Characterising the holdover phase of lightning-ignited wildfires in Catalonia

Pineda, Nicolau; Altube, Patricia; Alcasena, Fermín; Casellas, Enric; Segundo, Helen San; Montanyà, Joan

doi:10.1016/j.agrformet.2022.109111

Cited by 13 publications

(31 citation statements)

References 72 publications

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“…Fourth, the database does not include coordinates and dates of wildfires and lightning. Consequently, the database cannot be used to study holdover times of individual LIWs in full detail (e.g., Pineda et al, 2022). In that case, users of the database may contact the authors of the original studies, although often original data on wildfires and lightning may be difficult to share due to data privacy policies.…”

Section: Discussionmentioning

confidence: 99%

“…Morris (1947) already noticed the existence of local maxima 24 hours apart from each other. Pineda et al (2022) illustrated how these peaks are most likely associated with the diurnal heating and cooling cycle. The frequency distributions of both LIW detections and ignitions seem to follow bell-shaped distributions, with maximum values occurring between the late afternoon and early evening (i.e., 4-7 p.m.; Barrows, 1951;Pineda et al 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Pineda et al (2022) illustrated how these peaks are most likely associated with the diurnal heating and cooling cycle. The frequency distributions of both LIW detections and ignitions seem to follow bell-shaped distributions, with maximum values occurring between the late afternoon and early evening (i.e., 4-7 p.m.; Barrows, 1951;Pineda et al 2022). Therefore, LIWs that smoulder for one or more nights may be more likely to be reported in the afternoon hours, when the environmental conditions become progressively warmer and drier, favoring a transition to flaming combustion.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, recent holdover time data derived with the help of LLS are known to be affected by several data and methodological issues (Müller et al, 2013;Schultz et al, 2019;Moris et al, 2020;Pineda et al, 2022). Problems with wildfire data (e.g., misclassifications, low detection effort), lightning data (e.g., low detection efficiency and location accuracy of LLS), and the methodology to match wildfires and lightning (e.g., criteria and parameters applied to select the most probable lightning igniting LIWs) can influence the estimations of holdover time.…”

Section: Discussionmentioning

confidence: 99%

“…A typical example of LIWs with a survival phase are evening and night ignitions that smoulder overnight and are detected the day after in the afternoon when a higher temperature and lower relative humidity favor fire spread (Pineda et al, 2014;Pineda and Rigo, 2017). While it is commonly accepted that the majority of LIWs have short holdover times (e.g., < 24 hours; Dowdy and Mills, 2009;Schultz et al, 2019;Moris et al, 2020;Pineda et al, 2022), relative frequencies of LIWs with longer holdover durations are less generalizable, partially due to limited understanding on how often and how long LIWs can smoulder (Scholten et al, 2021). However, most studies show that holdover time follows a right-skewed distribution, with an exponential-like decay with increasing time (e.g., Nash and Johnson, 1996;Wotton and Martell, 2005;Schultz et al, 2019;Moris et al, 2020).…”

mentioning

confidence: 99%

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A global database on holdover time of lightning-ignited wildfires

Moris

Álvarez‐Álvarez

Conedera

et al. 2022

Preprint

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Abstract. Holdover fires are usually associated with lightning-ignited wildfires (LIWs), which can experience a smouldering phase or go undetected for several hours to days and weeks before being reported. Since the existence and duration of the smouldering combustion in LIWs is usually unknown, holdover time is conventionally defined as the time between the lightning event that ignited the fire and the time the fire is detected. Therefore, all LIWs have an associated holdover time, which may range from a few minutes to several days. However, we lack a comprehensive understanding of holdover times. Here, we introduce a global database on holdover times of LIWs. We have collected holdover time data from 29 different studies across the world through a literature review and datasets assembled by authors of the original studies. The database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). Censored data are the core of the database and consist of different frequency distributions reporting the number or relative frequency of LIWs per interval of holdover time. In addition, ancillary data provide further information to understand the methods and contexts in which the data were generated in the original studies. The first version of the database contains 42 frequency distributions of holdover time built with data on more than 152,375 LIWs from 13 countries in five continents covering a time span from 1921 to 2020. This database is the first freely available, harmonized, and ready-to-use global source of holdover time data, which may be used in different ways to investigate LIWs and model the holdover phenomenon. The complete database can be downloaded at https://doi.org/10.5281/zenodo.7352172 (Moris et al., 2022).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%