Analysis of prevailing atmospheric conditions during wildfire events in the Indian Himalayan region DOI Creative Commons

Anandu Prabhakaran,

Piyush Srivastava

Quarterly Journal of the Royal Meteorological Society, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Abstract Wildfire incidents have seen an exponential rise in the past few decades India, particularly over Indian Himalayan region, which has led to a huge loss of life and property. To mitigate manage impact wildfires, better understanding key physical atmospheric processes conducive spread wildfires is required. This study aims analyze conditions associated with propagation state Uttarakhand (India). For this, wildfire burned‐area data from (India) State Forest Department, in‐situ precipitation information India Meteorological variables (temperature, relative humidity, soil moisture) European Centre for Medium‐Range Weather Forecasts Reanalysis v5 Global Land Data Assimilation System datasets years 2000–2022 been critically analyzed infer cause unprecedented Uttarakhand. The analysis suggests that strength El Niño Southern Oscillation Ocean Dipole phases along pattern pre‐fire season due western disturbances are dominant factors fires. Further, bimodal distribution vapor pressure deficit, having peak during fire post‐monsoon period, indicates increased dryness fuels susceptibility vegetation wildfires. These findings could be utilized impacts vulnerable state.

Language: Английский

Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections DOI Creative Commons
Yan He, Zixuan Zhou, Eun‐Soon Im

et al.

Weather and Climate Extremes, Journal Year: 2025, Volume and Issue: unknown, P. 100751 - 100751

Published: Feb. 1, 2025

Language: Английский

Citations

1
A framework for defining fire danger to support fire management operations in Australia† DOI Creative Commons
J. J. Hollis,

Stuart Matthews,

Wendy R. Anderson

et al.

International Journal of Wildland Fire, Journal Year: 2024, Volume and Issue: 33(3)

Published: March 18, 2024

Background Development of the Australian Fire Danger Rating System began in 2017 with a project aimed at demonstrating feasibility new fire danger rating system through Research Prototype (AFDRSRP) that accounted for variability vegetation types, was nationally applicable, modular and open to continuous improvement. Aims In this manuscript, we identify define transition points categories AFDRSRP. We discuss user responses categorisation during live trial evaluation AFDRSRP reflect on limitations potential improvements. Methods A review available literature, broad consultation stakeholders reanalysis impact data were used determine suitable thresholds categorising within Key results transitions behaviour result application different management strategies or are associated variation serious consequences impacts. Conclusions The incorporated best science, supported by well-defined framework defining making it across jurisdictions range fuel types. Implications allows managers assess accuracy appropriateness forecasted danger.

Language: Английский

Citations

6
Live trial performance of the Australian Fire Danger Rating System – Research Prototype† DOI Creative Commons
Saskia Grootemaat,

Stuart Matthews,

B. J. Kenny

et al.

International Journal of Wildland Fire, Journal Year: 2024, Volume and Issue: 33(4)

Published: March 28, 2024

Background The Australian Fire Danger Rating System program (AFDRS) has built a new fire danger rating system for Australia. A live trial of the system’s Research Prototype (AFDRSRP), based on behaviour thresholds, was run and evaluated between October 2017 March 2018. Aims Live results are critically analysed, knowledge gaps recommendations future work discussed. Methods bushfire experts assessed wildfires prescribed burns across range vegetation types weather conditions. Forecast ratings calculated using: (1) AFDRSRP; (2) Forest Index (FFDI) Grassland (GFDI) were compared against derived by expert opinion each evaluation (n = 336). Key Overall performance AFDRSRP superior to FFDI/GFDI (56 vs 43% correct), with tendency over-predict rather than under-predict potential. also demonstrated its value assess in fuel not conforming current grassland or forest models; e.g. fuels that grouped use mallee-heath, spinifex shrubland spread models. Conclusions successful, outperforming existing operational system. Implications Identified improvements would further enhance performance, ensuring readiness implementation.

Language: Английский

Citations

5
Australian Fire Danger Rating System: implementing fire behaviour calculations to forecast fire danger in a research prototype† DOI Creative Commons

B. J. Kenny,

Stuart Matthews,

Stéphane Sauvage

et al.

International Journal of Wildland Fire, Journal Year: 2024, Volume and Issue: 33(4)

Published: April 10, 2024

Background The Australian Fire Danger Rating System (AFDRS) was implemented operationally throughout Australia in September 2022, providing calculation of fire danger forecasts based on peer-reviewed behaviour models. system is modular and allows for ongoing incorporation new scientific research improved datasets. Aims Prior to operational implementation the AFDRS, a Research Prototype (AFDRSRP), described here, built test input data systems evaluate performance potential outputs. Methods spread models were selected aligned with fuel types process that captured bioregional variation characteristics. National spatial datasets created identify history alignment existing weather forecast layers. Key results AFDRSRP demonstrated improvements over McArthur Forest Grass due its use models, as well more accurately reflecting fuels. Conclusions design robust allowed updates prior AFDRS.

Language: Английский

Citations

5
Interpretation of seasonal fire outlooks DOI Creative Commons
Naomi Benger, Paul A. Gregory, Paul Fox‐Hughes

et al.

Journal of Southern Hemisphere Earth System Science, Journal Year: 2025, Volume and Issue: 75(1)

Published: March 18, 2025

The Australian Fire Danger Rating System (AFDRS) is a nationally consistent approach to forecasting fire danger for all major vegetation types found in Australia. AFDRS climate outlooks (Fire Outlooks, FDOs) extending out 3 months ahead are the first such operational products of their kind world. use Bureau’s seasonal model Community Climate Earth simulator – Seasonal (ACCESS-S2). FDOs currently available agencies, and partner agencies involved land management prevention activities. To make sound planning decisions, should be used with other sources intelligence understand which components might driving risk. It prudent consult temperature rainfall as both these contributing factors conditions, but have differing data foundations (hindcast periods) that need understood correct interpretation. Previous comparative analysis showed hindcast period warmer during shoulder seasons some regions; thus, high chance above average not reflected expected outlooks. For this reason, it has been important provide users advice on how best interpret alongside In work, we continued determine differs over periods subsequent implications when interpreting strategic context.

Language: Английский

Citations

0
Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction DOI Creative Commons
Svetlana Illarionova, Dmitrii Shadrin,

Fedor Gubanov

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

Wildfires play a pivotal role in environmental processes and the sustainable development of ecosystems. Timely responses can significantly reduce damages consequences caused by their spread. Several critical issues wildfire behavior analysis include fire occurrence forecasting, early detection, spread prediction. In this study, we focus on which is valuable tool for facilitating earlier intervention. Conventional approaches primarily rely computation indices based weather conditions. However, solutions that utilize more comprehensive data, remote sensing information, artificial intelligence (AI) algorithms may offer substantial advantages rapid decision-making extensive territory monitoring. The wide variety spatial parameters great diversity geographical regions influence complicate task. Consequently, there no unified approach predicting occurrences using data AI techniques. goal study to explore potential various available - meteorological, geo-spatial, anthropogenic machine learning (ML) algorithms. We developed pipeline acquisition subsequent ML-based algorithm development. includes following algorithms: Random Forest, XGBoost, Autoencoder, ConvLSTM, Attention Multilayer Perceptron, RegNetX. addition, several metrics assess quality models case highly imbalanced spatio-temporal data. To conduct collected unique dataset covering large central Russia, incorporating than 17,000 verified events over period 10 years. findings underscore necessity developing individual ML tailored each region, taking into account specific features correlated with probability occurrence. achieved models, as measured F1-score, varies from 0.7 0.87 depending demonstrating integrating such emergency response systems.

Language: Английский

Citations

0
Meteorological analysis of an extreme pyroconvective wildfire at Dunalley-Forcett, Australia DOI Creative Commons
Ivana Čavlina Tomašević, Paul Fox‐Hughes, Kevin K. W. Cheung

et al.

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Language: Английский

Citations

0
An efficient and comprehensive field protocol for assessing fuel characteristics for fire behaviour modelling in Australian open forests DOI Creative Commons
J. J. Hollis, Miguel G. Cruz, Lachlan McCaw

et al.

MethodsX, Journal Year: 2025, Volume and Issue: unknown, P. 103345 - 103345

Published: April 1, 2025

Language: Английский

Citations

0
Limited spatial co-occurrence of wildfire and prescribed burning on moorlands in Scotland DOI Creative Commons
Debbie A. Fielding, Scott Newey, Robin J. Pakeman

et al.

Biological Conservation, Journal Year: 2024, Volume and Issue: 296, P. 110700 - 110700

Published: June 29, 2024

Prescribed burning of vegetation is a land management approach that can be used to prevent or reduce the risk wildfires yet burns escape and an ignition source for wildfires. In Scotland, muirburn form prescribed encourage new growth heather grouse livestock. Future regulation aims mitigate potential negative impacts on biodiversity, peat carbon stocks wildfire risk. this study, we assessed spatial co-occurrence across Scotland. Evidence was quantified using high-resolution aerial imagery from 2018 occurrence composite satellite detection covering 2015 2020. Fires were mapped dominant habitat types (acid grasslands, heathlands, peatbogs) soil. Overall, 96 % total area occurred outside moorlands managed by muirburn, with 1.1 moorland area. Heather peatbogs habitats soils had greater proportional than other non-peat soils. contrast, heathlands muirburn. We suggest limited wildfires, may due fuel load reduction following muirburns, but arise prevention strategies estates and/or lower recreational caused addition regulation, our findings emphasise need policy, management, awareness education areas

Language: Английский

Citations

2
Modernizing the US National Fire Danger Rating System (version 4): Simplified fuel models and improved live and dead fuel moisture calculations DOI Creative Commons
W. Matt Jolly, Patrick H. Freeborn,

Larry Bradshaw

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 181, P. 106181 - 106181

Published: Aug. 13, 2024

Language: Английский

Citations

2