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D.P. Turner 1 , W.D. Ritts 1 , J. Styles 1 , Z. Yang 1

Monitoring Effects of Interannual Variation in Climate and Fire Regime on Regional Net Ecosystem Production with Remote Sensing and Modeling. D.P. Turner 1 , W.D. Ritts 1 , J. Styles 1 , Z. Yang 1 W.B. Cohen 2 , B.E. Law 1 , P. Thornton 3 , M. Falk 4. 1 Oregon State University

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D.P. Turner 1 , W.D. Ritts 1 , J. Styles 1 , Z. Yang 1

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  1. Monitoring Effects of Interannual Variation in Climate and Fire Regime on Regional Net Ecosystem Production with Remote Sensing and Modeling D.P. Turner1, W.D. Ritts1, J. Styles1, Z. Yang1 W.B. Cohen2, B.E. Law1, P. Thornton3, M. Falk4 1Oregon State University 2USDA Forest Service 3National Center for Atmospheric Research 4University of California, Berkeley

  2. Western Oregon Study Area

  3. Chronosequences:NEP by Age Class wet NEP Uncertainty ~25% NEP = (NPPA + NPPB)  (RhSoil+RhCWD+RhFWD) NEP = NPPA + TBCA – Rs (RhCWD + RhFWD) dry Campbell et al. 2004

  4. Biome-BGC Simulation Conifer forest in West Cascades Turner et al. 2003

  5. Prognostic Modeling Approach to Bottom-up Scaling

  6. Land base carbon budgets for two representative (100 km2) study areas. (Units are gC/m2/yr) __________________________________________________________ Study Area A B C Net Ecosystem Harvest  Land Production Removals C Pool (A+B) Coast Range 199 -364 -165 West Cascades 177 -7 170 ___________________________________________________________ Turner et al. 2004

  7. Land Base Carbon Budget Western Oregon Forests (1995-2000) _________________5 yr mean_______________ Total NEP 13.8 TgC/yr Harvest Removals -5.5 Products (net) 1.4 Fire -0.1 Net 9.6 TgC/yr ________________________________________ Law et al. 2005

  8. Diagnostic Modeling Approach Application to larger domain Investigation of interannual variability in NEP Daily FPAR from satellite data Simpler process model (base rates for light use efficiency, Ra, Rh) No spin-ups Same distributed climate data Same land cover from satellite data (aggregated) Same stand age from satellite data (aggregated)

  9. NPP derived from USFS Inventory plot data Van Tuyl et al. 2005

  10. Biome-BGC Model Run How to include information on the disturbance regime? Metamodeling Approach GPP = ↓PAR * FPAR * eg * Ssa GPP = gross primary production ↓PAR = incoming PAR FPAR = fraction PAR absorbed eg = light use efficiency Ssa = stand age factor (0-1), output from Biome-BGC model Stand Age Factor for GPP

  11. Biome-BGC Model Run How to include information on the disturbance regime? Metamodeling Approach Rh = f (Rh-base, FPAR, Tsoil, SW, SA) Rh = heterotrophic respiration Rh-base = base rate of heterotrophic respiration FPAR = Fraction PAR absorbed Tsoil = soil temperature SW = soil water content SA = stand age factor, output from Biome-BGC model Stand Age Factor for Rh

  12. MODIS FPAR Spatial Resolution = 250m - 1 km Temporal Resolution = 8 day MODIS FPAR

  13. Diagnostic Model (“Fusion”) Parameter Optimization Daily time step • Daily GPP parameters optimized with tower GPP (or Biome-BGC GPP) • Daily Ra parameters optimized by reference to measured NPP (or Biome-BGC NPP) • Daily Rh parameters optimzed with tower NEE (or Biome-BGC NEE)

  14. Fusion daily NEP (line) compared to reference NEP (circles) At the Klamath Mountains ecozone conifer optimization site.

  15. Bars are mean NPP for FIA plots from Van Tuyl et al. 2005

  16. Bars are mean ecoregion NEP from Law et al. (2004)

  17. Validation Boundary Layer Budget Diagnostic NPP/NEP Model Bottom-up Flux Top-down Flux

  18. Boundary layer budget footprint Monthly mean from the STILT model Weighting (Courtesy of M. Goeckede, OSU)

  19. Conclusions Land-based carbon sinks significantly offset fossil carbon emissions in Oregon Post-fire increases in heterotrophic respiration reduce the regional carbon sink Interannual variation in climate can substantially modify regional NEP

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