Climate change effect on forests Modelling perspectiveTomáš HlásnyNational Forest Centre – Forest Research Institute in Zvolen, Slovakia
Introduction Forest cover 30 % of Earth`s land area
Important rule of in global carbon cycle (ca. 75% of C in terrestrial ecosystems) with large variation in C storage among forest types (boreal, temperate, tropical …. ) and in C partitioning between soil and vegetation
Complex responses to CC because of varying sensitivity of forest components and processes, i.e. tree species, ground vegetation, pests and pathogens, regeneration, water cycling, etc.
Large share of forests is managed, thus coupled effect of management and environmental change needs to be considered
Drought, forest fires, increasing frequency of windstorms, invasive species, changes in population dynamics of pests are among the most important threats
Forest longevity is important factor in the period of rapid environmental changes
Forest response to climate change
Beneficial and adverse effects
Differing along altitudinal and latitudinal gradients, depending on environmental limits to forest persistence
Direct effect, such as effect on CO2 increase on tree physiology, increased nutrient input due to increased decomposition rates, etc.
Indirect effects, acting mainly through changes in forest disturbances, fires, floods, windthrows, pest outbreaks ... all climate sensitive
Disruption of synchrony in multitrofic systems; host, pest, predator, parasitoid ….
Species shift, tree line expansion, low range retraction has received large attention
Ural
Hungary
Example of species shift projection
Hanewinkel, M., Cullmann, D. A., Schelhaas, M.-J., Nabuurs, G.-J., & Zimmermann, N. E. (2012). Climate change may cause severe loss in the economic value of European forest land. Nature Climate Change, advance on. doi:10.1038/nclimate1687
Forest vulnerability assessment Vulnerability is a degree to which a system is susceptible to be
affected by adverse effects of climate change
Assessment of forest vulnerability follows general principle of exposure, sensitivity, adaptive capacity◦ Exposure describes projected changes in relevant climate elements, i.e. drought
indices, biotemperature, etc.◦ Sensitivity describes degree to which a system may be affected◦ Adaptive capacity contains inherent mechanisms such as phenotypic plasticity
or migration, and socio-economic mechanisms,. i.e. adaptive forest management
As exposure and sensitivity are given, S-E adaptive capacity is of prime interests of foresters. It contains factors such as country`s economic development, awareness issues, cross-sectoral cooperation, forestry infrastructure, etc.
Regional vulnerability assessments are presently being developed for some regions of Europe under various initiatives, e.g. Carpathian Convention or Danube Strategy + national programmes
Example: Climatic exposure of the Carpathians
Example: Climatic exposure of European beech in the Carpathians based on 4 RCMs
Forest modelling To get knowledge about future forests development under diverse treatments
and environmental conditions, and to optimize management to reach desired forest structure, distribution and provision of ecosystem services
P-BM provide detailed description of BGC processes in ecosystems; needed for profound understanding of ecosystem functioning as well as for evaluation of some ecosystem services (carbon sequestration)
Rising importance of more robust multi-model estimates
Long history, starting with yield tables (1800) to stand simulators (2000) to present integrative hybrid models
Although various classification schemes exist, empirical and process based models are obviously distinguished
EM obviously suit better for forest management planning and optimization (usually simpler definition of inputs and calibration, based on yield tables thus familiar to foresters, etc.)
Spatial scale Stand-scale models Picus (BOKU), Silva (TUM), Sibyla (TU Zvolen), etc. Single tree vs. big leaf approaches Single tree-based approaches provide diverse information needed
to support the forest management; e.g. species composition, tree dimensions, stands vertical structure, diversity, dead wood and regeneration distribution, etc.
Information on stand structure allow for linking simulated stands to range of ecosystems services
Landscape models, such as LANDIS II (Portland University), Muscatella (Swiss Federal Institute for Forest, Snow and Landscape Research), LandMod (Oregon State University), etc.
Models consider landscape context and configuration Stress is laid on horizontal processes such as spread, migration,
flows as well as on effect of forest disturbances and succession on the landscape
Used for example for the assessment of forest effect on gravitation hazards, supporting habitats, etc.
Assessment of climate change impact on production of temperate forests
We use combined assessments by Biome-BGC and Sibyla tree growth simulator. Biome-BGC is well know here, so …
Sibyla is EM, single tree, 1-year step, climate sensitive, distance-based
Inter- and intra-specific competition is considered
Parameterized for 6 temperate tree species, other species can be simulated on the basis of physiological and morphologic resemblance
Versatile initial stand definition, number of standard stand treatments pre-defined
Outputs are production, carbon, assortments, stand structural indices, economic indicators, etc.
Experimental design European beech, oak(s) and Norway spruce were
addressed
Number of simulation plots arranged at elevation gradient with plausible species mixtures
Single climate change scenario, no management, plot structure characterizing vegetation zone
Three time periods addressed – 1961-1990, 2021-2050 and 2071-2100
Sibyla-based assessment of CC impact on forest production
Hlásny, T., Barcza, Z., Fabrika, M., Balázs, B., Churkina, G., Pajtík, J., Sedmák, R., Turčáni, M. (2011). Climate change impacts on growth and carbon balance of forests in Central Europe. Climate Research, 47(3), 219–236.
Biome-BGC based simulations Three forest monitoring plots addressed Calibration data 1997-2009 Continuous stand development 1925-2100
Combined assessment by Sibyla and Biome-BGC
Hlásny, T., Barcza, Z., Fabrika, M., Balázs, B., Churkina, G., Pajtík, J., Sedmák, R., Turčáni, M. (2011). Climate change impacts on growth and carbon balance of forests in Central Europe. Climate Research, 47(3), 219–236.
Unmanaged mountain Norway spruce stand, High Tatras Mts. Simulation period 1939-2100 for Biome-BGC, 1997-2100 for Sibyla 4 CC scenarios, 1 statistically generated stable climate 1939-2100 Simulation design: Sibyla × Biome BGC 1 × Biome BGC 11 × Biome
BGC 111 × 4 CC scenarios x 50 stochastic simulations
Combined assessment by Sibyla and Biome-BGC
Simulations under stable climate
Climate change effect on main carbon pools and fluxes
Differences “scenario – stable climate”
C pool Abovegrou nd C in living trees (stem, branches, leafs) Belowground C in living biomass (roots)
Period/Model Biome BGC Sibyla Integrated Biome
BGC Sibyla Integrated
1961-1990 4.6 ± 2.8 — 4.6 ± 2.8 1.1 ± 0.6 — 1.1 ± 0.6
2021-2050 23.2 ± 2.3 23.1 ± 1.7 23.1 ± 2.0 4.2 ± 0.4 3.9 ± 0.2 4.0 ± 0.4
2071-2100 28.1 ± 4.2 30.2 ± 2.1 29.1 ± 3.5 4.9 ± 0.8 4.9 ± 0.2 4.9 ± 0.6
C pool/flux C in dead trees (stem, branches) Soil C Litter C GPP NEE
Period/Model Biome BGC Sibyla Integrated Biome
BGCBiome BGC Biome BGC Biome BGC
1961-1990 1.0 ± 0.4 — 1.0 ± 0.4 13.3 ± 1.0 0.1 ± 0.1 -1467.8 ± 531.4 1164.5 ± 402.8
2021-2050 1.8 ± 0.8 4.3 ± 1.7 3.1 ± 1.8 13.5 ± 1.0 0.3 ± 0.0 -2101.0 ± 142.2 1767.3 ± 95.4
2071-2100 4.2 ± 0.7 7.7 ± 0.6 6.0 ± 1.9 14.1 ± 0.9 0.6 ± 0.1 -1757.9 ± 371.2 1665.6 ± 247.6
Variability decomposition
Variable
2021-2050 2071-2100
R2 Model (%)
CC scenario
(%)
Mortality (%)
Model ×scenario (%)
Res (%)
R2 Model (%)
CC scenario
(%)
Mortality (%)
Model × Scenario
(%)
Res (%)
ABG 0.28 6.7 27.0 62.9 3.2 0.3 0.94 34.3 4.5 60.6 0.6 0.0BWG 0.50 62.2 5.5 27.1 3.8 1.3 0.95 26.4 1.6 71.5 0.3 0.2DW 0.58 90.5 0.1 9.4 0.0 0.0 0.97 80.5 0.2 19.2 0.1 0.0Leafs 0.27 — 4.4 95.5 — 0.2 0.05 — 13.5 85.5 — 1.0Litter 0.35 — 40.8 59.1 — 0.2 0.92 — 16.00 82.6 — 1.5Soil 0.99 — 0.3 99.7 — 0.0 0.99 — 1.8 98.2 — 0.1GPP 0.52 — 99.7 0.1 — 0.3 0.95 — 7.8 92.1 — 0.1NEE 0.69 — 88.3 11.5 — 0.2 0.95 — 14.0 85.9 — 0.1
Effect of models architecture, CC scenarios, mortality assumptions and models stochasticity need to be explored to understand the presented simulations
We used specific ANOVA design to attribute parts of variability of final estimates to respective factors
Such control differed between quantities simulated and time periods
Link to ecosystem services and functions
Range of ESF is provided by forests; productive (biomass, roundwood, fruits, mushrooms), environmental (water, air quality, weather regulation, carbon sequestration, erosion preventions, biodiversity ...), and social (recreation, education, research …)
Effects of CC on forest structure and composition affects ESF; various linker functions are sought to describe such relationships
Coupled effect of CC and management modifies future ESF provided by forests, hence simulation exercises are used to evaluate such developments
Simulations are used to optimize forest management to reach the desired ESF provision; stakeholders` involvement is needed to specify the desired ESF portfolio
Concluding remarks Forest models are used to test the effect of various forest
treatments to reduce negative effects and take advantage of positive effects of climate change as well as to allow for fundamental understanding of forest functioning
Large uncertainty is associated to simulation outputs at longer runs; limited knowledge of the effects of management and future disturbance regimes are key factors
Multimodel approaches needs to be further developed to increase the robustness and reliability of modelling outputs
Linking modelling outputs to diverse ESF is becoming key concept of forest modelling
Although most of projects on CC impacts include stakeholder interactions, information transfer to practice is largely insufficient
Climate change effect on forests Modelling perspective
Tomáš HlásnyNational Forest Centre – Forest Research Institute in Zvolen, Slovakia
Thank you for your attention
Mortality and disturbances Disturbances largely limit the interpetation of Substantial simplification is needed to include complex
disturbance regimes into modeling Survival probability functions based on past records are
obviously used SP is evaluated either in each simulation step or in
simulation post-processing
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