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Climate Change and Wildfires Risk Assessment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Air, Climate Change and Sustainability".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 14321

Special Issue Editor

Environmental Research Laboratory, NCSR Demokritos, 15310 Athens, Greece
Interests: atmospheric modelling; regional impacts of climate change

Special Issue Information

Dear Colleagues,

It is an unequivocal fact that climate change due to anthropogenic activities has become one of the most pressing issues in recent decades, as it threatens ecosystems, human well-being and sustainable livelihoods. According to the World Meteorological Organisation News Report (September 2020) [1], there is clear evidence that climate change amplifies the risk of wildfires due to increased warming globally. Research findings [2] point to climate trends of global increases in the frequency, extent and intensity of heatwaves, as well as of regional increases in the frequency, intensity and duration of droughts. Such increases are known to enhance the potential for the formation of fire weather and climate conditions. The use of multiple Global Circulation Models (GCMs) for the investigation of global fire season severity has indicated significant increases in the length of fire season [3]. Studies have shown a pronounced influence of climate change on the total areas burned across the fire season [4].

Given the fact that the effects of human-induced climate change on fire weather are detectable and distinguishable from natural variability [5], and wildfire risk and severity is expected to increase [6], there is a growing need for a fire-resilient community. To improve the preparedness level of our society with suitable strategies for mitigation and prevention requires high-quality services and approaches to assess the impact of climate change on fire risk. Within this context, research efforts need to continue in the investigation of the assessment of wildfire risk in a changing climate at the regional and local scales, with high spatial and temporal resolutions, by employing newly emerging datasets and methods for climate reanalysis and future projections.

This Special Issue of Sustainability encourages high-quality research papers on the following topics:

  • Seasonal prediction of extreme fire weather;
  • Projected changes in daily fire spread and fire danger;
  • Climate change impacts on fire weather extremes and fire season length at regional and local scales;
  • Links between changes in atmospheric dynamics and wildfire patterns;
  • Potential climate change impacts on wildfire intensity and future fire-prone regimes;
  • Spatio-temporal changes in wildfire activity at regional and local scales;
  • Wildfire risk assessment under changing fire weather and climate conditions;
  • Preceding drought conditions and seasonal wildfire risk prediction;
  • Fire risk conditions in wildland–urban and wildland–rural interface areas;
  • Wildfire management and adaptation measures as a function of fire likelihood at regional and local scales.

Within these topics, interdisciplinary original research articles highlighting new ideas, review articles, study approaches, methods and innovations are welcomed.

References

[1] https://public.wmo.int/en/media/news/climate-change-increases-risk-of-wildfires

[2] Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) published in 2013.

[3] Mike Flannigan, Alan S. Cantin, William J. de Groot, Mike Wotton, Alison Newbery, Lynn M. Gowman, Global wildland fire season severity in the 21st century, Forest Ecology and Management, 294, 2013, 54-61, https://0-doi-org.brum.beds.ac.uk/10.1016/j.foreco.2012.10.022.

[4] Kirchmeier‐Young, M. C., Gillett, N. P., Zwiers, F. W., Cannon, A. J., & Anslow, F. S. (2019). Attribution of the influence of human‐induced climate change on an extreme fire season. Earth's Future, 7, 2– 10. https://0-doi-org.brum.beds.ac.uk/10.1029/2018EF001050

[5] Abatzoglou, J. T., Williams, A. P., & Barbero, R. (2019). Global emergence of anthropogenic climate change in fire weather indices. Geophysical Research Letters, 46, 326– 336. https://0-doi-org.brum.beds.ac.uk/10.1029/2018GL080959

[6] 2019 IPCC Special Report on Climate Change and Land, Technical Summary, https://www.ipcc.ch/srccl/chapter/technical-summary/

Dr. Diamando Vlachogiannis
Guest Editor

Manuscript Submission Information

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Keywords

  • fire risk
  • wildfire intensity
  • fire weather
  • fire climate conditions
  • projections
  • extreme fire weather

Published Papers (5 papers)

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Research

Jump to: Review

15 pages, 3323 KiB  
Article
Investigation of Fire Weather Danger under a Changing Climate at High Resolution in Greece
by Nadia Politi, Diamando Vlachogiannis, Athanasios Sfetsos, Nikolaos Gounaris and Vassiliki Varela
Sustainability 2023, 15(3), 2498; https://0-doi-org.brum.beds.ac.uk/10.3390/su15032498 - 30 Jan 2023
Cited by 4 | Viewed by 1861
Abstract
Future fire weather conditions under climate change were investigated based on the Fire Weather Index (FWI), Initial Spread Index (ISI) and threshold-specific indicators in Greece. The indices were calculated from climate datasets derived from high-resolution validated simulations of 5 km. The dynamical downscaled [...] Read more.
Future fire weather conditions under climate change were investigated based on the Fire Weather Index (FWI), Initial Spread Index (ISI) and threshold-specific indicators in Greece. The indices were calculated from climate datasets derived from high-resolution validated simulations of 5 km. The dynamical downscaled simulations with the WRF model were driven by EC-Earth output for historical (1980–2004) and future periods, under two Representative Concentration Pathways (RCPs), RCP4.5 and 8.5. The analysis showed that the FWI is expected to increase substantially, particularly in the southern parts with extreme values found above 100. In addition, the number of days with an FWI above the 90th percentile is projected to increase considerably (above 30 days), under both scenarios. Over the eastern and northern mainland, the increase is estimated with more than 70 days under RCP4.5, in the near future (2025–2049). Moreover, central and north-eastern parts of the country will be affected with 30 or more extreme consecutive days of prolonged fire weather, under RCP4.5, in the near future and under RCP8.5 in the far future (2075–2099). Finally, the expected rate of fire spread is more spatially extended all over the country and particularly from southern to northern parts compared to the historical state. Full article
(This article belongs to the Special Issue Climate Change and Wildfires Risk Assessment)
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16 pages, 4292 KiB  
Article
Wildfire Prediction Model Based on Spatial and Temporal Characteristics: A Case Study of a Wildfire in Portugal’s Montesinho Natural Park
by Hao Dong, Han Wu, Pengfei Sun and Yunhong Ding
Sustainability 2022, 14(16), 10107; https://0-doi-org.brum.beds.ac.uk/10.3390/su141610107 - 15 Aug 2022
Cited by 3 | Viewed by 2101
Abstract
Wildfires influence the global carbon cycle, and the regularity of wildfires is mostly determined by elements such as meteorological conditions, combustible material states, and human activities. The time series and spatial dispersion of wildfires have been studied by some scholars. Wildfire samples were [...] Read more.
Wildfires influence the global carbon cycle, and the regularity of wildfires is mostly determined by elements such as meteorological conditions, combustible material states, and human activities. The time series and spatial dispersion of wildfires have been studied by some scholars. Wildfire samples were acquired in a monthly series for the Montesinho Natural Park historical fire site dataset (January 2000 to December 2003), which can be used to assess the possible effects of geographical and temporal variations on forest fires. Based on the above dataset, dynamic wildfire distribution thresholds were examined using a K-means++ clustering technique for each subgroup, and monthly series data were categorized as flammable or non-flammable depending on the thresholds. A five-fold hierarchical cross-validation strategy was used to train four machine learning models: extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and decision tree (DT). Finally, to explore the performance of those we have mentioned, we used accuracy (ACC), F1 score (F1), and the values for the area under the curve (AUC) of the receiver operating characteristics (ROCs). The results depicted that the XGBoost model works best under the evaluation of the three metrics (ACC = 0.8132, F1 = 0.7862, and AUC = 0.8052). The model performance is significantly improved when compared to the approach of classifying wildfires by burned area size (ACC = 72.3%), demonstrating that spatiotemporal heterogeneity has a broad influence on wildfire occurrence. The law of a spatiotemporal distribution connection in wildfires could aid in the prediction and management of wildfires and fire disasters. Full article
(This article belongs to the Special Issue Climate Change and Wildfires Risk Assessment)
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17 pages, 3063 KiB  
Article
Wildfire Smoke, Air Quality, and Renewable Energy—Examining the Impacts of the 2020 Wildfire Season in Washington State
by Augusto Zanin Bertoletti, Theresa Phan and Josue Campos do Prado
Sustainability 2022, 14(15), 9037; https://0-doi-org.brum.beds.ac.uk/10.3390/su14159037 - 23 Jul 2022
Cited by 5 | Viewed by 2674
Abstract
The 2020 wildfire season was devastating, setting negative records in many states and regions around the world, especially in North America. Five of the six largest fires in California’s recorded history burned in 2020. In the Pacific Northwest region of the United States, [...] Read more.
The 2020 wildfire season was devastating, setting negative records in many states and regions around the world, especially in North America. Five of the six largest fires in California’s recorded history burned in 2020. In the Pacific Northwest region of the United States, Oregon and eastern Washington almost doubled their 10-year average of burnt acres recently. Depending on wind speed and direction conditions, the smoke from wildfires may significantly impact the air quality and reduce solar photovoltaic (PV) generation even in regions located hundreds of kilometers away from high-risk zones. Thus, during those periods, power system operators must ensure reliability and resilience across power generation, transmission, and distribution, while minimizing carbon emissions that can harm the air quality of the affected communities during wildfire events even more. This paper analyzes the impact of the 2020 wildfire season in the state of Washington, verifying the wind speed and solar irradiance data, and correlating these with the particulate matter 2.5 (PM 2.5) concentration and aerosol optical thickness (AOT) through a multi-variable regression model. The results show that PV production may be significantly reduced during the periods of high concentration of wildfire smoke and reduced wind speeds, thus highlighting the need for efficient and sustainable power system operations during wildfire events. Full article
(This article belongs to the Special Issue Climate Change and Wildfires Risk Assessment)
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17 pages, 3889 KiB  
Article
Deep Learning-Based Projection of Occurrence Frequency of Forest Fires under SSP Scenario: Exploring the Link between Drought Characteristics and Forest Fires
by Jang Hyun Sung, Seung Beom Seo and Young Ryu
Sustainability 2022, 14(9), 5494; https://0-doi-org.brum.beds.ac.uk/10.3390/su14095494 - 03 May 2022
Viewed by 1509
Abstract
The occurrence frequency of forest fires (OF) can be estimated using drought features because droughts are affected by climatic conditions. Previous studies have improved OF estimation performance by applying the meteorological drought index to climatic conditions. It is anticipated that the temperature will [...] Read more.
The occurrence frequency of forest fires (OF) can be estimated using drought features because droughts are affected by climatic conditions. Previous studies have improved OF estimation performance by applying the meteorological drought index to climatic conditions. It is anticipated that the temperature will rise in South Korea in the future and that drought will become severe on account of climate change. The future OF is expected to change accordingly. This study used the standard precipitation index, relative humidity, and wind speed as predictor variables for a deep-learning-based model to estimate the OF. Climate change scenarios under shared socioeconomic pathways were used to estimate future OF. As a result, it was projected that the OF in the summer season will increase in the future (2071–2100). In particular, there will be a 15% increase in July compared to the current climate. A decrease in relative humidity and increase in wind speed will also affect the OF. Finally, drought severity was found to be the most influential factor on the OF among the four drought characteristics (severity, duration, intensity, and inter-arrival), considering inter-model variability across all global climate models. Full article
(This article belongs to the Special Issue Climate Change and Wildfires Risk Assessment)
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Review

Jump to: Research

21 pages, 3429 KiB  
Review
Early Wildfire Detection Technologies in Practice—A Review
by Ankita Mohapatra and Timothy Trinh
Sustainability 2022, 14(19), 12270; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912270 - 27 Sep 2022
Cited by 19 | Viewed by 5060
Abstract
As fires grow in intensity and frequency each year, so has the resistance from their anthropic victims in the form of firefighting technology and research. Although it is impossible to completely prevent wildfires, the potential devastation can be minimized if fires are detected [...] Read more.
As fires grow in intensity and frequency each year, so has the resistance from their anthropic victims in the form of firefighting technology and research. Although it is impossible to completely prevent wildfires, the potential devastation can be minimized if fires are detected and precisely geolocated while still in their nascent phases. Furthermore, automated approaches without human involvement are comparatively more efficient, accurate and capable of monitoring extremely remote and vast areas. With this specific intention, many research groups have proposed numerous approaches in the last several years, which can be grouped broadly into these four distinct categories: sensor nodes, unmanned aerial vehicles, camera networks and satellite surveillance. This review paper discusses notable advancements and trends in these categories, with subsequent shortcomings and challenges. We also describe a technical overview of common prototypes and several analysis models used to diagnose a fire from the raw input data. By writing this paper, we hoped to create a synopsis of the current state of technology in this emergent research area and provide a reference for further developments to other interested researchers. Full article
(This article belongs to the Special Issue Climate Change and Wildfires Risk Assessment)
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