FOREST FIRES IN PORTUGAL — THE CONNECTION WITH THE ATLANTIC MULTIDECADAL OSCILLATION (AMO)

The data on forest fires in Portugal in the period 1980–2015 were used in the research: the annual number of forest fires, the annual burned area and the average burned area per fire. Increasing trend of the annual number of forest fires (statistically significant at p≤0.01), non-significant increasing trend of the annual burned area and decreasing trend of the average burned area per fire (statistically significant at p≤0.01) were recorded. Portugal is the most endangered country by forest fires in comparison with the other European countries. During the research period, fires in Portugal covered 23.6% of the total burned area in five the most affected European countries (Portugal, Spain, France, Italy and Greece). In the research of the connection between forest fires and the Atlantic Multidecadal Oscillation (AMO) Pearson correlation coefficient (R) was used. Monthly, seasonal and annual values of the AMO index were used in calculations. All R values recorded for the annual number of fires were positive and statistically significant at p≤0.01. The highest values were recorded for August (0.543) and for summer (0.525). With the annual burned area all R values were also positive and the highest one on the seasonal level was for summer (0.359). With the average burned area per fire all R values were negative (−0.428 was recorded for winter). The results of the research could be applied in the fire danger forecast for periods of several months. Other climate indices should also be considered in future research

ELECTRONS OR PROTONS: WHAT IS THE CAUSE OF FOREST FIRES IN WESTERN EUROPE ON JUNE 18, 2017?

Forest fires in Portugal (June 17–24, 2017) destroyed 64 lives and caused considerable material damage. The coronal hole CH807 and the energy region S5710 were in the geoeffective position on the Sun immediately before the outbreak of fires. In the period that preceded it, as well as at the time of the fires, increased values of the solar wind (SW) parameters (temperature, speed and density of particles) were recorded. In addition, a geomagnetic disorder was recorded. The shape and size of the burning areas, as well as the low air pressure over Portugal indicate the possibility of the effect of positively charged particles that came from the area south, i.e. southwest of Portugal. Nevertheless, it is a specific case that would have to be investigated in more detail

Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece

The impact of solar activity on environmental processes is difficult to understand and complex for empirical modeling. This study aimed to establish forecast models of the meteorological conditions in the forest fire areas based on the solar activity parameters applying the neural networks approach. During July and August 2018, severe forest fires simultaneously occurred in the State of California (USA), Portugal, and Greece. Air temperature and humidity data together with solar parameters (integral flux of solar protons, differential electron flux and proton flux, solar wind plasma parameters, and solar radio flux at 10.7 cm data) were used in long short-term memory (LSTM) recurrent neural network ensembles. It is found that solar activity mostly affects the humidity for two stations in California and Portugal (an increase in the integral flux of solar protons of > 30 MeV by 10% increases the humidity by 3.25%, 1.65%, and 1.57%, respectively). Furthermore, an increase in air temperature of 10% increases the humidity by 2.55%, 2.01%, and 0.26%, respectively. It is shown that temperature is less sensitive to changes in solar parameters but depends on previous conditions (previous increase of 10% increases the current temperature by 0.75%, 0.34%, and 0.33%, respectively). Humidity in Greece is mostly impacted by solar flux F10.7 cm and previous values of humidity. An increase in these factors by 10% will lead to a decrease in the humidity of 3.89% or an increase of 1.31%, while air temperature mostly depends on ion temperature. If this factor increases by 10%, it will lead to air temperature rising by 0.42%

Influence of summer temperatures on basic economic and tourism indicators of the Middle Mediterranean

The Middle Mediterranean is characterized by long, hot and dry summers, significant historical and cultural values and the warm Mediterranean Sea, making it attractive for coastal tourism. Given these characteristics, the goal of our paper is to analyze the influence of summer temperatures in the region of the Middle Mediterranean on the values of underlying economic and tourism indicators. The method of simple linear correlation and regression was used. Based on the results of testing, we came to the conclusion that the temperatures in the summer months have no significant influence on selected economic and tourism indicators. Also, we conclude that social factors have the greatest influence on these indicators. The coefficients of variation are calculated in the observed period to analyze the variability of the tested values. It could not be identified a statistically significant relationship of indicators with average temperatures. [Projekat Ministarstva nauke Republike Srbije, br. 47007

Hurricane genesis modeling based on the relationship between solar activity and hurricanes II

This research presents improved results on modelling relationship between the flow of charged particles that are coming from the Sun and hurricanes. For establishing eventual link, the methods of Big Data, such as Adaptive Neuro Fuzzy Inference System (ANFIS), Parallel Calculations, Fractal analysis etc., are applied. The parameters of solar activity were used as model input data, while data on hurricane phenomenon were used as model output, and both of these on daily level for May–October in period 1999–2013. The nonlinear R/S analysis was conducted to determine the degree of randomness for time series of input and output parameters. The time lag of 0–10 days was taken into account in the research. It led to growing input parameters up to 99. The problem of finding hidden dependencies in large databases refers to the problems of Data Mining. The ANFIS with Sugeno function of zero order was selected as a method of output fuzzy system. The “brute-force attack” method was used to find the most significant factors from all data. To do this, more than 3 million ANFIS models were tested on Computer Cluster using Parallel Calculation. Within the experiments, eight input factors were calculated as a base for building the final ANFIS models. These models can predict up to 39% of the hurricanes. This means, if causal link exists, approximately every third penetration of charged particles from coronary hole(s) or/and from the energetic region(s) toward the Earth precede the hurricanes