Integrated wildfire danger models and factors: A review

Zacharakis, Ioannis; Tsihrintzis, Vassiliοs A.

doi:10.1016/j.scitotenv.2023.165704

Cited by 9 publications

(8 citation statements)

References 238 publications

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“…Our selection of an accumulated fuel dryness index for prediction of fire occurrence seems to agree with studies that have observed higher correlations of fire activity with longer time lag fire weather indices (e.g., [35,98,99]), reinforcing the hypothesis that antecedent water balance and accumulated drought can influence fire activity (e.g., [3,32,33,100]). Interestingly, the selected period of 50 days, out of the candidate 10-90 days evaluated, corresponds directly with the time lag of some frequently used indices that have been found to be related to fire activity, such as Drought Code (DC) (with a time lag of 52 days) ( [35,99,101]), 1000 h dead fuel moisture ( [17,18]), or 2-month SPI [36]. Furthermore, some of the shorter time lag widely used fire danger indices that have been frequently related to fire activity such as FWI (e.g., [31,37,102]) or ERC (e.g., [4,8]) integrate those longer time lag codes into their weighted calculation (e.g., [2,15]).…”

Section: Discussionmentioning

confidence: 99%

“…We tested accumulated FDI (AcFDIi), calculated as the average FDI value for the evaluated i periods of 10, 20, 30, 40, 50, 60, 70, 80, and 90 days, for every Mexico state. Evaluated periods of 10-90 days for the AcFDIi were selected based on the more common range of accumulated periods for fire danger indices considered in the literature (e.g., [17,18]). The index AcFDI was assumed to be zero when the FDI value at the corresponding 10-day period was below a threshold FDI 99 .…”

Section: Accumulated Fdimentioning

confidence: 99%

“…The majority of the literature on fire occurrence forecasting has relied on weather variables such as temperature (e.g., [13,14]) or precipitation (e.g., [1,2]), or on the use of fire danger indices calculated from observed or forecasted weather (e.g., [15][16][17][18]). The most widely used weather-based fire danger indices include those from the Canadian Forest Fire Danger Rating System, (CFFDRS) (e.g., [14][15][16][17]) or the fire danger indices of the National Forest Fire Danger Rating System (NFFDRS) of the USA (e.g., [2,4,19]), but several challenges in fire prediction from those metrics of fuel dryness still remain [2].…”

Section: Introductionmentioning

confidence: 99%

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Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index

Vega-Nieva,

Briseño-Reyes,

López-Serrano

et al. 2023

Forests

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There is a need to convert fire danger indices into operational estimates of fire activity to support strategic fire management, particularly under climate change. Few studies have evaluated multiple accumulation times for indices that combine both dead and remotely sensed estimates of live fuel moisture, and relatively few studies have aimed at predicting fire activity from both such fuel moisture estimates and autoregressive terms of previous fires. The current study aimed at developing models to forecast the 10-day number of fires by state in Mexico, from an accumulated Fuel Dryness Index (FDI) and an autoregressive term from the previous 10-day observed number of fires. A period of 50 days of accumulated FDI (FDI50) provided the best results to forecast the 10-day number of fires from each state. The best predictions (R2 > 0.6–0.75) were obtained in the largest states, with higher fire activity, and the lower correlations were found in small or very dry states. Autoregressive models showed good skill (R2 of 0.99–0.81) to forecast FDI50 for the next 10 days based on previous fuel dryness observations. Maps of the expected number of fires showed potential to reproduce fire activity. Fire predictions might be enhanced with gridded weather forecasts in future studies.

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Section: Discussionmentioning

confidence: 99%

Section: Accumulated Fdimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index

Vega-Nieva,

Briseño-Reyes,

López-Serrano

et al. 2023

Forests

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“…Recently, fire danger has been calculated at large regional and global scales (Di Giuseppe et al 2016;Pettinari and Chuvieco 2017). Reviews of the structure and skill of fire danger rating systems have been presented by Chandler et al (1983), Viegas et al (1999), San-Miguel-Ayanz et al (2003), Fujioka et al (2009), de Groot et al (2015) and Zacharakis and Tsihrintzis (2023).…”

Section: Introductionmentioning

confidence: 99%

Introduction to the Australian Fire Danger Rating System†

Hollis,

Matthews,

Fox-Hughes

et al. 2024

Int. J. Wildland Fire

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Background Fire danger rating systems are used daily across Australia to support fire management operations and communications to the general public regarding potential fire danger. Aims In this paper, we introduce the Australian Fire Danger Rating System (AFDRS), providing a short historical account of fire danger rating in Australia as well as the requirements for an improved forecast system. Methods The AFDRS combines nationally consistent, spatially explicit fuel information with forecast weather and advanced fire behaviour models and knowledge to produce locally relevant ratings of fire behaviour potential. Key results A well-defined framework is essential for categorising and defining fire danger based on operational response, the potential for impact and observable characteristics of fire incidents. The AFDRS is modular, supporting continuous and incremental improvements and allowing upgrades to components in response to new science. Conclusions The AFDRS provides a new method to estimate fire danger based on the best available fire behaviour models, leading to potentially significant improvements in the way fire danger is calculated, forecast and interpreted. Implications The Australian Fire Danger Rating System was implemented in 2022, the most significant change to fire danger forecasting in Australia in more than 50 years.

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“…Previous studies on fires have highlighted the significance of the location or physical space where the fire occurs. Researchers have analyzed various aspects, such as the frequency of fires in a particular area [41][42][43][44][45][46][47], the danger or risk of wildfires [48][49][50][51][52], and the size and area burned [53][54][55][56][57]. The process of how fire approaches buildings [29] has been a major concern for researchers.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Trends in the Distance of Wildfires from Built-Up Areas in Spain and California (USA): 2007–2015

Marey-Perez,

López-Álvarez,

Franco-Vázquez

2024

Forests

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Wildfires present a significant risk to societies globally due to population growth, concentrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyzes 101,597 fires in Spain and California between 2007 and 2015, where ignition points and all built-up areas were precisely geolocated in 2014. The study employs the Mann–Kendall test to determine trends and analyze the relative distance of wildfires to human buildings over time. The results indicate that wildfires are becoming statistically significantly closer in several areas. It is also observed that the majority of wildfires are moving away from buildings, although this is not a significant finding. These results suggest the possibility of further research into the reasons behind these observations and possible future developments and their consequences.

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Integrated wildfire danger models and factors: A review

Cited by 9 publications

References 238 publications

Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index

Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index

Introduction to the Australian Fire Danger Rating System†

Analysis of Trends in the Distance of Wildfires from Built-Up Areas in Spain and California (USA): 2007–2015

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