Long-term temporal changes in the occurrence of a high forest fire danger in Finland

Abstract. Climate variation and change influence several ecosystem components including forest fires. To examine long-term temporal variations of forest fire danger, a fire danger day (FDD) model was developed. Using mean temperature and total precipitation of the Finnish wildfire season (June–August), the model describes the climatological preconditions of fire occurrence and gives the number of fire danger days during the same time period. The performance of the model varied between different regions in Finland being best in south and west. In the study period 1908–2011, the year-to-year variation of FDD was large and no significant increasing or decreasing tendencies could be found. Negative slopes of linear regression lines for FDD could be explained by the simultaneous, mostly not significant increases in precipitation. Years with the largest wildfires did not stand out from the FDD time series. This indicates that intra-seasonal variations of FDD enable occurrence of large-scale fires, despite the whole season's fire danger is on an average level. Based on available monthly climate data, it is possible to estimate the general fire conditions of a summer. However, more detailed input data about weather conditions, land use, prevailing forestry conventions and socio-economical factors would be needed to gain more specific information about a season's fire risk.

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Temporal variations and change in forest fire danger in Europe for 1960–2012

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1477-2014 ◽

2014 ◽

Vol 14 (6) ◽

pp. 1477-1490 ◽

Cited By ~ 39

Author(s):

A. Venäläinen ◽

N. Korhonen ◽

O. Hyvärinen ◽

N. Koutsias ◽

F. Xystrakis ◽

...

Keyword(s):

Forest Fire ◽

Forest Fires ◽

National Level ◽

Temporal Variations ◽

Fire Danger ◽

Fire Season ◽

Fire Weather ◽

Fire Weather Index ◽

Current Season ◽

Forest Fire Danger

Abstract. Understanding how fire weather danger indices changed in the past and how such changes affected forest fire activity is important in a changing climate. We used the Canadian Fire Weather Index (FWI), calculated from two reanalysis data sets, ERA-40 and ERA Interim, to examine the temporal variation of forest fire danger in Europe in 1960–2012. Additionally, we used national forest fire statistics from Greece, Spain and Finland to examine the relationship between fire danger and fires. There is no obvious trend in fire danger for the time period covered by ERA-40 (1960–1999), whereas for the period 1980–2012 covered by ERA Interim, the mean FWI shows an increasing trend for southern and eastern Europe which is significant at the 99% confidence level. The cross correlations calculated at the national level in Greece, Spain and Finland between total area burned and mean FWI of the current season is of the order of 0.6, demonstrating the extent to which the current fire-season weather can explain forest fires. To summarize, fire risk is multifaceted, and while climate is a major determinant, other factors can contribute to it, either positively or negatively.

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Risk of large-scale fires in boreal forests of Finland under changing climate

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-239-2016 ◽

2016 ◽

Vol 16 (1) ◽

pp. 239-253 ◽

Cited By ~ 17

Author(s):

I. Lehtonen ◽

A. Venäläinen ◽

M. Kämäräinen ◽

H. Peltola ◽

H. Gregow

Keyword(s):

Climate Change ◽

Forest Fire ◽

Forest Fires ◽

Large Scale ◽

Fire Danger ◽

Short Period ◽

Projected Change ◽

Fire Activity ◽

Model Variability ◽

The Impact

Abstract. The target of this work was to assess the impact of projected climate change on forest-fire activity in Finland with special emphasis on large-scale fires. In addition, we were particularly interested to examine the inter-model variability of the projected change of fire danger. For this purpose, we utilized fire statistics covering the period 1996–2014 and consisting of almost 20 000 forest fires, as well as daily meteorological data from five global climate models under representative concentration pathway RCP4.5 and RCP8.5 scenarios. The model data were statistically downscaled onto a high-resolution grid using the quantile-mapping method before performing the analysis. In examining the relationship between weather and fire danger, we applied the Canadian fire weather index (FWI) system. Our results suggest that the number of large forest fires may double or even triple during the present century. This would increase the risk that some of the fires could develop into real conflagrations which have become almost extinct in Finland due to active and efficient fire suppression. However, the results reveal substantial inter-model variability in the rate of the projected increase of forest-fire danger, emphasizing the large uncertainty related to the climate change signal in fire activity. We moreover showed that the majority of large fires in Finland occur within a relatively short period in May and June due to human activities and that FWI correlates poorer with the fire activity during this time of year than later in summer when lightning is a more important cause of fires.

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Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

Remote Sensing ◽

10.3390/rs11182101 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2101 ◽

Cited By ~ 8

Author(s):

M. Ahmed ◽

Quazi Hassan ◽

Masoud Abdollahi ◽

Anil Gupta

Keyword(s):

Remote Sensing ◽

Forest Fire ◽

Forest Fires ◽

Land Surface ◽

Vegetation Index ◽

Significant Risk ◽

Fire Danger ◽

Area Of Interest ◽

Forest Fire Danger ◽

Very High

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

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Large-scale weather types, forest fire danger, and wildfire occurrence in the Alps

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2012.08.011 ◽

2013 ◽

Vol 168 ◽

pp. 15-25 ◽

Cited By ~ 27

Author(s):

Clemens Wastl ◽

Christian Schunk ◽

Marvin Lüpke ◽

Giampaolo Cocca ◽

Marco Conedera ◽

...

Keyword(s):

Forest Fire ◽

Large Scale ◽

Fire Danger ◽

Weather Types ◽

Wildfire Occurrence ◽

The Alps ◽

Forest Fire Danger

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Classification of Territory on Forest Fire Danger Level Using GIS and Remote Sensing

Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks - Advances in Environmental Engineering and Green Technologies ◽

10.4018/978-1-7998-1867-0.ch011 ◽

2020 ◽

pp. 258-267 ◽

Cited By ~ 1

Author(s):

Elena Petrovna Yankovich ◽

Ksenia S. Yankovich

Keyword(s):

Remote Sensing ◽

Forest Fire ◽

Forest Fires ◽

Vegetation Cover ◽

Remote Sensing Data ◽

Fire Danger ◽

Gis And Remote Sensing ◽

Forest Fuels ◽

Forest Fire Danger

The vegetation cover is the most important factor in forest fires, because it reflects the presence of forest fuels. The study of the variability of the vegetation cover, as well as observation of its condition, allows estimating the level of fire danger of the forest quarter. The work presents a geo-information system containing a set of tools to determine the level of fire danger of the forest quarter. The system is able to predict (determine the probability) and classify forest quarters according to the level of fire danger. The assessment of forest fire danger of Tomsk forestry of Tomsk region has been carried out. Fire probability maps of forest quarters were created based on remote sensing data and ArcGIS software.

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Peer review report 1 On “Large-scale weather types, forest fire danger, and wildfire occurrence in the Alps”

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2015.07.109 ◽

2015 ◽

Vol 201 ◽

pp. 68

Author(s):

Anonymous

Keyword(s):

Peer Review ◽

Forest Fire ◽

Large Scale ◽

Fire Danger ◽

Weather Types ◽

Wildfire Occurrence ◽

The Alps ◽

Forest Fire Danger

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Meteorological conditions and wildfire-related houseloss in Australia

International Journal of Wildland Fire ◽

10.1071/wf08175 ◽

2010 ◽

Vol 19 (7) ◽

pp. 914 ◽

Cited By ~ 74

Author(s):

Raphaele Blanchi ◽

Chris Lucas ◽

Justin Leonard ◽

Klara Finkele

Keyword(s):

Forest Fire ◽

Weather Conditions ◽

Local Area ◽

Meteorological Conditions ◽

Severe Weather ◽

Fire Danger ◽

Fire Weather ◽

Statistical Relationship ◽

Historic Building ◽

Forest Fire Danger

Wildland fires or bushfires occurring under very severe weather conditions are likely to be destructive to infrastructure. This paper reports an analysis of the statistical relationship between house loss and the fire weather under which it occurred. A dataset was derived from 54 bushfires that occurred in Australia between 1957 and 2009, which resulted in the destruction of 8256 houses. The dataset was statistically compared with relevant local meteorological conditions, and a standardised calculation of the McArthur Forest Fire Danger Index (FFDI) applied. The analysis highlights how house loss statistics in Australia are dominated by a few iconic events that have occurred during very intense fire weather with the majority of losses occurring on days when the FFDI exceeds 100. Virtually all of the house loss has occurred above the 99.5th percentile level in the distribution of daily FFDI for each of the regions considered. Regulatory tools will need to focus on the most appropriate fire weather potential of a local area in order to ensure that infrastructure is adequately designed. In Australia, little house loss has occurred on days where the FFDI did not exceed 50, suggesting that historic building practices may be maintained in regions where this level is not likely to be exceeded.

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WWLLN Data Cluster Analysis Methods for Lightning-Caused Forest Fires Monitoring

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i6.pp3112-3120 ◽

2016 ◽

Vol 6 (6) ◽

pp. 3112

Author(s):

Baranovskiy Nikolay ◽

Krechetova Svetlana ◽

Belikova Marina ◽

Perelygin Anton

Keyword(s):

Cluster Analysis ◽

Forest Fire ◽

Forest Fires ◽

Research Area ◽

Fire Danger ◽

Storm Activity ◽

Lightning Discharges ◽

Current Article ◽

Forest Fire Danger ◽

New Generation

<p>Storm activity is the main reason for forest fires to occur in remote forested territories. The current article presents the results for cluster analysis of WWLLN data on lightning discharges. It provides the description for clusterization algorithms of lightning discharges over the controlled territory. Research area is Timiryazevskiy forestry of the Tomsk region (Siberia, Russia). We analyzed the applicability of cluster analysis results for monitoring of the forest fire danger caused by storm activity. As a result of the conducted research, we established that the following characteristics of storm activity can be included in deterministic-probabilistic criterion to assess the forest fire danger. The article gives the recommendations how to create new generation information-computer and geoinformation systems for monitoring of the forest fire danger caused by storm activity in the controlled forested territory.</p>

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