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Fire Monitoring: Overview and Fire forecasting Opha Pauline Dube, John Isaac Molefe and Sesafeleng Mosotho Department of Environmental Science, University of Botswana, African Monitoring of the Environment for Sustainable Development
Let’s start with the latest news on fire • The first global study human lives lost to wildfires published 19th Feb, 2012 by Johnston et al., in the Journal of Environmental Health Perspectives- • They assessed deaths in areas exposed to heavy smoke and landscape fire between 1997 and 2006 and found that: • About 339,000 people worldwide were killed by wildfires each year • Most of those deaths are concentrated in sub-SaharanAfrica, where ~ 157,000 people died as a result of being exposed to such fires annually, southeast Asia ranked 2nd with 110,000 deaths.
latest news on fire ……… • Johnston et al., 2012 results also: • signal a link between climate and fire mortality i.e. About twice as many people died (averaging 532,000)during El Nino years when the surface ocean temperature rises in the tropical eastern Pacific Ocean as opposed to during cooler La Nina years (averaging 262,000). • They warn that A warmer world due to climate change is going to see more fire. • "It is going to be incredibly difficult in the future to manage forest fires because the intensity of fires is going to be increasing and that changes the strategy of putting fires out." • “Current firefighting methods e.g. aerial suppression may have to be abandoned because they will not work against hotter, more intense fires”
Socioeconomic activities interact with natural climate variations and human-caused climate change to influence (fire) disaster risk • Disaster Risk: • the likelihood of severe • alterations in the normal • functioning of a • Community/due • to weather or climate (related) • events interacting with • vulnerable social • conditions • Vulnerability: • the predisposition of a • person or group to be • adversely affected • Exposure: • Presence of people, livelihoods, environmental services and resources etc, in places that could be adversely affected. IPCC First Joint WGI and WGII SPECIAL REPORT, on climate Extreme (SREX), 2011
Increasing vulnerability, exposure, or severity and frequency of climate related hazards increases disaster risk Disaster risk management and climate change adaptation can influence the degree to which meteorological hazards translate into impacts and disasters
Managing risks of disasters in a changing climate e.g. as in fire risks, benefits from an interative approach Monitoring Evaluation Innovation • The most effective strategies offer benefits in the relatively near term while reducing vulnerability over the longer term Learning IPCC First Joint WGI and WGII SPECIAL REPORT, SREX, 2011
Dimitris Doudoumis/ICON press 24/08/07) Impacts of hazards such as fire depend on: • -Nature and severity of the fire event – hence the need to understand, monitor and forecast the hazard • Vulnerability/suscetability– this too needs monitoring (but falls outside AMESD products) • Exposure – i.e. Also needs monitoring i.e. increase in urban fringes settlements • For exposed and vulnerable communities, even a non-extreme events can lead to a disaster
In Fire Management the aim should be for: • A fire that never escapes : • That is the fire we need - It will never need resources to fight nor will it cause any damage! • Fire risk assessment and • forecasting relates to your • fire management approach: • i.e. a Pro-active approach • As opposed to A re-active one i.e.we will act as and when there is a fire -then we put resources into fighting it
Fire Monitoring is an important part of a Pro-active Fire Management approach: • Monitor Fire Activity in order to: • Understand your fire regime i.e. nature of the hazard • Prevent unwanted ignitions through reduction of risk e.g. • Education, fire danger warning/active enforcement and rewarding good practices • Modifying fuel loads to reduce fire risk • Suppress small fires • Apply an informed rapid response to escaped fires • Assess damage and have plans for recovery
What to monitoring for fire management? • Monitor weather elements that drive fire i.e. Fire weather (Also long term change e.g. climate change): • - Rainfall- its role in built up of fuels but also as a fire suppressor during the fire season • - Temperature – influence fuel moisture and flammability • - Relative humidity - influence ability of fire to burn • - Wind – driver of rate of spread
Monitoring is mandatory at all levels of fire management • Monitor fuel: • Type ,density, fuel components e.g. litter, grass, shrubs etc • perceived value – for damage assessment & need to protect • Monitor land use: • As ignition source, • As a factor in fuel availability and condition • But also likelihood of damage e.g. need to protect • Monitor burning activity: • Frequency, timing, size, type, intensity • Monitor your fire management strategy: • Effect of prescribed fires i.e on species diversity • Effects of total fire exclusion on future fire hazards etc • Is it cost effective
Monitoring contributes to your understanding of fire behavior and types of fires occurring in your area as defined by fuel type, landscape and weather/climate Types of Fire:
Fire monitoring occurs at all levels of fire management: • Fire Risk Assessment • Fire Detection (tactical/strategic) • Post Fire Assessment • Post Fire Recovery
Fire Risk A measure of damage (expected losses) due to a fire hazard. Measured as both a CHANCE of a fire of a certain magnitude and severity to burn and resulting impact (driven by degree of vulnerability/suceptability) and exposure.
AMESD relies on satellite based information • i.e. biophysical features and deductions on land use from land cover information • There is less of socio-economic information such as policy i.e. that drives vulnerability, exposure, turning a natural hazard into a humaninduced disasters • The next slides cover Fire Forecasting within the framewok of Fire Risk Assessment - emphasizing satellite based information sources
Rainfall variability over 3 years at the landform level, computed as the sum of the differences in rainfall for each decade in a given year and the corresponding decade in the reference year (defined as the 10 year average rainfall per decade over the period 1994– 2004). Average total annual precipitation for the period 2000–2004 aggregated at the biome level. Serneels et al, w2007
Fire Danger Indices for fire forecasting • Definition: Fire danger rating quantifies the level of risk of a fire to start, spread and cause damage • Information for a Fire danger system: • 1.Type and density of fuel loads e.g. litter, grasslands, shrub- • land, woodlands forests: • Fuel condition - moisture content (live and dead) and rate of drying 2. Landscape – slope factor • 3. Weather conditions: • temperature, rainfall, relative humidity and wind • 4. Land use activities – as sources of ignition but also could influence fuel-loads considering that: • Fire management is about society interacting with land through the medium of fire
Meteorological Dead fuel moisture content Live fuel moisture content (LiveFMC) Fire danger Drivers of fire as inputs into a fire danger rating system to forecast fire occurrence • -Landscape & Fuel types: • -biomass loads, density, flammability • Fuel moisture content) • Land use & population density - Sources of ignition -Impact on fuel load Relative humidity, Air temperature, wind speed & direction All these factors will be influenced by climate change Adjusted from:: E. Chuvieco U. Alcala – Spain (Slide provided by Chris Justice UMD )
Most operational fire danger systems rely on meteorological parameters because temporal data is easier to access compared to • Data different types and levels of fuel e.g. – forest, litter on the floor, partly buried litter and their rate of drying • land use activity e.g. policy, population distribution etc With more variation that is difficult to measure
Climate change leads to change in meteorological parameters influencing fire danger • Since 1950, extreme hot days and heavy precipitation have become more common globally • Models project substantial warming in temperature extremes by the end of the 21st century. • There is likelihood that the frequency of heavy precipitation or the proportion of total rainfall from heavy falls will increase in the 21st century over many areas of the globe – resulting in more fuels to burn! IPCC First Joint WGI and WGII SPECIAL REPORT, SREX, 2011
Different fire danger models have been developed – mostly being area/region specific. • Increasing global challenges such as climate change have demanded for the need for a universally applicable fire danger index • In Southern Africa the most significant indices that have been considered are: • - Low veldt Fire Danger Model • - McArthur Fire Danger Index • - Canadian Fire Weather Model • Low veldt Fire Danger Index is expressed as; • FDI = (BI + Wind Factor) * RCF, • Where FDI = Fire Danger Index, BI is Burning Index, and the RCF is the rainfall correction provided. • * It is considered to be the most Southern Africa specific index
McArthur FDI - Australian • Based on an empirical model of fire behaviour in open air • Inputs to run the model: • airTemp., RH, wind speed, rainfall, time since last rain and a drought index • Originally – A lot of hope for Southern Africa for this model– but performance has been found wanting
Canadian Forest fire Danger Rating system (CFFDRS)- one of the oldest fire danger rating systems • Originally designed to use two subsystems: • Canadian Forest Weather Index (FWI) • The Canadian Forest Fire Behaviour Prediction system (FBP) The Canadian Fire Weather index (FWI) has been found to perfom far better than the Australian McAuthur index in Southern Africa and works satisfactorily in other parts of the world too – hence its adoption as a global Fire Index
FWI is a sophisticated but simple index that incorporates the interface between weather parameters e.g. • Temp. RH, wind speed, and 24 hr accumulated precipitation (as recorded at noon local standard time) • AND • Moisture levels and drying rates of different fuel loads – originally defined as per Canadian conditions (i.e. Pine forest) (Taylor and Alexander, 2006).
The three moisture codes and their corresponding fuels in the Canadian FWI: • 1. Fine Fuel Moisture Code (FFMC): moisture of the thin surface layer of fast-drying material i.e the • relative ease of ignition and flammability of fine fuels. • 2. Duff Moisture Code (DMC): moisture content of loosely compacted, decomposing organic matter • Drought Code (DC): a slow drying deep layer of compact organic matter: • When very dry or drier than the upper layers DC may results in persistent deep smoldering even though surface fire behavior may not be severe
Physical properties of forest ﬂoor layers associated with the fuel moisture codes of the Canadian Forest Fire Weather Index System Time lags of the fuel moisture codes vary with weather conditions. Tabulated values represent standard drying conditions (temperature 21.1º C, relative humidity 45%, wind speed 13 km/h, July) and were derived by Canadian Forest Service. FFMC = Fine Fuel Moisture Code: DMC = Duff Moisture Code: DC = Drought Code.
The three moisture codes plus wind are used to form two intermediates that in turn combined to yield the final index- FWI as follows: • Initial Spread Index (ISI): a combination of wind and the FFMC that represents initial rate of fire spread • The Buildup Index (BUI): A combination of the DMC and the DC that represents the total fuel available for spreading the fire Giving us the Fire Weather Index (FWI): a combination of the lSI and the ADMC that represents the intensity of the spreading fire as energy output rate per unit length of fire front.
What are the chances of having a bad fire today? For use at a global level- The scale of the Canadian FWI is adjusted to match the Locally available indices as in the Lowveld Index for the Southern Africa region Tiaan Pool, SAASVELD Campus
Spatial distribution of CAFWI for a single day in 04November 2011. The same data can be displayed as both class (categorical ) values and numerical values. Within GIS/ RS software the data can be visualized as a time series (TS) enabling appreciation of changes and trends. When working on TS data we can “count” the number of days a particular fire class value occurs (see next slide).
RECALL: Managing risks of disasters in a changing climate e.g. as in fire risks, benefits from an interative approach Monitoring Evaluation Innovation • The most effective strategies offer benefits in the relatively near term while reducing vulnerability over the longer term Learning IPCC First Joint WGI and WGII SPECIAL REPORT, SREX, 2011
Let’s Learn and Be Innovative: • The most effective fire forecasting approach is one that integrate various sources of evidence – than relying only e.g. on the fire danger index
Drought/crop product such as these valuable in determining and interpreting FDI - They signal potential fuel and air conditions
Knowledge of fire season based on monitoring e.g. active fires adds to the accuracy of forecasting
The FDI can also be linked to fire frequency to forecast and assessing potential fire risk: (needs validation) Frequency of burns over the 2001 and 2008 based on MODIS data. Areas that burn frequently can be considered high fire risk areas - especially where based on longer observations
Assessing NDVI trends over time and linking this to FDI and other evidence add to improving forecasting NOAA AVHRR based Normalized Difference Vegetation Index (NDVI) -A reasonable estimation of the density and coverage of green vegetation
Assessing trends in Net Primary Productivity and linking this with fire seasonality, history of burns etc will improve forecasting Map of global net primary productivity (g C m-2yr-1) from the International Global Biosphere Programme
NPP is determined from: estimate the absorbed photosynthetically active radiation An image with visible and near infrared bands is required. Many vegetation indices, particularly NDVI and EVI, are correlated with live biomass and are used in NPP models.
NPP determined from Linear Modeling - Relate reflectance data recorded by a sensor to field measurements of NPP using linear regression technique High correlation (R2=0.91) between the predicted NPP from NASA-CASA models based on EVI (enhanced vegetation index) and field measurements of NPP (source: Potter et al. 2007) • One similar approach to NPP is: • Leaf Area Index (LAI) - the ratio of upper leaf surface area to ground area
Linking FDI to the Vegetation Health Index based on a combination of VCI and TCI, which is also called Vegetation-Temparature Index. NOAA Based product An effective proxy data for monitoring vegetation health, drought, moisture and thermal condition etc – hence relevant for fire risk assessment .
Vci diff (20110301 and 20110201) VCI20110301 VCI20110301-VCI20110301 VCI20110201
Fire Forecasting is a valuable part of fire management • Fire risk assessment was done in the past using experience and rough observations of rainfall, biomass, weather and land use • It is now being perfected using Earth Observation based products • Fire forecasting in particular adds into early warning and fire preparedness – i.e. leading to disaster risk reduction
Fire Forecasting: Aids in decisions on where and when to construct fire breaks (Slide, DFRR, 2009)
Fighting fire with fire – Prescribed burning/fuel manipulation will benefit where there is fire risk information (Photo by Trollope)
Fire risk forecasting help to anticipate the severity of an active fire so as to respond appropriately (Photo by Trollope)
From fire forecasting: Decide in the event of a fire outbreak If suppression is the right response or Evacuation is best An aerial firefighter leaps from an An-2 biplane (parachuting into fires was a Soviet invention- where firefighters would climb out onto the wing of a plane, jump off, land in the nearest village, and rally the villagers to go fight the fire) http://environment.nationalgeographic.com/
Let’s Go and Learn, Innovate, Evaluate and Implement • THANK YOU The Sky is Your Limit