مجله مخاطرات محیط طبیعی
پیاپی 37 (پاییز 1402)

Under rapid climate change, desertification, and land degradation, a critical management issue in forests of northern Iran is how to increase forest resilience to wildfires. For decades, forest managers have been employing silvicultural treatments to restore the Hyrcanian forest stand's structure, composition, and diversity, but there is no scientific evidence to support that these treatments reduce the wildfire risk in the area. In this study, based on the wildfire behavior modeling approach using FlamMap MTT, we evaluated spatial relationships between silvicultural fuel reduction treatment (e.g., thinning) and surface and crown burn probabilities in the 6 and 7 Malekroud districts of Siahkal forest. We first analyzed fuel characteristics after the development of silvicultural treatments in the period 2007-2017 in the study area to evaluate low thinning and heavy thinning as restoration tools for similar fuel conditions. Furthermore, we conducted analyses of variance to determine the influence of the silvicultural thinning treatment on surface and canopy burn probabilities. Modeling results at the landscape scale confirm the important role of thinning in the mitigation of crown burn probability (almost 11%) in the study area. However, the effect of thinning on the surface burn probability was not statistically significant (P> 0.05). Comparing the two types of thinning in terms of intensity, heavy thinning had a greater effect on changing the surface and crown (i.e., 10% increase and 39% decrease, respectively, in conifer plantation) burn probability than the low thinning. The modeling approach in the study offers new insights to improve the spatial mapping of wildfire likelihood, include variables sensitive to thinning operation in the models, and provide useful information to prevent extreme wildfire behavior through effective silvicultural treatments.

Landslide is one of the environmental hazards that every year cause loss of life and financial losses in large parts of the mountainous regions of Iran. Therefore, identifying important and effective factors for the occurrence of this phenomenon can be used as a practical tool in reducing possible losses.  Modeling the susceptible area into landside hazards can be a useful tool for the police makers for protecting the prone areas. The Kameh watershed in the south of Isfahan province has been selected as one of the areas prone to landslides due to its special topography, geology, lithology, climate, and social-economic situation. This research aims to identify the effective factors in creating the phenomenon of landslides and to determine the areas with a high risk of landslides by applying the CART and TreeNet models with using of SPM and ArcGIS 10.8 software in the studied area. We have used 11 factors that affect the occurrence of landslides including geology, amount of precipitation, slope, distance from the river, TWI, land use, NDVI, elevation, aspect, distance from the faults, and distance from roads, as the independent variables and 311 landslides (as a polygon) collected from the study area were used as dependent variables. For running our models, 70% of the landslides were used as training and the remaining 30% of the landslide points were used for validation. The results of this study, according to the various statistical indicators that have been applied, show a higher accuracy of the CART model than the TreeNet model. The output of the model is a landslide peril map with 5 classes: very high, high, medium, low, and very low. It would be worth the managers and policymakers to use these results for protecting the susceptible areas.

Thunderstorms, also known as electrical storms or lightning storms, are one of the most damaging weather hazards and often damaging to the environment, crops, cities, and property. Therefore, forecasting them is very important for decreasing the possible damages. To forecast these phenomena, investigating the thermodynamic structure of the atmosphere and analyzing instability indices is necessary. The purpose of this study is to investigate the general characteristics of thunderstorms and the ability of GFS reanalysis data in identifying the spatial distribution of thunderstorms in Iran. For this aim in addition to GFS data, 8-year thunderstorm reports (2008 to 2015) in 361 weather stations in Iran from April to May were also used. At first, based on the thunderstorm frequency, each data series was divided into 4 quarters. Then, 10 atmospheric instability indices were calculated for daily GFS data and the results were analyzed. The results showed that the CIN index has the best performance. However, this index showed more error in Quarter-4 which had strong instability. The KI index showed moderate performance in forecasting weak instabilities but showed better results as instability increased in the fourth quarter. Both TT and 4LFTX indices had the same results and showed strong instabilities in the western parts of the country. Investigation of the average ability of the indices, induced that the four indices, including CIN, TT, 4LFTX, and KI had good performance in detecting instability. This performance varies from 90% in the CIN index to 60% in the KI index. The other investigated indices did not have an acceptable performance. It is recommended to redefine the threshold values of the instability indices according to the climate type in the studied stations.

Long term changes in climate parameters within the next few decades may affect the yield of many agricultural crops. In this aspect, few studies have been conducted on national scale to evaluate the proper strategies against the negative impacts of climate change for important crops. In the present study, different crop managements including changes in irrigation period, fertilizer volume and planting date in rice and soybean were simulated for two time-scale of near future (2021-2035) and far future (2036-2050) and the results evaluated as an adaptation strategy to oppose against the negative impact of climate change. We used downscaled meteorological parameters of MarkSimGCM data based on the output of three General Circulation Models (HadGEM2-ES, CSIRO-Mk3-6-0, GFDL-ESM2M) under three different RCP scenarios (RCP 2.6, 4.5, 8.5). In addition, we applied a 12-year phenological dataset of Hashemi and Alikazemi rice cultivars beside a two-year field data of Williams and Hobbit soybean cultivars and the simulations were carried out by DSSAT plant model. The results showed that changing the planting date from 10th of May to 30th of April in the RCP 8.5 scenario and Hashemi cultivar could improve the crop yield by 18.9% in the 5-day periodic irrigation, compared to flood irrigation. Moreover, the advance of planting date for 20 and 30 days will improve the crop yield, but is not effective as the 10-day acceleration. For far future window (2036-2050) and RCP 2.6 scenario, implementing the F3 fertilizer will improve the crop yield by 10.7%. Among the aforesaid strategies, the maximum yield increase was found under fertilizer managements and RCP 2.6 scenario. In general, although climate change will reduce the studied crop yields by 2050, but could compensate a part of this yield reduction, if appropriate crop managements is adopted.

Water scarcity has become a problem of concern for many cities in the world. Predicting water demand helps policymakers and water suppliers maintain the balance between the supply and demand of urban water resources, thereby preventing water wastage and shortage. Forecasting Urban Water Consumption (UWC) has a significant impress on efficient urban water management in cities in arid regions when considering the implications of climate change.  In this research, five firefly algorithms were used to estimate the consumption of drinking water in the Sistan region for the years 2006-2020 and were compared. The data from 2006 to 2015 were used for training and learning and finding the optimal weight of the model, and the remaining data from 2016 to 2020 were used to test the model. The results of the model showed that 5 different FA models can get possible answers. In the exponential and hybrid models, the relative error in the NDFA algorithm is 0.19, which has the lowest relative error among other algorithms, and in the linear model, the VSSFA algorithm has the lowest relative error with a relative error of 0.196. Therefore, the exponential model and NDFA method have better performance than other models and algorithms. And its prediction accuracy is above 81%. After ensuring the accuracy of the algorithm, the consumption of drinking water was predicted for the years 2023, 2024, and 2025. The results showed that the peak consumption is in July and August and the total consumption in 1402, 1403, and 1404 is equal to 7293, 7558, and 7674 thousand cubic meters, respectively.

In late March 2019, heavy rains occurred in Iran, which led to floods in several provinces of the country. In this paper, the spring floods of 2019 in the western basins of the country (Karkheh and Karun) have been investigated. These floods brought heavy losses to the country. Nation administration tried to dissect this event and know its causes and consequences. This experience showed that flood is an anthropogenic - physical phenomenon and it is naturally complex like all other human phenomena; Complex, in the sense that it is the result of the interaction of many different components. Here, we have examined partly one of the components of this complex phenomenon, that is, the part of precipitation from the climatological point of view. This investigation showed that the intensity of rain in the spring of 2019 did not exceed the historical events, but the amount of rain received by the basins was unprecedented. Therefore, from a climatological point of view, heavy rainfall and the location of the core of rainfall played the most important role in the occurrence of this flood. The amount of rainfall in the Karkheh and Karun basins in 9 days was equivalent to about 70% of the annual rainfall of these two basins. The establishment of the rain core in the eastern steep part of the basin has also contributed to the intensification of floods.

Ferrosilicon is used in the production of steel and cast iron as a modifier. The most important pollutants in the ferrosilicon industry are particulate matter (PM2.5 and PM10) and dust, carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx), and sulfur oxides (SOx). The purpose of this study was to investigate the environmental status of air pollution in Iran's ferroalloy industry by examining air pollutant control equipment. Iran's ferroalloy factory with a production capacity of 60 thousand tons of ferrosilicon is located in Lorestan province and air pollutants were measured in the years 2020 and 2021. The exhaust gases and dust were measured with the XL 350 and Westech portable particle detectors and environmental particulate matter with the Dust Trak 8520, respectively. The data analysis was done using SPSS and Excel software. So that the normality of the data was reviewed using the Kolmogorov-Smirnov test and the average of the data was compared with the pollution standards using the one-sample t-test. The results showed that all measured parameters of flue gases, dust, and ambient particulate matter PM2.5 and PM10 during the studied years 2020 and 2021 were according to the standard and there was no polluted air in the factory. Having suitable environmental equipment, including U-shaped pipes to cool the transfer smoke-containing gases from the furnace area to the dust collection hall (Baghouse), a cyclone separator for dust removal and the collection of particle matter (PM), and bag filters in the dust collection hall (Baghouse) have been used for collecting particle matter (PM) and filtering dust. Also, the green space, with more than 60% of the factory space, has played a very influential role in reducing the air pollution of the factory.

By assessing the trend of air temperature changes, it is possible to explore traces of climatic changes in the area of Iran. Climate change and temperature increase are important human-environmental issues. Based on the change point method, it is possible to identify the onset time of changes in basic variables such as minimum and maximum temperature. Therefore, the present research aims to analyze the change point of temperature thresholds of heat and cold waves in Iran. The temperature threshold means the 95th percentile for maximum temperature values and the 5th percentile for minimum values. For this purpose, the temperature data (minimum and maximum) of 43 synoptic stations in Iran, which have a long statistical period (1966-2018) and suitable distribution, were used. To identify the temperature threshold change time, three change point methods - Pettitt, SNHT, and Buishand's Range Test were used. The results showed that the temperature threshold of heat and cold waves has been increasing over the past few decades. The rising growth rate is equal to 0.019 and 0.052 degrees Celsius per year, respectively. Therefore, in the current study, it was found that the increasing trend of the temperature threshold led to an increase in the frequency of maximum temperatures, and on the other hand, it will lead to an increase in hot days and a decrease in cold nights, in addition to the higher frequency of events, it can be stated that the frequencies will move towards higher values. Based on the results of the spatial-temporal analysis of the thresholds, the increasing trend of the minimum temperature in the northwest, Zagros, and southeast regions of Iran is quite evident. And from the other results of this study, we can emphasize the time of the change point or jump of the temperature threshold of cold and heat waves around 1991. The mentioned year is in line with global studies