Performance Evaluation of an Operational Rapid Response Fire Spread Forecasting System in the Southeast Mediterranean (Greece)

Giannaros, Theodore M.; Lagouvardos, Konstantinos; Kotroni, Vassiliki

doi:10.3390/atmos11111264

Cited by 14 publications

(9 citation statements)

References 57 publications

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“…These values are much higher than the AGB values of the 13 fuel-type model that was developed for the Mediterranean climate of California [26,28] and that is used in the Israeli operational WRF-Fire forecasting system. The AGB values of timber and chaparral are fixed and lower than the spatially variable SAR product estimation (Figure 4c,g), which is one of the sources of errors in wildfire simulation [59,60].…”

Section: Fuel and Agbmentioning

confidence: 92%

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Yaron

Kozokaro²,

Brenner

et al. 2022

Remote Sensing

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Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations.

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Section: Fuel and Agbmentioning

confidence: 92%

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Yaron

Kozokaro²,

Brenner

et al. 2022

Remote Sensing

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“…Therefore, the vertical pattern of GPH and temperature anomalies can be used as a necessary weather condition for potential fire spread and extinguishing in northern California. Currently, some studies focus on the forecasting and simulation of wildfires (Coen et al, 2020;Giannaros et al, 2020;Worsnop et al, 2020). If we apply this method to a model forecast product, we might obtain a clearer early warning signal for a possible wildfire when the model is capable of predicting the anomalous synoptic pattern.…”

Section: Anomalous Synoptic Patterns For the Third Wildfire (Camp)mentioning

confidence: 99%

Opposite anomalous synoptic patterns for potential California large wildfire spread and extinguishing in 2018 cases

Qian

et al. 2021

Atmospheric Research

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The consecutive occurrence of three large wildfires in 1 year is rarely seen in northern California including the deadliest case during 8-25 November 2018 in Butte County. They happened under favorable fuel and meteorological conditions including long-term warming trends, normal summer-autumn warm-dry climate, and extreme heated air masses with strong offshore wind. The former two meteorological conditions contribute a background for large wildfires but extreme heated air masses and strong offshore winds are critical potentials for a fire spread. The relationship between extreme heated/cooled air masses and the three large wildfires was carefully examined from their starts to extinguishing. An anomaly-based synoptic analysis method is used by separating atmospheric variables into climatology and anomalies. A 3-dimensional anomalous synoptic pattern of atmospheric variables is established for the three wildfire cases in 2018. The analysis showed that one anomalous warm-air mass in the mid-to-low troposphere dynamically associated with a positive center of geopotential height (GPH) anomalies at the upper troposphere (150-300 hPa) is an anomalous synoptic pattern indicating a potential large wildfire spread, and conversely by negative anomalies when the fires were extinguished. The ECMWF model seems to be capable of predicting such anomalous temperature-pressure patterns to indicate possible fire spread and extinguishing.

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“…Like spatial fire patterns, historical trends in fire activity were largely driven by non-climatic drivers during the past decades in the EU-Med. While fire-weather increased over much of the area (Giannaros et al 2020), fire activity, including fire numbers and burnt areas, significantly decreased in most regions (Turco et al 2017;Silva et al 2019) because of co-evolving human factors that overwhelmed the impact of climate change. Negative trends can be explained, at least in part, by an increased effort in fire management and prevention in several countries, in response to the large and destructive fires that occurred during the 1980s and 1990s (Moreno et al 2014;Ruffault and Mouillot 2015), albeit this effect remains difficult to quantify at the local scale.…”

Section: Introductionmentioning

confidence: 99%

Disentangling the factors of spatio-temporal patterns of wildfire activity in south-eastern France

Castel-Clavera

Pimont²,

Opitz³

et al. 2022

Int. J. Wildland Fire

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Background. Identifying if and how climatic and non-climatic factors drive local changes in fire regimes is, as in many other human-dominated landscapes, challenging in south-eastern France where both heterogeneous spatial patterns and complex fire trends are observed. Aim. We sought to identify the factors driving the spatial-temporal patterns of fire activity in southeastern France. Methods. We incorporated several non-climatic variables into the probabilistic Firelihood model of fire activity and implemented an enhanced spatio-temporal component to quantitatively assess remaining unexplained variations in fire activity. Key results. Several non-climatic drivers (i.e. orography, land cover and human activities) contributed as much as fire-weather to the distribution of fire occurrence (>1 ha) but less to larger fires (>10, 100 and 1000 ha). Over the past decades, increased fire-weather induced a strong increase in wildfire probabilities, which was actually observed on the western part of the region but not so in the east and Corsican Island, most likely due to reinforced suppression policies. Conclusions. While spatial patterns in fire activity are driven by land-use and land-cover factors, temporal patterns were mostly driven by changes in fire-weather and unexplained effects potentially related to suppression policies but with large differences between regions.

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Performance Evaluation of an Operational Rapid Response Fire Spread Forecasting System in the Southeast Mediterranean (Greece)

Cited by 14 publications

References 57 publications

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Opposite anomalous synoptic patterns for potential California large wildfire spread and extinguishing in 2018 cases

Disentangling the factors of spatio-temporal patterns of wildfire activity in south-eastern France

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