Mr Mohammad Reza Salimi Sobhan,, Mrs Zahra Beygom Hejazizadeh , Mrs Fariba Sayadi, Mrs Fatemeh Qaderi,
Volume 24, Issue 75 (12-2024)
Abstract
In examining natural hazards, such as hail, statistical analyzes can play a significant role. Due to the great importance of economic and side losses of hail in the northern part of Zagros with maximum frequency and damage, the necessity of studying its temporal and spatial location is felt very distinctly. Therefore, in order to estimate and estimate the probability of occurrence of this phenomenon, 10 hail data data of 10 synoptic stations of the region were used during the statistical period of 2014- 1992. In choosing the best method for calculating the distribution of precipitation probabilities, different types of probability distributions of discrete random variables were tested by means of both Kolmogorov and Anderson-Darling testsThe results showed that the good Poisson distribution test had a good fit for hail occurrence at a high level of 90.99%. Baneh station with the maximum frequency of hail precipitation has the lowest probability (0.023%) and Pearnshahr station has the most probable days without hail (0.39%). Therefore, the probability of occurrence of hail in Baneh has a higher percentage. In the next round, the negative binomial model satisfies the observations of this type of precipitation well. The calculation of probabilistic distributions by these two methods showed that the probability of occurrence of hail with the frequency of 1 to 6 times and more in the region and the highest probability is related to the frequency of 3 occurrences of 0.20%. At a frequency of 1 to 6 times, the probability of occurrence of this phenomenon is 5 times more than the probability that it will not occur, which indicates the region's high vulnerability to this type of climate risk.
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Saeed Jahanbakhshasl, Ali Mohammadkhorshiddoust, Fatemeh Abbsighasrik, Zahra Abbasighasrik,
Volume 24, Issue 75 (12-2024)
Abstract
Assessing and predicting future climate change is of particular importance due to its adverse effects on water resources and the natural environment, as well as its environmental, economic and social effects. Meanwhile, rainfall is also an important climatic element that causes a lot of damage in excess conditions. West Azerbaijan Province is no exception. The aim of this study is to model and predict 30 years of rainfall in West Azerbaijan province. The statistical period studied is 32 years (2019-1987). Selected stations in the province include Urmia, Piranshahr, Takab, Khoy, Sardasht, Mahabad and Mako stations. Average slider time series models, Sarima (seasonal Arima), Health Winters were used for analysis and prediction and also linear regression and Mann-Kendall test were used to determine the data trend. The results show an increasing trend of precipitation in Urmia, Piranshahr, Khoy, Sardasht and Mako stations and a decreasing trend in Takab and Mahabad stations. According to the results of comparing the models used, the Health Winters model with the least error in the absolute mean of deviations, mean squared deviations and the percentage of absolute mean errors was introduced as the best precipitation forecasting model for West Azerbaijan province.
province. [A1]
Ali Hashemi, Hojjatollah Yazdanpanah, Mehdi Momeni,
Volume 24, Issue 75 (12-2024)
Abstract
This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error.
Hamed Heidari, Darush Yarahmadi, Hamid Mirhashemi,
Volume 24, Issue 75 (12-2024)
Abstract
Human interventions in natural areas as a change in land use have led to a domino effect of anomalies and then environmental hazards. These extensive and cumulative changes in land cover and land use have manifested themselves in the form of anomalies such as the formation of severe runoff, soil erosion, the spread of desertification, and salinization of the soil. The main purpose of this study is to reveal the temperature inductions of the land cover structure of Lorestan province and to analyze the effect of land use changes on the temperature structure of the province. In this regard, the data of land cover classes of MCD12Q2 composite product and ground temperature of MOD11A2 product of MODIS sensor were used. Also, in order to detect the temperature inductions of each land cover during the hot and cold seasons, cross-analysis matrix (CTM) technique was used. The results showed that in general in Lorestan province 5 cover classes including: forest lands, pastures, agricultural lands, constructed lands and barren lands could be detected. The results of cross-matrix analysis showed that in hot and cold seasons, forest cover (IGBP code 5) with a temperature of 48 ° C and urban and residential land cover (IGBP code 13) with a temperature of 16 ° C as the hottest land use, respectively. They count. In addition, it was observed that the thermal inductions of land cover in the warm season are minimized and there is no significant difference between the temperature structure of land cover classes; But in the cold season, the thermal impulses of land cover are more pronounced. The results of analysis of variance test showed that in the cold period of the year, unlike the warm period of the year, different land cover classes; Significantly (Sig = 0.026) has created different thermal impressions in the province. Scheffe's post hoc analysis indicated that this was the difference between rangeland cover classes and billet up cover.
Ms Akram Alinia, Dr Amir Gandomkar, Dr Alireza Abasi,
Volume 24, Issue 75 (12-2024)
Abstract
The main goal of this research is to analyze the time series trend of fire events in natural areas and reveal the relationship between these fire events and vegetation levels in Lorestan province. In this regard, the data of the fire product of the Madis sensor (MOD14A1) and the vegetation product (MOD13A3) of the Madis sensor were used during the statistical period of 2000-2020. The monthly and annual spatial distribution of fires in Lorestan province was investigated. Cross-information matrix analysis and spatial correlation matrix were used to reveal the relationship between fire occurrences and vegetation. The results showed that more than 70% of the total frequency of fire occurrences in natural resources fields (fires with code 2) in Lorestan province is related to June and then July. In terms of the long-term trend, the 21-year trend of the frequency of fire incidents in the province showed that the frequency of incidents in the natural resources areas of the province has generally increased with an annual slope of 3 incidents. The results of the correlation analysis between the monthly vegetation cover and the annual frequency of fire occurrences showed that the fire occurrences in the province showed a significant correlation with the vegetation cover changes in 4 months of the growing period, i.e. from May to August. Cross-matrix analysis between the spatial distribution of fire occurrence foci and NDVI index, both of which were products of MODIS measurement, indicated that, in general, the highest frequency of fire occurrences in Lorestan province in the period from May to August corresponds to Greenness range was 0.15 to 0.22. This range of vegetation generally corresponded to rainfed lands, weak pastures and low-density forest patches
Shamsallah Asgari, Tayeb Razi, Mohamadreza Jafari, Ali Akbar Noroozi,
Volume 25, Issue 76 (3-2025)
Abstract
Due to the significance of forests in both the natural and human environment, this study aims to investigate the impact of meteorological drought on oak forest dieback in Ilam province. Specifically, the study seeks to determine the relationship between Zagros Forest drought and droughts in this particular region. The analysis utilizes the Standard Precipitation Index (SPI) to identify the frequency of droughts during different time periods. The results indicate that the years 2007, 2008, 2011, 2015, and 2016 experienced the highest occurrence of droughts. Additionally, remote sensing data from MODIS images were employed to examine the trend in tree greenness (NDVI) from 2000 to 2016. The analysis reveals a significant correlation (R2 = 0.9999) between the greenness trend and the drought index (SPI). Moreover, a land survey of oak drying points and simulation using Landsat satellite images, with a 15×15 pixel output from GIS software, indicate that approximately 17,894 hectares of forests in the region experienced drying and destruction between 2000 and 2016. By combining the oak forest drying layer with the output layers derived from drought zoning, visual indicators were created, and statistical analysis was conducted for three 5-year time series. The results demonstrate a correlation coefficient of 96.6% and an explanation coefficient of R2 = 0.985 for the 2002-2006 time series, a correlation coefficient of 95.4% and an explanation coefficient of R2 = 0.980 for the 2007-2011 time series, and a correlation coefficient of 98.8% and an explanation coefficient of R2 = 0.995 for the 2012-2016 time series. These findings illustrate the influence of drought and its variations in terms of intensity and duration on oak forests in the Zagros region of Ilam. Based on the study results, it is predicted that if the drought persists with the same trend, approximately 1,118.4 hectares of oak forests in Ilam province will dry up and be destroyed annually.
Zeinab Mokhayeri, Ebrahim Fatahi, Reza Borna,
Volume 25, Issue 76 (3-2025)
Abstract
To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs. The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts. The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.
Mohammad Baaghideh, Motahhareh Zargari,
Volume 25, Issue 76 (3-2025)
Abstract
The performance of broiler chickens is directly influenced by temperature changes and the occurrence of heat stress, whether it is cold or hot. The present study aims to assess the cooling and heating degree days in different stages of broiler chicken production in Khorasan Razavi province. To achieve this objective, daily average temperature data were collected from 13 synoptic stations during the statistical period of 1988-2018. Cooling and heating degree days were calculated for each week of production using specific thresholds, and their spatial distribution was analyzed. Furthermore, the relationship between cooling and heating degree days and geographical features was evaluated. The findings indicate a decrease in cooling degree days and an increase in heating degree days as latitude increases. The northern and western regions of the province exhibited a greater need for heating throughout all stages of broiler chicken production, whereas the eastern and southern regions had higher cooling requirements at different production stages. Overall, the northeastern, southern, and western marginal areas of the province, including Khaf, Gonabad, Kashmar, Sarakhs, and Sabzevar, exhibited the lowest cooling and heating needs during the 6-week period of broiler chicken production, making them suitable climates for this economic activity.
Doc. Zahra Ghassabi, Doc. Hoshang Ghaemi, Mr. Ebrahim Mirzaei,
Volume 25, Issue 76 (3-2025)
Abstract
The structure of deep moist convection can be influenced by various factors, including wind shear, available potential energy of convection, relative humidity, and vertical distribution of these variables. Among these factors, wind shear plays a more significant role in the creation of convection. The interaction between large-scale and synoptic-scale processes, along with the adjustment of available potential energy for convection and the presence of convection inhibitors, creates conditions suitable for the development of convection. The large-scale average reduces the convection inhibitor, while even small vertical velocities, such as a few centimeters per second, can have a noticeable impact on the environment's sounding. The presence of potential instability is also considered an important factor in initiating deep moist convection. When the temperature reaches the critical point and the convection inhibitor is removed, moist deep convection begins. If an air parcel rises above the lower stable layer with low relative convective inhibition energy and high relative free convective potential energy, it supports the development of deep moist convection. The initiation of updrafts by warm air masses and the subsequent development of convection depend on parameters like vertical wind shear and the inversion cap of the environment, among others. Large-scale convective systems can be triggered with less forcing due to the significant uplift of the air mass from the surface to the convection level along the front.
Dr Abolhassan Gheibi, Mr Ali Soleymani, Hossein Malakooti,
Volume 25, Issue 76 (3-2025)
Abstract
Nitrogen dioxide is a significant factor affecting air quality in various regions worldwide. The aim of this study is to examine the concentration and trends of nitrogen dioxide pollution between 2005 and 2018, and explore its association with precipitation levels in the region. Based on data derived from the OMI sensor in Iran, the average vertical column concentration of nitrogen dioxide during this period revealed that the highest concentration was observed in the troposphere. Megacities, particularly Tehran metropolis, exhibited elevated levels of nitrogen dioxide due to the high population density and extensive road transportation. Analyzing the annual changes in nitrogen dioxide concentration in the troposphere alongside the average annual precipitation in Iran, it was observed that the pollutant concentration increased from 2005 to 2016 and subsequently decreased from 2016 to 2018, primarily due to population growth. However, when considering the overall trend, there was an upward trend with a slope of 3.53× -2. In contrast, the time series analysis of average annual precipitation in Iran demonstrated a declining trend with a slope of (-0.159 mm × ). Comparing the trends of these two variables, it can be deduced that they exhibit a negative correlation.
Mohammadsaleh Ekhlasi, Dr. Somayeh Soltani-Gerdefaramarzi, Dr. Abolfazl Azizian, Morteza Gheysouri,
Volume 25, Issue 76 (3-2025)
Abstract
In this study, we examined the impact of climate change on the virtual water content of key crops in Kerman province for future periods. Specifically, we utilized the climatic data from the HadCM3 model under the RCP4.5 radiative forcing scenario. The model was calibrated and validated for the base period of 1991-2011. We predicted the precipitation levels, as well as the maximum and minimum temperatures, for selected stations from 2011 to 2070 using data from LARS-WG. These predictions were then compared to the base period. The virtual water content was calculated for three selected crops: alfalfa, barley, and wheat. Our findings indicate that climate change has a significant impact on evapotranspiration and the performance of these crops, consequently affecting future agricultural water productivity. As we project an increase in average temperature during the growing season due to climate change, it is worth noting that the maximum temperature parameter will be more affected by this phenomenon than the minimum temperature. This, in turn, will lead to increased water requirements and plant evaporation-transpiration during this period. Our research also reveals a decrease in precipitation during hot seasons and an increase during cold seasons across all study stations. Notably, the virtual water content for all crops studied demonstrates an upward trend, with barley and wheat showing the greatest average increase in the future period. Specifically, the Kerman station exhibits a substantial increase in virtual water content for barley and alfalfa products, at a minimum of 30% higher than the base period.
