Analysis of the Spatial Pattern of Fire Points Distribution with Foehn Wind in the Rasht City

Introduction

Among the many human and biophysical factors in fire, climate and weather are also the main drivers of fire initiation and spread. But the separation of atmospheric processes affecting fire complicates the possibility of fire modeling and makes its management difficult. Considering the importance of fire and its close relationship with weather conditions, it seems that a comprehensive analysis of the spatial distribution of fire points along with the wind in Rasht city has a suitable position for further study. Therefore, in this research, we will try to provide a correct analysis of the spatial pattern of fires in Rasht city. Also, by examining how the fire points are distributed in this city, it is possible to determine the high-risk areas in terms of the occurrence of fire events in order to perform a better management to deal with possible incidents and fire extinguishing during Foehn wind in this city.

Materials and methods

In order to identify Foehn wind days in Rasht, the data of maximum temperature, relative humidity, wind speed and direction were used for 8 years (1392 to 1400). In this way, the Foehn wind days were divided into three groups of moderate, severe and very severe days based on the criteria of 1sd, 2sd and 3sd of the maximum daily temperature of the period compared to the maximum temperature of the Foehn day. Then, using the average nearest neighbor method (ANN) and Ripley's K function, the spatial pattern of fire points was determined for moderate, severe and very severe winds.



Results and discussion

The results showed that out of 160 Foehn winds that occurred in this area, 72 were moderate, 59 were severe, and 29 were very severe. The results showed that 72, 59, and 29 of the 160 Foehn winds that occurred in this area were moderate, severe, and very severe, respectively. In total, 23.6, 6.9, 0, 0, 0, 0, 2.8, 1.4, 9.7, 13.9, 16.7, and 25% of 100% of Medium Foehn winds happened from April until March, respectively. Also, 11.9, 5.1, 0, 0, 0, 0, 1.7, 1.7, 13.6, 22, 16.9 and 27.1 percent of the total Severe Foehn winds, , 10.3, 0, 0, 0, 0, 0, 6.9, 0, 13.8, 34.5, 13.8 and 20.7 percent of the total very severe Foehn winds occurred from April to March, Respectively. In this regard, the relationship between the maximum daily temperature and the average maximum temperature of the period (40 years) is 0.608 and is a direct relationship and is significant at both α=0.05 and α=0.01 levels. This relationship between the daily maximum temperature and the minimum relative humidity that occurs at noon is negative, inverse and is -0.504, which indicates that the relative humidity of the air decreases during the occurrence of a Foehn wind days in this area. The results of the multivariate regression model to determine the effect of each of the climatic variables on the maximum daily temperature showed that the Adj.R2 value of this relationship is equal to 0.566. Considering the large value of F and the value of Sig=0.000<0.05, we conclude that the regression model is suitable and most of the changes in the dependent variable have been seen in the regression model. Also, the two variables of average maximum temperature of the period and minimum relative humidity have a relationship with the variable of maximum daily temperature, and the effect of the first is direct and the second is inverse. As a result, with the increase in temperature caused by the Foehn wind, the relative humidity decreases greatly. The two variables of wind direction and average wind speed do not have significant effects on the daily maximum temperature caused by the Foehn wind. The results of calculating the average index of the nearest neighbor indicate that all three types of random, cluster and scattered patterns can be seen in the fire points of medium, severe and very severe Foehn winds. But the pattern governing the distribution of all the fire points of the period is also cluster type. Also, the results of Ripley's K function showed that the K value observed in the 10 investigated steps is greater than the expected K value, and this confirms the clustering of fire points in Rasht city.

Conclusion

In this research, it was found that the pattern governing the spatial distribution of fire spots in Rasht city and its surroundings in the months of January, February, March, April, October, November and December is of all three types of random, cluster and scattered patterns. However, the frequency pattern of the entire distribution of fire points for medium, severe and very severe Foehn wind was obtained as a cluster type. In other words, the clustering of the total frequency distribution indicates that there are different fire hotspots in the city of Rasht and its surroundings, which can be identified as different clusters. This feature of fire points has a great impact on the construction of new fire stations and it can be used in locating new fire stations to speed up the arrival of firefighters to fire situations. Finally, it should be said that the spatio-temporal information about the Foehn winds can help to understand its various effects at the local, regional and global scale.

Keywords: Foehn wind, fire, average nearest neighbor, Ripley's K function, Rasht city