvest data were estimated from the USDA Forest Service Forest Inventory and Analysis (FIA) program, which we accessed through the FIA DataMart (USDA Forest Service, 2019) using the rFIA package (Stanke et al., 2020) in the R programming environment (R Core Team, 2020), which enables data exploration and user-defined spatio-temporal queries and estimation of the FIA database (FIADB). Methodologies derived from Bechto..., , # Modeling Climate-Smart Forest Management and Wood Use for Climate Mitigation Potential in Maryland and Pennsylvania

[https://doi.org/10.5061/dryad.8w9ghx3sg](https://doi.org/10.5061/dryad.8w9ghx3sg)

This dataset provides formatted input databases and post-processed results databases for the CBM-CFS3 ecosystem model portion of this analysis. Additionally, the post processed data from both the CBM-HWP-MD and CBM-HWP-PA harvest wood product models are provided. Please see the methods section and appendices for more detailed descriptions of data sources and data processing.

To be compatible with the CBM-CFS3 software, ecosystem model inputs were processed and reformatted into a MS access database format. The input databases are then run through the simulation software using a Python API where results are exported in CSV format. Ecosystem model results are post-processed to be inputted into the associated harvested wood product models which then provide additional CSV files.

Ecosystem ...

州和地方政府愈发关注明晰森林与采伐木材产品（harvested wood products）在区域碳汇及碳储存中所发挥的作用、其对州级温室气体（Greenhouse Gas, GHG）减排的潜在贡献，以及温室气体减排目标与潜在经济机遇之间的相互作用。我们采用基于经验驱动的过程性森林碳动态模型与采伐木材产品模型，结合系统论方法，对2007年至2100年间马里兰州与宾夕法尼亚州的各类森林管理及木材利用活动的碳影响进行了预测。为量化全州范围的森林碳动态，我们采用参与式建模方法，整合了森林清查数据、采伐与管理活动数据，以及遥感获取的土地利用变化与自然森林扰动指标。我们核算了以下四类的净温室气体排放：(1) 森林生态系统；(2) 采伐木材产品；(3) 木材产品利用带来的替代效益；(4) 相关碳泄漏（leakage）。CBM-CFS3生态系统模型（CBM-CFS3）的主要原始输入数据包括详细的森林清查、用于估算森林生产力的生长-收获关系、采伐产量与强度、土地利用变化以及自然扰动数据。清查数据按照一系列森林分类器进行分类，这些分类器定义了相关特征，如空间参考边界、所有权、森林类型、立地生产力或保护地状态。异速生长方程被用于预测树木体积与生物量的关系（Boudewyn等，2007）。本研究所需的森林清查、生长-收获曲线以及采伐数据，均通过R编程环境（R Core Team, 2020）中的rFIA软件包（rFIA）获取，我们通过FIA DataMart（美国农业部森林服务局，2019）进行数据访问，并借助该包在R编程环境中实现数据探索、用户自定义时空查询以及FIA数据库（FIADB）的估算。研究方法源自Bechto…… # 马里兰州与宾夕法尼亚州气候智能型森林管理与木材利用的气候减缓潜力建模

[https://doi.org/10.5061/dryad.8w9ghx3sg](https://doi.org/10.5061/dryad.8w9ghx3sg)

本数据集为本次分析中CBM-CFS3生态系统模型部分提供格式化后的输入数据库与后处理结果数据库。此外，还提供了CBM-HWP-MD与CBM-HWP-PA两款采伐木材产品模型的后处理数据。有关数据源与数据处理的详细说明，请参阅方法章节与附录。

为适配CBM-CFS3软件，生态系统模型的输入数据经过处理并重新格式化为Microsoft Access数据库格式。随后，通过Python应用程序编程接口（API）运行输入数据库以启动模拟软件，最终结果以逗号分隔值（Comma-Separated Values, CSV）格式导出。生态系统模型的结果将经过后处理，作为关联采伐木材产品模型的输入数据，进而生成额外的CSV文件。

生态系统……