WMO Commission for Agricultural Meteorology Expert Team on Forestry (Fire Weather, Fire danger rating): Agromet

Services and Products

Contents 1. Introduction 2. Current Status of wild land fire danger ratings by WMO region 1. (Should be improved by regions' representation) 3. Impact of wild land Fire 4. Indicators of fire danger 1. Agro met 2. Pest and disease 5. Indicators of wild land fire danger on sensitive ecosystems 1. Delivery systems (messaging/communication) 2. Implications for land management 6. Wild land fire and climate change (fold into discussion rather than separate) 7. Conclusion 8. Bibliography

1. Introduction Wildland fire is a driver of change in the natural environment. Although destructive, the power of wildland fire is not in what it can take away but in what it can leave behind. It is a vital component in the health of ecosystems, helping to maintain a balance among the many varieties of plant and animal life. It clears the forest floor of undergrowth to allow animals to forage and seek cover from the elements as well as enemies. It opens stands by removing dead and diseased vegetation, allowing the remaining vegetation to thrive. It maintains the continuity of grasslands by improving nutrients and slowing the introduction of woody plant species. In many ecosystems some species have become fire adapted, requiring periodic fire to propagate and survive. This balance has existed for millennia. However, things changed with the advent of agriculture. Man used fire to clear large tracts of land for farming. Over time human-caused fire disrupted the delicate balance in fire adapted ecosystems and decimated those ecosystems that were not fire tolerant. Landscapes changed. Non-native species that were fire adapted replaced those that were not. Large areas of forests were lost to make room to expanding agriculture, ranching, and development. Fire does not have to be present to contribute to the changes. Man's efforts to exclude fire from the landscape for self preservation or as good stewardship come with negative consequences. Ecosystems that rely on periodic fire for maintaining a steady-state, such as grasslands, become overgrown with brush and other woody vegetation that choke off the native grasses when fire is removed. Forests become dense and unhealthy with increasing amounts of organic matter available to burn when fire does return, creating more intense and more destructive conflagrations that alter the landscape to the point where it can no longer regenerate to its original state. Wildland fire can be described as any fire on the landscape that consumes natural resources, regardless of the ignition source. It has the capacity to do tremendous damage to an ecosystem or it can help maintain it, make it more resilient. How wildland fire is managed is largely determined by the conditions in which the fire exists and the expected or desired outcomes of the management efforts. Fire danger ratings systems allow land managers to assess those environmental conditions that contribute to fire danger (or potential) and fire behavior.

2. Current Status of wild land fire danger ratings by WMO region In the following are provided the main Fire Danger Index applied in the WMO regions (Fig. XX). It has to be argued that no effort is made to quantify the degree of complexity and reliability of those indices.

Figure XX – WMO regions. Region I – Africa Among several initiatives in region I, South Africa’s power supply company initiated the development of an Advanced Fire Information System (AFIS) in 2004. The AFIS system provides detection (near real time fire detection), assessment (burn area mapping), prediction (Fire Danger Index), alerting, planning and reporting capabilities through the use of Earth observation satellites, weather forecast models and Information and Communication Technologies. It’s also used as a research tool for the alert service for fire suppression, with a focus on Southern Africa(http://www.afis.co.za).Multiple satellite sensors are employed to carry out these actions. Continuous observation of Africa is managed via geostationary satellite, projecting images at 15 minutes interval (MSG ABBA)(www.csir.co.za, 2013)at 4.8 km resolution. Fire sizes of 5-10 ha can be detected. AFIS products under development include an operational burned area mapping tool, Flashover Probability Index, a daily FDI product based on the Lowveld model and DB (direct broadcast) CRAS forecast data [DB CRAS is a numerical

weather prediction model that is unique in its ability to assimilate satellite products to improve the initial conditions used in the model forecast physics], and integration of 100 automated weather stations in South Africa into AFIS for 15 minute wind speed and direction updates. Dissemination of fire products via GEONETCAST’s network of Eumetsat is also underway. In Tunisia, it’s since 1989 that the National Institute of Meteorology (NIM) provides a daily bulletin for fire risk rating at the request of the forest authorities and the civil protection. Nowadays, the bulletin covers 21 forest areas and it is provided from the beginning of June to the end of September. In fact, according to a recent study, Sebei (2015) concluded that the month of June is marked by the highest degree of fire forest risk and the month of August reached the peak of fire forests. For the elaboration of the final bulletin, three methods are used. The first method is based on the recorded meteorological data (temperature, moisture, rain and weather forecasts). The second method is based on a developed model for calculation of risk degree using climatic data and weather forecasts of ALADIN Tunisia. The third method uses the Orieux index requiring wind speed and water reserve in the soil. A software based on the Orieux index (Arif, 1992 a and 1993), called INCENDIE, was developed at the NIM for the determination of the meteorological degree of the forest fires risk (MDFER) and its frequency (FMDFER) for each forest zone from the local model. The bulletin is intended for forestry service in the Ministry of Agriculture, for the civil protection services and for all regional and local authorities involved in the monitoring of fire forests (Ministry of Environment, regional and local meteorological representation...). In Mali, a research was conducted to establish a period of risk of wildland fires. The method used in this study was based on the use of MODIS products. It consisted of counting the number of pixels burned per month in Mali during the period 2000-2013 (Anonyme, 2013). Therefore, a four-month period from February to May was defined for the development of the map of occurrence of late brush fires. The MODIS satellite is currently the most efficient tool for characterizing the spatial andtemporal distribution of bush fires using its two TERRA and AQUA carriers. In practice, the Forest Information System Unit (SIFOR) is in partnership with the Institute of Rural Economy to identify the different cases of fires of fire throughout the national territory (Anonyme, 2015).

In Morocco, and according to the authorities in charge of forests and desertification(Anonyme, 2013)(http://www.eauxetforets.gov.ma/fr/text.aspx?id=1157&uid=37), the system of warning on fire forests is based on frequent observations in the season of wildland fires. Every fire detection is mentioned by a first SMS including a detailed description, location, name of the forest … a second SMS describing the importance of the fire (type of trees, burned area …). Otherwise, according to Faleh and al. (2012) to acquire adequate facilities for more effective control of forest fire and to prevent heavy damages, the development of forest fire risk maps by GIS and remote sensing using Dagorne Y. Duche method (1994). In Algeria, Statistical analysis of information on forest fires over several years in the fourty departments of the northern Algeria revealed that the country is strongly affected by forest fires during the period between 1985 and 2010 (Meddour Sahar, 2012). The results showed that wildfires occur mostly in August. The main responsibility in fire fighting is assigned to the Civil Protection Department, whereas the Forestry Service is in charge of first intervention and of the prevention and management activities (Meddour Sahar, 2013).

REGION II – ASIA

Since the year 2005, the Forest Survey of India (FSI) has been monitoring forest fires across the country using inputs received from MODIS satellite system, a joint collaboration of NASA and Geography Department of University ofMaryland. In March 2010, FSI started a system of sending SMS/emailalerts through its website www.fsi.nic.in. Any user can register forthe alert system by providing his/her mobile phone number and emailaddress and the names of district/state/UT for which the information is sought. Every day, between 1100 -1200 hrs email and SMS alerts reachthe registered users giving a summary of total number of forest firesdetected in their chosen areas. The detection of forest fires is made on the daily basis through the website http://maps.geog.umd.edu. After collecting the coordinates of the fire spots, FSI maps the forest firesthrough GIS analysis. The coordinates of all the forest fire spots are thensent to the respective State Forest Departments through fax and email. Based on the feedback receivedfrom SFDs, it has been observed that the detected forest fires are correcton more than 95% points.

Numbers of initiatives have been taken under the plan to strengthenthe forest fire management system in the country. A strong centralcomponent for the development of an Early Warning Fire ForecastingSystem using satellite data and Fire Danger Rating System for earlydetection of forest fire has been introduced. The FSI is working in collaboration with the National Centre for Medium RangeWeather Forecasting (NCMRWF) for this project. The Forest ResearchInstitute (FRI), based at Dehradun is also being involved in the plan toassess the impact of forest fire on vegetation and micro-climate. HERE MORE INFORMATIONS ARE NEEDED FOR RUSSIA, JAPAN, KOREA AND CHINA. Nabansu contacted an potential co-author to report. REGION III – South America

In early 90’s, the National Institute of Space Research in Brazil (INPE) put forward a method to indicate the atmospheric favorability to fire occurrence, namely the Potential Fire Index (PFI). Currently this method is delivered in all countries in the American continent (https://queimadas.dgi.inpe.br/queimadas/outros-produtos/risco-de-fogo-e-meteorologia), in exception of USA and Canada. The principle of the PFI is that more days without rain, higher the risk of vegetation burning. To compute the PFI are included type and effects of the natural cycle of vegetation dryness (defoliation), daily maximum temperature and minimum air relative humidity, as well as the presence of fire in the area of interest occurred in the previous day. The PFI is based on analysis of hundreds of thousands of burning/fires on the main biomes (vegetation types) in the country, during the last two decades (Sismanoglu and Setzer, 2004). The basis of the calculations is the "Days of dryness", or "Dry", which is the number of days in a row without any precipitation during the past 120 days on the date of calculation. In case of occurrence of precipitation during the period analyzed, the PES calculates a hypothetical number of consecutive days without rain (Justino et al. 2010, 2014). It is essential to take into account that the PFI indicates the susceptibility of the vegetation to be burned and that the burning in the vast majority of cases is initiated by humans, and not naturally by lightning. In this context, the wind speed is not considered in the calculations because it will be more related to the spread of fire.

Despite the availability of the PFI, since 2000, theServicio Provincial de Manejodel Fuego (SPMF) in Argentina applies the Canadian FWI as a fire potential tool to support operational activities. FWI was implemented in the Province as a part of the Project “Argentina 2000, of Fire Management and Technology Transfer” in a Cooperative Agreement between the British Columbia Fire Service (BCFS), Canada and the Plan Nacional de Manejodel Fuego (PNMF), Argentina. At present, SPMF has 11 Fire Brigade Operational Bases where the FWI is calculated. For this purpose, real time meteorological data are provided by weather stations belonging to different agencies. The resulting codes and indexes of the day and those for the next three days, obtained through forecasted weather data provided by the ServicioMeteorológicoNacional (SMN), are analyzed by fire personnel in operational meetings, held twice a week and danger classes determined for five fuel types. Through a discussion process a final fire danger class is assigned for the area represented by each meteorological station, for the day and the next three following days. Considering the fire danger class and the probability of fire occurrence due to either anthropogenic or natural causes and fire expected behavior, prevention and pre-suppression decisions are taken following established guidelines. This process results in relevant benefits to SPMF as it provides objective information for decision making process of pre-suppression and prevention, thus, making the resources administration more efficient and improving safety conditions. However, due to limited number of weather station the FWI is available for all municipalities in country, but for areas characterized as highly vulnerable.

REGION IV – North America, Central America and Caribbean, The broad north-to-south extent of Region IV encompasses virtually all the major climate and sub-climate zones. Most of Canada and Alaska are subartic and generally span the boreal forest belt. The western interior of the United States, southwestern interior Canada, and northern Mexico are mainly arid and semiarid. The eastern United States and southeastern Canada are mainly humid continental or subtropical climates. Along the west coast from British Columbia to northern California is a narrow band of moderate marine west coast, which transitions to a Mediterranean climate along the southern California coast. The remainder of the region—southern Mexico, Central America and the Caribbean islands—is generally tropical. Wildland fire affects most of the region. Naturally caused wildland fire is most prominent in the northern and mid-latitudes, especially the western half of the continent. Alaska, western Canada and the western United States typically experience a rapid increase in wildland fires

during the warmer periods of the year, from March to October. Fire generally begins in the southwestern United States and northern Mexico in the spring months with activity migrating northward into the early and mid-summer before decreasing from north to south in the late summer and early fall. The eastern half of the continent similarly experiences fires in the warm season but with a more bi-modal occurrence. In the early spring before greenup begins, dormant and dry vegetation is susceptible to fire. In the northern regions, after snow cover is gone, dry duff layers can be receptive to ignitions and spread. Activity decreases with greenup but can resume in the dry fall conditions with leaf drop. Wildland fires are largely started by natural causes, such as lightning, but there is a significant proportion of ignitions from human activities (recreation, industry, agriculture/land management practices). In the tropical regions of southern Mexico, Central America, and the Caribbean, wildland fire is mainly influenced by distinct wet and dry seasons. However, unlike the northern and mid-latitude regions, fire occurrence is largely driven by human activities, especially agricultural burning. In the United States from 2001-2015, the yearly average number of fires was near 73,000 with a yearly average 2.5 million hectares burned (National Interagency Fire Center, www.nifc.gov). In Canada, the 15-year average (2001-2015) number of fires is about 7000 fires, burning over 2.4 million hectares (National Forestry Database, http://nfdp.ccfm.org/index_e.php). In Mexico, the average (2001-2015) number of fires was about 7400, burning around 216,700 hectares per year (Reporte seminal y acumulado de Incendios Forestales 2017, http://www.camafu.org.mx/index.php/noticias/articles/reporte-semanal-y-acumulado-de-incendios-forestales-2009.html). Statistics from other countries are incomplete or generally unreliable if available at all. Wildland fire warning systems are a critical part of land management efforts in Canada and the United States and, to a lesser degree, in Mexico. Elsewhere around the region, land management practices are more focused on prevention, detection and control of wildland fire, with less emphasis on advanced warning (source citation?). In the United States, the primary warning effort is based on the National Fire Danger Ratings System (NFDRS 2016, http://www.wfas.net/nfdrs2016/index.php/en/; Bradshaw, Larry S.; Deeming, John E.; Burgan, Robert E.; Cohen, Jack D., compilers 1984. The 1978 National Fire-Danger Rating System: technical documentation. General Technical Report INT-169. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment

Station. 44 p.). It is a model of the relative measure of conditions over a large area that reflect the potential for a fire to start, spread, and require an effort to suppress it. The National Weather Service (NWS) produces weather forecasts specifically for wildland fire. When conditions meet regionally-determined thresholds, NWS will issue Red Flag Warnings or Fire Weather Watches to highlight a significant threat of extreme fire behavior up to 48 hours in advance. The Predictive Services units of the federal land agencies produce a running 7-day and a monthly outlook of weather and fuels used to identify areas of elevated risk of significant wildland fires. In Canada, the primary warning system is the Canadian Forest Fire Danger Rating System (CFFDRS, http://cwfis.cfs.nrcan.gc.ca/background/summary/fdr). Similar to the NFDRS, it is a model that reflects the risk of forest fires in Canada. The provinces and territories manage their own forest weather station networks and use the observations to create their regional fire danger products. Natural Resources Canada creates national fire weather and behavior products using weather observations from about 2500 stations across the country and the United States. A CFFDRS-based prediction model generates a month-by-month seasonal outlook using weather forecasts from Environment Canada to predict several fire risk parameters, such as severity and severity anomaly. Canadian fire science and systems have been applied to many global regions, including parts of the United States, Europe, Mexico, New Zealand, and Southeast Asia. The most recent international application is development of an African prototype for a global fire early warning system. Mexico monitors weather for periods where conditions may increase (or decrease) the potential for wildland fires. The Servicio Meteorológico Nacional (SMN) also uses satellite-derived vegetation health index to gauge fire potential. Canada, the United States, and Mexico work closely to produce a three-month wildland fire outlook. The North American Seasonal Fire Assessment and Outlook (http://www.predictiveservices.nifc.gov/outlooks/NA_Outlook.pdf) identifies factors that contribute to increases (or decreases) in fire potential and highlights areas of elevated (or lowered) risk. The product is issued monthly. Among the remaining members of the region, many are working toward or have implemented forest management plans that include public education, prevention, detection and control of

wildland fire, and restoration. However, only a few (Cuba, St. Lucia, St. Vincent and the Grenadines) include a prediction and warning component.

REGION V – South West Pacific

WMO Regional Association V (RA V) is the South-West Pacific Region and consists of 23 countries: Australia, NZ, Papua New Guinea, Indonesia, Malaysia, Philippines, and smaller Island Nations in south-west Pacific Ocean west of 120oW. There is significant diversity across the region in terms of the physical geography and the political, cultural, and economic landscapes. Based on geographic location and development status, the members of RA V are assigned to three sub-groups: South-East Asia (SEA: Indonesia, Malaysia, Philippines, Singapore, Brunei Darussalam, and Timor Leste), Pacific Island Territories and Countries (PITC: Cook Islands, Federated States of Micronesia, Fiji, French Polynesia, Kiribati, New Caledonia, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu), other advanced countries (Other: Australia and New Zealand) (http://www.wmo.int/pages/prog/dra/documents/RA_V-RECO6-surveyreport.pdf). In all countries, operational agricultural meteorological services for the forestry sector are provided by the National Meteorological and Hydrological Services (NMHS). The legislative frameworks, mandates, and scope varies significantly between the various NMHS http://www.wmo.int/pages/prog/dra/documents/RA_V-RECO6-surveyreport.pdf, page 14). In general the PICT countries have relatively limited meteorological services, by world standards, in terms of resources, budgets, and staff. Almost all countries have a meteorological services but the climatological services are minimal. Therefore many countries have some form of cooperative programs supporting some of their services, especially climatological services: these programs have various formats including bilateral, multilateral, regional, or larger international programs (http://www.wmo.int/pages/prog/dra/rap/regionV_more.php). In Australia, the Australian Bureau of Meteorology (BoM) routinely provides the state and regional fire agencies with forecasts of daily Fire Weather Indices (FWIs). These forecasts are based on the McArthur Mk V Forest and a modified CSIRO Mk IV grassland fire danger meters ((McArthur, 1967, 1973)). The State fire agencies use these forecasts in conjunction with other information to determine the Fire Danger Rating (FDR) which is then disseminated to the

public. The fire danger rating scale was introduced by Lucas et al(2010). The original scale had five levels: low, moderate, high, very high and severe. However, after the February 2009 Black Saturday fires in Victoria, and the extreme fire danger weather recorded during the event, the fire danger rating scale was changed in October 2010 to include two additional levels: extreme and catastrophic (Bureau of Meteorology, 2014a). There are also other fire spread/danger models used regionally in Australia for a range of fuel type and burning conditions. Gridded and location specific daily and 3-hourly fire weather information can be accessed in the internet by the public via a system called MetEye; an interactive weather resource (http://www.bom.gov.au/australia/meteye). MetEye allows users to view real-time weather observations and seven-day forecast information as generated via the NexGen FWS (Next Generation Forecast and Warning System). As well as the suite of weather variables forecast for the general public and user-groups, this system also includes hourly, 3-hourly, and daily forecasts out to seven days of fire weather variables and a forest fuel dryness factor. Forecasts are updated twice daily. Gridded forecasts from the Australian Digital Forecast Database are also available. The spatial resolution of the forecast varies depending on the State: National forecasts are provided at 6km resolution as are all state forecasts except Victoria and Tasmania forecasts which are on a 3 km grid. The forecasts can also be received in text format. Additional information is available such weather summaries, the synoptic situation, and fire danger. The forestry sector can also utilise a Seasonal Bushfire Outlook provided by the Australasian Fire and Emergency Service Authorities Council (AFAC) via a workshop process. Contributing agencies include BoM, the Bushfire and Natural Hazards Cooperative Research Centre (CRC), state fire agencies and government agencies. The seasonal assessments include 3-month climate risk assessments from BoM of key fire weather variables such as rainfall and temperature, and also assessment of ENSO development. Smoke management services are also available for the Australian region, provided as a specialised service usually for prescribed burning for fuel reduction, forest management, or flora and fauna management. Both BoM and CSIRO have smoke trajectory prediction services (Wain & Mills, 2006). Agro-meteorological products and services for the fire-related sectors of the ASEAN nations (Association of Southeast Asian Nations), are primarily provided by the NHMS of individual

countries as well as the ASEAN Specialised Meteorological Centre (ASMC), hosted by the Meteorological Service Singapore (a WMO Specialized Regional Meteorological Centre). This centre serves the region with services of regional early warning of fires, weather prediction, and detection of hot spots with an aim of addressing both fire and haze disasters. Its role is to monitor and assess vegetation fires, and the occurrence of transboundary smoke haze, as well as to conduct seasonal and climatological predictions, for the ASEAN region. The Fire Danger Rating System (FDRS) for ASMC is produced by the Malaysian Meteorological Services (MetMalaysia). In general the FDRS are used by forestry, agriculture, environment, and fire and rescue agencies to develop and implement fire prevention, detection, and suppression plans ((De Groot et al., 2007). The regional Fire Danger Rating System (FDRS) is based on the Canadian Forest Fire Danger Rating System (Stocks et al., 1989). Components of the CFFDRS have been adapted to local vegetation, climate and fire regimes (De Groot et al., 2007). These adapted components are the Canadian Forest Fire Weather Index System (FWI), the Canadian Forest Fire Behaviour Prediction System (FBP), and the Fine Fuel Moisture Code (FFMC) and Drought Code (DC) components of the FWI. The Indonesian and Malaysian FDRS are operated by the Indonesian Agency for Meteorological, Climatological and Geophysics and The Malaysian Meteorological Service respectively. An additional tool for regional and global scale analyses is the Global Wildland Fire Early Warning System (Global Fire EWS) hosted at the University of Freiburg (http://www.fire.uni-freiburg.de/gwfews/index.html). The Global Fire EWS provides 1-10 day forecasts of fire danger indicators using the Canadian Meteorological Centre’s (CMC) Global Deterministic Forecast System (GDPS). The fire danger indicators are components of the Canadian Forest Fire Weather Index (FWI). One of the benefits of this program is to provide an operational fire danger rating system for countries that do not have a national fire danger rating system (de Groot et al., 2010). The PICT region has a wide range of agro-met services for forestry reflecting the diversity of NHMS across the Southwest Pacific region. The Samoa Meteorological Division has developed a Climate Early Warning System (CLEWS), which enables provision of climate and weather information specifically targeted to climate-sensitive economic sectors. CLEWS was supported by the Least Developed Countries Fund within the Global Environment Facility and was

integrated with programs such as the Pacific-Australia Climate Change Science and Adaptation Planning (PACCSAP) Program, Climate Oceans Support Program for the Pacific (COSPPac), and the Pacific Climate Change Science Program. The Fire Weather System (FWSYS) provides forest fire danger indices and fire weather information for key locations across Samoa. In 1997/98 El-Nino event, extensive forest fire in Kalimantan and Sumatra had caused widespread haze over Southeast Asia region. This has significantly affected the tourism industries, the health of the people, transportation, sport and educational activities. In respond to this environmental disaster, the Southeast Asia Environmental Ministers initiated Regional Haze Action Plan. As part of the plan, a monitoring and warning system for forest / vegetation fires need to be developed and implemented. Adopted from Canadian Forest Fire Danger Rating System (CFFDRS), the Southeast Asia Fire Danger Rating System(SEA FDRS) and Malaysian Fire Danger Rating System (MFDRS) were subsequently developed and implemented. Initially the SEA FDRS was generated daily by the Canadian Forest Service (CFS). However, this responsibility was handed over to Malaysian Meteorological Department (MMD) in September 2003. Since then MMD has been producing SEA FDRS product on a daily basis. At present MFDRS is running operationally in MMD and the output of this system is being displayed on MMD website since February 2003. The product of FRDS is being used as a guide to policy maker in developing action to protect life, property and the environment. REGION VI – Europe

Due to geographic characteristic, which involves topography, climate and land cover difference, several FDI has been developed and adapted in European countries. The Global Fire Monitoring Centre (GFMC) is the foremost pioneer organization providing individual support and relevant information for strengthening international cooperation. The Centre is hosted by the Fire Ecology and Biomass Burning Research Group of the Max Planck Institute of Chemistry, Bio-geochemistry Department, Freiburg, Germany. The GFMC, carrying out extensive research and demonstration in the subject of forest fire is providing consultancy services to many countries. Cooperation with Deutsche Gesellschaft fur TechnischeZusammenarbeit (GTZ) projects was implemented in Brazil 1980-82, Indonesia-1987, Algeria-1992, Argentina-1991-97, Srilanka-1991 etc. The Global Fire Monitoring Centre (GFMC) provides a global portal for wild land fire

documentation, information and monitoring. The regularly updated national to global wild land fire products of the GFMC are generated by a worldwide network of cooperating institutions. The online and offline products include early warning of fire danger and near-realtime monitoring of fire events. The European Forest Fire Information System (EFFIS) has been established in 2000, by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC), to support services in charge of protection of forests against fires in the EU and neighbour countries. The core of EFFIS consists of a scientific and technical infrastructure at the JRC doing research on forest fires and operating a web based platform (http://effis.jrc.it). In addition EFFIS is supported by a network of Experts on Forest Fires from 22 EU countries that meet regularly with the EC services. Fire danger rating has been introduced in EFFIS to provide the states’ fire agencies with a common, EU wide harmonized assessment of fire danger, of crucial importance especially in case of mutual assistance among countries. In this context, the Fire Weather Index (FWI) developed by the Canadian Forest Service was adopted in 2007 as the method to assess the fire danger level in a harmonized way throughout Europe. The original FWI formulas have been slightly changed to better suit the remarkable differences in day length in EU when going from the Mediterranean to the Boreal countries. The fire danger module of EFFIS currently generates daily maps of 1 to 6 days projected fire danger level in EU using weather forecast data. Fire danger is mapped in 5 classes with a spatial resolution of about 45 km (MF data) and 36 km (DWD data). The fire danger classes are the same for all countries and maps show a harmonized picture of the spatial distribution of fire danger level throughout EU. Calibration studies are on going based on the analysis of recent larger fires and also on the relative frequency of FWI values in the different areas. In addition to standardized fire danger classes, EFFIS offers maps of FWI anomalies and absolute ranking, which are based on the comparison of the daily fire danger level with the last 50 years of daily FWI values that have been recalculated using the ECMWF ERA40 dataset. Turning to particular countries, it has be noticed that the UK is not extremely affected, based on international standards, by the frequency or ferocity of wild land fires which many of its European counterparts and other overseas countries are faced with. However, when wildfires do occur, they can be devastating and on occasions, cause many millions of pounds worth of damage and affecting large areas of valuable land and ecosystems. In response to legislation regarding public access to the countryside, the Met Office has implemented the Canadian Fire

Weather Index for predicting daily fire danger across the country on a 10km2 grid. Forecasts are used to determine countryside exclusion zones for the public. Numerical Weather Prediction models are used to produce five days ahead forecasts which are disseminated freely on the web. The Spanish Meteorological Service (AEMET) has been in close cooperation with the regional and National Forestry Services and Civil Protection Authorities since the 90s, this has resulted in a close collaboration to provide meteorological support for forest fires prevention activities at both regional and national level during the Forest Fires Campaign. Duration and other characteristics of these Campaigns are yearly by the State Committee for the Operational Coordination (CECO). This Committee is responsible for forest fires matters at national level, integrating State Institutions from different Ministries, which have competences on this issue. In this context, fire-weather bulletins are currently issued for around 100 zones covering the whole country; it contains the predicted values for D+1 of the different meteorological parameters which are relevant to characterize fire danger conditions and fire-spreading such as 2m-temperature, wind, relative humidity and probability of thunderstorms. The predicted values of the probability of ignition as well as the final risk value (classified in four categories) based upon the Spanish Forest Fire Index (an adaptation of the NFDRS ignition component) are included. Based on these bulletins a daily national fire danger map is produced and sent to the National Civil Protection Authorities and National Forestry Services. A new meteorological information system, including a webpage specifically devoted to fire weather conditions, is now in an advanced stage of development and will substitute in a near future the current system. This system is being developed in the framework of an agreement between AEMET and the Spanish National Forestry Service. The Fire Danger maps will be included in the page as well as other different graphic products containing basic meteorological and climatological information of interest in fire prevention and fire suppression activities. The new Fire danger rating System is based on the automatic production of gridded analysis and short-term predictions of FWI; the analyses are produced through the use of the data from the Automatic Weather Station Network of AEMET. A bilinear interpolation scheme is employed to produce gridded values of the different meteorological variables that integrate the FWI. Short-term gridded forecasts of FWI are produced from the numerical outputs of the high resolution numerical weather model HIRLAM which is in operation at AEMET. Both FWI analyzed and predicted gridded values are transformed into FWI mean areal values for a set of predefined zones covering the whole country. The FWI has been calibrated making use of daily

data of forest fires occurrence and total burned area for each zone, to obtain the threshold values for a total of five danger classes. Since 1981,Croatian forest fire protection has been run by the Meteorological and Hydrological Service at coastal region, using the Canadian Forest Fire Weather Index System (CFFWIS), in the frame of the Governmental Programme of Open-air Fires Prevention. Canadian method is applied for the fire weather indices once a day, from June to September. Indices for the current date are based on real-time meteorological data, while predicted indices for the next day are based on the products of the ALADIN/HR limited area numerical weather prediction model. Both, actual and predicted fire weather indices are sent automatically each day to the Fire Department of the National Protection and Rescue Directorate, during the fire prevention season. Actual fire weather indices are public available on the web site of the Meteorological and Hydrological Service. In Italy, among other models RISICO has been extensively used in the framework of Foundation to the National Civil Protection Department. RISICO consists of a module very similar to the FFMC of FWI, modified to include characteristics of the vegetation cover of the Mediterranean. However, the only dynamic component of the system is the moisture of combustible material related to different types of vegetation cover. This is applied because in areas frequently dominated by fire, moisture can go from saturation values, following precipitations, to values below 10% in daily intervals. In Germany, the international Forest Fire Danger Index M-68 is used to forecast the forest fire danger situation. This method determines or predicts the actual forest fire danger and is used by the German Meteorological Service to provide country. The operational calculation is carried out during the forest fire season from February/March until October and is based on official climate data from selected territorial weather stations belonging to the German Meteorological Service. Country wide forest fire danger forecasts for the current day and following four days are published online by the German Meteorological Service as tables and maps on their "agro-weather page". Every day at 2pm, a valid and officially binding Forest Fire Danger Index for individual forest fire regions is calculated for the next day, as well as a prognosis for the following four days. The regional hazard situation is categorized by the German Meteorological Service into five forest fire danger classifications according to the international standard from 1 "very low" to 5 "very high". The next morning another Forest Fire Danger Index is made for the current day with any amendments due to weather conditions. This enables a flexible

organizational response to changed meteorological conditions.The Forest Fire Danger Index is used to inform the public via the usual media channels and by forest and fire fighting authorities as well as rescue and emergency services to implement their forest fire prevention measures in a timely manner. Furthermore, forest workers can be better prepared and the potential danger of recreational activities in forests can be more realistically estimated. The German states of Mecklenburg-Western Pomerania and Brandenburg classify their state wide forecasts based on the regionally determined Forest Fire Danger Index not according to the international standard but according to the traditionally used M-68 Forest Fire Danger Scale on a scale from 0 to 4. The actual Forest Fire Danger Scale is published on the homepages of the Mecklenburg-Western Pomerania and Brandenburg state forest enterprises. Furthermore, the actual values are available at all necessary locations (e.g. rural district and municipalities centres, German Rail Agency, state utilities etc.) as well as published by the media. Other countries: Ireland, and so on 3. Impacts of wild land fires Lightning is the primary cause of natural fire. Globally, however, human activity causes more wildland fire (Whitlock, C., 2004. Forests, fires and climate. Nature 432, 28–29.). Historical evidence suggests that the deterioration of forests did not begin until about 1000 years ago when the forests covered about 34 percent of the land (FAO, 2012; in state of world’s forest). But with the onset of industrialization and demand for land and timber, forest cover declined across scattered parts of Europe, Central America, China and India. Two centuries ago, further decline left parts of Europe and China bare, while only a century ago in the wake of the Industrial Revolution, eastern North American woodlands began shrinking. Nevertheless, almost 32 per cent of the total land mass remained forested. Of the tropical countries, Africa and South America were the continents with the largest annual loss of green forest between 2010 and 2015, with 2.8 and 2 million hectares lost, respectively. But deforestation has shown

reduction trends over the last five years because areas of environmental preservation and ecological planning. However, over the last 50 years, this loss of forested land accelerated. Vast tracts of forest vanished from virtually every continent. Net loss decreased globally between 2000 and 2010 but South America and Africa continued to show very high losses of 4 million and 3.4 million hectares per year, respectively (GFRA report, 2010). During the same period, Oceania reported significant net loss increases. Only Europe and China showed net gains in forested area for the period. It has been estimated that almost 900 million hectares of forest land have been lost from anthropogenic causes since man began farming. (source?)[1] In the wide-ranging forestry field, there are several areas in which agrometeorological applications can be used, including: fire behavior/danger, fire management, prescribed burning and fire effects, climate change, smoke management and air quality, and forest health and productivity. Fire is one tool that forestry managers use for the sustainable management of forest resources. One of the main concerns in forestry is fire’s potential, behavior, management and output. Chemical reactions of the gases released by fire leads to an increase in atmospheric ozone and the deposition of acidic compounds downwind from fires, which in turn can affect the physiology of plants and ecosystems in these areas. Global Scenario The overall impact of large scale deforestation has been very devastating, while on one hand the indigenous people in the tropical forests have faced poverty and alienation due to diminishing supplies of forest products and farm yields, on the otherit caused immense loss to environment and ecology. There have been profound ecological effects of forest loss, as demonstrated by the

Table 1- Annual change in forest area by region (area in sq km)

Fig: 1 Change in forest cover during 1990-2010 (area in sq km) source GFRA-2010 frequent droughts and floods, release of heat trapping temperatures, advent of new pests into cropped lands, much sedimentation in river beds and hydroelectric reservoirs, and loss of productive fisheries. The decline in forests along with other adverse effects also threatened the genetic diversity of the world’s plants and animals. The World Conservation Union calculated that about 12.5 per cent of the world’s 270,000 species of plants and about 75 per cent of the world’s mammals are threatened by forest decline (McNeely et al., 1990). The Commission concluded that “forests

can no longer be used in the same way as they have been in the past. Forest products and services

must be assured through new political choices and policy decisions that ensure the survival of

forests.” The impacts of forest fires can have global consequences: Forest fires also produce gaseous and particle emissions that impact the composition and functioning of the jetstream and the global

atmosphere, exacerbating climate change. Tropical forest destruction, through fire, could also spiral our weather systems in new and unpredictable directions. [AW2] Forest fires also cause widespread damage and are responsible for large emissions of carbon into the atmosphere. In some regions, there is evidence of increasing in number of fires affecting larger areas and burning with greater severity. Climate change and the lack of sustainable land use policies are contributing factors in this increase. The El Niño effect is also a factor, as it contributes to increases in the frequency of drought and lightning strikes. The majority of fires are caused by agricultural burning to convert forests to ranch or cropland, by careless burning of residues and waste to improve hunting. In many regions, the spread of urban development is also associated with increased incidence of fires (Bistinas et al 2013). As deforested areas expand, changes in the landscape and micro-climate occur: the forest floor dries up which, in turn, makes it more vulnerable to fire. Ecosystems can change as a result of fires. Fires in the understory of humid rainforests can cause tree mortality and canopy openness. Frequent fires and land over-use means forests are increasingly impoverished and, in some cases, such land becomes savannah (Mountinho and Schwartzmann 2005). In summary, degradation of forest related to fire has wide ranging adverse ecological, economic and social impacts, including: loss of valuable timber resources degradation of catchment areas loss of biodiversity and extinction of plants and animals loss of wildlife habitat and depletion of wildlife loss of natural regeneration and reduction in forest cover global warming loss of carbon sink resource and increase in percentage of CO2 in atmosphere change in the microclimate of the area with unhealthy living conditions soil erosion affecting productivity of soils and production ozone layer depletion health problems leading to diseases loss of livelihood for tribal people and the rural poor, as approximately 300 million people are directly dependent upon collection of non-timber forest products from forest areas for their livelihood.

4. Indicators of fire danger

a. Agro met (Arif, 2012 b)

The weather elements needed for fire weather Index calculations are those that influence (i) the situation with which fires can be started, (ii) the rate of spread and difficulty of control of fires and (iii) the effects of fire on the environment. The weather main elements are:

Precipitation Precipitation is one of the elements of weather used to predict fire occurrence, because of its direct relationships with fuel moisture content within wildlands or forest stands.

Temperature Although there may be little or no rain for a fairly long period, other climatic factors like high humidity and low temperatures may delay or prevent fine fuel ignition. After the effects of rain have been overcome, temperature and relative humidity and wind speed have direct effects on the moisture content of the fine fuel of plant materials. The drying factor is required for the prediction of a fire danger index. Temperature affects the evapotranspiration process it therefore speeds up the rate at which dry combustible plant matter is made available for ignition.

Relative Humidity Relative Humidity has been used for long periods in countries to give a quick assessment of the degree of fire danger. Air relative humidity correlated at noon has influence on the state of dryness of available combustible plant matter. In addition to temperature and wind speed relative humidity has influences on the fire weather.

Wind Speed Fire weather index is also influenced by wind speed by affecting the rate of drying of the combustible fuel and the rate of spread of the fire, which is also influenced by the moisture

content of the fire susceptible fuels. It has to be noticed that the wind influence in fire activity is more related to fire spread instead of fire initiation. b. Pest and disease Weather is closely related to the outcome and development of pests and disease of forest trees (Strand 2000). It is important to be informed on the frequency of favourable conditions for the development of important pests and diseases, as well as of the periods suitable for successful control practices, such as spraying. Basic weather data used to interpret the forecasting of pests and disease in silvicultural systems are also used in decision making, regarding its control and protection. Forest insects often have impacts that are obvious and easy to track, making them useful indicators of forest health. While these natural disturbances are a normal part of healthy forest ecosystems, they can also affect productivity in commercial forests and have a potentially negative impact on environmental values associated with forested landscapes. In 2014, 20.3 million hectares (ha) of forest have been damaged by insects. This is virtually unchanged from the total in 2013. While the area damaged by some insects has decreased, the area damaged by other insects has increased. Temperate forests accounted for the largest area of forest reported damaged by insect pests, 69.6 million hectares (Table 2). Recent outbreaks of bark beetles in North America (Hicke et al., 2012; Walton, 2013) appear to have been the main contributor to temperate forest damage. Insect pests were the leading cause of forest disturbance other than worldwide. In North America, they are the leading cause of forest disturbance.

Table 2: Forest area affected by other disturbances by climatic domain as reported to FRA 2015

North America reported the largest area of forest disturbed by insect pests, 57 million hectares (Table 3), again demonstrating the continuing significance of damage by bark beetles (Walton, 2013). African countries reported over 9 million hectares of forest damaged by insect pests. Severe weather events were the leading cause of reported forest disturbance other than fire in Asia. Nearly 18 million hectares of forest were damaged by severe weather in this region. North and Central America reported over 13 million hectares of forest damaged by severe weather. Diseases affected a relatively small extent of forests in all geographic regions. Asia reported over 5 million hectares of forest affected by diseases, and Europe reported just less than 5 million hectares affected. Dale et al. (2001) found that in the temperate forests of North America insect pests and diseases affected almost 50 times as much forest as burning annually. Logan et al. (2003) corroborated the large-scale impacts of insect pests and diseases on forest land in North America, and indicated that most global climate change scenarios favor the increased incidence of outbreaks in temperate forests in the future.

Table 3: Forest area affected by other disturbances by region as reported to FRA 2015 Since peaking in 2007, the area affected by the mountain pine beetle in British Columbia continued to decline in 2014, shrinking by 765,000 ha to 2.2 million ha. A spruce beetle infestation in northern British Columbia increased significantly in 2014 to 290,000 ha and has the potential for further expansion. However, it is still quite small compared to current or past areas affected by mountain pine beetle. Forest area affected by mountain pine beetle in British Columbia, 2004-2014

The forest tent caterpillar outbreak that expanded so rapidly in 2013 continues to affect nearly 6 million ha of predominantly aspen forest in central and western Canada. This defoliator exhibits classic population cycles 8 to 11 years in length, with the last maximum occurring in 2006. Populations in Alberta appear to have peaked in 2013 and were on the decline in 2014, while the amount of area defoliated to the east and west was still increasing, particularly in Manitoba and Ontario. The eastern spruce budworm outbreak in Quebec continues to increase, with 3.4 million ha undergoing moderate to severe defoliation in 2014. The abundant foliage of the host trees for spruce budworm and forest tent caterpillars sustains population increases, and the resulting defoliation decreases tree growth. If defoliation is severe or persists for several years, tree mortality can occur. Areas near the origin of the spruce budworm outbreak have now experienced seven to eight years of annual defoliation, and significant mortality is beginning to occur. Forest area containing defoliated trees for three insects in Canada, 2004-2014

The mountain pine beetle outbreak in British Columbia seems to have run its course and will likely continue its decline to historical background levels, in part because the host tree species (lodgepole pine) has been largely decimated. Populations are persisting in Alberta, but it remains unclear how far east and north the beetles will spread. The spruce budworm outbreak in eastern Canada is expected to continue increasing in area. If recent trends continue, the

outbreak will have encompassed 6 million ha in 2015 and is predicted to cover 10 million ha in 2016. Tree mortality in the core areas will also likely increase. While the area defoliated by forest tent caterpillar in Alberta will likely decline, simultaneous growth in Manitoba and Ontario could substantially increase the total area defoliated. Previous outbreaks in Ontario have involved approximately 15 million ha, and Manitoba experienced more than 10 million ha of defoliation in 1975–1976. Although short-lived tent caterpillar outbreaks rarely result in much tree mortality, potential interactions with regional droughts could exacerbate the impact of this species. By consuming trees and other plant material, forest insects and micro-organisms contribute to healthy change and regeneration in forest ecosystems. They help renew forests by removing old or otherwise susceptible trees, recycling nutrients and providing new habitat and food for wildlife. However, it’s not for their ecological benefits that forest insects and diseases sometimes make national news. When infestations are so severe they destroy or damage large areas of commercially valuable forest, or infest Canadian forest products bound for export, then insects and diseases—whether native or alien—become “pests.” Mountain pine beetle, spruce budworm, gypsy moth and Dutch elm disease are all examples of well-known forest pests that have led to significant losses in value of Canadian forests. The challenge for forest resource managers is therefore two-fold. First is to assess the risks posed by potential and actual outbreaks and spread. Second is to apply risk-based decision-making to manage forest ecosystems in a way that minimizes the negative impacts of outbreaks and maximizes the positive impacts. Positive impacts

Forest insects and diseases:

• help renew forests by removing old, weakened or otherwise vulnerable trees • help in soil formation by breaking down dead trees and other plant material and recycling the nutrients • provide new habitat and food for wildlife • pollination

Negative impacts

Forest insects and diseases:

• cause radial and height growth loss, volume loss, dieback and deformity • through that damage, can kill individual trees or entire forests • through widespread killing of existing forests, can result in the displacement of existing tree species

Forest pest control Pest management programs involving pest and disease pests aim to decrease damages caused to forests. According to Liebhold (2012) forest pest management must take into account among other criterias, the human dimension and the silvicultural practices. Klapwijk and al. (2016) have evaluated three alternative management methods affecting the natural control of forest inscts by studying the effect of forest management o natural enemy pressure. These alternative methods are based on (1) short rotation forestry (2) mixed forests (3) continuous cover forestry.

Fire danger rating The Fire Danger Rating System (FDRS) is a forest/vegetation fire monitoring system that provides information to support fire management. FDRS products are used as a guide to predict fire behaviour, with the objective to help stakeholders make informed decisions on fire mitigation and smoke haze pollution. FDRS uses measured meteorological variables such as temperature, relative humidity, rainfall and wind speed collected from meteorological stations in the Southeast Asia region. Fire danger rating systems produce qualitative and/or numerical indices of fire potential that can be used for guides in a variety of fire management activities including early warning of fire threat. Different systems of widely varying complexity have been developed throughout the world which reflect both the severity of the fire climate and the needs of fire management. The simplest systems use only temperature and relative humidity to provide an index of the potential for fire starts (e.g. see the Angstrom index, cf. Chandler et al., 1983). Fire danger rating systems of intermediate complexity combine measures of drought and weather as applied to a standard fuel type to predict the speed of a fire or its difficulty of suppression (e.g., McArthur 1966, 1967; Sneeuwjagt and Peet, 1985). The most complex systems have been

developed in Canada (Forestry Canada Fire Danger Group, 1992) and the United States (Deeming et al., 1978) which combine measures of fuel, topography, weather and risk of ignition (both lightning and human-caused) to provide indices of fire occurrence or fire behaviour which can be used either separately or combined to produce a single index of fire load. Fire danger rating on the base of meteorological data is more precise when it is expressed on the base of weather forecast of the previous evening or on the very same day. Long-term forecasts are also possible. There are many methods that give either a numerical index directly or an alarm level, which increases with the danger conditions. Generally, the numerical index is also translated, for practical reasons, into a danger scale with 3 to 6 levels.

Fire Danger Rating Index Fire danger is the resultant of factors affecting the inception, spread and difficulty of control of fires and the damage they cause' (Chandler et al. 1983). If any of these factors are absent, then there is no fire danger (Cheney and Gould1995). Various factors of fuels, weather, topography and risk are combined to assess the daily fire potential on an area. Fire Danger is usually expressed in numeric or adjective terms. Fire danger indices are an important tool for fire and land managers. Effective Forest-fire management is based on sound knowledge of the potential for ignition, behaviour, difficulty of control, and impact of fire in a given situation. Forest-fire danger-rating systems provide a framework for organizing and integrating scientific knowledge and operational experience, and they are a cornerstone of modern fire management (S.W. Taylor et al 2001.) Fire danger rating systems are used by fire and land management agencies to determine levels of preparedness, to issue public warnings, and to provide an appropriate scale for management, research, and law for fire related matters (Cheney and Gould 1995). All these systems integrate weather variables to assess fire danger, calculated as a numerical index. A variety of fire danger ratings are used around the world, including the McArthur Forest Fire Danger Index (FFDI, McArthur 1967), used in the eastern parts of Australia, the Forest Fire Behavior Tables (FFBT, Sneeuwjagt and Peet 1998), developed for use in Western Australia, the Fire Weather Index (FWI, van Wagner 1987) used in Canada, the National Fire Danger Rating System (Deeming et al. 1977) used in the USA, the Nestrov Fire Danger Index System used in the Russia and the PFI which is currently used in Brazil (Justino et al 2010).

Nesterov Index The most widely used fire danger rating system in Russia is a relatively simple ignition index called the Nesterov Index (NI). It provides a general index of ignition potential (Fosberg et al 1996; Stocks et al 1996). The daily weather requirements for this index are: • Dry-Bulb Temperature • Dew-Point Temperature (calculated from relative humidity and temperature) • Precipitation. The index is initialized at zero and is determined by taking the difference between the daily air (dry bulb) and dew point temperatures, multiplying this difference by the air temperature and then cumulatively summing up the values over the number of days since 3mm of precipitation has fallen. Once 3 mm or more of daily precipitation has fallen the index returns to zero (Buchholz and Weidemann 2000). The index is expressed mathematical (from Buchholz and Weidemann 2000) by W NI = Σ (Ti – Di) * Ti i=1 where NI = Nesterov Index W = number of days since 3mm of rainfall T = Temperature (oC) D = Dewpoint temperature (oC) According to Fosberg et al (1996) the index scale is : • Less than 300 – Low Ignition Potential • 300 to 1000 – Moderate Ignition Potential • 1000 to 4000 - High Ignition Potential • Greater than 4000 – Extreme Ignition Potential According to Pyne et al (1996), the NI is used to schedule daily fire operations in the Russian Federation. From Stocks and Lynham (1996), the NI has been calibrated by various Russian researchers for regional conditions and a logarithmic version of the index was produced to decrease the disparity between the regional scales. The KeetchByram Drought Index

The Keetch-Byram Drought Index (KBDI) (Keetch and Byram 1968) expresses drought as an index on a scale from 0 to 2000, based on the moisture content of the soil. Zero is the point of no moisture deficiency and 2000 is the maximum drought level possible. The major advantage of the KBDI is that only three variables are required to compute the Drought Index: 1. Mean annual rainfall of a station, 2. Today's maximum temperature, and 3. Todays rainfall The KBDI itself of a given day is the sum of yesterdays rating reduced by 10 times rainfall added to today's Drought Factor (DF). The Fire Danger, which is expressed through the KBDI, can range from 0 to 2000. To start calculating the KBDI for a given region, one has to go back to a period when the KBDI dropped to "0", meaning the soil was saturated by water. Keetch and Byram (1968) indicate that point as the day after a rainy period with 150 to 200 mm rainfall within one week. The index was originally divided into three fire danger classes, for practical reasons and with the focus on the potential end user concessionaires the fire danger rating class "extreme" will be added to the classes:

Tab.1. Fire Danger Rating Classes Numeric scale Adjective scale 0-999 Low 1000-1499 Moderate 1500-1750 High 1750-2000 Extreme The KBDI was incorporated into the United States National Fire-Danger Rating System (NFDRS) in 1988 to modify the amount of dead fuel available for consumption (Burgan 1988). A modified KBDI has been used for a Fire Danger Rating System in East Kalimantan on the island of Boreno in Indonesia for five years on an operational basis (Buchholz and Weidemann 2000), and for fire management in Sabah, Malaysia (Solibun and Lagan 1998). The KBDI is also used in the calculations of the Australian McArthur Fire danger meters for grasslands and forests.

Angstron Index The Anstronindex has beendeveloped in Sweden, it is based primarily on temperature and relative humidity, both measured daily at 13:00.It is not a cumulative index and can be computed as follows: B = 0, 5:00-0.1 (T-27) Where, B = Angstron index H = relative air humidity in% T = air temperature in° C Whenever the value of "B" is less than 2.5 there will be risk of fire, that is, the weather conditions of the day will be conducive to the occurrence of fires. Telicyn Logarithmic Index

Basic equation: I = ∑ log (ti – ri),i=1,n I = Telicyn index t = Air temperature °Cat 13:00 and15:00 h, r = Dewpoint temperature °C In case of precipitation higher than 2.5 mm the index should be computed from next day, or when the rain stops. For rainy days the index is zero (0). McArthur Fire Danger Meters In Australia, McArthur (1966, 1967) developed a widely used fire danger and behavior index. It is based on over 800 experimental fires in a wide variety of fuel types, including eucalypt. The index is calculated by using fire danger meters for forests and grasslands. The Commonwealth Scientific and Industrial Research Organization (CSIRO) has updated these meters into the following: Grassland Fire Danger Meter (modified McArthur); Grassland Fire Spread Meter; Fire Spread Meter for Northern Australia; and Forest Fire Danger Meter (CSIRO 2000). These meters have been programmed into computer programs complete with help files and are compatible with Windows 95 & 98, Windows NT, and Windows 2000 (CSIRO 2000). CSIRO based the forest fire danger index (FFDI) equations from Noble et al (1980). It should be pointed out that the equations used in the McArthur meters are not based on the original data, but represent a reasonable fit to the meters since many of the original data was not available

(Noble et al 1980). The forest fire danger index is based on the following equations from (Noble et al1980): D = 0.191 * (I + 104) * (N + 1)1.5 / (3.52 * (N + 1) 1.5 + P – 1) FFDI = 2.0 * exp(-0.450 + 0.987 * ln(D) – 0.0345 * H + 0.0338 * T + 0.0234 * V) Where, H = relative humidity (percent) T = air temperature (degrees C) V = average wind velocity at 10 m (km/hour) P = Amount of precipitation (mm) D = Drought factor I = Keetch-Byram Drought Index (mm equivalents) N = Number days since rain From McArthur (1968), the FFDI is based on a scale from 0-100, where a rating of 1 represents a fire that will not burn, or that will burn so slowly that control is very easy, and 100 represents a fire that will burn so fast and so hot that control is virtually impossible. The meter also can be used to determine the rate of forward spread of fire on level and sloping ground, flame height, and the distance of spot from flame front (Noble et al 1980). The advantage of these meters is that they can be used in the field applying real-time or hourly weather data. The current Grassland Fire Danger Meter uses some of the same relationships as McArthur’s, but omits the rate of spread and makes it into a separate CSIRO Grassland Fire Spread Meter. Also, the index value is open-ended and can exceed 100 (CSIRO 2000). The Grassland Fire Danger Meter uses only one fuel variable (the percent of curing) and is then combined with temperature, relative humidity, and wind speed. This creates an index of the degree of difficulty of suppressing fire in a standard average pasture. There are five fire danger rating classes: low, moderate, high, very high, and extreme (CSIRO 2000). The Grassland Fire Spread meter was developed because it was found that the conditions that affect relative fire danger are not applicable to fire spread in the same way (Cheney and Sullivan 1997). This meter predicts a fire’s potential rate of forward spread over periods of 15-20 minutes across continuous grassland. The slope of the ground is not included (CSIRO 2000; Cheney and Sullivan 1997). The Fire Spread Meter for Northern Australia was designed to predict the rate of spread of fires in open grassland, woodland, and open forest with a grass understory. It is not suitable for predicting fire spread in tall, closed forest or a tall forest with substantial shrub and litter. However, it is suitable to predict fire spread for grass fuels that are largely undisturbed or only grazed lightly (CSIRO 2000).

Hall and Gwalema (1985) derived a drought index and fire danger rating for Tanzania based on the methods of McArthur Fire Danger Meter. By using 12-years of data and the drought index, they concluded that controlled burning is feasible in Tanzania after the rainy season. United States National Fire-Danger Rating System (NFDRS) The NFDRS is used by the United States Forest Service for the past thirty years. The current system was developed in 1972 (Deeming et al 1972) and revised in 1978 to include a better response to short-term drought, increased seasonal sensitivity, additional fuel models and other modifications (Bradshaw et al 1984). The NFDRS was further revised in 1988 with the addition of Keetch-Byram Drought Index to account for weather and climatic conditions in the eastern US (Burgan 1988). The following description of the NFDRS was based on Bradshaw et al (1984), Burgan (1988), and Pyne et al (1996). There are eleven weather parameters that drive the various models of the NFDRS. Parameters observed at the observation time of mid to early afternoon that reflect the worst case scenario include: • Air Temperature (F) • Relative humidity (%) • State of the weather (cloud cover and type of precipitation) • Ten-minute average 20-ft windspeed (mph) • Fuel Stick Moisture (%) – can be measured or estimated Daily parameters for the 24-hour period ending at the observation time are: • Duration of Precipitation (hours) • Amounts of Precipitation (inches) • Maximum and Minimum Air Temperature (F) • Maximum and Minimum Relative Humidity (%) NFDRS Fire Behavior Components

• Spread Component (SC) – Spread rate, emphasizes 1-hour and 10-hour fuelmoisture content • Energy Release Component (ERC) - Energy Release, emphasizes 100-hour and1000-hour fuel moisture content • Burning Index (BI) - flame length or fireline intensity In the NFDRS, 20 fuel models representing the following vegetation and fuel types can be selected: annual grass and herbs; perennial grass; sawgrass; open timber/grass; southern rough; sagebrush/grass; mature chaparral; intermediate brush; winter and summer

hardwoods; southern plantation; long- and short needled confier; heavy, medium, and light slash; pocosin; Alaskan tundra; and black spruce (Andrews and Bradshaw 1997; Bradshaw et al 1984). These fuels models are derived by using a combination of dead fuel moisture (1-hour, 10-hour, 100-hour, and 1000-hour) and live moisture (woody and grass) models (Burgan 1988). The moisture content of live fuel and four size classes of dead fuel are calculated from weather data and moisture values. Dead fuel is determined by the diameter of the wood or timelag. The moisture content of the 10-hour fuel is based on observed stick weight. The 1000-hour moisture content is used to calculate the live fuel moisture. In Figure 2, the wedges below the dead fuel moisture boxes indicate the mathematical weighting of the dead fuel moisture content on the various indices. Note that the 1000-hour fuel has no influence on the SC. Windspeed influences the Spread Component (SC), but not the Energy Release Component (ERC). The NFDRS also has several fire control components: the Man-caused Fire Occurrence Index (MCOI); the Lightning-caused Fire Occurrence Index (LOI); and the Fire Load Index (FLI). However, these components are little used and are designed to be replaced by the lightning ignition potential and lightning on fire danger maps (Latham et al 1997). Presently, lightning ignition efficiency algorithm (Latham and Schlieter1989) is being used by the U.S. Wildland Fire Assessment System (WFAS 2000). Canadian Wildland Fire Information System This rating system has been under development since 1968 by the Canadian Forest Service and consists of two modules: The Canadian Forest Fire Weather Index (FWI) System and the Canadian Forest Fire Behavior Prediction System (FBP) System (Stocks et al 1989). The FWI System has been used in Canada since 1970 and consists of six detailed moisture and fire behavior codes that account for the effects of fuel moisture and wind on fire behavior in a standardized fuel type (mature pine stand). The following description of the CFFDRS was based from Stocks et al (1989), Pyne et al (1996), and Van Wagner (1990).The FBP system will be discussed in the following section on Fire Behavior. The FWI system components need the following daily (noon local standard time) weather parameter input: • Dry-bulb Temperature • Relative humidity (%) • 10- meter Wind Speed • 24 hour cumulative precipitation

FWI System Moisture Components • Fine Fuel Moisture Code (FFMC) – Numerical rating of the fuel moisture content offine surface litter and is an indicator of the relative ease of ignition and flammability of fine fuel. Timelag of 2/3 of a day. • Duff Moisture Code (DMC) - Numerical rating of the average moisture content of loosely compacted organic layers of moderate depth. Timelag of 12 days. • Drought Code (DC) - Rates the fuel moisture content of deep, compact organic matter. Time lag of 52 days. Duff is the humus layer on the forest floor consisting of decomposing litter (needles, leaves, and other dead vegetation) and mineral soil. Surface fuel includes standing trees up to2 meters; herbaceous vegetation (grasses); forest floor litter; and fallen woody material. The three moisture components are bookkeeping systems that add moisture for rain and subtract moisture for drying. Since the three codes have different time lags, rates, and rain amounts required for saturation, any one of them may be high or low in relation to the others. FWI System Fire Behavior Components • Initial Spread Index (ISI) – represents the rate of fire spread • Buildup Index (BUI) – represents the fuel available for combustion • Fire Weather Index (FWI) – represents the frontal fire intensity The FWI, a combination of the ISI and BUI, represents a relative measure of the potential intensity of a single spreading fire with a standard fuel source on level terrain. The FWI systems components have different interpretations in different fuel types, since the system was developed to represent fire behavior in a generalized, standard fuel type. Each of the components of the FWI system needs to be examined for proper interpretation of past and present fire effects on fuel flammability; each component conveys direct information about certain aspects of wildland fire potential. Oklahoma Fire Danger Model (U.S.) With the Oklahoma Fire Danger Model, Carlson and Engle (1998) provided a good overview of the next generation of indices that integrate satellite data with a high-density weather station network to produce a fire danger model based on the US NFDRS. The model calculates live fuel moisture from relative and visual greenness values based on the Normalized Difference

Vegetative Index (NDVI) from weekly 1-kilometer resolution Advanced Very High Resolution Radiometer (AVHRR) satellite data. Every kilometer pixel ofland has been assigned to a NFDRS fuel model representative of native Oklahoma vegetation.The weekly satellite data are integrated with calculations based from a high-density weather station network (over 100 stations) throughout the state. Hourly NFDRS fire danger indices with a 1-km resolution are produced including the Spread Component (SC); Energy Release Component (ERC); Burning Index (BI); Ignition Component (IC); and dead fuel moisture (FM).From this a daily map of the Keetch-Byram Drought Index (KBDI) is created.

5. Indicators of wild land fire danger on sensitive ecosystems

5.1 Implications for land management Early warning systems for fire and smoke management for local, regional, and global application require early warning information at various levels. Information on current weather and vegetation dryness conditions provides the starting point of any predictive assessment. From this information the probability of risk of wildfire starts and prediction of the possibility of current fire behaviour and fire impacts can be derived. Short- to long-range fire weather forecasts allow the assessment of fire risk and severity within the forecasting period. Advanced space borne remote sensing technologies allow fire weather forecasts and vegetation dryness assessment covering large areas (local to global), at economic levels and with accuracy which otherwise cannot be met by ground-based collection and dissemination of information. Remote sensing provides also capabilities for detecting new wildfire starts, monitoring ongoing active wildfires, and, in conjunction with fire-weather forecasts, providing an early warning tool for escalating, extreme wildfire events.

a. Forest Fire Management Although fire has been the primary agent of deforestation, yet as a natural process it serves an important function in maintaining the health of certain ecosystems. The traditional view of fire as a destructive agent requiring immediate suppression has given way to the view that fire can and should be used to meet land management goals under specific ecological conditions. For

decades, controlled burning has been used as a genuine forest management measure in the developed countries. In western countries, especially Britain, U.S.A., Canada etc. controlled fires are burnt at intervals of 10-12 years to maintain uniform growth. In South and Southeast Asia, including India, “Slash and Burn” method of farming is used by the tribals of hilly areas, in which they cut down and burn small areas of the forest and use the cleared land for cultivation. This method of burning offers them not only the cheapest means to clear the forest, but also supplies free fertilizers in the form of ash from the burnt vegetation on limited scales. Over the past few decades, ecologists, foresters, and conservationists have reconsidered the wisdom of the all-out effort to eliminate fire from our landscape. Without the ongoing occurrence of fire and other human-induced disturbances the vegetation and landscape may change quite rapidly and populations of valued species that depend on the open conditions and specific structures created by fire may decline. Although fire suppression, especially near dwellings, remains a major concern in many of our forest areas, we are beginning to use our knowledge of fire behavior under controlled conditions to manage some New England landscape purposefully with fire. To introduce a well-coordinated and integrated fire-management program includes the following components: • Prevention of human-caused fires through education and environmental modification. It will include silvicultural activities, engineering works, people participation, and education and enforcement. It is proposed that more emphasis be given to people participation through Joint Forest Fire Management for fire prevention. • Prompt detection of fires through a well coordinated network of observation points, efficient ground patrolling, and communication networks. Remote sensing technology is to be given due importance in fire detection. For successful fire management and administration, a National Fire Danger Rating System (NFDRS) and Fire Forecasting System are to be developed.

b. Prevention of Forest Fires

• To master technology of prevention, monitoring, alertness, readiness, early warning system, early detection, early fire extinguishing, and postfire handling. • To utilize all potential resources to overcome forest fire nationally supported by appropriate software and hardware. • To improve coordination and cooperation nationally, regionally and internationally.

• training of fire fighters and other personnel; • fire-prevention work in the forests (construction, reconstruction of the forest roads, maintenance of observation towers, building of preventive fire lines; creation of fire prevention water reservoirs, etc.); • providing acquisitions of fire-fighting equipment and engines, communication systems, etc.; • providing fire management publicity for use in the mass media; • preparations of fire management plans of forest units and regional resources; • Preparations of interregional and interagency agreements on fire management.

Forest fires can and do occur naturally and play a number of important roles in ecosystems, and are commonly referred to as “wildfires”. These fires can start through natural disturbances such as lightning strikes. Many types of forests have evolved to utilize fire disturbances to maintain ecosystem health and to regenerate. For example, many tree species actually require fire to germinate their seeds, and forest fires return important nutrients to the forest soil that was previously being stored in biomass. Wildfires help to clear out dead wood and other materials that would otherwise have taken much longer to break down and provide soil nutrition for the next generation of trees and plants living in that forest. This process helps to keep a forest ecosystem healthy. Burned forests serve as important habitat for many species, such as the Black-backed Woodpecker, Picoides arcticus, that is specialized to live and thrive in forests that have experienced severe burning. After a forest fire occurs, a process called ecological succession takes place, where the ecosystem goes through a series of changes and eventually develops into a mature forest again. Typically, the first species that recolonize a site after a fire are pioneer herbaceous species, such as fast-growing grasses and weeds. Next, slower-growing and taller types of plants come in. Later, early successional tree species, such as small pine trees come in, and then larger pine trees become established. Eventually, long-lived hardwood tree species like oak and hickory become established, the forest canopy closes overhead, and you have a mature climax forest. This process can take a long time, ranging from several decades to even hundreds of years to move from early pioneer to climax stage habitat¹. At each

stage of succession, the changing forest provides habitat for many types of species, including plants, animals, and birds. At one time in the not-too-distant past, it was common forest management policy to suppress and control forest fires as much as possible due a general lack of understanding of fire’s important role in the ecological health of forest ecosystems. When forest fires are regularly suppressed, large amounts of dead biomass accumulates on the forest floor, increasing the risk for more frequent and much more intense wildfires than otherwise when they finally do occur. This puts human communities at an increased risk for damage from these more intense fires. Also, the trees that do grow in such forests are much more densely packed than they would otherwise have been. With the current understanding of forest fires as a natural and healthy part of forest ecosystem ecology, forest management efforts typically are now focusing on a combination of containment where necessary to protect human communities, as well as periodic fires for the sustainability and health of forest ecosystems. 5.2 Communication/delivery systems An effective high quality communications network allows a good communication and coordination between the various actors of fire prevention and suppression. If well organized, it allows reducing delay times until initial attack. The transmissions system for information generally used for forest fire protection is the radio operator system. However, a particular telephone network is sometimes also used. In any transmission network, the quality of the procedures and their precise definition are essential, so that only information circulates that is necessary, clear, precise, and concise. Radio

Choice of the Frequencies : The most used frequencies vary from 30 MHz (low frequencies) to 3000 MHz (very high frequencies). The waves having the lowest frequencies have the best direct carrying distance but they are less easily reflected and less penetrating. Distribution of the stations

The stationary radios will be located at the level of headquarters, coordination centers or communication units having the goal to centralize various calls. With regard to mobile or portable stations, the personnel ensuring the surveillance (lookout towers, patrols...) must be equipped in priority. When the fire danger is high, it is advised, in order to avoid the saturation of the network or confusion, to set up specialized cells tasked to handle the communications coming from the personnel ensuring the surveillance. If the equipment is insufficient or defective to ensure a total cover of the territory, it is necessary to resort to indirect communications (from station to station), but this requires much more time.

Telephone

Telephone Network The national telephone network can be used to transmit information, but its use for the surveillance remains generally limited, because it can saturate very quickly in the event of significant fire risks. A specialized telephone network can supplement the radio operator network. E.g., in Cyprus, the forest service has its own telephone network with a manual switching standard, effective and free of charge except for maintenance costs. This network connects the various forest units, divisions to the lookout towers. Moreover, phone terminals connected to the forest office are at the disposal of the public in the forest, and particularly close to picnic areas. Mobile Phones Mobile phones have been increasingly utilized by the public in charge of forest fire management. However, it does not ensure a total coverage of the territory. Moreover, the operational standard can sometimes be saturated at the time of a fire occurrence (cases of Tunisia, Morocco, Mali, …). Call Free Number A call free number can be placed at the disposal of the public SMS through Mobile The near real time monitoring of forest fires involves dissemination of forest fire alerts through mobile SMS system.

Web based geographic information system

Remote sensing data processing for forest/land fires monitoring and mapping can be distributed to users through a web based geographic information system (WebGIS). This system is interactive, where users can select different types of information and date, zoom in/out locations, measure distance and provide markers. Users also have the option to print or download the information. 5.3. Future perspective

a. Global System Development and Implementation There are a number of satellite data and modeling enhancements to the Global EWS-Fire that are being explored. Advances in measuring spatial precipitation from space likely offer the single largest improvement to the accuracy of fire danger maps and could reduce or eliminate the need for spatial interpolation of precipitation from ground-based point sources. Remotely sensed fuel mapping is also being pursued to develop a global fuel type map, which would be a first step towards developing global fire behavior prediction models. Monitoring of live fuel moisture (Ceccato et al. 2003) can contribute to establishing fuel flammability and seasonal criteria that are important to fire behaviour models and monitoring/modeling of fuel consumption, fire spread rate, and carbon emissions. The use of remotely sensed fire radiative energy to estimate fuel consumption and carbon emissions is currently being studied (Wooster 2002, Wooster et al. 2003). Fuel consumption could also potentially be combined with satellite-monitored daily fire spread data to calculate fire intensity. b. Development of a Global Early Warning System for Wildland Fire Wildland fires burn several hundred million hectares of vegetation every year, and increased fire activity has been reported in many global regions. Many of these fires have had serious negative impacts on human safety, health, regional economies, global climate change, and ecosystems in non-fire-prone biomes. Worldwide fire suppression expenditures are rapidly increasing in an attempt to limit the impact of wildland fires. To mitigate fire-related problems and costs, forest and land management agencies, as well as land owners and communities, require an early warning system to identify critical periods of extreme fire danger in advance

of their potential occurrence. Early warning of these conditions allows fire managers to implement fire prevention, detection, and pre-suppression plans before fire problems begin. Fire danger rating is commonly used to provide early warning of the potential for serious wildfires based on daily weather data. Fire danger information is often enhanced with satellite data, such as hot spots for early fire detection, and with spectral data on land cover and fuel conditions. Normally, these systems provide a 4 to 6hour early warning of the highest fire danger for any particular day, that the weather data is supplied. However, by using forecasted weather data, as much as 2 weeks of early warning can be provided. c. Forest Fires and Climate Change Climate plays a vital role in determining fire patterns and intensity and, in turn, fire influences the climate system via the release of carbon. Forest fires and global warming have created dangerous relationship. The close linkage between high fire activity and inter annual and decadal-scale climate oscillations indicates that fire occurrence increases during the La Nina phase of the ENSO southern United States and Patagonia, Argentina, whereas a marked increase in fire activity occurs in tropical rainforests during EI Nino phases. Sedimentary charcoal records also show a strong link between climate and fire activity, with reduced fire in cold intervals and increased fire in warm intervals, regardless of whether humans were present. The changing weather pattern in one of the major factor is contributing to current increase in instances of forest fires. The main reason for this is change in overall increase in the temperature; change in precipitation pattern and moisture content in the atmosphere. Drier soil leads less evaporation and so the heat goes into higher temperatures, less recycled moisture in the atmosphere, and hence less rain during summer. This results into increased heat waves and thus increased risk of wildfires. The Climate change is affecting various climate related variables like – soil moisture content, vegetation density, affecting the fire season severity. Extended periods of above normal temperatures and below – normal rainfall are key factors that contribute to an active wildfire season. Forest fires as contributor global warming is not only the global warming that is affecting the forest fire, but is true in the reverse way also. The forest fires are contributing to global warming. As per an assessment based on scientific research, the combination of intentional and unintentional fires – by burning carbon-storing vegetation has contributing to 20% of all human caused greenhouse gas emission since the Industrial Revolution.

Not only are forest fires a significant source of carbon emitted into the atmosphere which

exacerbates climate change, but forests are an irreplaceable sink of carbon too. So when forests burn, there is a double negative effect on the climate because instead of actually absorbing carbon dioxide, the gas is emitted by the burning biomass. It must not be forgotten that forest fires themselves are a significant source of carbon emissions, which fuel climate change. Estimates vary but biomass burning is now recognized as a significant source of carbon dioxide generally considered by most authorities as being around 20% for both fires and land use change. One study in its early stages named World Fire Web being co-ordinated by the European Commission’s Global Vegetation Monitoring Unit even notes that fires may perhaps account for 40% of annual global greenhouse emissions in severe fire years. Of all burning from all types of vegetation (generating nearly 4000 million tonnes of carbon) tropical and boreal forest could release some 700 million tonnes of carbon in a bad fire year into the atmosphere. Other estimates exist: UNEP has calculated that from the forest fires in Kalimantan and Sumatra, an estimated 11 million tonnes of carbon dioxide was released, out of a total of 191 million tonnes from forests, agriculture and peat fires combined51. It has been estimated that the Amazon forest fires in 1998 could have been responsible for 10% of the net annual carbon emissions stemming from human activities worldwide that year.

If all the fires from tropical forests in 1998 were added up, one estimate is that they could have produced some 1 to 2 billion tonnes of carbon, which is equivalent to one third of the emissions from fossil fuel burning across the world. While fires accelerate climate change, some scientists argue that El Niño events give usa preview of what it will be like to live in a higher carbon world, with the possibility of El Niño becoming a violent and annual event. Indeed, modellers from the Max Planck Institute believe that the average climate in the 21st Century will become more like the El Niño conditions experienced in the last few years. Conclusion (MAYBE NOT NEEDED!) Forest fires cause a significant environmental damage while threatening human lives. In the last two decades, a substantial effort was made to build automatic detection tools that could assist Fire Management Systems (FMS). The three major trends are the use of satellite data, infrared/smoke scanners and local sensors (e.g. meteorological). Space-based information and technologies are one of the most powerful tools to support the full cycle of forest/land fire management suchas documenting fire prone areas, providing early warning, emergency response and postfire assessment. Bibliography (Reference materials) Bistinas I., Oom, D., Sa, A. C.L., Harrison, S. P., Prentice, I. C. and Pereira, J. M.C.

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