Climate change induced drought effects on forest growth and vulnerability Climforisk
forest growth and vulnerability ‐
Mikko Peltoniemi, Sanna Härkönen, Aleksi Lehtonen, ,
,
,
and rest of the research group at Metla
At FAO 2.10.2011
www.metla.fi/life/climforisk
et a / e/c
o s
LIFE09 ENV/FI/000571
Climate change induced drought effects on forest growth and
vulnerability (Climforisk, www.metla.fi/life/climforisk)
Contents
• Introduction
– Climate change in Finland
– Climate change and Finnish forests
Climate change and Finnish forests
– Aims of Climforisk project
• Materials and Methodology
Materials and Methodology
– Data‐model approach in Climforisk
– Model development
– Pest/Pathogen modelling
• Methodology premiers in test regions (Sanna Härkönen)
• Few words about broad scale applicability of the method (Aleksi Lehtonen)
Climate change in Finland Temperatures
Climate change in Finland ‐
• Climate has already changed – + 1 C in Finland in past 100 a [1]
1 C in Finland in past 100 a [1]
– More extreme temperatures [2]
• + 5‐6 C increase expected [3]
5 6Ci
t d [3]
– Less extreme frost – More heat waves
[1] Tietäväinen, H, Tuomenvirta, H, Venäläinen, A., 2010. Annual and seasonal mean temperatures in Finland during the last 160 years based on gridded
temperature
p
data. Int. J. Climatol.30: 15, 2247-2256
[2] Räisänen, J. ja L. Ruokolainen, 2008a, Estimating present climate in a warming world: a model-based approach. Climate Dynamics, 31, 573-585
Climate change in Finland seasons
Climate change in Finland ‐
[3] Jylhä, K., Ruosteenoja, K., Räisänen, J., Venäläinen, A., Ruokolainen, L., Saku, S. ja Seitola, T., Arvioita Suomen
muuttuvasta ilmastosta sopeutumistutkimuksia varten, ACCLIM-hankkeen raportti 2009.
GROWING
G SEASON DECR
REASE BY
END OF C
CENTURY
• North: considerable winter warming
• Snow cover reduces by 20% (north) ‐ 60% (south)
G o g seaso
• Growing season lengthtens throughout the country by 30‐50 y y
days
MAX. SNOW
W WATER CONTE
ENT DECREASE
BY END OF CENTURY
FEBRUARY, MEAN TEMPERATURE CHANGE
• Precipitation increases
– distribution changes little
• RH%
RH% remains
remains same [4] Æ
[4] Æ
VPD increases Æ
Evapotranspiration
increases
• Soil water is uncertain
CHANGE-% R
RELATIVE TO
CURRET
Climate change in Finland water
Climate change in Finland ‐
RAINFALL
[4] Dressler and Sherwood, 2009, A Matter of Humidity, Science, vol 323, 1020-1021
Climate change and (Finnish) forests
Climate change and (Finnish) forests
•
•
•
•
CO2 and T changes promote photosynthesis
Season changes allow longer growth periods
Season changes allow longer growth periods
Æ Increased growth and biomass
Soils: – Soil ΔC Soil ΔC
• Increasing T promotes soil respiration
• Increased growth and biomass infers more C to soil
Increased growth and biomass infers more C to soil
Finnish forests get denser: management effect
GROWINGPuuston
STOCK
IN FOREST
tilavuus
metsä- jaLAND
kitumaalla
TOTAL VOLUME
Kokonaistilavuus
milj. m³
3500
Scots pine
Manty
3000
Norway Spruce
Kuusi
2500
Deciduous
Lehtipuu
2000
1500
1000
500
0
2006
2016
2021
2026
2036
2046
2051
Fig: Salminen, 2008, MELA-estimates
Forest damages
Forest damages
• Driven by climate and forest structure
• Most pests/pathogens benefit from longer seasons
y
g
p
• Many insects benefit from drought and temperature increases
Abiotic: weather extremes
• Abiotic: weather extremes
Acantholyda posticalis
• New species from south, e.g.
Dothistroma septosporum
Fig:
g Antti Pouttu, Metla
Fig: Michael Müller, Metla
Topical questions
Topical questions
z
How will climate change influence forests in Finland?
z
z
Information to support decision making needed
How changes in carbon balances and forest How
changes in carbon balances and forest
vulnerabilities to pest/pathogen are distributed in Finland?
Climforisk Aims
Climforisk ‐
z
Providing tools for climate assessments of forests
z
z
Evaluates how climate and climate change influence:
g
z
z
z
z
Collect and merge forest related data sources and models together
forest carbon sinks p /p
pest/pathogen vulnerability
g
y
Evaluate lacks in current data and models – what more/else is needed?
more/else is needed?
Disseminates results to the public
(Fig, E. Oksanen
n/Metla)
Forest structure data
Forest structure data
NFI plot data
LANDSAT + CLC
((Fig.
g NFI))
• NFI plot level data
• Plot
Plot‐level
level predictions of predictions of
biomass, LAI
• NFI plot level data provides the basis for kNN‐
generalization of data to wall‐to‐wall
wall
to wall maps
maps
Soil data
Soil data
Topographical map +
DEM (Fig. Paikkatietoikkuna)
Soil map + elements from
topgraphical map
• Digital
Digital soil map: variable‐sized soil map: variable sized
polygons > 6.25 ha; mean soil depth texture
depth, texture
• Topographical map and DEM, resolution ~ 25 m
l i
2
• Bring high resolution elements from topo‐map to soil map
g
,
• Drought index, shallow soils
Modelling GPP and water balance
Modelling GPP and water balance
• Predicting GPP, ET and soil water • Additionally: – soil C model
– growth allocation submodel
– Drought effect proxies?
Drought effect proxies?
• Predictions will be made for NFI sample plots and scaled wall‐to‐
sample plots and scaled wall
to
wall Finland
– kNN vs. direct prediction on map cells. – Use soil map + DEM
Modelling GPP and water balance
Modelling GPP and water balance
• An ecosystem model that links carbon and water balances • Model inputs are minimal:
– Climate
Climate data
data
– LAI from NFI plots
– Soil depth from NFI plots Soil depth from NFI plots
(shallow, medium, deep)
Submodel: GPP
Submodel: GPP
• Model based on LUE‐appoarch (Mäkelä et al 2008, GCB)
• Transparent and ’easy’ to calibrate!
• P = βfaPPFDφfLfTmin{fD, fW}
–
–
–
–
–
–
–
–
P = GPP
β = potential LUE
t ti l LUE
Φ = PPFD
faPPFD o act o o
abso bed
aPPFD for fraction of PPFD absorbed
fL = for ligth saturation of photosynthesis
fT for season and temperature fD for vapour pressure deficit, VPD
fW for soil water (estimated from REW)
Environmental
modifiers [0
[0,1]
1]
Account for
suboptimal
Growing conditions
-Simple empirical
functions
-
Submodel: ET
Submodel: ET
• E = βE
P
D
κ
D + α (1 - f aPPFD ) f W, Eφ
Transpiration
• Driven by VPD
Evaporation
• Driven by PPFD reaching soil
• Predictive power equals to Penman-Monteith at two
forested eddy-covariance sites (south and north Finland)
• Does not need Rnet or windspeed as Penman-Monteith
• Will provide basis for drought index in the project
Model’ss other water balance components
Model
other water balance components
• Rain
Rain fills small canopy water fills small canopy water
storage until it pours over to soil
• Single layer model for soil water
• Rain is snow in winter
• Snow melts according to temperature coeff.
• Evapotranspiration empties:
1.
2.
3
3.
CW
snow soil water
soil water
Model calibration
Model calibration
• Two forested eddy‐covariance sites (Hyytiälä y )
and Sodankylä)
• Bayesian calibration assimilates different data
– Adaptive Markov‐Chain Monte‐Carlo Ad ti M k Ch i M t C l
– Allows calibration of the GPP, ET and Soil water components at the same time
• Joint likelihood of all predictions and measurements used
• Non‐informative prior distributions of parameters
Calibration study (poster* at Atrium)
• Joint calibration
Joint calibration
– All 16 parameters calibrated in one long MCMC run
• Separate calibration
p
– Submodels calibrated separately against measurements (GPP: 8, ET: 5, SW: 3 parameters)
• Model performance tested in application case: – no measurements of GPP, ET and SW
as inputs
i
t
* Peltoniemi, M., Pulkkinen, M., Mäkelä, A.: Joint vs. separate calibration of GPP, ET and soil water model.
Soil watter
GPP and ET
GPP and ET
• Joint calibration of model: GPP and ET predictions as good as they get
• Separate calibration of submodels: GPP and ET predictions S
t
lib ti
f b d l GPP d ET
di ti
suffer from poor SW predictions. • Joint calibration provides better predictive power, especially Joint calibration provides better predictive power especially
for cases where the effect of soil water gets important (= under drought)
g )
PESTS AND PATHOGENS
Pest/Pathogen observations
Pest/Pathogen observations
• ICP level I plots, N~3000 – Permanently monitored
y
– Tree and site properties as in NFI
in NFI
– Forest health measurements on > 600
measurements on > 600 plots
– Pest/pathogens identified
P / h
id ifi d
• NFI plot data
p
Life cycle of a pest: D. sertifer
Climate factors
Egg parasites
Winter minimum T
Summer T
eggs
Predators
Winter
Birds, Birds
mammals, ants, etc.
N. N. sertifer
Summer
Parasites
Diseases
N
Resin
Phenological
changes
Needle quality /tree vitality
Scots pine tree / stand
SURROUNDING
LANDSCAPE
Rainfall
Soil factors
Life cycle of a pest: D. sertifer
Climate factors
Egg parasites
Winter minimum T
Summer T
Predators
Parasites
Diseases
N
Resin
Phenological
changes
Needle quality /tree vitality
Scots pine tree/stand
SURROUNDING
LANDSCAPE
Rainfall
Pest damage
g
caused
by adult
N. sertifer
D. sertifer
Birds, Birds
mammals, ants, etc.
Soil factors
Predicting pests/pathogens vulnerability
Predicting pests/pathogens vulnerability
• Identify key variables influencing life‐cycle of specific pests/pathogens
• Formulate prediction models based on collected data
GPP
SNOW, mmH2O
SOIL VARIABILITY
Feasibility test: Model predictions and pests/pathogens
t / th
FIG: Model predictions of drought
days vs. drought damage
observations at ICP
C I level plots
(fig. P. Muukkonen, T. Linkosalo)
• Correlating modelled drought and measured drought damages show positive relationship between the years
– Spatial correlations within a year marginal or non‐existent
S i l
l i
i hi
i l
i
– Soil type seems to be decisive
www.metla.fi/life/climforisk
Research group:
Mikko Peltoniemi, Aleksi Lehtonen,
Seppo Neuvonen, Eeva Karjalainen, Sanna
Härkönen, Petteri Muukkonen, Kalle Eerikäinen,
Heikki Parikka, Sakari Tuominen, Martti
Lindgren Pekka Tamminen
Lindgren,
Tamminen, Jukka Pöntinen
Pöntinen,
Seppo Nevalainen, Juha Heikkinen, Paula
Puolakka, Tapio Linkosalo, Risto Sievänen,
Minna Pulkkinen ((UH),
) Eero Nikinmaa (UH),
( )
Annikki Mäkelä (UH)
Contacts:
Mikko Peltoniemi (Metla)
[email protected]
p. 040-801 5329
Aleksi Lehtonen, (Metla)
[email protected]
p. 050-391
050 391 2362
Annikki Mäkelä (University of Helsinki)
[email protected]
LIFE09 ENV/FI/000571
Climate change induced drought effects on
forest growth and vulnerability
(Climforisk)
Thank you
h k
LIFE09 ENV/FI/000571
Cli t change
Climate
h
induced
i d
dd
drought
ht effects
ff t on fforestt growth
th
and vulnerability
(Climforisk, www.metla.fi/life/climforisk)