High-resolution fire danger forecast for Poland based on the Weather Research and Forecasting Model

Mandal, Alan; Nykiel, Grzegorz; Strzyżewski, Tomasz; Kochanski, Adam K.; Wrońska, Weronika; Gruszczyńska, M; Figurski, Mariusz

doi:10.1071/wf21106

Cited by 9 publications

(5 citation statements)

References 46 publications

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“…The weather research and forecasting (WRF) model, an open-source atmospheric simulation model that integrates regional land-atmosphere phenomena [19], can overcome many limitations of the observed stations in providing information relevant to local and regional fire management at a finer spatial scale. WRF has widely been applied in fire research by providing weather input data of drought-fire models, such as in Australia [20], Poland [21], and the United States [22]. In Indonesia, the applicability of the WRF of fire assessment has been initiated recently in Sumatra peatlands [23,24].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Rewetting Peatland on Fire Hazard in Riau, Indonesia

et al. 2023

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Rewetting peatland is an ongoing effort in Indonesia to restore the hydrological cycle and carbon balance of the ecosystem. However, quantifying the impact of rewetting on mitigating fire remains a challenge. Here, we assess the impact of large-scale rewetting on fire risks and occurrences (duration, coverage area, and the number of events) in 2015–2021. The weather research and forecasting (WRF) model was integrated with a drought–fire model to spatially quantify fire hazards in Riau, Sumatra. The results show that rewetting has resulted in decreasing the frequency of extreme events in the study area (pre- and post-rewetting, respectively, were seven and four events). Although the area influenced by extreme events reduced following rewetting by 5%, the mean duration of extreme events increased. Our findings reveal that widespread prolonged extreme fire hazards only occurred during drying El Niño events in 2015 and 2019. The findings obtained in this case study provide quantitative evidence of the reduced fire hazard resulting from peat restoration in Indonesia. Further, the findings assist in assessing the success of peatland restoration programs and improve our knowledge of the ability to monitor and forecast fire risks in tropical peatlands.

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Section: Introductionmentioning

confidence: 99%

The Impact of Rewetting Peatland on Fire Hazard in Riau, Indonesia

et al. 2023

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“…It has been observed that in Mediterranean ecosystems, climate variability is the leading factor regarding wildfire seasonality and fuel layer flammability [48]. Increased fuel flammability due to prolonged drought and higher overall temperatures is considered one of the main ecosystem responses to climate change [49].…”

Section: Study Regionmentioning

confidence: 99%

Fusion of Remotely-Sensed Fire-Related Indices for Wildfire Prediction through the Contribution of Artificial Intelligence

et al. 2023

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Wildfires are a natural phenomenon, which nowadays, due to the synergistic effect of increased human intervention and the escalation of climate change, are displaying an ever-increasing intensity and frequency. The underlying mechanisms present increased complexity, with the phenomenon itself being characterized by a significant degree of stochasticity. For the above reasons, machine learning models and neural networks are being implemented. In the current study, two types of neural networks are implemented, namely, Artificial Neural Networks (ANN) and Radial Basis Function Networks (RBF). These neural networks utilize information from the Fire Weather Index (FWI), Fosberg Fire Weather Index (FFWI), Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), aiming to predict ignitions in a region of Greece. All indices have been developed through the Google Earth Engine platform (GEE). In addition, a new index is proposed named “Vegetation-Enhanced FWI” (FWIveg) in order to enhance the FWI with vegetation information from the NDVI. To increase the robustness of the methodology, a genetic algorithm-based approach was used in order to obtain algorithms for the calculation of the new index. Finally, an artificial neural network was implemented in order to predict the Mati wildfire in Attica, Greece (23 July 2018) by applying the new index FWIveg, aiming to assess both the effectiveness of the new index as well as the ability to predict ignition events using neural networks. Results highlight the effectiveness of the two indices in providing joint information for fire prediction through artificial intelligence-based approaches.

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“…The FWI, based on temperature, relative humidity, wind speed and precipitation (Van Wagner 1987;Anderson 2005;Mandal et al 2021) was calculated daily using the gridded and station data. The median FWI values were calculated for each location over the year-long period.…”

Section: Vector Wind Change and Fire Weather Indexmentioning

confidence: 99%

Wind vector change and fire weather index in New Zealand as a modified metric in evaluating fire danger

Brody-Heine

Zhang

Katurji

et al. 2023

Int. J. Wildland Fire

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Background. Wildfire spread is influenced significantly by the weather variability. Wind speed and direction changes, resulting from synoptic weather systems and small-scale meteorological processes in complex terrain, can drastically alter fire intensity and spread. Aims. To investigate the use of vector wind change (VWC) integrated with the Fire Weather Index (FWI) as a new metric in fire danger. Methods. A 20-year FWI and modified FWI was calculated from weather station and gridded numerical weather simulation data. Key results. High VWC is found primarily on the South Island, inland and in areas of complex terrain. After incorporating VWC into the FWI, data from the modified FWI show spatiotemporal patterns that highlight the impact of wind variability in the fire danger. Conclusions. High VWC station data mapped with synoptic type suggest the primary factor in determining high VWC is meso-and micro-scale terrain-driven meteorology, not larger synoptic regimes. Implications. The current fire danger metric, the Fire Weather Index (FWI), does not include wind direction changes for high wind speeds. Therefore, the inclusion of VWC as an additional metric in fire danger calculations in a modified FWI could increase operational understanding of high-danger locations and terrain impacts on extreme and unpredictable fire behaviour.

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High-resolution fire danger forecast for Poland based on the Weather Research and Forecasting Model

Cited by 9 publications

References 46 publications

The Impact of Rewetting Peatland on Fire Hazard in Riau, Indonesia

The Impact of Rewetting Peatland on Fire Hazard in Riau, Indonesia

Fusion of Remotely-Sensed Fire-Related Indices for Wildfire Prediction through the Contribution of Artificial Intelligence

Wind vector change and fire weather index in New Zealand as a modified metric in evaluating fire danger

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