FDR Implementation and Calibration in the Alpine Region of Italy

Transcription

1 FDR Implementation and Calibration in the Alpine Region of Italy International Fire Danger Rating Workshop July Edmonton Eva Valese University of Padua eva.valese@unipd.it

2 Chapters Background: what s about the Alpine Region? Projects related to the FWI System Calibration: approach n. 1

3 BACKGROUND 20 Administratives Regions. Every region is responsible for Fire Management Actually, only some of them implement a FDR System In the Alpine Context (Northern Italy): Every region implements or is going to implement a FDR System A SUB-REGIONAL ALPINE NETWORK is starting, both at Country (Italy) and European level (GFMC and Alpine Space European Project)

4 NATIONAL LEVEL Civil Protection National FDR System is accessible only at central level. Testing phase

5 EUROPEAN LEVEL ALPine Forest FIre Rating System

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8 Common environment, common issues Snow cover and winter fires Complex terrain and continental gradient Eterogeneous forest ecosystems Land abandonment (land use change) Ground fires and persisting smouldering phase Boundary regions Lack of fire culture! Main issue are landslides and floods Morgex 17/3/97 % burnt area forest non forest month Rates of monthly forest and non forest burnt area in the Aosta Valley from 1986 to Source: Aosta Valley Fire Fighting Service. Photo Cesti Photo Cesti

9 GERMANY FRANCE SWITZERLAND AUSTRIA SLOVENIJA

10 Examples of projects related to the FWI System Aosta Valley Region Lombardia Region Veneto Region CONSOLIDATED SYSTEM 1994 STARTING SYSTEM In progress study about LARGE FIRES PATTERNS AND NEW MANAGEMENT TECHNOLOGIES (as implementation of FWI) JUST STARTED SYSTEM 2006

11 Aosta Valley Implementation of FWI started in 1994 Motivation: concern about the few but wild LARGE FIRES in pine stands. Challange: to provide Forest Service with easy PREDICTION MODELS FOR CRITICAL NEEDLES MOISTURE BY USING THE FWI SYSTEM CODES Crown Fires occur even when pine needles moisture is quite high ( % depending on needle type: old or young needle) Crown Fire Intensity increases when pine needles moisture decreases to % depending on needle type Results: FWI can be used as predictor of eruptive behaviour, even if the results must be Photo Cesti reinforced Under % (critical live needles moisture) crown fires develope more severe (Cesti 2004) Photo Cesti

12 Spearman correlation coefficients (semiquantitative variables) P < Sampling Sites Needle Type M.C. Morgex Quart St. Christophe St. Denis Predicted (y) x r x r x r x r FWI BUI VPD DMC South Current yr South Last yr North Current yr North Last yr DSR ISI level_fwi DMC BUI VPD VPD BUI DSR FWI DMC FWI DSR FWI ISI level _FWI DMC FFMC DMC level_fwi VPD BUI DMC DMC FFMC DMC VPD VPD BUI DMC level _FWI DMC ISI FWI DSR DSR FWI Data collection period: 3 years and half (1999, October 14th 2003, May 31st); every 15 days Different names given to pine needles depending on position (north, south) and growing period year (current, past). FW DW LFFMC = 100 DW Vapour Pressure Deficit T RH = T VPD e = the difference (deficit) between the amount of moisture in the air and how much moisture the air can hold when it is saturated

13 Implementation of FWI System to Veneto Region Before 2006 fire danger was not monitored PHASE 1: to find a simple way to spatialize the index; PHASE 2: to start an automatic and user-friendly rating system; HOW?

14 PRELIMINARY CLIMATIC ANALYSIS: Principal Components Analysis interpolated by Cokriging method: elevation latitude distance from the sea dataset: monthly min & max temperatures; monthly cumulated rains; => 11 HOMOGENEOUS AREAS Selection of 11 representative meteo stations (providing input data) / Residual Analysis 62 meteo stations 30 years long data-series Output: Web-accessible daily map of fire danger and table A SAS 9.1 application, supported by GIS (polygones coordinates)

15 Calibration 1 REGION (CENTRAL SERVICE) Test Area 5 FORESTRY PERIPHERICAL SERVICES

16 Calibration 1 REGION (CENTRAL SERVICE) Test Area 5 FORESTRY PERIPHERICAL SERVICES

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19 Climatic Area n. 7 - Monthly values ( ) T (C ) T (C ) 100 RAIN (mm) Month Tmax RAIN Month Tmax RAIN RAIN (mm) 60 Climatic Area n. 9 - Monthly values ( )

20 Danger Levels 7 DANGER LEVELS Danger levels change depending on the Month

21 Contingency Tables tabella di contingenza: Malo 100% % 80% % 60% 50% % 30% % 10% 0% P A P A tabella di contingenza: Grezzana 100% % % % 60% 50% % % % 10% 0% P A classe di pericolo Area n : 7 yrs ca Area n : 14 yrs Danger Level 7 (Extreme Danger) is not represented at all!

22 Percentile Analysis PL Andreaws et al Datasets: 1. All days 2. Fire days 3. Multiple fire days 4. Large fire days 25 percentile 50 percentile 90 percentile 90 th and 97 th percentieles values are oftern used as the lower limits for the highest staffing class levels (Holfman et al. 1987) THE AMOUNT OF DATA IS WHAT LIMITS THE ANALYSIS

23 Percentile Analysis PL Andreaws 100 et al Percentile (%) percentile percentile 90 percentile 0 4 Datasets: 1. All days 2. Fire days 3. Multiple fire days 4. Large fire days 90 th and 97 th percentieles values are oftern used as the lower limits for the highest staffing FWI class levels (Holfman et Multi al. 1987) dd 4ha dd tuttigg gginc ggmulti gif4ha All dd 90 th pct Fire dd THE AMOUNT OF DATA IS WHAT LIMITS THE ANALYSIS

24 Cumulated Frecuency curves th pct Percentile (%) FWI tuttigg All dd gginc Fire dd Multi ggmulti dd gif4ha dd

25 All days 25 pct 50 pct 90 pct Taking into account the whole dataset (all days of the years, n years)

26 Fire days The amount of data decreases. FWI values are not well represented. Difficult to fix the limit value

27 Multiple fire days

28 Large fires days LargeFire> 4 ha

29 Large Fire definition 4 ha N fires Area Burnt CLASS 0 area < 1 ha N fires Area Burnt CLASS 1 1 ha area < 4 ha N fires Area Burnt CLASS 2 area 4 ha

30 Monthly analysis Approximate results due to small dataset available 90 th and 97 th percentieles values

31 12 Months Threshold Values Highest class levels NEW Jen Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 90th pct th pct December Not represented VERSUS Danger Level Jen Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Extreme >26 >30 >43 >58 >64 >67 >68 >73 >67 >52 >40 >37 Very high Aosta Valley Threshold values Highest class levels should be changed to lower FWI values Such difference depends on climate. Verona is placed closer to the Adriatic Sea, Aosta Valley in the most continental Alps (fall wind, föhn)

32 Tutti i giorni Giorni con incendio/i 90 PERCENTILE Giorni più incendi Giorni GIF 4 ha

33 DEFINIZIONE DI GIF GIF = GRANDE INCENDIO FORESTALE Comunemente un GIF ha una superficie di almeno 100 ha Ridefinizione per la Provincia di VERONA Incendio più vasto (nelle aree analizzate): ha Superficie Boscata 70 ha Superficie Totale 35 ha Superficie Non Boscata

34 COME DEFINIRE LA SOGLIA DI UN GIF? Approccio n.1: 30 ha CLASSE 0 sup < 1 ha CLASSE 1 1 sup < 5 ha CLASSE 2 5 sup < 30 ha CLASSE 3 sup 30 ha

35 Number of Danger Levels: From 7 To 5 1 Minimum or very low danger 2 Low danger 3 Moderate danger 4 High danger 5 Very high or extreme danger

36 Conclusions (1) A specific calibration is necessary for Veneto Region (NE Italy) Pilot dataset should be extended to get reliable results The test indicates that the metodology is suitable BUT other metodologies should be taken into account the comparison between different methods could lead to better resuls, even when the amount of data is skinny also a comparison between all days/fire days/multiple-firedays/large-fire days (90th & 97th pct) NEXT APPROACH will consist in merge winter months data (FIRE SEASON) inside a more sound dataset, To simplify threshold danger levels (just one season)

37 Conclusions (2) The continental gradient (distance from the sea) and threshold values Sensitivity of the Alpine Environment to climate anomalies (droughts, global warming) should be taken into account CHALLANGES: To get a landscape view about fires To improve and promote coordination between Regions To promote a CULTURE OF FIRE, still missing Towards a multi-scale approach and an ALPine Forest FIre Rating System