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Showing 16 results for Alijani
Farzad Shirzad, Mr Bohlol Alijani, Mehry Akbary, Mohammad Saligheh,
Volume 0, Issue 0 (3-1921)
Volume 0, Issue 0 (3-1921)
Abstract
Climate change and global warming are very important issues of the present century. Climate change process, especially temperature and precipitation changes, the most important issue is environmental science. Climate change means a change in the long-term average. Iran is located in the subtropical high pressure zone in arid and semi-arid regions and the Hyrcanian forest is a green area between the Caspian Sea and the Alborz mountain range. At the 43rd UNESCO Summit, the Hyrcanian forests were registered as the second natural heritage of Iran. Beech is one of the most important tree species and the most industrial species of Hyrcanian forests It accounts for about 18 percent of the northern forest volume (from Astara to Gorgan with a life span of about 250 years). The study area is located in the Shanderman basin in western Guilan province. In this research using tree dendroclimatology, Use of vegetative width of beech tree rings, Weather station statistics located in the study area, And Mann-Kendall nonparametric statistical method, To Investigate Climate Change Trend on Growth Time Series and Pearson Statistical Method, in order to evaluate the correlation of diameter growth of beech tree rings with climate variables in the region, an attempt was made. Results of time series of beech tree growth rings over 202 years. Using the nonparametric method Mann- Kendall showed, Changes in growth rings of beech trees have a downward and negative trend, at level 5 %, it was significant. Temperature Minimum, Average, Maximum, and Evaporation during the growing season, there was an upward trend and Annual precipitation there was a downward trend. Using the Pearson method Fit correlation of growth ring diameter with temperature, For the average monthly in February and the average minimum temperature in July, August and September and Negative correlation, for average maximum temperature in February, July, August and September at 95% level, it was significant and precipitation in June, the correlation was 95% positive and significant.
Dr Hassan Kharajpour, Dr Zahra Hejazizadeh, Dr Bohloul Alijani, Dr Mohammad Hossein Nasserzadeh,
Volume 0, Issue 0 (3-1921)
Volume 0, Issue 0 (3-1921)
Abstract
Considering the undeniable impact of agricultural plants on climatic and regional changes, it seems necessary to conduct regional research to understand the reaction of each agricultural plant in different stages of growth in relation to weather elements. If the temperature of the air along with the warm cloud is lower or higher than a certain threshold, its development will stop. Between the two limits, there is an optimal temperature where the plant grows the fastest. Temperature and clouds are both the most important climatic elements in agriculture. Both climatic parameters together cause stress in wheat and lower the productivity of the product. Considering the strategic nature of wheat, in order to increase the level of production, in the present research, while taking advantage of the experiences and methods and models used in foreign and domestic researches, it was practical in Kermanshah province due to the extent of the land under wheat cultivation and The significant amount of production, which has a special place in this field at the level of the country, the determination of the statistical threshold and the synoptic analysis of warem cloud temperatures on the performance of the wheat crop are investigated. According to the investigations and consultations with agricultural engineers, the maximum temperature along with cloudy days causes the phenomenon of greenhouse and excessive heat, which causes the fall of flowers, rot, sterility of pollen grains, fruit reduction, Premature aging and poverty become seeds, and this phenomenon occurs mostly in the months of May and June.
Sharifeh Zarei, Dr. Bohloul Alijani, Dr. Zahra Hejazizadeh, Dr. Bakhtiar Mohammadi,
Volume 0, Issue 0 (3-1921)
Volume 0, Issue 0 (3-1921)
Abstract
In this research, the most important synoptic patterns of widespread snowfall in the eastern half of Iran have been investigated. For this purpose, the current weather code data and snow depth of synoptic stations in the eastern half of the country during the statistical period of 1371-1400, for the months of October to March, were received from the country's meteorological organization. In order to investigate widespread snowfall, the days when more than 70% of the studied area saw snowfall at the same time were extracted as a widespread day. In order to perform synoptic-dynamic analysis of widespread snowfall in the eastern half of Iran, the classification method using cluster analysis was used and the maps of the representative days including atmospheric temperature, moisture flux, geopotential height, vorticity, front formation, jet stream, omega index and orbital and meridian wind data were drawn. Trend analysis was also performed using the Mann-Kendall test. The results showed that 3 patterns justify the snow cover in the studied area. These patterns are: high pressure in Siberia and central Europe-low pressure in eastern Iran, high pressure in western Iran-low pressure in Sudan, high pressure in central Europe-low pressure in eastern Iran and Afghanistan. In all the patterns in the middle of the atmosphere, the intensification of the meridian currents of the western winds along with the formation of high pressure and low-pressure centers has caused blocking in the path of the western currents and has provided the conditions for the ascent of the air. The concentration of the negative omega field and the relative positive advection, along with the location of the northeastern region of Iran in the left half of the outlet of the Subtropical Jet, have caused severe instabilities and widespread snowfall in the region. Also, the results showed that despite the absence of a trend in the number of snow days in the northeast of Iran, the number of snow days has decreased over time.
Esmaeail Ahmadi, Zahra Hejazizadeh, Bohlol Alijani, Mohammad Saligheh, Hassan Danaie Fard,
Volume 15, Issue 36 (6-2015)
Volume 15, Issue 36 (6-2015)
Abstract
The more exposure to Climate change / variability, the more vulnerability and a community with low adaptive capacity and high sensitivity is more vulnerable. Vulnerability reduction depends on adaptation policy and strategies. Designing and assessing these strategies require climate vulnerability (CV) measuring. To produce a new CV index, as a main propose of this study, first: The score of exposure factor for two five span years was calculated based on four synoptic stations data (Zabol, Zahedan, Iranshahr and Chabahar). Second: The scores of adaptive capacity and climate sensitivity were determined using all of the country census and yearbook data for 1385 and 1390. Third: Due to the nature and factors of vulnerability, a climate vulnerability index was developed based on the multiplicative-exponential model (CVIMEM). Forth: The index was calculated for the province and sub regions. The result shows, although the Sistan and Baluchistan (SB) adaptation capacity was increased, but because of the increased exposure and sensitivity, this province is 16.3% more vulnerable. Area with very high vulnerability label expanded from 57.5% to 100%, which reflects the spatial expansion of vulnerability over SB. The overall result is that vulnerability reduction needs for accurate and continuous measurement, on the increase adaptation capacity and mitigate climate sensitivity.
Mohsen Hamidianpour, Abass Mofidi, Mohammad Saligheh, Bohloul Alijani,
Volume 16, Issue 43 (16 2016)
Volume 16, Issue 43 (16 2016)
Abstract
In this study, the interaction between atmosphere and earth surface and its effect on the simulation of Sistan wind structure in the East of Iranian plateau is investigated. For this purpose, four experiments have been carried out with RegCM4, with horizontal resolution of 20 km. In non-topography experiments, the model was implemented in three different conditions. The results indicated that the Sistan wind is a multi-scale climate phenomenon which will be affected by topography both in wind intensity and in wind direction in the lower troposphere. In the synoptic scale, the pressure gradient which dominates between Pakistan heat low and the Turkmenistan anticyclone (Caspian Sea high pressure) can create a large scale background northerly flow in the lower troposphere which will be passing through the whole area in the east of Iranian Plateau. Furthermore, in meso to regional scales, the topography of the area would be responsible for creation and maintenance of a Low Level Jet (LLJ) through a mechanical and thermal forcing. the mechanical forcing of mountains are responsible for appearance of two LLJ cores across eastern borders of Iran which their preference locations would be around Atishan Desert in the north and upstream of Hamon Lake in the south. As a general result, by eliminating the topography in all non-topography experiments, the LLJ core will disappear on upstream of Hamon Lake as the most important mechanical forcing of the mountains. However, eliminating only the southern Khorasan Mountains will accelerate the north LLJ core in the Atishan Desert, while the LLJ core on the upstream of Hamon Lake will disappear over the Iran Borders at the same time. To evaluate the influence of thermal forcing of the mountains on Sistan wind structure, the total heating, as a residual term of thermodynamic equation, is calculated. The results indicate that mountains have a significant role to building a local low level circulation in the east of Iranian Plateau.
Naseh Qaderi, Bohloul Alijani, Zahra Hejazizadeh, Mohammad Saligheh,
Volume 18, Issue 48 (4-2018)
Volume 18, Issue 48 (4-2018)
Abstract
Wheat is the main focus of the economy of Kurdistan province in which the annual fluctuation of wheat yield is 4/11 times as affected by the climatic elements of the site. This study investigated the role of agro-climatic variables and indices on rainfed wheat yield in Kurdistan province. The data of planting area, amount of production, damages and yield of wheat of 31-year in 10 regions of Kurdistan along with the hourly, daily, decade, monthly, seasonal and yearly levels data of 22 synoptic stations were collected. The correlation between wheat yield and 128 independent variables was calculated. The effect of variables on yield evaluated by multivariate regression. The spatial analysis of variables was performed and the spatial model of wheat yield was introduced for province and regions. The results showed that climatic elements in various regions are different, in a 99% confidence. Most of the independent variables alone have a significant effect on wheat yield, but in the stepwise model, 7 variables such as: the number of rainy days of the year, the sum of the degree hours (of temperature less than -11 ° C) in germination and tilling stage, annual precipitation and the precipitation of November are determinants of the yield. Yield and effective independent variables have significant spatial differences even in a cluster climate type. The highest and lowest coefficient of variation of wheat yield is related to Bijar and Kamyaran areas, respectively. Kamyaran and Sanandaj regions have the highest and lowest yield, respectively. Bijar is the highest risk region of the province for wheat production.
The results of this study showed that with a 99 percent confidence, climatic elements (variables) vary in different regions. Most of the independent variables have a significant effect on wheat yield in simple linear regression, but in Stepwise method, due to the internal correlation between variables, just variables entered that have insignificant correlation with each other and have more effects than other variables. The variables affecting the performance are differentin various regions, and from the point of view of effectiveness, the arrangement of the variables in different areas vary too. In other words, even in two regions with a climatic type (based on the Modified De Martonne method), both agro-climatic indices and wheat yield are significantly different. The impact of effective variables on yield at any time and place depends on the time of year and the phonological stage of wheat. At one time the environmental conditions of different regions in terms of temperature, humidity and precipitation differ, based on phonological stages of the regions. The time of the vulnerability of wheat varies from place to place. Wheat vulnerability at flowering stage is more than other stages. The effect of independent variables on yield at different times of year is proportional to the phonological stage in years Different and different in different regions. In Kurdistan province, the number of rainy days of the year, total degree hours the temperature reaches below -11 °C (sum of hours with below -11 °C temperature) from germination to tillering stage, the annual precipitation, the rainfall in the fifth decade of the water year (the precipitation of 11-20 of November), annual relative humidity and total degree hours the temperature reaches above 30°Ctemperature (sum of hours with above 30 °C temperature) in milky and dough stage is the determinants of the production of rainfed wheat. In Baneh and Marivan areas, the coefficient of variation (CV) is lower and in Bijar and Divandareh regions CV is more than other regions. Kamyaran region has the highest yield, Baneh and Marivan were ranked secondjointly. Sanandaj and then Bijarhave the lowest yield. Each region has a model for wheat yield and determinant factors vary from region to region. Although the annual production of Bijar is higher than other areas, wheat production in the Bijar region has a higher risk than other areas.
The results of this study showed that with a 99 percent confidence, climatic elements (variables) vary in different regions. Most of the independent variables have a significant effect on wheat yield in simple linear regression, but in Stepwise method, due to the internal correlation between variables, just variables entered that have insignificant correlation with each other and have more effects than other variables. The variables affecting the performance are differentin various regions, and from the point of view of effectiveness, the arrangement of the variables in different areas vary too. In other words, even in two regions with a climatic type (based on the Modified De Martonne method), both agro-climatic indices and wheat yield are significantly different. The impact of effective variables on yield at any time and place depends on the time of year and the phonological stage of wheat. At one time the environmental conditions of different regions in terms of temperature, humidity and precipitation differ, based on phonological stages of the regions. The time of the vulnerability of wheat varies from place to place. Wheat vulnerability at flowering stage is more than other stages. The effect of independent variables on yield at different times of year is proportional to the phonological stage in years Different and different in different regions. In Kurdistan province, the number of rainy days of the year, total degree hours the temperature reaches below -11 °C (sum of hours with below -11 °C temperature) from germination to tillering stage, the annual precipitation, the rainfall in the fifth decade of the water year (the precipitation of 11-20 of November), annual relative humidity and total degree hours the temperature reaches above 30°Ctemperature (sum of hours with above 30 °C temperature) in milky and dough stage is the determinants of the production of rainfed wheat. In Baneh and Marivan areas, the coefficient of variation (CV) is lower and in Bijar and Divandareh regions CV is more than other regions. Kamyaran region has the highest yield, Baneh and Marivan were ranked secondjointly. Sanandaj and then Bijarhave the lowest yield. Each region has a model for wheat yield and determinant factors vary from region to region. Although the annual production of Bijar is higher than other areas, wheat production in the Bijar region has a higher risk than other areas.
Tayebeh Dehghani, Mohammad Saligheh, Bohloul Alijani,
Volume 18, Issue 49 (5-2018)
Volume 18, Issue 49 (5-2018)
Abstract
In order to detect climate change, a variety of climate indicators can be used which is often considered temperature and precipitation. In order to investigate the effect of climate change on the amount of precipitation in the north coast of the Persian Gulf, it simulated the precipitable water for 2017-2050 based on the RCP4.5 model of the Hadcm3 model. The NCEP / NCAR base-station data with an arc-value of 0.125 was used to analyze the past and present precipitable water patterns and to reveal the process of this time series. Time series analysis of precipitable water was performed using two SENS tilt estimators and Man-Kendall test. The results indicated that the annual time series of rain water was increasing in the region, every year, 0.05 mm, the precipitated water increased and it tended to become more homogeneous, this increase in the significance level of 0.95. The precipitation rate in the eastern part of the region was higher than other areas. Before 1989, several fluctuations were observed in the rainy season of precipitable water, but none was statistically significant at the confidence level of 95%, but since 1989, the trend has increased significantly at a confidence level of 0.95. This spatial behavior of precipitable water can actually have occurred in response to the increase in the overall temperature of the area and can be considered as a profile of climate change in the region.
Mr Ali Mohammadpourzeidi, Professor Bohloul Alijani, Associate Professor of Climatology Mohammad Saligheh, Mr Mohammadsaleh Gerami,
Volume 19, Issue 52 (3-2019)
Volume 19, Issue 52 (3-2019)
Abstract
owledge of spatial rainfall behavior in environmental, land planning is effective. These changes in the later place in the form of time later and in the climate of the area. The Target of this study was to reveal the presence or absence of precipitation trend in the ratio of the height of local precipitation behavior and identify province mazandarn. Therefore, the purpose of the rainfall data station 32 (Meteorological Agency and Department of energy), the statistical period 1988-2010. To get the regression analysis of precipitation process was used to identify the local behavior of precipitation, the method of spatial statistics were used. The results obtained from the behavior of precipitation, the existence of the process within the scope of the study and the emphasis is most consistent with the Be modified regression model at adjustment indicate. According to the regional behavior of precipitation, using local spatial statistics, spatial Moran well hot spots check this behavior. The results showed that precipitation in the province of Mazandaran has the pattern of clusters with high value. According to the local hot spots and methods Moran, West Coast up to a height of 700 m has positive z score and clusters with high value, 99% confidence level. This range includes 15% of the total of the province. The range of the Southern Highlands as well as the negative z score and clusters with low value with a confidence level shows 99%. This range is also about 20 per cent of the province's total. About 65 percent of the total area of the province as well as the lack of a significant trend show.
Mohammad Hossein Nasserzadeh, Zahra Hejazizadeh, Zahra Gholampour, Bohloul Alijani,
Volume 20, Issue 57 (6-2020)
Volume 20, Issue 57 (6-2020)
Abstract
The plant community in an area is the most sensitive indicator of climate. A visual comparison of climate and vegetation on a global scale immediately reveals a strong correlation between climatic and vegetation zones and this relationship, of course, are not co-incidental. The main object of this study is to reveal the spatiotemporal association between climatic factors andvegetation Cover (NDVI) incorporate MODIS and TRMM product in Kohkiloyeh O Boirahmad province of Iran. So that the in this paer we use MOD13Q1 of MODIS product as NDVI layer for study area. MOD11A2 as landsurface temperature and 3B43 TRMM as meanmonthly accumulative rainfall for study area during 2002 to 2012 in 0.25° spatial resolution also were used as climatic factors. We use the correlation and cross-correlation analysis in 0.95 confident level(P_value =0.05) to detection the spatial and temporal association between the NDVI and 2 climatic Factor(LST and rainfall). The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months the pixels which have the high value of NDVI are spatiallyassociated with the pixels which have highest value of LST (6 to 14C°).As can be seen in table 1. Season the spatial correlation among NDVI and LST is so high which is statistical significant in 0.99 confident level in winter. In transient months such as May, October and November,(temperate months in study region ) the spatial correlation among NDVI and LST is falling to 0.30 to 0.35 which is not statistical significant in 0.95 confident level. Finally in summer season or warm months including Jun to September, we found the minimum spatial association among the NDVI and LST.. In temporal aspect we found that the maximum correlation between NDVI and LST simultaneously appears and not whit lag time. The spatial correlation of NDVI and TRMM monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time in remain months we don’t find any significant correlation between NDVI and rainfall.
Hamideh Afsharmanesh, Zahra Hejazizadeh, Bohloul Alijani,
Volume 21, Issue 61 (6-2021)
Volume 21, Issue 61 (6-2021)
Abstract
Climate predictions have been made in global, regional and local simulation, and climatic parameters have changed in terms of trends and models in climatology, futures studies are less visible in literature and climatology literature therefore environmental planning and futures analysis are an attempt to look at the long-term future in the field of climatology. Today, one of the most important challenges of the present and future is the increase in temperature and is the lack of climatic comfort. The growth of Tehran's metropolitan area, improving living standards, expanding urbanization and industry, climate change, and the energy shortage crisis are important. The survey forms were prepared by the climatologists and managers of Tehran and data analysis, futuristic techniques such as scenario for data analysis tool in this study was MICMAC software. have been used. In the research process, the most important key factors and drivers in relation to futures studies were identified in relation to the increase of temperature in the city of Tehran.
Mini scenarios and a comprehensive scenario were defined in three cases:
- Improvement of the Micro-Climatic Conditions of Tehran City + Climatic Comfort of Citizens
- Lack of good micro-climate in Tehran + low climatic comfort of citizens
- The lack of improvement in the micro-climatic situation in Tehran + the lack of climate comfort for citizens and increased energy consumption
Mahnaz Aziz Ebrahim, Mohammad Saligheh, Mohammad Hossein Nassrzadeh, Bohlol Alijani,
Volume 22, Issue 64 (4-2022)
Volume 22, Issue 64 (4-2022)
Abstract
In this research, we are trying to determine the “beginning time” as well as the “end” of the climatic seasons; and we will focus on identifying the displacement of these dates, which is influenced by the “climate changes” and “descriptionAbstract
The purpose of this study is to investigate possible changes and displacements in Iran's climatic seasons due to climate change. To do this, temperature, relative humidity, water vapor, wind and cloud data for 36 stations were received from the Meteorological Agency over 40 years. The data were divided into two 20-year series to allow comparison. Daily temperature data for each clustering time series were determined, then by considering 7-day sequences, the beginning and end of the seasons. The designated times were tested using the Rayman model. The results of comparing the seasons in the two time series indicated that in all stations, changes in climatic seasons occurred from Insignificant to significant. Climatic seasons in Iran do not correspond to calendar seasons, and climate change, especially temperature changes in recent decades, has caused the seasons to shift and shorten and lengthen. Although the beginning and end of the seasons do not generally correspond to their calendar dates, most of the days of these seasons occur in its calendar periods. The changes that have taken place have not only affected the length of the seasons, and these shifts have also changed the quality of the natural seasons.
Keywords: Climate change, natural seasons, cluster analysis, Rayman model of the qualitative conditions” created in them, compared to the past climatic periods. “Meteorological Organization” data has been used in this research. Forty years of received data, was divided into two groups of 20. Applying SPSS, each group was divided into four stages representing each seasons. From these stages, the beginning time and the end of seasons were determined and the accuracy of the obtained dates was controlled with the comfort indicators of the Rayman model. The results of the comparison of seasons in two time series indicated that, the changes occurred in natural seasons from an almost non-existent one in all stations. Climatic seasons in Iran are not compatible with the summer season and climate change, especially the change in temperature in recent decades, has caused changes and shortening of seasons. Most of the days in these seasons occur during its monthly periods, although the beginning and end of the seasons generally do not match their calendar dates. Changes have not only affected the duration of the season, and these changes have also led to a change in the natural quality of the season.
The purpose of this study is to investigate possible changes and displacements in Iran's climatic seasons due to climate change. To do this, temperature, relative humidity, water vapor, wind and cloud data for 36 stations were received from the Meteorological Agency over 40 years. The data were divided into two 20-year series to allow comparison. Daily temperature data for each clustering time series were determined, then by considering 7-day sequences, the beginning and end of the seasons. The designated times were tested using the Rayman model. The results of comparing the seasons in the two time series indicated that in all stations, changes in climatic seasons occurred from Insignificant to significant. Climatic seasons in Iran do not correspond to calendar seasons, and climate change, especially temperature changes in recent decades, has caused the seasons to shift and shorten and lengthen. Although the beginning and end of the seasons do not generally correspond to their calendar dates, most of the days of these seasons occur in its calendar periods. The changes that have taken place have not only affected the length of the seasons, and these shifts have also changed the quality of the natural seasons.
Keywords: Climate change, natural seasons, cluster analysis, Rayman model of the qualitative conditions” created in them, compared to the past climatic periods. “Meteorological Organization” data has been used in this research. Forty years of received data, was divided into two groups of 20. Applying SPSS, each group was divided into four stages representing each seasons. From these stages, the beginning time and the end of seasons were determined and the accuracy of the obtained dates was controlled with the comfort indicators of the Rayman model. The results of the comparison of seasons in two time series indicated that, the changes occurred in natural seasons from an almost non-existent one in all stations. Climatic seasons in Iran are not compatible with the summer season and climate change, especially the change in temperature in recent decades, has caused changes and shortening of seasons. Most of the days in these seasons occur during its monthly periods, although the beginning and end of the seasons generally do not match their calendar dates. Changes have not only affected the duration of the season, and these changes have also led to a change in the natural quality of the season.
Mr Mahmood Hosseinzadeh Kermani, Dr Bohlul Alijani, Dr Zahra Bigom Hejazizadeh, Dr Mohammad Saligheh,
Volume 22, Issue 67 (12-2022)
Volume 22, Issue 67 (12-2022)
Abstract
The main aim of this paper is to determine the capable areas for cultivating pistachio through considering of Geo statistical Analysis the major effective factors. The necessary climatic daily data of weather stations For the 300 synoptic stations, the station was set up by 2016. The topographic data include relief, slope, aspect, and TIN layers extracts from 1:250000 topographic maps of the region. The maps of land use and vegetation land cover were prepared from the 1:250000 maps of national soil and water Research Institute. The spatial analysis facilities of GIS were utilized for numerical calculation and the spatial geodatabase of the region was established. Then spatial and description data was entered into the data bank. Finally by overlaying analysis in ArcGIS, cultivated area was classified according to its capabilities. The results showed that 707273/88 KM2 Of the area (43%), Not suitable for spreading pistachio cultivation (Including altitudes and urban use and steep slopes, seaside and riverside streams, shoals, saline and swampy lands) and 585130/39 KM2 (35/57%) From the country of Iran Area Including plain areas and agricultural use) was recognized as suitable for the expansion of pistachio cultivation. These areas are located in the east and south east, center and northeastern Iran.
Faryad Shayesteh, Mohammad Saligheh, Bohloul Alijani, Amanollah Fathnia,
Volume 23, Issue 70 (10-2023)
Volume 23, Issue 70 (10-2023)
Abstract
The smallest change in energy exchange of Earth System Shifts the balance of life. In order to be aware of the solar radiation Balance, Recognition of the measure of Balance level of the input and output components of radiation of input Short wavelength to the surface of the earth and Long Output Wavelength, it is necessary from the earth. To study the energy balance of input and output in Iranian plateau, the input and output radiation data of NCEP / NCAR site was used With a resolution of 2.5 * 2.5 *, including 46 cells in Iran,. For each season, a representative month was considered And correlation, confidence level, coefficient of determination and amount of oscillation of input and output radiation were calculated in different regions of Iran. Finally, some calculations were presented spatially with the IDW method. The results showed that the maximum short-wave wavelength was 230 watts per square meter in August and the lowest was 52 watts per square meter in November. The highest long-wavelength output in August was 65 watts per square meter, and the lowest amount was January and November with 20 watts per square meter. The highest the amount of output increase has been occurred in August in the east of province South Khorasan with a correlation of 0.59 to 112 watts per square meter in 2001. In decreasing output changes, except for May, there was a decrease in the rest of the months. The highest long-wavelength output was in the northwest and in the provinces of Ardabil and Guilan.
Maryam Saghafi, Gholamreza Barati, Bohloul Alijani, Mohammad Moradi,
Volume 23, Issue 71 (1-2024)
Volume 23, Issue 71 (1-2024)
Abstract
Precipitation is a phenomenon resulting from complex atmospheric interactions and among climatic events, due to its vital role, it has special importance. The importance of precipitation durability, especially in arid and semi-arid regions, which includes most of Iran, is greater than its volume. The purpose of this study is to identify Iran's precipitation areas in terms of precipitation durability and its characteristics in each area. In order to investigate the durability of Iran's precipitation and to define a precipitation day as " a day with equal precipitation or greater than 0.5 mm", used from daily precipitation data of 80 synoptic stations of the country during the 6 cold months of the year from October to March in a period of 30 years (2016 - 1987). Setting data in daily tables in the first step, made possible to program in MATLAB environment to separate precipitation in ten groups from "one day" to "ten days" and in the second step in SPSS environment based on frequency characteristics, amount and precipitations average in the mentioned groups was done by the method of Ward merging and clustering. The process of the clustering on Iran's durability precipitation showed that there are seven almost homogeneous precipitation zones in Iran; the geographical arrangement of Iran's precipitation areas, reveals the dependence of Iran's precipitations amount on roughness, the path of precipitation systems, its proximity to humidity sources, and the effect of the sea. In terms of area’s location, it can be said that; the settlement of the four zones in the western half of Iran, despite its small size in front of the eastern half, is a reason for its heterogeneity.
Prospective of climate change impact on water resources and sugar beet yield in the Urmia Lake basin
Mrs Somayeh Naderi, Prof. Bohloul Alijani, Prof. Zahra Hedjazizadeh, Dr. Hasan Heidari, Dr. Karim Abbaspour,
Volume 24, Issue 73 (8-2024)
Volume 24, Issue 73 (8-2024)
Abstract
Evidence suggests that climate change will create uncertain regional agricultural production stability in the coming decades. This research investigated the impact of climate change on hydrology and sugar beet yield as one of the main crops in the Urmia lake basin using the Soil and Water Assessment Tool (SWAT). To address this, a baseline SWAT model was setup for 1986-2014. Afterward, the output was calibrated (1989-2004) and validated (2005-2014) in the SWAT-CUP software using the SUFI2 algorithm to simulate streamflow of 23 gauging stations and crop yield. The Nash-Sutcliffe efficiency was 0.43 and 0.53 for calibration and validation periods, sequentially. The Percent Bias was 45% and 16% for calibration and validation periods, respectively. As well as the agreement indices of 0.71 and the little Percent Bias (-6% to 10%) for crop production, verified the model's efficiency. The next step was downscaling and bias-correction of the precipitation and temperature data received from 3 climate models, namely GFDL, HadGEM2, and IPSL under RCP4.5 and RCP8.5 using CCT program. Then, the downscaled data were fed to SWAT, and Finally, hydrological fluxes and sugar beet yield were estimated for 2021-2050. Despite a dispersion of precipitation changes ranging from -12% to +35% in most scenarios, results highlight the pivotal role that the warmer temperature (+2.7°C) increases evaporation, resulting in sharpened pressure on water resources and runoff, especially, at the beginning of crop growth season. Finally, the negative impacts on crop productivity (-45%) is not unexpected. This means that sugar beet may suffer from climate change impacts, and the production of this plant will change over the next period in this region.
Keywords: Climate Change, Sugar Beet, Urmia Lake Basin, Sensitivity Analysis, SWAT.
Zoleikha Khezerluei Mohammadyar, , Bohloul Alijani,
Volume 24, Issue 73 (8-2024)
Volume 24, Issue 73 (8-2024)
Abstract
The purpose of this article is to analyze the frequency and severity of the one to six days of rainfall in Iran. The trend of frequency changes and severity of each course was identified using my-candle test and the slope estimator during the 1968-1988 period. Then, using the main component analysis method and cluster analysis method, the entire stations were categorized in five clusters (abundance) and four (intensity) based on the annual changes of frequency indicators and intensity of precipitation. Cluster 1 and 2 stations represent the frequency of precipitation periods with a severe or without trend. The two clusters were mostly established in the southern half of Iran. Cluster 4 and 5 stations represent the frequency of precipitation periods with a positive (mild) trend, mainly in the northern part of the country. Cluster 3 stations represent the frequency of precipitation periods with decreased (mild) trends, which are mostly focused on west and southwestern Iran. The clustering results of the stations based on the intensity index of precipitation periods, contrary to many results; do not show a specific pattern. But in the cluster, there has been a severe decrease in the last half century. The stations of this cluster are mostly concentrated in the northern parts of the country. Other clusters are scattered in almost all parts of the country. Accordingly, it can be concluded that the frequency of precipitation periods in the northern latitudes of incremental processes (average or weak) and the severity of precipitation periods in these latitudes (north of the country) had severe declining trends.
Keywords: Frequency of precipitation, intensity of precipitation, analysis of main components, clustering, process.
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