Climate change induced drought effects on

Transcription

1 Climate change induced drought effects on forest growth and vulnerability Climforisk Mikko Peltoniemi, Sanna Härkönen, Aleksi Lehtonen, and rest of the research group at Metla At FAO e/c LIFE09 ENV/FI/ Climate change induced drought effects on forest growth and vulnerability (Climforisk,

2 Contents Introduction Climate change in Finland Climate change and Finnish forests Aims of Climforisk project 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)

3 Climate change in Finland Temperatures Climate has already changed + 1 C in Finland in past 100 a [1] More extreme temperatures [2] C increase expected td[3] Less extreme frost More heat waves [1] Tietäväinen, H, Tuomenvirta, H, Venäläinen, A., Annual and seasonal mean temperatures in Finland during the last 160 years based on gridded temperature data. Int. J. Climatol.30: 15, [2] Räisänen, J. ja L. Ruokolainen, 2008a, Estimating present climate in a warming world: a model-based approach. Climate Dynamics, 31,

4 Climate change in Finland seasons FEBRUARY, MEAN TEMPERATURE CHANGE North: considerable winter warming Snow cover reduces by 20% (north) 60% (south) Growing Go gseason lengthtens throughout the country by days MAX. SNOW WATER CONTE ENT DECREASE BY END OF CENTURY GROWING G SEASON DECR REASE BY END OF CENTURY [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.

5 Climate change in Finland water Precipitation increases distribution changes little RH% remains same [4] VPD increases Evapotranspiration increases Soil water is uncertain CHANGE-% RELATIVE TO CURRET RAINFALL [4] Dressler and Sherwood, 2009, A Matter of Humidity, Science, vol 323,

6 Climate change and (Finnish) forests CO 2 and T changes promote photosynthesis Season changes allow longer growth periods Increased growth and biomass Soils: Soil ΔC Increasing T promotes soil respiration Increased growth and biomass infers more C to soil

7 Finnish forests get denser: management effect milj. m³ GROWING STOCK IN FOREST LAND Puuston tilavuus metsä- ja kitumaalla Kokonaistilavuus TOTAL VOLUME Manty Scots pine Kuusi Norway Spruce Lehtipuu Deciduous Fig: Salminen, 2008, MELA-estimates

8 Forest damages Driven by climate and forest structure Most pests/pathogens benefit from longer seasons Many insects benefit from drought and temperature increases Abiotic: weather extremes New species from south, e.g. Acantholyda posticalis Dothistroma septosporum Fig: Michael Müller, Metla Fig: Antti Pouttu, Metla

9 Topical questions How will climate change influence forests in Finland? 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?

10 Climforisk Aims Providing tools for climate assessments of forests Collect and merge forest related data sources and models together Evaluates how climate and climate change influence: forest carbon sinks pest/pathogen vulnerability Evaluate lacks in current data and models what more/else is needed? Disseminates results to the public

11 Forest structure data (Fig, E. Oksanen n/metla) NFI plot data NFI plot level data Plot level level predictions of biomass, LAI NFI plot level data provides the basis for knngeneralization of data to wall to wall maps LANDSAT + CLC (Fig. NFI)

12 Soil data Topographical map + DEM (Fig. Paikkatietoikkuna) Digital soil map: variable sized polygons > 6.25 ha; mean soil depth, texture Topographical map and DEM, resolution ~ 25 m Bring high resolution elements from topo map to soil map Drought index, shallow soils Soil map + elements from topgraphical map

13 Modelling GPP and water balance Predicting GPP, ET and soil water Additionally: soil C model growth allocation submodel Droughteffect proxies? Predictions will be made for NFI sampleplotsandscaled plots and scaled wall to wall Finland knn vs. direct prediction on map cells. Use soil map + DEM

14 Modelling GPP and water balance An ecosystem model that links carbon and water balances Model inputs are minimal: Climate data LAI from NFI plots Soil depth from NFI plots (shallow, medium, deep)

15 Submodel: GPP Model based on LUE appoarch (Mäkelä et al 2008, GCB) Transparent and easy to calibrate! P = βf appfd φf L f T min{f D, f W } P = GPP β = potential ti llue Φ = PPFD f appfd for fraction of PPFD absorbed bed f L = for ligth saturation of photosynthesis f T for season and temperature f D for vapour pressure deficit, VPD f W for soil water (estimated from REW) Environmental modifiers [0,1] Account for suboptimal Growing conditions -Simple empirical functions -

16 Submodel: ET P E = β D + α (1- f ) f φ βe κ appfd W, E D 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

17 Model s other water balance components Rainfillssmallcanopy small 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. CW 2. snow 3. soil water

18 Model calibration Two forested eddy covariance sites (Hyytiälä and Sodankylä) Bayesian calibration assimilates different data Adaptive Markov Chain Monte Carlo 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

19 Calibration study (poster* at Joint calibration Atrium) All 16 parameters calibrated in one long MCMC run Separate calibration 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 * Peltoniemi, M., Pulkkinen, M., Mäkelä, A.: Joint vs. separate calibration of GPP, ET and soil water model.

20 Soil water

21 GPP and ET Joint calibration of model: GPP and ET predictions as good as they get Separate calibration of submodels: GPP and ET predictions suffer from poor SW predictions. Jointcalibrationprovides better predictive power, especially for cases where the effect of soil water gets important (= under drought)

22 PESTS AND PATHOGENS

23 Pest/Pathogen observations ICP level I plots, N~3000 Permanently monitored Tree and site properties as in NFI Forest health measurements on > 600 plots Pest/pathogens identified ifi d NFI plot data

24 Life cycle of a pest: D. sertifer Predators Birds, mammals, ants, etc. Egg parasites eggs Winter N. sertifer N. Summer Climate factors Winter minimum T Summer T Rainfall Parasites Diseases SURROUNDING LANDSCAPE N Resin Phenological changes Needle quality /tree vitality Scots pine tree / stand Soil factors

25 Life cycle of a pest: D. sertifer Predators Birds, mammals, ants, etc. Egg parasites Pest damage caused N. sertifer by adult D. sertifer Climate factors Winter minimum T Summer T Rainfall Parasites Diseases SURROUNDING LANDSCAPE N Resin Phenological changes Needle quality /tree vitality Scots pine tree/stand Soil factors

26 Predicting pests/pathogens vulnerability Identify key variables influencing life cycle of specific pests/pathogens Formulate prediction models based on collected data SOIL VARIABILITY SNOW, mmh 2 O GPP

27 Feasibility test: Model predictions and pests/pathogens t/ th FIG: Model predictions of drought days vs. drought damage observations at ICP I level plots (fig. P. Muukkonen, T. Linkosalo) Correlating modelled drought and measured drought damages show positive relationship between the years Spatial correlations within ihi a year marginal or non existent Soil type seems to be decisive

28 Research group: Mikko Peltoniemi, Aleksi Lehtonen, Seppo Neuvonen, Eeva Karjalainen, Sanna Härkönen, Petteri Muukkonen, Kalle Eerikäinen, Heikki Parikka, Sakari Tuominen, Martti Lindgren, Pekka Tamminen, Jukka Pöntinen, Seppo Nevalainen, Juha Heikkinen, Paula Puolakka, Tapio Linkosalo, Risto Sievänen, Minna Pulkkinen (UH), Eero Nikinmaa (UH), Annikki Mäkelä (UH) Contacts: Mikko Peltoniemi (Metla) p Aleksi Lehtonen, (Metla) p Annikki Mäkelä (University of Helsinki) LIFE09 ENV/FI/ Climate change induced drought effects on forest growth and vulnerability (Climforisk)

29 Thank you LIFE09 ENV/FI/ Climate change induced d drought effects on forest growth and vulnerability (Climforisk,