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Κεραυνική δραστηριότητα και δασικές πυρκαγιές στο όρος Μαίναλο - Lightning-caused fires on Mount Mainalo: Analysis of identified igniting strokes (In Greek with English abstract)

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Μελετήθηκαν 76 δασικές πυρκαγιές (N=76) οι οποίες προκλήθηκαν από κεραυνούς στο όρος Μαίναλο Αρκαδίας και πέριξ αυτού, σε χρονικό διάστημα 23 ετών (1998 έως 2020), από τον Μάϊο έως και τον Νοέμβριο κάθε έτους. Προέκυψαν δυνητικά χρήσιμα συμπεράσματα για τη συχνότητα και το υψόμετρο (Elv) εμφάνισής τους, τις καμένες εκτάσεις (BuA), τις συντεταγμένες των θέσεων των κεραυνικών πληγμάτων, την ώρα εντοπισμού-αναγγελίας των πυρκαγιών (Fdt) και τη χρονική καθυστέρηση μεταξύ της εκδήλωσης πυρκαγιάς από κεραυνό και του εντοπισμού της (holdover time). Βάσει των αποτελεσμάτων της ανάλυσης, η συχνότητα εμφάνισης πυρκαγιών που προκαλούνται από κεραυνούς, είναι αυξημένη τον Αύγουστο και τον Ιούλιο, οι περισσότερες έχουν εκδηλωθεί στο νότιο τμήμα του Μαινάλου και εντοπίστηκαν τις απογευματινές ώρες. This paper concerns seventy-six (76) lightning-ignited wildfires on mount Mainalo and its peripheral zone, during the May to November period of the years 1998 to 2020. Descriptive statistics (mean, standard error, median, mode, standard deviation, minimum and maximum) and frequency distribution histograms were used to describe the number of fires per year or month, the burned area per fire, the total burned area per month or year, absolute or mean elevation of lightning-caused fire occurrence, wildfire detection time, and the holdover time (the phase between ignition and detection). The analysis shows that the frequency of lightning-caused wildfires is increased in August and July while most of the fires have taken place at the south part of the mountain and have been detected in the afternoon hours. The results and the preliminary conclusions regarding the spatial and temporal distribution of lightning-caused wildfires and the rest of the data series of this paper, represent the first approach of this type to the lightning-ignited wildfires in Greece.
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ISBN: 978-618-84551-2-2
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1ikoyr@hotmail.gr
2Wildfire Management Consulting and Training, 8, 13673 , info@m-
athanasiou.gr
(Elv BuA
- Fdt
hold overtime
Pyne Pyne Scott
cloud to ground lightning: CGL
Reineking -
-
mm/h
Mazarakis relative
flash density km2
intra-cloud lightning cloud to cloud lightning)
CGL
Ogilvie
ignition
Nash Johnson (1996), Anderson (2002), Pineda Rigo Schultz
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(detection
(holdover time) (Wotton Martell
(Komarek 1968).
(Moris
(Pineda Rigo 2017, Schultz
Chen
N
Fdt
m)
Alt, m
BuA, ha holdover, h
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BuA BuA Elv,
BuA
BuA
BuA ha
ha BuA (=3.951,1 ha
BuA=57,1 ha
BuA
Fdt
BuA BuA
Figure 1. Frequency distribution of wildfires for burned area (BuA) classes and the total burned area per class (N=76)
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Figure 2. a) Frequency distribution of wildfires and maximum
elevation of lightning caused ignition (Elv) per month (N=76)
b) Frequency distribution of wildfires and the total BuA
per month (N=76)
Figure 3. Frequency distribution of wildfires and the total BuA per year (N=76)
Figure 4. a) Frequency distribution of wildfires and mean Elv
15,9 ha)
b) Frequency distribution of wildfires and the total BuA
per month (N=72,
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ha)
Figure 5.
BuA Elv.
Table 3. Descriptive statistics of number of fires, BuA and Elv
(ha)
Elv (
m)
Elv
m) ha)
Elv
m) ha)
ha) /
(Elv, m)
(Mean) 4 52 1.253 10/(1.234) 8,2 4 / 1.293 3 0,8/(1.248)
S.E.)
0,6 42 27 4/(30) 5,2 0,6 / 39 1 0,4/(28)
(Median)
4 0,005 1.252 10/(1.244) 0,6 4 / 1.302 0,06 0,005/(1.241)
(Mode) 4 0,0001 1.418 10/(-) - 4 / (-) 0,05 0,0001/(1.342)
(S.D.)
3 367 236 10/(79) 13,8 3 / (170) 6,4 3/(241)
(min) 1 0,0001 420 1/(1.088) 0,0002 1 / (951) 0,004 0,0001/(420)
(max) 12 3.180 1.842 27/(1.340) 33,6 12 / (1.624) 20 15,9/(1.842)
76 3.951 - 72/(-) 57,1 72 / (-) 57,1 57,1/(-)
19 76 76 7/(7) 7 19 / (19) 19 72/(72)
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Figure 6. a) Frequency distribution of wildfires
for fire detection time (Fdt) classes (N=72)
b) Frequency distribution of wildfires for holdover classes and the
total BuA per class(N=44)
Fdt, Elv, BuA holdover
Table 2. Descriptive statistics of Fdt, Elv, BuA and holdover (of Figure 6)
Fdt
(
hh
mm
)
Elv (m)
ha
)
Fdt (hh:mm) Elv (m) holdover (h) BuA
ha
)
Mean 15:47 1.243 10,4 15:26 1.202 22,5 17
S.E. 23 min 28 6 31 min 36 7 10
Median 17:09 1.241 0,005 15:45 1.186 1,6 0,005
Mode 17:30 1.342 0,005 17:30 - 0,2 0,0001
S.D. 3h &
19min 239 50,8 3h & 29min 240 46,1 64
min 08:02 420 0,0001 08:02 420 0,08 0,0001
max 21:30 1.842 300 21:30 1.842 222,8 300
- - 751,1 - - - 749
72 72 72 44 44 44 44
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- 2020.
Figure 7: Spatial and temporal (per month) distribution of lightning caused wildfires on mount Mainalo, Arcadia Greece, for
the period 1998 to 2020.
( )
holdover time)
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holdover time).
Kotroni Lagouvardos
Abstract
This paper concerns seventy-six (76) lightning-ignited wildfires on mount Mainalo and its peripheral
zone, during the May to November period of the years 1998 to 2020. Descriptive statistics (mean,
standard error, median, mode, standard deviation, minimum and maximum) and frequency distribution
histograms were used to describe the number of fires per year or month, the burned area per fire, the
total burned area per month or year, absolute or mean elevation of lightning-caused fire occurrence,
wildfire detection time, and the holdover time (the phase between ignition and detection). The analysis
shows that the frequency of lightning-caused wildfires is increased in August and July while most of the
fires have taken place at the south part of the mountain and have been detected in the afternoon hours.
The results and the preliminary conclusions regarding the spatial and temporal distribution of lightning-
caused wildfires and the rest of the data series of this paper, represent the first approach of this type to
the lightning-ignited wildfires in Greece.
Anderson K., 2002. A model to predict lightning-caused fire occurrences. Int. J. Wildland Fire, 11,
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Moris, J.V., Conedera, M., Nisi, L., Bernardi, M., Cesti, G. and Pezzatti, G., 2020. Lightning-caused
fires in the Alps: Identifying the igniting strokes. Agric. For. Meteorol. 290. 107990.
https://doi.org/10.1016/j.agrformet.2020.107990.
Nash, C.H. and Johnson, E.A., 1996. Synoptic climatology of lightning-caused forest fires in
subalpine and boreal forests. Can. J. For. Res. 26. 1859-1874. https://doi.org/10.1139/x26-211.
Ogilvie, C.J. 1989 Lightning fires in Saskatchewan forests. Fire Management Notes 50(1): 31-32.
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ResearchGate has not been able to resolve any citations for this publication.
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Full-text available
Lightning is the major cause of natural ignition of wildfires worldwide and produces the largest wildfires in some regions. Lightning strokes produce about 5 % of forest fires in the Mediterranean basin and are one of the most important precursors of the largest forest fires during the summer. Lightning-ignited wildfires produce significant emissions of aerosols, black carbon and trace gases, such as CO, SO2, CH4 and O3, affecting air quality. Characterization of the meteorological and cloud conditions of lightning-ignited wildfires in the Mediterranean basin can serve to improve fire forecasting models and to upgrade the implementation of fire emissions in atmospheric models. This study investigates the meteorological and cloud conditions of Lightning-Ignited Wildfires (LIW) and Long-Continuing-Current (LCC) lightning flashes in the Iberian Peninsula and Greece. LCC lightning and lightning in dry thunderstorms with low precipitation rate have been proposed to be the main precursors of the largest wildfires. We use lightning data provided by the World Wide Lightning Location Network (WWLLN), the Earth Network Total Lightning Network (ENTLN) and the Lightning Imaging Sensor (LIS) onboard the International Space Station (ISS) together with four databases of wildfires produced in Spain, Portugal, Southern France and Greece, respectively, in order to produce a climatology of LIW and LCC lightning over the Mediterranean basin. In addition, we use meteorological data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-reanalysis data set and by the Spanish State Meteorological Agency (AEMET) together with the Cloud Top Height (CTH) product derived from Meteosat Second Generation (MSG) satellites measurements to investigate the meteorological conditions of LIW and LCC lightning. According to our results, LIW and a significant amount of LCC lightning flashes tend to occur in dry thunderstorms with weak updrafts. Our results suggest that lightning-ignited wildfires tend to occur in high-based clouds with a vertical content of moisture lower than the climatological value, as well as with a higher temperature and a lower precipitation rate. Meteorological conditions of LIW from the Iberian Peninsula and Greece are in agreement, although some differences possibly caused by highly variable topography in Greece and a more humid environment are observed. These results show the possibility of using the typical meteorological and cloud conditions of LCC lightning flashes as proxy to parameterize the ignition of wildfires in atmospheric or forecasting models.
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Analysis was performed to determine whether a lightning flash could be associated with every reported lightning-initiated wildfire that grew to at least 4 km2. In total, 905 lightning-initiated wildfires within the Continental United States (CONUS) between 2012 and 2015 were analyzed. Fixed and fire radius search methods showed that 81–88% of wildfires had a corresponding lightning flash within a 14 day period prior to the report date. The two methods showed that 52–60% of lightning-initiated wildfires were reported on the same day as the closest lightning flash. The fire radius method indicated the most promising spatial results, where the median distance between the closest lightning and the wildfire start location was 0.83 km, followed by a 75th percentile of 1.6 km and a 95th percentile of 5.86 km. Ninety percent of the closest lightning flashes to wildfires were negative polarity. Maximum flash densities were less than 0.41 flashes km2 for the 24 h period at the fire start location. The majority of lightning-initiated holdover events were observed in the Western CONUS, with a peak density in north-central Idaho. A twelve day holdover event in New Mexico was also discussed, outlining the opportunities and limitations of using lightning data to characterize wildfires.
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Understanding the environmental and human determinants of forest fire ignitions is crucial for landscape management. In this study, we consider lightning-and human-induced fires separately and evaluate the relative importance of weather, forest composition and human activities on the occurrence of forest fire ignitions in the most fire-prone region of Switzerland, the Canton Ticino. Independent variables included 14 drought and fire weather indices, forest composition and human influences. Logistic regression models were used to relate these independent variables to records of forest fires over a 37-year period (1969-2005). We found large differences in the importance of environmental and human controls on forest fire ignitions between lightning- and human-induced events: lightning-induced fires occurred in a small range of weather conditions well captured by the Duff Moisture Code from the Canadian Forest Fire Weather Index System and the LandClim Drought Index, and with negligible influence of distance to human infrastructure, whereas human-induced fires occurred in a wider range of weather conditions well captured by the Angstroem and the Fosberg Fire Weather Index, mainly in deciduous forests, and strongly depending on proximity to human infrastructure. We conclude that the suitability of fire indices can vary dramatically between ignition sources, suggesting that some of these indices are useful within certain regions and fire types only. The ignition source is an important factor that needs to be taken into account by fire managers and when developing models of forest fire occurrence.
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Lightning strike, fire weather, and fire occurrence data were used to model (i) the probability that a lightning strike causes a sustainable ignition on the forest floor and (ii) the probability of an ignition being detected and reported to the fire management agency for each ecoregion in the province of Ontario. An index that tracks duff moisture content in very sheltered areas of a forest stand (near the tree boles) was the most significant predictor in each ignition model. The presence of positive cloud-to-ground lightning strikes was also found to have a significant and positive influence on the probability of ignition in most areas of the province with the exception of the far northwest. Weather conditions following a lightning storm influence the probability that a lightning strike causes a sustainable ignition. Models of the probability of detecting a fire ignited by lightning were also created for each of the ecoregions across Ontario. The form of these models varied somewhat among ecoregions, but contained an indicator of receptive surface fire spread conditions and an indicator of the dryness of the heavier fuels (the organic layer) in the forest floor.
Lightning and lightning fires as ecological forces
  • E V Komarek
Komarek, E.V., 1968. Lightning and lightning fires as ecological forces. In: Annual Tall Timbers Fire Ecology Conference Number 7. Tall Timbers Research Station, Tallahassee, FL, pp. 169-198.
Synoptic climatology of lightning-caused forest fires in subalpine and boreal forests
  • J V Moris
  • M Conedera
  • L Nisi
  • M Bernardi
  • G Cesti
  • G Pezzatti
  • C H Nash
  • E A Johnson
Moris, J.V., Conedera, M., Nisi, L., Bernardi, M., Cesti, G. and Pezzatti, G., 2020. Lightning-caused fires in the Alps: Identifying the igniting strokes. Agric. For. Meteorol. 290. 107990. https://doi.org/10.1016/j.agrformet.2020.107990. Nash, C.H. and Johnson, E.A., 1996. Synoptic climatology of lightning-caused forest fires in subalpine and boreal forests. Can. J. For. Res. 26. 1859-1874. https://doi.org/10.1139/x26-211.
Lightning fires in Saskatchewan forests
  • C J Ogilvie
Ogilvie, C.J. 1989 Lightning fires in Saskatchewan forests. Fire Management Notes 50(1): 31-32.