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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/nhess-2020-353
https://doi.org/10.5194/nhess-2020-353

  11 Dec 2020

11 Dec 2020

Review status: this preprint is currently under review for the journal NHESS.

Modeling of wildfire occurrence by using climate data and effect of temperature increments

Amir Hossein Sadat Razavi, Majid Shafiepour Motlagh, Alireza Noorpoor, and Amir Houshang Ehsani Amir Hossein Sadat Razavi et al.
  • Department of Environmental Engineering, University of Tehran, Tehran, Iran

Abstract. Forest fires are assumed as one of the key natural hazards in the globe since it causes great losses in ecology, economy, and human lives; recent fire cases in US and their vast damages are vivid reasons to study the wildfires more deeply One of the basic requirements to manage the threats and protect wildlife is the ability to predict wildfire spots which is necessary to prioritize forest management. In this study, a 25-year period natural wildfire database and a wide array of environmental variables are used to develop an artificial neural network model with the aim of predicting potential fire spots. This study focuses on non-human reasons of wildfires (natural) to compute global warming effects on wildfires. Among the environmental variables, this study shows the significance of temperature for predicting wildfire cases while other parameters are presented in the next study. The study area of this study includes all forest fire cases in united states from 1992 to 2015 excluding tropical forests. The data of eight days including the day fire occurred and 7 previous days are used as input to the model to forecast fire occurrence probability of that day. The climatic inputs are extracted from ECMWF. The inputs of the model are temperature at 2 meter above surface, relative humidity, Total pressure, evaporation, volumetric soil water layer 1, snow melt, Keetch–Byram drought index, total precipitation, wind speed (along U and V direction), and NDVI. The results show there is a transient temperature span for each forest type which acts like a threshold to predict fire occurrence. In Temperate forests, A 0.1-degree Celsius increase in temperature relative to 7-day average temperature before a fire occurrence results in prediction model output of greater than 0.8 for 4.75 % of fire forest cases. In Boreal forests, the model output for temperature increase of less than 1 degree relative to past 7-day average temperature represents no chance of wildfire. But the non-zero fire forest starts at 2 degrees increase of temperature which ends to 2.62 % of fire forest cases with model output of larger than 0.8. It is concluded that other variables except temperature are more determinant to predict wildfires in temperate forests rather than in boreal forests.

Amir Hossein Sadat Razavi et al.

 
Status: open (until 22 Jan 2021)
Status: open (until 22 Jan 2021)
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Amir Hossein Sadat Razavi et al.

Amir Hossein Sadat Razavi et al.

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