Modeling Grow and Yield for Lake States Forests

1. The Carbon Budget GPP RA RH NPP NEE GPP = Gross Primary Production RA = Autotrophic(Plant) Respiration NPP = Net Primary Production RH = Heterotrophic(Microbial) Respiration NEE = Net Ecosystem Change(Sequestrated Carbon) R-sqr = 0.66 WLEF Eddy Flux Tower Modeling Grow and Yield for Lake States Forests Zhilong Yuan*, Stith T. Gower*, John A. Roth*, Kenneth Davis+ *:Department of Forest Ecology and Management, University of Wisconsin - Madison +: Department of Meteorology, Pennsylvania State University - University Park Who we are a group of forest ecologists at UW-Madison affiliated with the Upper-Midwest Regional Earth Science Application Center(RESAC). Model Simulation 1. NPP and LAI simulation for 24 Northern Wisconsin Plots (1) Study Area (2) Stand Characteristics (4 plots / habitat type) Habitat Type Forest Cover (3) Model Revision i) Problems: simulating plant functional type (PFT) competitions for mixed PFT plots ii)Facts: different PFTs have different response to water and nutrient limitations. To account for basic ecophysiological differences among plant functional types we developed a simple competition index: 2. Daily NEE simulation (1) Study Area Willow Creek site of Chequamegon Ecosystem- Atmosphere Study (ChEAS) (2) Stand Characteristics Broad-leaf Deciduous Forest (4) Simulation results Seasonal Vmax Adjustment No Yes QAE Conifer AQV Mixed PMV Mixed AVVib Hardwood ATD Hardwood AViO Hardwood Introduction To satisfy the increasing demand for managing forest for sustainable production, we need to understand the mechanisms regulating forest growth and production. Our objectives are: (3) Model Revision Vmax in IBIS is controlled by instantaneous temperature, which produced an incorrect seasonal pattern of daily NEE. To correct this problem, we used a short-term and 10 day-low temperature night temperature to adjust Vmax. (1). Modifying an ecosystem process-based model(IBIS) to estimate ecological parameters such as Leaf Area Index(LAI), Net Primary Production (NPP), and Net Ecosystem Change (NEE) for Lake States forests; where, i = 1, 2, . . ., nplot; j = 1, 2, . . ., npft; som: soil organic matter content; availw: plant available water; bsom: base som; bavailw: base availw; a, b are constant (4) Simulation results (2).Coupling IBIS with Remote Sensing and Geographic Information System(GIS) technology to estimate NPP, LAI and NEE. Conclusion 1. Simulated NPPs and LAIs agreed well with field measurements for pure stands, but the model predicted PFT composition poorly, hence NPP and LAI. A stress response factor was introduced, which partly solved this problem, but the NPP predication was still poor. 2. By allowing seasonal adjustment of Vmax, simulated daily NEE agreed well (r2 = 0.66) with flux tower measurement for the ChEAS study. Contact Dr.Stith T.Gower, University of Wisconsin-Madison (608)262-0532 stgower@facstaff.wisc.edu What is IBIS IBIS is a process-based terrestrial biosphere model developed by Dr. John Foley and colleagues at the University of Wisconsin-Madison. Using a hierarchical modular structure, IBIS effectively integrates the complex biophysical, physiological and ecological processes in a single physically consistent modeling framework.