Predicting wind damage risks to forests from stand to regional level using mechanistic wind risk model HWIND

Transcription

1 Predicting wind damage risks to forests from stand to regional level using mechanistic wind risk model HWIND Workshop on the Mathematical Modelling of the Risk of Wind Damage to Forests, 29 th October 2015 Dr. Veli-Pekka Ikonen, Prof. Heli Peltola UEF // University of Eastern Finland

2 Part 1. Predicting tree damage in fragmented landscapes using a wind risk model HWIND coupled with an airflow model Aquilon (Dupont et al. 2015) Schematic representation of the studied stand configurations UEF // University of Eastern Finland Predicting wind damage risks.. / V-P Ikonen

3 Extrapolating the critical wind speed of tree top at the stand edge to 10 m height above an open lawn surface of a fictive meteorological station

4 Examples of mean wind velocity fields simulated by Aquilon

5 Examples of turbulent kinetic energy simulated by Aquilon

6 Wind profiles in HWIND based on Aquilon simulations

7 Some examples wind speeds at a fictive meteorological station - HWIND Uprooting, CWS at 10 m height: 8.8 ms -1 Uprooting, CWS at tree top: 9.8 ms -1 Uprooting, CWS at 10 m height: 25.2 ms -1 Uprooting, CWS at tree top: 16.1 ms -1

8 Part 2. Assessment of wind risks to Finnish forests considering uncertainties of climate change Component models: Forest Ecosystem model SIMA and Mechanistic Wind Risk Model HWIND HWIND logarithmic wind profiles (not Aquilon profiles) Diameter distribution of trees in each stand (plot) Minimum CWS calculated (not averages)

9 Different forest management scenarios: Baseline management (business as usual) Preference of Scots pine in regeneration on medium fertile sites (MT) Preference of Norway spruce on medium fertile sites (MT) Preference of Silver birch on medium fertile sites (MT) MT = Myrtillus type 16 different climate scenarios

10 Alternative climate change projections (CMIP5, IPCC 2013) North Finland Central Finland South Finland Climate scenarios CU (current climate) HadGEM2 4.5 HadGEM2 8.5 MPI 4.5 MPI 8.5 CanESM2 4.5 CanESM2 8.5 MIROC5 4.5 MIROC5 8.5 CNRM 8.5 GFDL 8.5 Average of 28 Rcp 4.5 and Rcp 8.5 model runs As a comparison used: Average of 19 SRES B1, A1B and A2 model runs Precipitation change (%) Precipitation change (%) CU In summer North Finland GFDL 8.5 CNRM 8.5 A1B A2 Rcp 8.5 MIROC5 8.5 B1 CanESM2 4.5 CanESM2 8.5 MIROC5 4.5 Rcp 4.5 HadGEM2 4.5 HadGEM2 8.5 MPI 4.5 MPI CU Temperature change (degrees) South Finland CNRM 8.5 A1B A2 MIROC5 8.5 CanESM2 4.5 GFDL 8.5 B1 Rcp 8.5 Rcp 4.5 MIROC5 4.5 CanESM2 8.5 MPI 8.5 MPI 4.5 HadGEM HadGEM2 8.5 Temperature change (degrees)

11 Baseline manag. Annual Volume Growth Current climate (m3 ha-1 a-1) CNRM 8.5 HadGEM2 8.5 GFDL Baseline manag Pref. Scots pine Pref. Norway spruce Pref. Silver birch

12 Average minimum CWS s in summer (m s -1 ) Current climate CNRM Baseline manag. Baseline manag. Pref. Scots pine Pref. Norway spruce Pref. Silver birch HadGEM2 8.5 GFDL 8.5

13 Effects of tree species composition on wind risks HadGEM Share of Scots pine Baseline Pref.SP Pref.NS Pref.SB Baseline Average minimum CWS s in summer (m s-1) Share of Norway spruce Average minimum CWS s in autumn (m s-1) Share of Birch Pref.SP Pref.NS Pref.SB

14 Factors affecting wind risks Concluding remarks Tree level: Tree species, dbh, height, height/dbh ratio, position in the stand, Landscape level: Tree species, terrain, height difference between neighboring stands, Regional level: Tree species composition, UEF // University of Eastern Finland Predicting wind damage risks.. / V-P Ikonen

15 References Dupont, S., Ikonen, V-P., Väisänen, H., Peltola, H Predicting tree damage in fragmented landscapes using a wind risk model coupled with an airflow model. Canadian Journal of Forest Research 45: Peltola, H., Ikonen, V-P., Gregow, H., Strandman, H., Kilpeläinen, A., Venäläinen, A., Kellomäki, S Impacts of climate change on timber production and regional risks of wind-induced damage to forests in Finland. Forest Ecology and Management 260(5): New manuscript UEF // University of Eastern Finland Predicting wind damage risks.. / V-P Ikonen

16 Thank you! uef.fi

17 Additional info for presentation

18 Climate change projections (CMIP5, IPCC 2013)

19 Climate scenario Current climate Abbreviation CU HadGEM2-ES rcp 4.5 HadGEM2 4.5 HadGEM2-ES rcp 8.5 HadGEM2 8.5 MPI-ESM-MR rcp 4.5 MPI 4.5 MPI-ESM-MR rcp 8.5 MPI 8.5 CanESM2 rcp 4.5 CanESM2 4.5 CanESM2 rcp 8.5 CanESM2 8.5 MIROC5 rcp 4.5 MIROC5 4.5 MIROC5 rcp 8.5 MIROC5 8.5 CNRM-CM5 rcp 8.5 CNRM 8.5 GFDL-CM3 rcp 8.5 GFDL 8.5 Average of 28 rcp 4.5 scenarios Rcp 4.5 Average of 28 rcp 8.5 scenarios Rcp 8.5 SRES B1 SRES A1B SRES A2 B1 A1B A2

20 Main characteristics of studied cases (Dupont et al. 2015) UEF // University of Eastern Finland Predicting wind damage risks.. / V-P Ikonen