Towards assessment and optimisation of the French network "Moisture content of Mediterranean wildland fuels"

Transcription

1 Towards assessment and optimisation of the French network "Moisture content of Mediterranean wildland fuels" INRA: C MORO, D PORTIER, E RIGOLOT, JC VALETTE CEMAGREF / CIRAD / ENGREF: C DELENNE, M DESHAYES Météo-France: B SOL, E BERTRAND ONF Mission Zonale DFCI: Y DUCHE, R SAVAZZI MTDA: D ALEXANDRIAN, M ALEXANDRIAN The ignorant asserts, the savant doubts and the wise thinks Aristote FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 1

2 Content 1. Context 2. Objectives 3. French Mediterranean network 4. Material and Methods 5. Results 6. Conclusions and Perspectives FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 2

3 Context: Importance of wildland fires FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 3

4 Context: Weather conditions FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 4

5 Context: Weather conditions FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 5

6 Context: Weather conditions FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 6

7 Context: Flammability and FMC FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 7

8 Objectives 1. To standardise and disseminate the methods and procedures for: collecting, storing and transporting the wildland fuel samples from the field to the laboratory determining in the laboratory the fuel moisture content of each sample entering the data in the wildland fuel data base available under the Web site 2. To enhance the quality of the data collected on the plots and the pertinence of the French Mediterranean network 3. To improve the analysis of the temporal and spatial variations of the specific moisture content of wildland fuels: for determining the specific rates of moisture content decreases and consequently for better predicting the contribution of the wildland fuel to wildland fire risk ignition and initial propagation 4. To establish statistical relationships between FMC of wildland fuel and: climatic parameters like Soil Water Reserve and codes like Duff moisture code DMC and Drought code DC FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 8

9 Objectives 5. To determine the nature and pertinence of biological data to the prevention and the prediction of wildland fires danger 6. To look for linkages between FMC, wildland fires occurrences and data extracted from satellite images 7. To replace progressively and partially the field measures by automatic analysis of the satellites images for enhancing the location of wildland fire risk 8. To provide pertinent information in term of wildland fire risks of ignition and initial propagation towards managers of wildland areas, wildland fires fighting organisations the teams in charge of predicting the wildland fire danger 9. To made these data available to end-users and stakeholders through an user-friendly Web site FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 9

10 French Mediterranean network: plots 30 plots + 3 INRA plots initially 2 per department now, 3 plots in more threatened areas (06, 2A, 2B, 83) FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 10

11 French Mediterranean network: species Species Plots Cistus monspelliensis 10 Erica arborea Rosmarinus officinalis 6 Cistus albidus 5 Arbutus unedo Quercus coccifera 5 Quercus ilex Erica scoparia 3 Genista cinerea 3 Juniperus oxycedrus 3 Buxus sempervirens Species Plots Acacia dealbata 1 Calluna vulgaris 1 Cistus salvaefolius 1 Cytisus scoparius 1 Cytisus sessiliflora 1 Erica cinerea 1 Genista purgens 1 Genista scorpius 1 Quercus lanuginosa 1 FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 11

12 French Mediterranean network: databases 1. FMC (per plot, per species and per date) Up to five validated values FMC1 to FMC5 FMC: the average of the validated values available on the WEB site 2. Soil Water Reserve SWR (daily calculation) Using the data of the four nearest weather stations to each plot Interpolating the values following a 1/d 2 law Determining a local SWR for each plot Storing the values of each plot and for each date 3. Duff Moisture Code DMC and Drought Code DC (daily) Following the same procedure as SWR FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 12

13 Materials and methods 1. To collect wildland fuel To prepare containers, bags, balance, forms, and tools To sample the wildland fuel To fill and store the containers To fill the form FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 13

14 Materials and methods 2. Back to the laboratory, to determine the mass of the container and of the fuel (FM +T) to store the opened container in the oven at 60 C during 24h to separately determine the oven-dried mass of the fuel ODM and of the container T FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 14

15 Materials and methods 2. Back to the laboratory, to enter these parameters and comments in the form available under the Web site and, consequently, update the database FM +T ODM T FMC 100 * ((FM+T) T ) ODM TEf = (FM+T) T 100 * ((FM+T) T ) ODM FMC= ODM Indicate here local observations, precipitation, and any useful information Determining the average of the validated data Validating/ invalidating the data FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 15

16 Materials and methods 3. To register local weather conditions, Air temperature and humidity Wind speed and direction 4. To determine Soil water reserve SWR Duff Moisture Code DMC Drought Code DC SWR If Precipitation j-1 > 50 mm then Precipitation j-1 = 50 mm SWR j = SWR j-1 + Precipitation j-1 - PETth j-1 * SWR j-1 / 150 If SWR j > 150 mm then SWR j = 150 mm FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 16

17 Materials and methods Canadian Fire Weather Index Duff Moisture Code DMC Drought Code DC FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 17

18 Results 1: Fuel Moisture Content Database Year Species Five moisture contents Tef Plot D2BS1 Average Invalidated data Modifying the row Deleting the row Protecting the row Printing the row (PDF file) Adding a comment No measure due to rain FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 18

19 Seasonal variation Results 2: Variation of Fuel Moisture Content One of the five valid values Invalidated value Average value FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 19

20 Seasonal variation with a long drought period: one local rain between August 20 th and August 24 th Results 2: Variation of Fuel Moisture Content FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 20

21 Results 2: Variation of fuel moisture content Spatial variation of the average four plots D13S1, D13S2, D04S1, D84S2 FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 21

22 Yearly variation four years: 2003, 2004, 2006, 2007 Results 2: Variation of fuel moisture content 2006 s spring is drier than 2003 s one; Erica arborea s FMC is lower FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 22

23 Results 3: FMC and SWR versus time FMC decrease laws FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 23

24 Results 4: FMC versus SWR, DC, DMC D84S4, 2007, Arbutus unedo, Erica arborea FMC=f(dmc) FMC=f(swr) FMC=f(dc) FMC=f(dmc) FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 24

25 Results 5: FMC thresholds and wildland fire risks Rule of thumb, for all species Level FMC TEf Low if the two moisture content are higher than Medium if at least one moisture content is between High if at least one moisture content is between Extreme if at least one moisture content is lower than FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 25

26 Results 5: FMC thresholds and wildland fire risks Twice a week synthesis Five values and average Previous average Difference = if Idif I < or + if 0.5 < Idif I < 5 -- or ++ if idif I > 5 Minimum value on this plot and for this species since the beginning of the network Minimum value on this plot and for this species during the current ten days period since the beginning of the network Low Medium High Extreme Unknown Rain FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 26

27 Results 5: FMC thresholds and wildland fire risks Risk classes low medium high extreme unknown no measure rainy period Wildland fire risk classes according to four FMC levels, the map is updated twice a week during each Summer campaign FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 27

28 Results 6: FMC and satellite data To compare FMC data to data delivered by MODIS satellite Major constraints Spatial resolution of MODIS images is not enough accurate compare to local heterogeneities Temporal resolution of MODIS images (every 16 days) is not adapted to FMC weekly variations Daily MODIS data are adequate but directional effects of the sensor are not corrected and atmospheric effects are not well corrected Most of Mediterranean shrub species are adapted to summer drought, amplitude of FMC variations is of the same order of magnitude as the surrounding noise FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 28

29 Results 6: FMC and satellite data To compare FMC data to satellites data Needs Improvement of the characteristics of MODIS and SPOT-4 HVR1 data To select and monitor species on homogeneous large area (1 km 2 ) To select plots where the shrub cover is total (100%) To focus on drought sensitive species FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 29

30 Conclusions and Perspectives Positive 1. Methods and procedures have been successfully standardised and disseminated towards the professionals: - they collect, store and transport the wildland fuel samples from the field to the laboratories and - some of them determine by themselves the fuel moisture content of each sample and enter the data in the wildland fuel data base available under the Web site - but, this effort must be maintained during the future campaigns 2. Thanks common efforts, the quality of the collected data and FMC, and the pertinence of the French Mediterranean network have been enhanced 3. An user-friendly, operational and innovative Web site has been implemented and able to be used during the following campaigns FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 30

31 Conclusions and Perspectives Equal 4. The analysis of temporal and spatial variations of the specific FMC of wildland fuels is still on going: fitting the data with polynomial equations permits to determine yearly and specific laws of FMC decreases and consequently elaborating models easy to be used by professionals for predicting the contribution of the wildland fuel to wildland fire risk ignition and initial propagation 5. Statistical relationships between FMC, SWR and DC have been established per plot, species and year, the synthesis is on going; the relation with DMC is not so regular 6. The nature of the contribution of biological data to the prevention and the prediction of wildland fires danger has been identified 7. The pertinence of the information in term of wildland fire risks provided towards foresters and wildland fires fighters and those predicting the wildland fire danger, has been recognised by these end-users FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 31

32 Conclusions and Perspectives Negative 8. The added value of biological data to the prevention and the prediction of wildland fires danger is not clear compared to those of meteorological parameters 9. No clear linkages between FMC, wildland fires occurrences and data extracted from satellite images mainly due to: the occurrence and location of catastrophic wildland fires is not a relevant indicator spatial and temporal resolution of satellite images is not adapted to the heterogeneities of the French Mediterranean wildland areas which are largely structured by human activities, and this, since many centuries FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 32

33 Conclusions and Perspectives In the future 1. To improve the rules on thumbs and adjust them to the behaviours and traits of the most important species 2. To identify and focus on sentinel species like Rosmarinus officinalis 3. Monitoring species developing superficial roots 4. Monitoring species presenting a FMC closely related to DC 5. To elaborate biological risk indexes based on FMC decrease laws 6. To compare the advantages and disadvantages of destructive methods to non-destructive ones for determining FMC, and to substitute the first by the second if the balance is positive for the second ones 7. To maintain active the Web site for following summer campaigns FOREST FOCUS symposium, Brussels, October 22nd 2007 Moisture content of wildland fuel 33