11 forest fire - moisture content of mediterranean...

FOREST FOCUS symposium, Brussels, October 22nd 2007 1Moisture content of wildland fuel

Towards assessmentand optimisation

of the French network "Moisture content of Mediterranean wildland fuels"

INRA: C MORO, D PORTIER, E RIGOLOT, JC VALETTECEMAGREF / CIRAD / ENGREF: C DELENNE, M DESHAYES

Météo-France: B SOL, E BERTRANDONF – Mission Zonale DFCI: Y DUCHE, R SAVAZZI

MTDA: D ALEXANDRIAN, M ALEXANDRIAN

The ignorant asserts, the savant doubts and the wise thinksAristote


Content

1. Context2. Objectives3. French Mediterranean network 4. Material and Methods5. Results 6. Conclusions and Perspectives


Context: Importance of wildland fires


Context: Weather conditions


Context: Flammability and FMC


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 propagation4. 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


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 wildlandfire 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


French Mediterranean network: plots30

plo

ts +

3 IN

RA

plo

tsin

itial

ly 2

per

dep

artm

ent

now

, 3 p

lots

in m

ore

thre

aten

ed

area

s (0

6, 2

A, 2

B, 8

3)


French Mediterranean network: species

Species PlotsCistus monspelliensis 10 Erica arborea 7 + 2Rosmarinus officinalis 6Cistus albidus 5 Arbutus unedo 3 + 2Quercus coccifera 5 Quercus ilex 4 + 1Erica scoparia 3Genista cinerea 3 Juniperus oxycedrus 3Buxus sempervirens 1 + 1

Species PlotsAcacia dealbata 1 Calluna vulgaris 1 Cistus salvaefolius 1Cytisus scoparius 1Cytisus sessiliflora 1Erica cinerea 1 Genista purgens 1Genista scorpius 1Quercus lanuginosa 1


French Mediterranean network: databases

1. FMC (per plot, per species and per date)Up to five validated values FMC1 to FMC5FMC: the average of the validated valuesavailable on the WEB site

2. Soil Water Reserve SWR (daily calculation)Using the data of the four nearest weather stations to each plotInterpolating the values following a 1/d2 lawDetermining a “local” SWR for each plotStoring 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


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



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



2. Back to the laboratory, – to enter these parameters and

comments in the form availableunder the Web site and, consequently, update the database

FM +T ODM T FMC

Indicate here local observations, precipitation, and any useful information

Validating/invalidating

the data

Determiningthe average of the validated

data

100 * ((FM+T) – T ) – ODM TEf = ------------------------------------

(FM+T) – T

100 * ((FM+T) – T ) – ODMFMC= -----------------------------------

ODM



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 Precipitationj-1 > 50 mm then Precipitationj-1 = 50 mm

SWRj = SWRj-1 + Precipitationj-1 - PETthj-1 * SWRj-1 / 150If SWRj > 150 mm then SWRj = 150 mm



Canadian Fire Weather Index

– Duff Moisture Code DMC

– Drought Code DC

http://fire.nofc.cfs.nrcan.gc.ca/

http://fire.nofc.cfs.nrcan.gc.ca/


Results 1: Fuel Moisture Content DatabasePlot D2BS1

SpeciesYear

Five moisture contents Tef Average

Modifyingthe row

Deletingthe row

Protectingthe row

Printingthe row (PDF file)

Adding a comment

No measure due to rain

Invalidateddata


Results 2: Variation of Fuel Moisture Content

Seasonalvariation

One of the fivevalid values

Invalidatedvalue

Averagevalue


Results 2: Variation of Fuel Moisture Content

Seasonalvariation

with a long drought period:

one local rain between

August 20th

andAugust 24th


Results 2: Variation of fuel moisture contentS

patia

l var

iatio

n of

the

aver

age

four

plo

ts D

13S

1, D

13S

2, D

04S

1, D

84S

2


Results 2: Variation of fuel moisture contentY

early

var

iatio

nfo

ur y

ears

: 200

3, 2

004,

200

6, 2

007

2006’s spring is drier than 2003’s one;

Erica arborea’s FMC is lower


Results 3: FMC and SWR versus time

FMC decrease laws


Results 4: FMC versus SWR, DC, DMCD

84S

4, 2

007,

Arb

utus

une

do, E

rica

arbo

rea

FMC=f(dmc)FMC=f(dmc)

FMC=f(swr) FMC=f(dc)


Results 5: FMC thresholds and wildland fire risks

Rule of thumb, for all species

Level FMC TEfLow if the two moisture content are higher than 70 40Medium if at least one moisture content is between 70-55 40-35High if at least one moisture content is between 55-40 35-30Extreme if at least one moisture content is lower than 40 30


Twice a week synthesisResults 5: FMC thresholds and wildland fire risks

Five valuesand average

Previousaverage

Difference= if Idif I < 0.5

- 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

LowMediumHighExtremeUnknownRain


Results 5: FMC thresholds and wildland fire risksW

ildla

nd fi

re ri

sk c

lass

esac

cord

ing

to fo

ur F

MC

leve

ls,

the

map

is u

pdat

ed tw

ice

a w

eek

durin

g ea

ch S

umm

er c

ampa

ign

Risk classeslowmediumhighextremeunknownno measure

rainy period


Results 6: FMC and satellite data

To compare FMC data to data delivered by MODIS satelliteMajor 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


Results 6: FMC and satellite data

To compare FMC data to satellites dataNeeds

Improvement of the characteristics of MODIS and SPOT-4 HVR1 data

To select and monitor species on homogeneous large area (1 km2)To select plots where the shrub cover is total (100%)To focus on drought sensitive species


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



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



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



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 officinalis3. Monitoring species developing superficial roots4. Monitoring species presenting a FMC closely related to DC5. To elaborate biological risk indexes based on FMC decrease laws6. 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

11 forest fire - moisture content of mediterranean...

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