Forest fires under climate, social and economic changes in Europe, the Mediterranean and other fireaffected (FUME)

Transcription

1 Forest fires under climate, social and economic changes in Europe, the Mediterranean and other fireaffected areas of the world (FUME) IP FP7 GA January 1st, 2010-December 31st, 2014

2 THE CONCEPT

3 Socioeconomics Fire regime Drivers of change Climate Fire danger Fire size Fire Ignitions LULC Vegetation Landscape fire hazard Fire intensity Ecosystem services Fire prevention and manag. Local level Global level

4

5

6 We are a big and diverse group!!!

7 Key Questions and Main Results

8 Understanding the past Fires and the land

9 Key questions What socioeconomic changes could have affected fire activity? How these affected the landscapes? How do they interact with what fires burn?

10 Socio-economic changes and annual area burned in Greece during the last century Fig. 1. Socio-economic changes during the last century, and especially after 1950s and 1970s, and annual area burned in Greece. Note the changes in the people devoted to various activities, particularly agriculture, significantly decreased, while those devoted to non-rural activities, in particular the tertiary sector, increased.

11 LAND USE-LAND COVER (LULC) CHANGES FROM 50s TO 70s AND LAND PROCESSES RELATED TO LANDSCAPE HAZARD Fig. 3. Changes in landscape processes in relation to landscape fire hazard in Central Spain from the years 1950 s to the 1970 s. A) 1950; B) 1978; C) Differences among the two years.

12 LAND PROCESSES RELATED TO LANDSCAPE HAZARD from 50s to 70s AND FIRE OCCURRENCE ( ) Fig. 4. Net change in fire hazard as a result of land process in Central Spain from the years 1950 s to the 1970 s, and distribution of forest fires from 1976 to 2009.

13 Resource Selection Function 2,5 2 What is being burned? FIRE SELECTIVITY 1,5 1 0,5 0 CHANGES FROM 50s TO 70s Fig. 5. Fire selectivy by fires between 1976 and 2009 of areas differing in land use change processes between 1950 s and 1970 s. Note that values above 1 indicate burning above the area available.

14 Reconstruction of recent fire history in Attica, Greece ( ) Fig. 6. Spatially explicit reconstruction of recent fire history at local scale (Attica region, Greece, ).

15 Corine L3 Differential selectivity of burning High-fire year Low-fire year Transitional woodland shrub Sclerophyllous vegetation Natural grassland Mixed forest Coniferous forest Broad-leaved forest Agro-forestry areas Land principally occupied by agriculture, with significant areas of Complex cultivation patterns Olive groves Vineyards Non-irrigated arable land 0.00% 50.00% % Frequency Fig. 7. Corinne L land-cover types selectivity by fires across the Mediterranean for high and low fire years. Note that depending on the fire-year different types are selected.

16 Mapping fires >500 ha in Europe Fig. 9. Spatially explicit reconstruction of recent fire history at EU scale (southern Europe, fires >500 ha; ).

17 % Frequency % Frequency % Frequency % Frequency Differential selectivity of burning 50% Old burnings (334) 30% Transitional woodland shrub (324) 40% 30% Lanscape Fires 20% Lanscape Fires 20% 10% 10% 0% % Year Year 30% 20% Coniferous forest (312) Lanscape Fires 10% Olive groves (223) Lanscape Fires 2007 Peloponnese fires 10% 0% % Year Year

18 Take-home messages What we do, and where do it, in the landscape matters Fires differentially affect landscape features and this varies among years To understand future impacts we need to have fire maps from as long as possible!

19 Understanding the past Fire and weather

20 Key questions Under which weather conditions are fire more likely to occur? Can we model fire occurrence based on weather to allow early warning? How fire risk occurs throuhgout the Mediterranean?

21 Synoptic patterns and fire risk at local level: Valencia region Burned area by day ( ) Burned area (ha) Month Continental backward advection Maritime backward advection Convective situation Atlantic frontal advecion Fig. 10. Annual distribution of daily burned area (ha) by synoptic situation ( ) in Valencia region, Spain. Note that some patterns are critical for area burned

22 Fire danger indices and megafires risk Fig. 11. Number of very large fires (>500 ha) and the fire danger index FWI in Greece, during the megafire event of 2007.

23 Fire danger indices and burned area in Portugal ha ha Log 10 BA=a + bdsr (July) + cdsr (May, & June) Log 10 BA=a + bdsr (August) + cdsr (May, June, July) Fig. 12. Modeling area burned as a function of the FWI (DSR= Daily severity rating of the Canadian FWI system) during the months of July and August.

24 Areas differ in their sensitivity to FWI Fig. 13. Modeling annual number of fires and area burned as a function of the FWI during the months of the fire season in various regions of Spain. Note the different thresholds among regions.

25 Patterns of risk across Europe A July 27th, 2007 B August 25th, 2007 C August 31st, 2009 Fig. 14. Maps of fire risk based on meteorological information, vegetation and actual fires (derived from satellites) for three selected dates showing variations in fire risk across southern Europe during the summer: A) July 27th, 2007; B) August 25th, 2007; C) August 31st, 2009.

26 Take-home messages Fire risk is closely related to weather, which can allow projecting future fires Fire indices do not carry the same message across southern Europe Risk does not manifest itself homogeneously throughout Europe

27 Projecting the future Land-use/land cover change, climate change and fires

28 Land-use/land cover scenarios and fires

29 Key questions How LULC will change through time based on economic and climate? Can we project the impact of future LULC changes on fire?

30 Modeling land-use/land cover change 2000 Fig. 15 A. Modeling land-use/land cover based on static or dynamic factors including climate (A1B), population density, landdemand (ICES, A1B) at a spatial resolution 5 min (ca. 10 km): A) year 2000

31 Modeling land-use/land cover change share of LUC dynamics Share of changes 89.83% 4.11% 2.85% 3.21% Agriculture Pastureland Forest&Shrubs Stable 31.6% (+21%) 28% (-28.7%) 40.4% (-5.8%) Agriculture Pastureland Forest&Shrubs 2100 Fig. 15 B. Modeling land-use/land cover based on static or dynamic factors including climate (A1B), population density, landdemand (ICES, A1B) at a spatial resolution 5 min (ca. 10 km): b) year 2100

32 LULC in 2025 Fig. 16B. Modeling future fires based on socioeconomics and LULC data.

33 Take home messages Landscapes will continue to change through time and this will likely affect fires

34 Climate change and fires

35 Key questions How will fire danger vary across Europe during the next century? How will fire danger vary in the rest of the world? How will future climate affect fires across Europe? What additional factors can play a role?

36 RCP8.5 A1B RCP6.0 RCP4.5 RCP2.6 Fig. 17. Projected forcings and global surface temperature during this century. Note the correspondence between SRES and RCPs scenarios

37 Future change (%) in fire danger A1B Scenario (high CC) % Change Fig. 18. Mean delta (%) change with respect to the control run ( ) of various FWI-derived indices for three time slices and an ensemble of five RCM-GCM regional models for the fire season (June to September) and the A1B scenario.

38 Future fire danger in the world RCP 4.5 RCP 8.5 Figure 19. Global future projections of Fire Weather Index (FWI) based on a multi-model ensemble of five CMIP5 ESMs and the RCPs 4.5 and 8.5 scenarios. The upper panel represents the observed mean FWI ( ) according to the WATCH Forcing Dataset. The intermediate panels correspond to the ensemble mean relative FWI increments (deltas), expressed in % with regard to the FWI of the historical experiments ( ), for the future periods and The uncertainty of the future projections (lower panels), is expressed in terms of the signal-to-noise ratio (SNR). SNR can be interpreted in the usual way of multi-model agreement (frequency of models with the same sign as the ensemble mean) previously used in, e.g. IPCC-AR4. The ESMs of the ensemble include: ACCESS-1.0-r1, GFDL-ESM2G-r1, IPSL-CM5A-MR-r1, MIROC-ESM-r1 and MRI-CGCM3-r1.

39 Future burning in Portugal Extreme year Fig. 20. Probability distribution of the decimal logarithm of monthly burnt areas in Portugal (summer) for present conditions ( ) and for two temporal slices ( and ) of future B1 climate scenario. Note the disproportionate increase of extreme fire years that are modelled by the end of the century.

40 Modeling future ( ) change (%) in area burned * 100 [%] Low Climate Change More fire Less fire Area burned slightly increases or decreases in the Mediterranean, but increases in central Europe High Climate Change More fire Less fire Area burned increases in central and boreal Europe, but decreases in the Mediterranean due to fuel limitations LPJmL-SPITFIRE Fig. 21. Percentage change in area burned by the end of the century ( ) for low (RCP2.6) or high (RCP8.5) climate change using two GCMs (MPI, IPSL) and the LPJmL-SPITFIRE model.

41 Atmospheric CO 2 & climate change most important drivers Future biomass burned Difference of individual factors to overall change [gc/m²] High Climate Change Climate change Increases fire Limits fire Low Climate Change LPJmL-SPITFIRE Fig. 22. Future biomass burned by the end of the century ( ) for low (RCP2.6) or high (RCP8.5) climate change and one GCM (MPI) and the LPJmL-SPITFIRE model, differencing the effects of CO2 and climate.

42 Take-home messages Fire hazardousness will increase in most of Europe due to socioeconomics Fire danger, season and extremes will also increase with climate change throughout Europe Fire activity is very likely to increase, mainly large fires Fires are very likely to expand in non-fire areas of Europe

43 The impacts of climate change and fires on the vegetation

44 Key questions How plant moisture will be affected by future climate, namely drought? Will plants with different regeneration strategy vary in their response to drought? How will changes in fire regime affect the vegetation?

45 Various approaches and systems A) Large mobile shelter. Puchabon B) Throughfall interception: Fontblanche C) Manual shelter: Souk-el-Jemaa D) Automatic system: Quintos de Mora Fig. 23. Approaches used in FUME to simulate future droughts impacts, without (A, B) and with (C, D) fire, in Mediterranean forests and shrublands. A) Total rainfall exclusion during specific months using a large mobile structure in a Quercus ilex forest (Puchabon, southern France); B) Throughfall interception (30%) in a Pinus-halepensis Quercus ilex forest (Fontblanche, southern France); C) Manual rainfall interception shelters simulating various levels of droughts in a Quercus coccifera maquis (Souk-el-Jemaa, northern Tunissia); D) Automatic drought manipulation system (shelters and irrigation equipment) in a Cistus-Erica shrubland (Quintos de Mora, Central Spain).

46 Rainfall (mm) Plants moisture content with drought varies depending on strategy Rainfall (mm) Quercus ilex Quercus ilex Dry Buxus sempervirens Buxus sempervirens Dry Rosmarinus oficinalis Relative moisture content Puechabon 2010 Quercus ilex Rosmarinus officinalis Month 0 Fig. 24. Relative plant moisture of various species in southern France, after one season of 30% water interception. Note that some species (Rosmarinus, seeder) varied largely through the year but others (Quercus) were practically not affected independent of treatment.

47 Fuel moisture content among species varies with drought 2 Species types Fig. 25. Live fuel moisture content or seeders and resprouters and relationship to weather variables..

48 Plant sensitivity to drought depends on postfire regeneration strategy Fig. 27. Recruitment of a seeder (Cistus ladanfier) and resprouter (Phillyrea angustifolia) subjected to various levels of drought before fire and after fire at the Quintos de Mora experimental site (Toledo, ES).

49 Fig. 28. Recruitment of a seeder (Cistus ladanfier) three years after fire as a function of emergence the first year after fire in plots subjected to no change in rainfall (blue), historical rainfal (green), moderate (Pc10; orange) or severe (Pc1, red) drought. before fire and after fire at the Quintos de Mora experimental site (Toledo, ES).

50 Cover(%) Cover(%) Cover(%) Fire recurrence can and vegetation 100 Herbaceous species Hemicriptophytes 0 NQ Rec 1 Rec 2 Rec 3 Recurrence Terophytes NQ Rec 1 Rec 2 Rec 3 Recurrence 0 NQ Rec 1 Rec 2 Rec 3 Recurrence Fig. 29. Vegetation cover of main groups in Pinus pinaster woodlands after fire recurrence in Sierra de Gredos (Rec 1: 38 yr; Rec2: 28; Rec3:27 yr)

51 Take-home messages Vegetation and fuel sensitive to drought, but variable amon species Vegetation regeneration very resilient to drought, resprouting but also seeding Regeration by seeding will be less certain, depending on weather patterns after fire Fire recurrence appears to reduce resilience, inducing vegetation change

52 The RUI: a risky area

53 Key questions What procedures are amenable for identifying risk at the RUI How will the RUI change in the future Anticipating risks to the climate and other changes (under development)

54 Approaches for characterizing the RUI Fig. 30. Approaches for developing a toolbox for characterizing and mapping the RUI.

55 In practice: RUI mapping tool Community of Alghero Sardinia Fig. 31. Applications of both methods (IRSTEA, TRAGASATEC) to a common area at local scale (community of Alghero, Sardinia, Italy) and global scale (Sardinia, Italy)

56 Projecting RUI T+10 T+20 T+30 Fig. 32. Modeling change in the RUI in 11 areas in southern France: % RUI of total area in the next 30 years.

57 Key messages A number of techniques are available to map the RUI at various levels RUI projections are possible based on past trends

58 Coping with change

59 Key questions How can we integrate pre-, and post-fire management to reduce future fire risk and increase resilience? What species traits best suit future climate and future fire regime, and how can we select them? Can we develop protocols to guide restoration needs, including techniques better suited for restoring under future climate?

60 Scheme of actions to increase fire-resilience of pine forests mono-specific pine plantation Forest management Holm oak nursery culture Species selection and cultivation deep rooting high resilience to fire pine thinning & planting hardwoods Efficient resprouting after burning MIXED FOREST: lower flammability higher resilience higher biodiversity Fig. 33. Scheme of actions at various levels to increase resilience of pine forest..

61 Stem diameter (mm) Height (cm) Height / Diameter (cm/cm) Integrating pre- and post-fire management for restoration & for reducing future wildfire risk Clearing of pine stands + hardwoods introduction Marseille Valencia Hammamet dense (DS) medium (MS) light (LS) shelterwood Quercus pubescens b c a b a Quercus ilex DS MS LS c c b a c b a DS MS LS a a a,b b b c DS MS LS Marseille site Figure 34. Clearing of pin stands and hardwoods growth. Example of Quercus ilex and Q. pubescens at the Marseille site. Note that light shelterwood favors growth, more in Q. ilex than Q. pubescens.

62 Cavitation (%) Species trait selection: Drought tolerance of species used in restoration Acer granatense Fraxinus ornus Quercus faginea Arbutus unedo Arbutus 6.1 Risk of xylem cavitation: PLC (water potential at 50% percent loss of hydraulic conductance) Mediterranean woody species show large differences in their resistance to drought Water potential (-MPa) + DROUGHT tolerance - - Risk of xylem cavitation PLC50 (-MPa) + Arbutus unedo (6.1) Rhamnus alaternus (8.1) Quercus ilex (5.7) Quercus faginea (5.6) Fraxinus ornus (4.0) Acer granatense (5.6) Fig. 35. Native Meditierranean species widely differ in their resistance to cavitation under water stress conditions. Percent loss of xylem hydraulic conductance is produced at a range of water potentials, from quite low (-8,1 MPa Rhamnus) to relatively high ( -4MPa Fraxinus).

63 POST-FIRE DECISION SUPPORT SERVICE PRELIMINARY ASSESSMENT SHORT-TERM URGENT REPORT EMERGENCY ACTIONS REGENERATION REINFORCEMENT FOREST RESTORATION Potential vulnerable areas Identification vulnerable areas Vulnerable areas Spatial prioritization Spatial prioritization Maps Report: impact forest fire Data base Protocols FOREST FIRE INFORMATION SYSTEM Monitoring and evaluation Fig. 36. Postfire decision support service for restoration.

64 Take-home messages Postfire regeneration may not be compatible with desired management objectives, and could prompt degradation loops more likely under drought and increased fire regime Restoration must aim at reducing hazard and increased resilience to future climate and changed fire regime Management actions must be based on careful assessment of fire impacts and precised management objectives FUME has developed an operational protocol for the decision-making-process on restoration actions.

65 Assessing users needs In FUME we have passed and analyzed a set of questionaires to end-users regarding fire protocols A course was dedicated to managers to acquaint them with climate change Two rounds of meetings discussing their way of operating and how this would be affected by climate change Final document in preparation

66 Final mesasge Fires are sensitive to climate and socioeconomics by way of the landscape All of these changes are real Addressing fire risks needs to take them into account now!

67 For more information, visit: