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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