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INSEA Forestry Forest Modelling Across Scales

INSEA Forestry Forest Modelling Across Scales. Presentation: Other contributors:. Oskar Franklin Georgii Alexandrov Steffen Fritz Florian Kraxner Elena Moltchanova Michael Obersteiner Dimiti Rokityanskiy. Rupert Seidl Manfred J. Lexer Werner Rammer. INSEA forestry project layout.

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INSEA Forestry Forest Modelling Across Scales

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  1. INSEA Forestry Forest Modelling Across Scales Presentation: Other contributors: Oskar Franklin Georgii AlexandrovSteffen FritzFlorian KraxnerElena MoltchanovaMichael ObersteinerDimiti Rokityanskiy Rupert Seidl Manfred J. Lexer Werner Rammer

  2. INSEA forestryproject layout

  3. INSEA forestryproject layout

  4. INSEA forestryproject layout forest management TsuBiMo 50 x 50 km grid PICUS plot level

  5. INSEA forestryin words • TsuBiMo • large scale simulations (continental / global scale) • interface to economic model • PICUS • help calibrating TsuBiMo • sample gridcell comparison • assessment of uncertainty • effect of management

  6. a 3D hybrid patch model... ...for decision support in multi-purpose forestry PICUS v1.3

  7. PICUS v1.3model description

  8. location: Austria best available soil & climate data reference and initial condition: long-term growth and yield records PICUS v1.3evaluation

  9. issue remedy climate used for TsuBiMo not available two independent climate files deployed pH: agri-soil (biased) WHC: soil map classes Nitrogen: not available pH: agri-soil (biased) WHC: soil map classes Nitrogen: constant (50kg/ha/a) 3 species 2500 st/ha age=10 yrs. no information about state of forest available PICUS v1.3data issues for T9 • climate • soil • forest

  10. TsuBiMo – Global NPP and forest model* • Original purpose: Prediction of NPP and forest growth globally • Potential NPP based on climate • General relations between NPP and forest stand growth • Soil - litter decomposition taken from the Osnabrück model • Our ultimate aim: Prediction of carbon sequestration and forestry production in response to climate and economic factors • Focus today: Tsubimo forest growth prediction in Europe • How can the PICUS model help *TsuBiMo was developed mainly by Georgi Alexandrov, See e.g: Alexandrov G A, Oikawa T, Yamagata Y. 2002. The scheme for globalization of a process-based model explaining gradations in terrestrial NPP and its application. Ecological Modelling 148, 293-306.

  11. TsuBiMo structure • Tsubimo predicts NPP and forest growth on a 0.5 degree scale globally • Two parts: • Potential NPP and decomposition from climate data • Forest growth and soil carbon dynamics using NPP and decomposition as starting points

  12. Global NPP and decomposition rate model • Predicts NPP of the potential vegetation on a global grid from climate (temperature, PAR, water, growing season) • Based on inter-biome relations between light use efficiency of photosynthesis and climate

  13. Global NPP and decomposition rate model • Predicts NPP of the potential vegetation on a global grid from climate (temperature, PAR, water, growing season) • Based on inter-biome relations between light use efficiency of photosynthesis and climate • The Osnabrueck NPP dataset was used for calibration • Basic decomposition rate is taken from the Osnabrueck model, which is based on climate similarly to NPP.

  14. Biomass growth and litter production • Total and woody biomass growth rate is proportional to NPP and is a slightly decreasing function of age. • Total herbaceous biomass, i.e. tree leaves and herbaceous vegetation is assumed to be constant • Total litter production, L= NPP – dB/dt • Herbaceous litter production = L at age=0 (before there is any woody biomass), the rest is woody litter NPP - herb. litter Biomass NPP Accumulated production of litter and biomass

  15. Litter decomposition • Litter inputs each year + harvest residues at each harvest • Three pools: herbaceous litter, woody litter and resistant litter • Decomposition rates of all pools are scaled by an overall decomposition rate, which depends on climate (from the Osnabrueck model) Herbaceous rdr = 1 Woody rdr = 0.3 50% 18% Resistant rdr = 0.08 rdr = relative decomposition rate

  16. NPP = 500 decomposition = 0.7 NPP = 250 decomposition = 0.35 Upscaling of managed forests • Assumes a normal forest = a uniform distribution of stands of all ages up to the harvest age. • The amount of standing biomass is then largely controlled by the age of harvest (the rotation period).

  17. Strengths of the TsuBiMo approach • Simple and transparent makes it: • Understandable • Easy to modify • Easy to combine with other models • Fast to run on a global scale • “Simple minded” and constrained by measured values. Therefore it is robust and doesn’t do anything too strange • Potential vegetation concept should work well for forests (?)

  18. Distribution of differences (%) between TsuBiMo and measured valuesLines are mean and 95% confidence limits. East Asia, mainly China North America – eastern part Scandinavia Does it work - validation NPP compared with dataset of site measurements converted to 0.5 degree grid scale for forests in North America, Scandinavia, and East Asia (Zheng et al. 2003).

  19. T9 simulation experiment • test plausibility of models across Europe • 30 plots • productivity, biomass stock (100 years) • comparison of results • gradients • „direct“ comparison • not adressed • management • scaling issues

  20. ? from plot to continent?issues of scale • calibration to range of conditions • linear up-scaling? (cf. Bugmann et al. 2000) • hierarchical levels of organisation • spatial heterogeneity • ecosystem processes non-linear

  21. PICUS resultsecoregions Source: Mayer 1984 Otto 1994

  22. PICUS resultsproductivity estimate MAI in m³/ha/a

  23. PICUS resultsproductivity estimate MAI in m³/ha/a

  24. PICUS – TsuBiMolatitude gradient

  25. PICUS – TsuBiModirect comparison

  26. PICUS – TsuBiModiscussion

  27. PICUS – TsuBiModiscussion

  28. PICUS – TsuBiModiscussion

  29. PICUS – TsuBiMo interaction example • Tsubimo biomass predictions can be improved with help of PICUS • This is important for modeling of optimal rotation period Relative biomass growth patterns for the best growing species at each plot predicted by PICUS. Stands were 10 years old at the start of the simulation.

  30. Coming TsuBiMo improvements • Include species /functional type specific growth patterns with the help of PICUS • Implement unmanaged forests, using a modified growth function fitted to equilibrium biomass data. • Include thinning effects with help of PICUS, e.g. amount of biomass that can be taken out without changing the final harvest • Include effects of elevated CO2. To do this better in the future a new model is being developed based optimization principles. • More sophisticated soil modeling, using global soil data

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