Grantee Research Project Results
Final Report: Regional Vulnerability of Forest Resources to Current and Projected Environmental Stresses in the Southeastern U.S.
EPA Grant Number: R828785Title: Regional Vulnerability of Forest Resources to Current and Projected Environmental Stresses in the Southeastern U.S.
Investigators: Abt, Robert , Myers, Jennifer Moore , Sommer, Allan , Murray, Brian , Bunch, Corey , Yang, Jui-Chen , Beach, Robert , Ahn, SoEun , McNulty, Steve , Pattanayak, Subhrendu
Institution: North Carolina State University , USDA , Desert Research Institute
Current Institution: North Carolina State University , Desert Research Institute , USDA
EPA Project Officer: Packard, Benjamin H
Project Period: March 19, 2001 through March 18, 2003 (Extended to March 18, 2004)
Project Amount: $399,365
RFA: Futures Research in Socio-Economics (2001) RFA Text | Recipients Lists
Research Category: Environmental Justice
Objective:
The primary objectives of this research project were to: (1) develop an integrated modeling framework that links climate, biological, and economic process models of the southeastern U.S. forest resource; (2) perform integrated ecological assessments of the biological and economic effects of chemical and physical environmental stressors and land use change on the forest resources in the 13 southeastern states; (3) develop regional-level assessments to identify and prioritize the role of environmental stresses, climate change, and forest market responses on forest systems; and (4) assess the benefits and service forest systems provide to the American public.
Summary/Accomplishments (Outputs/Outcomes):
Climate, ecological, and economic phenomena are inextricably linked, especially in the forest sector. The essence of economic value within the sector derives from the biophysical productivity of the resource. And the productivity, diversity, and extent of forest ecosystems are affected substantially by climatic conditions (macro and micro). Moreover, economic factors affect forest productivity via economically motivated interventions, such as land use change, land management, species selection, and harvesting rates. The economy feeds back to the climate system by altering the balance of greenhouse gases (GHG) in the atmosphere through GHG emissions. Ecosystem feedbacks to the climate system include emissions of GHG from land use change, the capacity to sequester carbon, the albedo effect, and other complex processes.
Because of these complex feedbacks, an integrated assessment approach was needed to better understand the vulnerability of forest ecosystems to climate change, economic externalities, and other environmental stressors.
To address these important issues, a team of economists and ecology, hydrology, and forestry scientists engaged in several distinct but interrelated research tasks, including: (1) improving the conceptual and empirical foundation of the system’s economic model of forest resources and timber supply (the Subregional Timber Supply [SRTS] model); (2) improving the conceptual and empirical foundation of the system’s ecological process model of productivity, species composition, and hydrology (PnET); (3) coupling the economic and ecological models into a common integrated framework for simulating forest economic and ecological outcomes from the plot to the regional scale (PEcon); and (4) developing an approach for monetizing the impacts of climate change on forest resources through statistical inference of the relationship between climate variables, forest productivity, and the economic value of land¾called the “Ricardian” or “hedonic” approach to economic valuation of climate change impacts.
Improve the Conceptual and Empirical Foundation of the System’s Economic Model of Forest Resources and Timber Supply
The SRTS Modeling System was developed originally by Robert Abt and colleagues (Abt, et al., 2000) to assess regional forest resource factors in the southeastern and south-central United States (referenced herein as “the South” or “southern”). SRTS was developed initially to serve as an economic overlay to traditional forest inventory models such as ATLAS (Mills and Kincaid, 1992). In addition to providing economic structure to a forest management problem, SRTS evaluates economic and biophysical phenomena with detail at the level of 51 United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) subregions in the South. At its inception in the late 1980s, SRTS’ level of subregional detail was an enhancement of the spatial resolution typically used to evaluate forest resource issues (the Timber Assessment Market Model of Adams and Haynes, 1980), which evaluated the southeastern and south-central United States as distinct regions but did not examine subregional variation within the South. Research efforts initiated as part of the USDA Forest Service’s Fourth Forest study in the 1980s led to the development of SRTS’ capabilities to examine sectoral activity at finer levels of sub-regional detail.
At the outset of this research project, SRTS had retained the same basic model structure since its inception. Although this structure provided unique insights into forest resource allocation within the South, there were a number of aspects of the model that needed to be improved to rigorously examine the complex nature of economic and ecological responses to environmental change. The main areas identified for improvement in this study were: (1) estimating the relationship between inventory age class, species distribution, and timber supply; (2) testing for the importance of model changes on economic and ecological outcomes; (3) developing a multiple-product timber supply characterization; and (4) endogenizing management responses to market and ecological conditions.
Those enhancements largely were accomplished in this research project, and the results were disseminated to the research community through several presentations and three peer-review publications.
Improve the Conceptual and Empirical Foundation of the System’s Ecological Process Model of Productivity, Species Composition, and Hydrology
PnET-II is a forest process model developed to predict forest productivity and hydrology across a range of climates and site conditions (Aber and Federer, 1992; Ollinger, et al., 1998; McNulty, et al., 2000). PnET-II uses site-specific soil water holding capacity (SWHC) and four monthly climate parameters (minimum and maximum air temperature, total precipitation, and solar radiation) along with tree species-specific attributes to predict changes in forest growth, carbon storage, and water yield.
Predicted forest growth is calculated as total gross photosynthesis minus growth and maintenance respiration for leaf, wood, and root compartments. Gross photosynthesis is first calculated without water stress effects as a function of temperature, foliar nitrogen concentration, and vapor pressure deficit. The optimal temperature for net photosynthesis varies from 24° to 28° C depending on forest type. As temperature increases beyond the optimal photosynthetic temperature, respiration rate increases, whereas gross photosynthesis increases slightly or decreases, and proportionally less net carbon per unit leaf area is fixed. PnET-II calculates the maximum amount of leaf area that can be supported on a site based on the soil, climate, and tree species-specific vegetation attributes (Aber, et al., 1995). The model assumes that leaf area is equal to the maximum amount of foliage that can be supported by SWHC, species, and climate limitations. Fixed carbon is divided into three tree compartments within the models. First, the plant demands for root and leaf carbon storage are met. Once sufficient fixed carbon has been allocated for the maintenance and growth of tree roots and foliage, the remaining carbon is allocated to stem wood within the tree. If environmental conditions are poor, there will be little or no carbon to allocate to stem wood growth. Under very poor environmental conditions, there may not be sufficient available carbon for complete foliar and root growth. This will result is a reduction in leaf and root mass relative to the previous year.
Improvements in the Ecological Process Model, PnET-II
Species Dynamics. In the past, the PnET-II model simulated growth for an ecosystem with broad forest types (Aber, et al., 1995), resulting in little or no species dynamics or competition for light or other resources. In addition to coupling the economic and ecological models into a common framework, species level growth and interaction were incorporated into PnET-II. Not only does this enhanced resolution permit species dynamics analysis, it also addresses species important to southern timber markets.
The first effort in this research study was expanding the tree species-specific attributes and focusing on a main driver of the PnET-II model, foliar nitrogen. Tree species were largely southern and were limited to species in the FIA database for the 11 southern states in the study area. An extensive literature search resulted in foliar nitrogen parameters for 15 (61%) of the softwood species and 83 (64%) of the hardwood species in our attribute database.
A modification was made to the PnET-II model to facilitate competition for light resources, which in turn introduced species dynamics in the overall modeling framework. First, two light allocation equations were established, one each for hardwood and softwood species. These equations distribute light throughout the canopy as a function of standing volume. To establish these curves, the model was run for a hardwood-dominated stand and a softwood-dominated stand with known FIA volume measurements, and full sunlight was allocated to each species. A mean net primary productivity (NPP) was recorded annually for both stands. The runs were then repeated, but light allocation for each species was based on the order-specific allometry of light extinction through the canopy as a function of individual species volume relative to total stand volume. The sum of individual species productivity should equal the single forest type productivity for the stand when the light allocation by species volume is correct. The resultant light distribution by species volume relationships were then finalized (Figure 1). Conifers have a higher percent light transmittance for any given volume relative to hardwoods because conifer foliage is more clumped and because hardwoods have a higher leaf area index (i.e., the relative amount of leaf area relative to ground area) for any given volume. These two factors increase the relative light absorption capacity of hardwood relative to conifers at each volume.
Figure 1. Light Distribution as a Function of Hardwood and Softwood Tree Species Volume
Mortality. Previous versions of PnET-II assumed a closed-canopy, mature forest (Aber, et al., 1995). To simulate the competitive environment needed by our coupled ecological and economic modeling framework, the issue of mortality was addressed. Inherent mortality results from the aforementioned light competition process. As more volume is added to certain species, more light is allocated to that species instead of other less competitive (i.e., lower volume) species. If a species volume relative to the sum of all other species present on a plot becomes proportionately low, then the species may not receive a sufficient amount of solar radiation to survive. Falling below this light compensation point causes tree respiration to exceed gross photosynthesis, which then results in tree species mortality on the plot.
Other mortality factors, such as insect invasion, disease, and extreme weather events, have to be accounted for as well, and for this we applied a static but stochastic mortality rate each year. Mean mortality rates were developed from the FIA database. Knowing that age and forest management influence mortality, the SRTS model’s management type definitions were used to assign management types to each plot. Mortality rates were then calculated for each management type and age class using estimates from the FIA database matching those qualifications. The calculated rates range from 1 to 7 plots per 1,000. Annual iterations of the model include a mortality subroutine that randomly selects plots and removes volume from all management type and age class combinations. The randomly selected plots are reset to zero volume for the following year to simulate stand death.
Spatial Framework. Previous versions of PnET models, such as PnETGIS, have addressed the spatial nature of the ecological modeling process (Aber, at al.,1995). For this analysis, however, a more complex spatial framework was needed to incorporate the differing scales of climate data, FIA database plot information, and SRTS economic units. To accomplish this, a relational database schema was implemented to store and link information about these different entities.
The three main components in this linking process were the FIA data, SRTS economic unit, and climate databases. FIA data is recorded at a plot level, where specific measurements are gathered in cycles to record stand composition, growth, death, and other factors. This plot level structure was maintained to store records relating to each plot and species growth, death, and harvest. The FIA plot database includes scaling factors to produce output at county, survey unit (collections of counties), and state levels.
SRTS input data, although derived from the FIA database, reside in economic units, the most common being a grouping of state, survey unit, ownership class, and management type (Abt, et al., 2000). Because plots have ownership classes and management type designations and can be associated with states and survey units, our framework allows plots to be aggregated and related to SRTS units.
The climate scenarios used in the PnET-II model were derived from the Vegetation and Ecosystem Modeling and Analysis Project (VEMAP). The spatial resolution of the VEMAP data is a 0.5-degree latitude x 0.5 degree-longitude grid covering the conterminous United States (Kittel, et al., 2000). The county identifiers of FIA plots were used to assign VEMAP climate data to each FIA plot.
Couple the Economic and Ecological Models Into a Common Integrated Framework for Simulating Forest Economic and Ecological Outcomes from the Plot to the Regional Scale
The purpose of this research project was to provide an integrated approach to ecosystem and economic modeling of forest resources while accounting for other factors such as climate change, ozone, and other disturbances. The models chosen for integration were the SRTS and PnET-II. We replaced the original SRTS growth and yield table-derived growth rates (Abt, et al., 2000) with dynamically modeled growth estimates from PnET-II. The PnET suite of models predicts changes in ecosystem health and forest growth based on factors such as climate change and disturbance. These two models were coupled and a framework was designed using climate change scenarios, multiple species parameter sets, and FIA measurement data. The PnET and SRTS models were written in Microsoft’s Visual Basic language, and a relational database engine, Microsoft SQL Server, was used to organize the input, runtime, and output data for access by both models and other applications. There are five main processes in the integrated framework: initialization, annual loop, plot loop, vegetation loop, and feedback loop for market prices and inventory levels.
Initialization. The model starts by initializing the basic framework. SRTS input data is read into memory. The data include harvest predictions for softwoods and hardwoods, acreage estimates, and designation of SRTS units, which are groups of states, survey units, and ownership types. The subset of FIA plots included in the 11-state study area is retrieved from the database. Because SRTS predicts forest resource trends for privately owned lands, only FIA plots with private ownership are included.
Annual Loop. The annual loop begins after initialization at year 1990, the mean date of all periodic FIA surveys for the study area states, with survey dates ranging from 1988 to 1999 and the approximate start date of the climate change scenario data and SRTS input data. The number of iterations in this loop corresponds to the number of years in a model run.
The first subroutine executed in the annual loop deals with mortality. In a pre-processing step, every management type and age class combination was assigned a mortality rate based on FIA measurements. These rates are used in the mortality subroutine to randomly select the appropriate percentage of plots in each management type and age class to experience mortality. These plots are flagged, and volume is reduced to zero.
Plot Loop. The FIA plots used for this modeling work totaled 37,995 in the study region. The plot loop occurs for each plot in the subset. First, plot-related information, including latitude of its county centroid, soil water holding capacity, and monthly climate for the current year, is retrieved from the database. Next, volume, harvest, and mortality information is retrieved for plots; the site and volume information subroutine, competition and regeneration subroutine, and plot run information subroutine is executed. If a threshold is reached in cumulative plot volume, light competition is turned off. If the plot was harvested or died the previous year, then regeneration occurs. Regeneration adds the top five species from this management type to the plot if not already present, as well as one random species. Because pine plantations are heavily managed, regeneration does not occur when a plot is classified as planted pine management type. If harvest or mortality flags are set from the previous year, then volume is assumed to be zero, regeneration occurs, and light competition is enabled. Finally, settings and variables specific to the PnET portion of the model from the previous year are retrieved for this plot.
Vegetation Loop. The vegetation loop occurs for all species present on the current plot during the current model year. There are four main parts to this loop. First, species-specific values for foliar nitrogen, leaf area index, and foliar mass are retrieved for the previous model year. Second, the model calculates the percentage volume on this stand to be used at a later point if light competition is enabled. Third, the core PnET-II growth subroutines are executed. Twelve months of climate data are read into the model, along with vegetation- and site-specific parameters, and annual growth is calculated. Finally, growth estimates are written to the database. This included converting growth units from grams/meter2 to feet3/acre using wood density values (Wenger, 1984; Burns and Honkala, 1990).
Market Price and Inventory Feedback Loop. After PnET-II has estimated growth for the 37,995 plots in the study region, the SRTS subroutine is executed to simulate harvest requests. Harvest information is sent to the growth database, and plots are harvested based on demand at the SRTS unit level. SRTS inventory information is updated with PEcon-estimated growth. The flow chart below illustrates the flow of these processes in the integrated PEcon framework.
PEcon Findings
Performance Under Historic Climate. Results of the PEcon modeling system simulations were compared to FIA volume estimates to gauge the accuracy of model performance and interactions. Mortality estimates also were compared. No harvest comparison was done, because those levels are established a priori and are not altered by growth dynamics with this version of the integration.
Baseline years for the growth and mortality comparisons were selected for each state and based on the most current FIA survey cycle year since the model start year of 1990. For some states, this year was beyond the range of the historic climate dataset, which ends in 1993; the Had2CMSul climate scenario data were used in these situations. To determine which of the two climate scenarios was more appropriate for these cases, mean annual temperature and precipitation records for 1994 through 2004, from a random selection of National Climatic Data Center (NCDC) stations across all study region states, were compared to Had2CMSul and CGC1 means for those same years. Independent means tests found more similarity between the NCDC and Had2CMSul temperature and precipitation means overall and for 9 out of 10 states for both variables, with Florida being the exception.
PEcon performed very well for most of the states in the survey units. We believed that there was sufficient agreement between PEcon-predicted and FIA-measured forest volume, growth, and mortality (given the limitations to comparison previously stated) to warrant the use of PEcon in projecting future forest volume, growth, harvest, and species composition across the southern United States to 2050.
Performance Under Climate Change Scenarios. PEcon predicted increased volume for both hardwoods and softwoods under both climate change scenarios for the 2000, 2010, and 2025 decades. By 2050, however, the Had2CMSul scenario continued to produce increased volume, whereas the CGC1 scenario resulted in decreased volume for both hardwoods and softwoods. This is expected, as the latter years of the CGC1 scenario includes more severe decreases in precipitation accompanied by increases in air temperature larger than those modeled by Had2CMSul.
At the FIA survey unit scale, effects of climate change on growth rates are still visible, as are differences in supply and demand across the region. Our findings suggest that southern U.S. forest volume will increase until at least 2025 for both the Hadley (Had2CMSul) and Canadian (CGC1) climate change scenarios. In both cases, softwood volume increases more than hardwood volume, but after 2025, timber volume predictions for the two scenarios diverge. By 2050, PEcon predicts that hardwood and softwood volume will increase by 11 percent and 18 percent, respectively, from 1990 levels, given the Had2CMSul climate change scenario. PEcon predicts, however, that hardwood and softwood volume will decrease by 11 percent and 6 percent, respectively, by 2050 if the CGC1 climate change scenario is applied. The CGC1 scenario is much drier and slightly warmer that the Had2CMSul scenario and could be considered a worse case scenario.
Key Research Findings and Foundation for Future Research
This report summarizes a suite of research projects using a consistent and integrated economic/ ecological framework to address the effect of environmental stressors on southern forested ecosystems.
This modeling framework allows us to examine how a changing climate and forest harvest will impact species composition and distribution, forest growth and volume, timber harvest, and value across the southern United States. We found that both the Hadley and Canadian climate change scenarios predicted similar changes in forest structure until 2025 and then began to diverge from each other. The slightly warmer, much drier Canadian scenario predicted a reduction in forest volume associated with harvest and climate change after 2025, whereas the warm, much wetter Hadley climate scenario predicted increased harvest and standing timber volume through 2050. We also observed that not all species will be represented equally until future climate and harvesting demands. High demand species, such as loblolly pine, only increased in volume by 4 percent by 2050 for the Hadley scenario and decreased in volume by 20 percent for the Canadian climate scenario. Our findings suggest that forest management will have an equal or greater impact on southern forest composition, volume, and distribution than will climate change over the next 50 years.
Future versions of PEcon will incorporate the impacts of both ozone, and nitrogen deposition on forest productivity and species composition. In addition to nitrogen and ozone, land use change is a major factor in forest distribution across the region. Improved projections of forest conversion to agriculture and rural developments need to be added to the existing modeling framework to examine more fully how all of these factors will combine to shape future southern forests.
References:
Aber JD, Ollinger SV, Federer CA, Reich PB, et al. Predicting the effects of climate change on water yield and forest production in the northeastern United States. Climate Research 1995;5:207-222.
Aber JD, Federer CA. A generalized, lump-parameter model of photosynthesis, evapotranspiration and net primary production in forest ecosystems. Oecologia 1992;92:463-474.
Abt RC, Cubbage FW, Pacheco G. Southern forest resource assessment using the subregional timber supply model (SRTS). Forest Products Journal 2000;50(4):25-33.
McNulty SG, Moore JA, Iverson L, Prasad A, et al. Application of linked regional scale growth, biogeography, and economic models for southeastern United States pine forests. World Resource Review 2000;12(2):298-320.
Burns RM, Honkala BH, Technical coordinators. Silvics of North America: 1. Conifers; 2. Hardwoods. Agriculture Handbook 654. Washington, DC: USDA Forest Service 1990;2:877.
Kittel TGF, Rosenbloom NA, Kaufman C, Royle JA, et al. VEMAP Phase 2 Historical and Future Scenario Climate Database. Available online at http://www.cgd.ucar.edu/vemap Exit from the VEMAP Data Group, National Center for Atmospheric Research, Boulder, CO, 2000.
Ollinger SV, Aber JD, Federer CA. Estimating regional forest productivity and water yield using an ecosystem model linked to a GIS. Landscape Ecology 1998;13:323-334.
Rudis VA. Ecological subregion codes by county, coterminous United States. Gen. Tech. Rep. SRS-36. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. (A technical document supporting the 2000 USDA Forest Service RPA Assessment). U.S. Department of Agriculture Forest Service, 1999, p. 102.
Wenger KF, ed. Society of American Foresters. Forestry Handbook (Second Edition). New York: John Wiley & Sons, Inc., 1984, p. 1355
Journal Articles on this Report : 3 Displayed | Download in RIS Format
Other project views: | All 17 publications | 10 publications in selected types | All 9 journal articles |
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Beach RH, Pattanayak SK, Yang JC, Murray BC. Econometric studies of non-industrial private forest management: a review and synthesis. Forest Policy and Economics 2005;7(3):261-281. |
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Pattanayak SK, Murray BC, Abt RC. How joint is joint forest production? An econometric analysis of timber supply conditional on endogenous amenity values. Forest Science 2002;48(3):479-491. |
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Pattanayak SK, Abt RC, Sommer AJ, Cubbage F, Murray BC, Yang J-C, Wear D, Ahn SE. Forest forecasts: does individual heterogeneity matter for market and landscape outcomes? Forest Policy and Economics 2004;6(3-4):243-260. |
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Supplemental Keywords:
forest resources, forest ecosystems, timber supply, greenhouse gases GHGs, Forest Inventory and Analysis, FIA, Subregional Timber Supply Modeling System, SRTS,, RFA, Scientific Discipline, Economic, Social, & Behavioral Science Research Program, Air, Ecosystem Protection/Environmental Exposure & Risk, Environmental Chemistry, Ecosystem/Assessment/Indicators, Ecosystem Protection, climate change, Economics, Ecological Effects - Environmental Exposure & Risk, Ecological Risk Assessment, decision-making, Economics & Decision Making, Ecological Indicators, anthropogenic stress, ecological exposure, environmental monitoring, meteorology, carbon emissions, climate change impact, ozone, climate studies, forest ecosystems, socioeconomic indicators, socioeconomics, forest reources, economic models, carbon dioxide, land use change, environmental stressors, changing environmental conditions, landscape characterization, regional scale model, forest resources, forest productivity model, ecological models, climate variability, climatic models, integrated ecological economic modelRelevant Websites:
http://www.cgd.ucar.edu/vemap Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.