Recipient Organization
UNIVERSITY OF MONTANA
COLLEGE OF FORESTRY AND CONSERVATION
MISSOULA,MT 59812
Performing Department
College of Forestry and Conservation
Non Technical Summary
Forest ecosystems play critical role in the global carbon cycle. Through the processes of growth and respiration, forests exchange carbon with the atmosphere and store carbon in biomass and soils. The rates at which these processes occur are climate sensitive. Consequently, forests both respond to climatic changes and have important feedbacks to the climate system. In order to predict forest responses to climatic forcings and appropriately manage forests in anticipation of changes, accurate and reliable simulation models are required. Because anticipated conditions are expected to vary from those observed historically, models should be based off of fundamental physical and biological principles. This study is designed to process-based improve simulation models of forest ecosystems response to climate. The results of the study will inform improved models of the global climate system, as well as regional models that can inform management decisions regarding forest growth, hydrology, and fire risk.
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
Biome-BGC is a process-based model designed to simulate the carbon, nitrogen and water dynamics of terrestrial ecosystems. The model is fully prognostic, enabling analysis of current, past, future, and hypothetical scenarios. Biome-BGC has been applied to a wide variety of ecosystem types, spatial scales, and research questions, and has particular relevance for investigating the response of the terrestrial biosphere to uncertain future climatic conditions. Many versions of Biome-BGC exist. Some of the major versions include Biome-BGC 4.2, the current 'release' version of the model; Agro-BGC (NTSG, publication in press), Wetland-BGC(NTSG, status dormant); UW Biome-BGC(University of Wisconsin-Madison); a version of Biome-BGC used for simulation of commercial timber species(Pietsch et al); and BGC5, an unfinished 'next generation' BGC model that includes the ability to simulate multiple competing vegetation types and a flexible disturbance handling routine. Additional versions are probably in use in other research groups. Further, the CLM model is closely related to Biome-BGC and has been in active development for several years; many advances in CLM algorithms are applicable to Biome-BGC. Only BGC 4.2 is widely available to the scientific community at present. The proposed project will combine the most useful advances from each of these model variants, as well as additional algorithmic advances from the recent literature, to produce a next generation general purpose ecosystem simulation model (BGC 5).
Project Methods
The project will focus on development and implementation of new algorithms based on concepts provided from the scientific literature or adapted from other existing models; incorporation of multiple, phenology submodels, including a growing degree day model that can be easily applied to agricultural simulations, simulation of the effects of a killing frost, and simulation of drought-deciduous phenology (e.g. using growing season index model); incorporation of the improved C4 photosynthesis routine, reproductive carbon pools, senescence routine, and annual and perennial grass allocation schemes from Agro-BGC; update the autotrophic respiration submodel to reflect the findings of Reich et al 2008 (new, organ-specific generalized scaling relationships for leaves, stems and roots); implementation of the improved canopy radiative transfer model described by Thornton and Zimmermann (2007), incorporating a vertical gradient of specific leaf area; an option for calculation of photosynthesis and evapotranspiration at a user-specified sub-daily time step, using the logic of the MtClim model to estimate meteorological forcing variables at high temporal resolution; age and stress dependant mortality; dynamic carbon allocation; belowground competition for water and nitrogen; a dynamic nitrogen fixation submodel. Additionally, Biome-BGC requires a large number of vegetation-specific parameters that can be difficult to obtain for a given study site. Typically, these parameters are derived from comprehensive literature reviews, combining estimates from geographically diverse studies to produce average parameter values for a type. However, variability in parameters within a functional group can at times be large and individual parameters are not independent. Ecological literature has established that traits such as leaf longevity, leaf nitrogen content, photosynthetic capacity etc. are closely related both within and across species. Consequently, mean parameter values may not provide the most realistic parameterizations available. Numerical optimization schemes, combined with flux data available from eddy covariance and field based carbon stock estimates, can be used to improve model parameterization. Such methods allow us to ask the question "What parameters minimize the difference between stocks and fluxes simulated by Biome-BGC and those estimated by field measurement campaigns" Bayesian methods allow the incorporation of prior knowledge into the optimization. This allows parameterizations to be constrained not only algorithmically, but also based on existing knowledge, such as the White et al Biome-BGC parameter literature review and the plant trait covariance database of Reich and Wright. Applying such methods to a broad range of study sites (i.e. performing a joint optimization across all deciduous broadleaf forest Ameriflux sites) may result in more robust estimates of vegetation type parameterizations than those based on literature review alone.