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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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slide1

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

insea forestry project layout4
INSEA forestryproject layout

forest management

TsuBiMo

50 x 50 km grid

PICUS

plot level

insea forestry in words
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
picus v1 3

a 3D hybrid patch model...

...for decision support in multi-purpose forestry

PICUS v1.3
picus v1 3 evaluation
location: Austria

best available soil & climate data

reference and initial condition: long-term growth and yield records

PICUS v1.3evaluation
picus v1 3 data issues for t9

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
tsubimo global npp and forest model
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.

tsubimo structure
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
global npp and decomposition rate model
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
global npp and decomposition rate model14
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.
biomass growth and litter production
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

litter decomposition
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

upscaling of managed forests

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).
strengths of the tsubimo approach
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 (?)
does it work validation

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).

t9 simulation experiment
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
from plot to continent issues of scale

?

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
picus results ecoregions
PICUS resultsecoregions

Source: Mayer 1984 Otto 1994

picus tsubimo interaction example
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.

coming tsubimo improvements
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