DOI: 10.5061/dryad.8w9ghx3sg
title: Modeling climate-smart forest management and wood use for climate mitigation potential in Maryland and Pennsylvania

description: 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 associated with reduced instate harvesting activities. Based on
 state agency partner input, a total of 15 management scenarios were
 modeled for Maryland and 13 for Pennsylvania, along with two climate
 change scenarios and two bioenergy scenarios for each state. Our findings
 show that both strategic forest management and wood utilization can
 provide substantial climate mitigation potential relative to
 business-as-usual practices, increasing the forest C sink by 29% in
 Maryland and 38% in Pennsylvania by 2030 without disrupting timber
 supplies. Key climate-smart forest management activities include
 maintaining and increasing forest extent, fostering forest resiliency and
 natural regeneration, encouraging sustainable harvest practices, balancing
 timber supply and wood utilization with tree growth, and preparing for
 future climate impacts. This study adds to a growing body of work that
 quantifies the relationships between forest growth, forest disturbance,
 and harvested wood products. 
description: 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<em> </em>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<em> </em>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
 Bechtold & Patterson, 2005 and Pugh et al., 2018 were used
 to estimate each state’s forest inventory by a predetermined list of
 classifiers. Natural disturbance history was estimated from both the FIADB
 and LANDFIRE (USGS,
 2016) datasets to better constrain initial belowground and
 soil carbon parameters during what the modeling framework refers to as the
 model spin-up period (Kurz et
 al., 2009). Estimates of merchantable volume and
 corresponding biomass from FIADB were used to calibrate the model’s
 allometric volume-to-biomass assumptions to match forest type groups and
 growth conditions in Maryland and Pennsylvania. Harvest
 removals were estimated as average annual removal of merchantable timber
 in cubic feet between 2007 and 2019, converted to metric tons of carbon
 using methodologies and specific gravities reported by Smith et al., 2006. To
 assign a harvest type and intensity to each record of volumetric removal,
 stand age at the time of removal was calculated by taking the mid-point
 average between time <em>t<sub>1</sub> </em>and
 <em>t<sub>2 </sub></em>(Bechtold &
 Patterson, 2005)<em><sub> </sub></em>where
 <em>t<sub>1 </sub></em>is the year the unharvested
 stand was measured and <em>t<sub>2</sub></em> is
 the repeat interval year measurement post-harvest. In collaboration with
 state partners, harvest type and intensity were determined heuristically
 for each forest type based upon state-level management documentation,
 peer-reviewed literature, and expert input. A complete list of harvest
 types and intensities prescribed to each forest type group as a product of
 stand age can be found in Table S3 of the manuscript.
 Longer-term averages from 2007-2019 were used to estimate annual
 area targets for all land-use change (LUC) and natural disturbance events
 including wind, fire, disease, and insects. Annual LUC average rates by
 ownership and forest type group were derived by overlaying a geospatial
 forestland ownership dataset (Sass et al., 2020), the
 Protected Areas Database of the U.S. (PAD-US), a national geodatabase of
 protected areas (USGS,
 2018), and the National Land Cover Database (NLCD), a
 remotely-sensed data product used to characterize land cover and land
 cover change (Wickham et al.,
 2021). Wind disturbance events were calculated using the
 LANDFIRE Historic Disturbance dataset (USGS, 2016), a
 remotely-sensed data product provided by the USGS that estimates annual
 disturbance events. Annual averages for wildfire disturbances were derived
 from the LANDFIRE Historic Disturbance dataset (USGS, 2016) and validated
 through annual reports from the National Interagency Fire Center (NIFC).
 Annual prescribed fire acres were estimated from reports provided by the
 Maryland DNR Forest Service and Pennsylvania DCNR Bureau of Forestry and
 scaled to represent treatments on forestlands only. Annual acreages of
 insect and disease disturbance were derived from the National Insect
 & Disease Detection Survey (USDA Forest Service,
 2020), a spatial data product produced by USDA that collects
 and reports data on forest insects, diseases, and other disturbances. For
 more information on all input and activity data see Supplementary
 Materials 1.2. A complete list of BAU parameters can be found
 in<strong> </strong>Table S2.
 State-specific trade and commodity data from Resource Planning
 Act (RPA) assessments (USDA
 Forest Service, 2021), US Commodity Flow Surveys (US Department of Transportation et al.,
 2020), US International Trade Commission export data (US International Trade Commission,
 2021), and published peer-reviewed data (Howard &
 Liang, 2019) when available, or US averages from the same sources, were
 used to adapt and parameterize both HWP models. FAOSTAT data (FAO, 2021) were utilized
 to determine the commodity distributions of exported roundwood. Softwood
 products were parameterized and modeled separately from hardwood products,
 as the two wood types differ in exports and commodities produced as well
 as their associated product half-lives and displacement (Dymond, 2012; Howard et al.,
 2017). Published data were used to calculate softwood- and
 hardwood-specific half-lives for Maryland and Pennsylvania sawn wood and
 veneer products, while we relied on literature estimates for other
 products (Skog, 2008; J. E.
 Smith et al., 2006b). To calculate substitution benefits, we
 coupled region-specific data (USDA Forest Service, 2021), US
 consumption rates (Howard et al., 2017), product weights (C. Smyth et al.,
 2017), and LCA data (Bala et al., 2010; Dylewski &
 Adamczyk, 2013; Hubbard et al., 2020; Meil & Bushi, 2013;
 Puettmann, 2020a, 2020b; Puettmann et al., 2016; Puettmann &
 Salazar, 2018, 2019), following
 methods developed by Smyth et al. (2017). Landfill
 CO­<sub>2</sub> and CH<sub>4</sub> emissions rely
 on PICC defaults for methane generation (<em>k</em>) and
 landfill half-lives for wet, temperate climates (Pingoud et al., 2006).
 See Supplementary Materials 1.3 for more details on
 substitution and leakage calculation methods.
description: # 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 results provide a variety of information disaggregated by a list of forest characteristics such as forest type, ownership, site indices, etc., on annual timesteps starting in 2007 until 2150. HWP results are from 2007-2100. Result databases include information such as carbon fluxes split by pool, carbon pools, forest area, area disturbed, carbon transferred to harvested wood products, age demographics, and biomass. All area units are in hectares while carbon units can be in metric tons of carbon or tons of CO<sub>2</sub>e. ## Description of the data and file structure Users should be very aware of the sign (negative or positive) of each column for both ecosystem and HWP model results in flux and emission files to correctly sum on annual timesteps. For ecosystem files, Timestep 1 is equivalent to the year 2007. A comprehensive list of scenarios and descriptions is located in 'Scenario\_list.xlxs' file. ### Input Databases All input databases are in '.mdb' formats. Input databases can be run through the CBM-CFS3 simulation software using python API and python code provided in the American Forest GitHub page to generate the same the results databases provided in the Ecosystem model outputs section. The 'mdb\_tablenames.xlxs' file contains all of the table names included in the input databases. Users can open the raw '.mdb' formats utilizing the 'R coding environment' and the following code chunk; however, mdb files are included primarily as the simulation input to be able to run through the software providing users the same output tables provided if using the python API and code provided below. ``` ## read in the data install.packages('RODBC') library(RODBC) db = odbcDriverConnect("Driver={Microsoft Access Driver (*.mdb, *.accdb)}; DBQ= Your Directory and File Name Here .mdb") ## Get data data = as_tibble(sqlFetch (db , "Table Name", rownames=TRUE)) ## Insert Table Name to call it ``` ### Ecosystem model outputs Ecosystem model outputs have four associated CSV files per scenario with the following format STATENAME\_SCENARIO\_FILE.csv (for example: MD\_baseline\_flux.csv). The three ecosystem files have the following names: * XX\_XXXX\_age.csv * XX\_XXXX\_dist.csv * XX\_XXXX\_flux.csv Detailed descriptions of columns and units contained in the age, dist, and flux CSVs can be found in the 'CBMoutputs.pdf' file as well as the 'Column\_list.xlxs' file. Further detailed descriptions of each results database file and for each column including further detailed definitions and descriptions of fluxes and pools contained with the model can be found in Kull, S.J., Rampley, G.J., Morken, S., Metsaranta, J., Neilson, E.T., Kurz, W.A. (2020). Operational-Scale Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) Version 1.2: User's Guide. Natural Resources Canada, Canadian Forest Service, Alberta, Canada. ### DistProduct Data DistProduct Data from the "XX\_XXXX\_dist.csv" files were aggregated into softwood (SW) and hardwood (HW) categories to be inputted into the HWP models. Harvest wood product files are split between softwood (SW) and hardwood (HW) as species specific wood characteristics and growth forms often dictate the product stream the timber supply enters. For each state, there are XX files where the XX's represent the state or the wood type (SW versus HW). * XX.DE.bio.tC\_XX - Displaced emissions from bioenergy HW/SW in tons of carbon, in CO2e * XX.DE.wood.tC\_XX - Displaced emissions from wood products HW/SW in tons of carbon, in CO2e * XX.Emissions\_XX\_XX\_07.19 - HWP emissions from 2007-2019, in tons of carbon * XX.Emissions\_XX\_XX\_2020 - HWP emissions from 2020-2100., in tons of carbon * XX.Emissions\_XX\_p2007 - HWP emissions inherited from wood in use pre-2007 (starts in 1950), in tons of carbon. * XX\_Stocks\_XX - HWP carbon pool split by product stream for 2020-2100, in tons of carbon * XX.stocks.disagg.tc\_07 - HWP carbon pool from 2007-2019, in tons of carbon * Leakage.XX - Assumed additional annual emissions of carbon leaving the HWP from the other states (includes emissions associated with 1) harvest disturbance and 2) HWP emissions) stemming from a 63.9% leakage rate * XX.emissions\_cum.disagg.tC\_07 - Cumulative emissions by scenario and product stream for years 2030, 2050, 2100 starting in year 2007 * XX.emissions\_cum.disagg.tC\_20 - Cumulative emissions by scenario and product stream for years 2030, 2050, 2100 starting in year 2020 ## Code/Software All code, scripts, and notebooks used in this analysis can be found at https://github.com/American-Forests Additionally, the CBM-CFS3 software is freely available here: https://natural-resources.canada.ca/climate-change/climate-change-impacts-forests/carbon-accounting/carbon-budget-model/13107