Climatology
Ali Mohammad Khorshiddoust; saeed jahanbakhshasl; zahra abbasighasrik; fatemeh abbasighasrik
Abstract
Today, long-term forecasting of climate variables has received much attention in order to be aware of the extent of change and, consequently, to take the necessary measures to mitigate the adverse effects of climate change. In this study, minimum temperatures in Kurdistan province were predicted using ...
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Today, long-term forecasting of climate variables has received much attention in order to be aware of the extent of change and, consequently, to take the necessary measures to mitigate the adverse effects of climate change. In this study, minimum temperatures in Kurdistan province were predicted using LARS-WG6 downscaling for the next three 20-year periods (2040-2021, 2060-2041, 2080-2061). For this purpose, the HadGEM2-ES general circulation model and three scenarios of RCP8.5, RCP4.5 and RCP2.6 were used. To generate the time series of future periods, daily data for the statistical period 1989-2019 were used and the trend of its changes was analyzed using Mann-Kendall test. The results showed that LARS-WG6 software simulates the minimum values of the minimum temperature well with low error indicators. Also, based on the results of the HadGM2-ES global model output in the study area, the minimum temperature in the future period will be higher than the base period in all scenarios and periods. The intensity of this increase under the RCP8.5 scenario is related to the last period of the century (2080-2061) and its lesser extent is related to the period (2060-2041) under the RCP4.5 scenario. Examination of seasonal averages also shows that spring has a lower temperature increase and autumn has a higher temperature increase. The trend of changes shows that the trend is positive and negative in both directions, so that in most stations and scenarios in different forecast periods, spring will have the most positive trend and autumn will have the most negative trend. Therefore, it can be concluded that the temperature will increase in future periods and the effect of cold waves will decrease.
Climatology
Mojtaba Nassaji Zavareh; Hossein Hokmabadi; Alireza Asadolahi
Abstract
Late spring frost cause a lot of damage to the agricultural sector every year. Prediction of this phenomenon is needed to active protection of plants. In this research, using FAO experimental method, daily and hourly data of two meteorological stations were used to determine the coefficients of the experimental ...
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Late spring frost cause a lot of damage to the agricultural sector every year. Prediction of this phenomenon is needed to active protection of plants. In this research, using FAO experimental method, daily and hourly data of two meteorological stations were used to determine the coefficients of the experimental model for prediction radiation frost in Qazvin Plain. also, in order to investigate the climatic condition of spring frost, the daily minimum temperature data of Qazvin and Buinzahra stations were used. The analysis of sixty years data in Qazvin stations showed that the intensity of frost has decreased during these years, but frequency of frost in Ordibehesht month has increased. Air, dew-point at two hours after sunset and minimum temperature relate 25 events of radiation frost at Simorgh station were used for regional coefficient calculation based on two models. These models were evaluated using 14 events of radiation frost at Tat stations. The mean absolute error(MAE) for testing and evaluating of Model1 was 0.71℃ and 1.21℃ and for Model2 was 0.67℃ and 1.09℃. The findings also showed that both models have acceptable accuracy in estimating the minimum temperature of the next day. It is proposed that these two models can be used for prediction of radiation frost in other regions.
Climatology
Mehdi pourahmad; mostafa karampour; behroz nasiri
Abstract
The aim of this study was to reveal the relationship between land cover changes and changes in aerosol optical depth index in the Middle Zagros. In this regard, two categories of MODIS sensor remote sensing products were used. First, land cover changes in the study area were performed using MODIS sensor ...
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The aim of this study was to reveal the relationship between land cover changes and changes in aerosol optical depth index in the Middle Zagros. In this regard, two categories of MODIS sensor remote sensing products were used. First, land cover changes in the study area were performed using MODIS sensor land use classification derivative product. In the second part of the research, the trends of dust events were investigated based on the station data of the dust codes of 4 stations of Khorramabad, Shahrekord, Yasuj and Abadeh. In addition, the trend of Aerosol Optical Depth Index (AOD) was examined using MOD04-L2 Madis sensor product for the statistical period 2000 to 2020. The results showed that there were 6 layers of rangeland, forest, agricultural, urban, residential, barren and water areas in the Central Zagros, in which the forest floor has decreased by about 123 square kilometers per year. Rangeland cover, which is the main cover of the study area, has remained relatively stable, and agricultural land uses have increased significantly, from 7% in 2000 to 9.5% in 2020. Urban and residential lands had also increased. On the other hand, a review of the 21-year time series trend of the AOD index indicates an upward trend over the last 21 years. Among the land use classes, the two categories of pastures and forests, which in fact occupy more than 90% of the study area, have shown an inverse relationship with the AOD index. But the class of agricultural lands was directly related to the AOD index. Therefore, the decreasing trend of forest floor in the region has been significantly associated with the increasing trend of AOD in the region and on the other hand, the increasing trend of agricultural land has been associated with the increase of AOD in the region.
Climatology
behrouz sobhani; minoo ahmadyan; Saeed jahanbakhsh
Abstract
the statistics of the ECMWF database were used for the observation data of the two stations of Semiram and Urmia during a 21-year period (1996-20016).In order to investigate the effects of climate fluctuations, the daily data of dynamic micro-rotation of the CORDEX project was used for the output of ...
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the statistics of the ECMWF database were used for the observation data of the two stations of Semiram and Urmia during a 21-year period (1996-20016).In order to investigate the effects of climate fluctuations, the daily data of dynamic micro-rotation of the CORDEX project was used for the output of the ICHEC-EC-EARTH model under the RCP8.5 and RCP4.5 radiative forcing (RCP) scenarios for the period (2017-2037). By using the data of the stations and the outputs of the micro scale model, and by using the perceptron neural network and linear regression, the performance was simulated. Then, to evaluate the efficiency of the models, R, R2, MSE, RMSE, and NRMSE statistics were used, and the non-parametric Menkendall test and age slope were used for the performance trend. The result of comparing the output of artificial neural networks with the linear regression model shows that the error rate of the neural network is less and the simulated results are close to the real observations to a very acceptable extent. The phenological stages, including bud blooming to fruit ripening in the stations under both scenarios, and in all the phenological stages in the future period will be completed earlier than the base period, and the length of the growth period will also decrease. The amount of future yield in Urmia station under RCP4.5 and RCP8.5 scenarios respectively yield 3.7 and 2.2 tons per hectare and in Semiram station yield 1.4 and 3 respectively tons per hectare will decrease. The results show that in the future in the study areas, with the change in the time of occurrence of the length of the growth period, all the phenological stages as well as the declining performance of apple trees will be subject to climatic fluctuations
Climatology
Shirin Mahdavian; Batool Zeynali; Bromand Salahi
Abstract
Climate diversity and land use / land cover change have a significant impact on hydrological regimes, especially in arid and semi-arid regions with critical water shortage problems. Therefore, estimating and evaluating climate change and land use and its consequences in each catchment is essential. This ...
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Climate diversity and land use / land cover change have a significant impact on hydrological regimes, especially in arid and semi-arid regions with critical water shortage problems. Therefore, estimating and evaluating climate change and land use and its consequences in each catchment is essential. This study examined the climate change of Kiwi Tea Basin using the data of four models of the Fifth Climate Change Assessment Report (CMIP5) under both optimistic and pessimistic scenarios (RCP8.5 and RCP4.5) using the LARS-WG6 microcirculation model. Changes in precipitation and temperature during three different periods (2040-2021, 2060-2041 and 2080-2061) compared to the base period (2019-1987) have been studied and for calibration and validation of LARS-WG6 model, observational data and output data of models with The use of F and T tests as well as RMSE, MSE, MAE and R2 indices were compared and evaluated. Based on the results of most of the models and the average of the studied models, in general, it is expected that the amount of precipitation and the minimum and maximum temperature in all the studied models will increase compared to the base period. Also, the results of evaluating land use changes with object-oriented classification showed that rangeland use with an area of 1224.18 and 1046.59 square kilometers, respectively, covered the largest area in both periods, while in 1987, residential use with an area of 3.66 square kilometers and in In 2019, water use with an area of 3.77 square kilometers had the lowest area. Also, the most modified use of rangeland use was dryland agriculture (181 square kilometers), which indicates thedestruction of rangelands
Climatology
shahnaz Rashedi; Saeed jahanbakhsh; Ali Khorshiddoust; Gholam Hasan Mohammadi
Abstract
For this purpose, data on the type, amount, and height of different cloud layers and daily precipitation of 36 synoptic stations located on the southern coast of the Caspian Sea were received from the Meteorological Organization. MODIS images were used to investigate the relationship between precipitation ...
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For this purpose, data on the type, amount, and height of different cloud layers and daily precipitation of 36 synoptic stations located on the southern coast of the Caspian Sea were received from the Meteorological Organization. MODIS images were used to investigate the relationship between precipitation and cloud microphysical parameters (CTT, CTH, COT, CER, CWP). ERA5 and NCEP/NCAR data were also used to identify synoptic patterns leading to cloud formation. Finally, HYSPLIT model and regression method were used to identify the path of moisture flow. The results of observational data showed that Caspian clouds were observed in the form of low Stratus clouds and middle clouds of Altocumulus type in the region. So that among the low clouds, the heights of 750 and 900 meters and among the middle clouds, the heights of 2700 meters had the highest frequency. The results of Caspian clouds rainfall showed that in most areas, 1 to 5 mm of precipitation has occurred. Correlation results showed that precipitation was positively correlated with CTH,COT, CER and CWP, and negatively correlated with CTT. Multivariate regression predicted 17% of precipitation by cloud parameters. The results of the study of synoptic maps showed that with the establishment of a 1012 hPa high pressure core in the north of the Caspian Sea, the north-south wind flow along with the transfer of sea moisture to the south shore of the Caspian Sea, ascending the air mass and the formation of clouds and limited rainfall in the region. Vertical profiles showed maximum specific humidity in the lower levels of the atmosphere (1000 to 900 hPa). The results of HYSPLIT model moisture flow path showed that the main source of regional moisture was the Caspian Sea.
Climatology
Mohammadreza Rafighi; Mehry Akbary; Mohammad Hassan Fakharnia; Mohammad Hassan Vahidnia
Abstract
IntroductionAlthough the air layer adjacent to the earth's surface - the boundary layer - is a small fraction of the entire atmosphere, the processes that take place on a small scale are very important to human life and activites. Among living organisms, plants and especially trees have undeniable effects ...
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IntroductionAlthough the air layer adjacent to the earth's surface - the boundary layer - is a small fraction of the entire atmosphere, the processes that take place on a small scale are very important to human life and activites. Among living organisms, plants and especially trees have undeniable effects on surface temperature and especially in urban environments have several balancing effects. This research was carried out using Landsat 8 satellite imagery and with Arc GIS software to compare the surface temperature of the earth in two areas with vegetation of coniferous trees (Chitgar Park) and broadleaf trees (Shahid Chamran Park). The values of Radiance, Reflectance, Brightness Temperature, Normalized Difference Vegetation Index, Proportion of Vegetation and Emissivity and then Land Surface Temperature were calculated and generated. A total of 1700 points were harvested from Chitgar Park and 800 points from Chamran Park. In SPSS software, Leven test (F) statistics was used to prove the homogeneity of variances of the samples and parametric tests (T with two independent samples) were used to prove the significant difference between the surface temperature of the earth in the mentioned areas. According to Leven test, the value was Sig = 0.409 (P_value), which confirms the homogeneity and equality of variance of the studied samples. Also, in the T test, the value was Sig = 0.000, which is less than 0.05, which means a significant difference. Therefore, the difference between the surface temperature data of the two parks was proved. Also, by comparing the graphs of LST values in the two groups, we found that Chitgar Park has a higher surface temperature than Chamran Park. In the current dilemma of the century, global warming, knowing these local realities and providing logical solutions to reduce surface temperature at the regional and regional scales as a whole can effectively solve the problem of global warming on a global scale.Data and Method The data used in this study is a Landsat 8 satellite imagery with the acronym: 8 (LC08_L1TP_165035_20190706) is LANDSAT.Retrieved July 6, 2019 from the USGS website.Production of component images for Shahid Chamran Parks in Karaj and Chitgar in Tehran: The surface temperature image was generated step by step using the Landsat 8 satellite image using the Raster Calculator command in the ArcMap software environment. First, relevant and effective indicators in calculating the surface temperature of the earth, Top of atmospheric radiance, reflectance, Brightness Temperature, normalized difference vegetation index, proportion of vegetation, emission coefficient (emissivity), calculation and their images are produced and then the land surface temperature, It was calculated and produced according to the following mathematical formulas.Step 1: Produce a spectral radius image from above the atmosphere To obtain the brightness temperature, the image must first be converted to radius. Therefore, the gray DN values of bands 10 and 11 of the Landsat 8 satellite TIRS sensor should be converted to high atmospheric radius separately with the help of the MTL file, which is an extension of the Landsat image (Tables 1, 2 and 3).Formula (1) :Calculate the radius of the upper atmosphere TOA (Lλ) = ML * Qcal + ALLλ = (Watts / (m2 * srad * μm)) The radius of the atmosphere in terms ofML = Multi-band radius_ 10 bandStep 2: Produce an image of the light temperature above the atmosphere After converting the DN values of bands 10 and 11 to high atmospheric radii, we converted these two corrected bands to Brightness Temperature.BT = (K2 / (ln (K1 / L) + 1)) - 273.15 Formula (2): Calculation of Brightness Temperature BT = Atmospheric Brightness Temperature (° C)Lλ = (Watts / (m2 * srad * μm)) Radius of the atmosphere in terms ofBT = (1321.0789 / Ln ((774.8853 / “% TOA%”) + 1)) - 273.15K1 = K1 Constant Band (No.), K2 = K2 Constant Band (No.)Step 3: Produce vegetation index image formula (3): normalized difference vegetation index image was generated usingNDVI = (Band 5 - Band 4) / (Band 5 + Band 4)Step 4: Produce a proportion of vegetation imageThe proportion of vegetation image was generated using normalized difference vegetation index.formulas (4):Calculate the proportion of vegetation PV = (NDVI - NDVImin / NDVImax- NDVImin) 2PV = Square (("NDVI" - 0.216901) / (0.632267 - 0.216901))Step 5: Produce the Emissivity image Emissivity image was generated using formula (5)ε = 0.004 * PV + 0.986 Formula (5): Calculate the Emissivity coefficientStep 6: Produce an image of the earth's surface temperature Land surface temperature image was generated using formula (6).Formula (6) :Calculate ground land surface temperatureLST = (BT / (1 + (0.00115 * BT / 1.4388) * Ln (e)))Results and Discussion Text Comparison of surface temperature phenomena (LST) According to Table (6), the highest land surface temperature with 44.42 ° C belongs to Chitgar Park, which is covered with coniferous trees, and the lowest in Shahid Chamran Park, in Karaj with 28.09 ° C with broadleaf trees. Has been. According to Tables (7) and (8), the lowest temperature of Chamran Park is 28.09 ° C and the highest is 36.51 ° C and the lowest temperature of Chitgar Park is 34.74 ° C and the highest is 44.42 ° C. . According to Figure (22), Chitgar Park with an average surface temperature of 38.92 ° C is warmer than Shahid Chamran Park with an average land surface temperature of 31.39 ° C. Figure (23) shows a red graphic showing the surface temperature of the ground in Chitgar Park with coniferous species (pine) and the blue diagram shows the surface temperature of Shahid Chamran Park in Karaj with broadleaf species. It is clear that the temperature is significantly higher in Chitgarh Park. The range of temperature fluctuations in Shahid Chamran Park is between 36.51 - 28.09 ° C and in Chitgar Park is between 42 / 44-74 / 34 which is exactly shown in the diagram. The fact that the red chart is higher than the blue chart explains this correctly. This is due to the lower density of trees in Chitgarh Park as well as the predominant tree species (needle-shaped) due to less shading and more input radiation. T test with two independent samples: This test, which is a parametric test, was used to prove a significant difference between the earth's surface temperature in areas with coniferous and deciduous trees. Leven test (F) was used to prove the homogeneity of sample variances and t-test with two independent samples was used to examine the homogeneity of the means of the two statistical populations, which resulted in the following results. As can be seen in Table (12), the value = 0.409 Sig, which is the same value as P_value, is greater than 0.05, ie the variance of the communities is homogeneous and equal. 0.05 is less, which means that the difference is significant. Due to religion, the difference between the land surface temperature data of Shahid Chamran and Chitgar parks is proved.ConclusionAccording to all the findings, Chitgar Park has a higher land surface temperature than Chamran Park, which is due to the lower density of trees and also the type of dominant tree species (needle-shaped). Coniferous species that take up less space than broadleaf species and have less shading. They also make it possible for the sun to collide with the ground due to the fact that the leaves of the adjacent trees do not meet, and this is an important factor in raising the surface temperature in the mentioned park. Species compatible with the climate of the study areas are broadleaf species because they have more leaves shading and care than coniferous species and ultimately cause more climate adjustment. The difference in temperature between the two parks confirms this fact. In the current dilemma of the century, global warming, knowing these local realities and providing logical solutions to reduce surface temperature at the regional and regional scales as a whole can effectively solve the problem of global warming on a global scale.
Climatology
abdolreza hosseini; sayed mohammad hosseini; Rahman Zandi; hasan hajimohammadi
Abstract
IntroductionSnow, as one of the important climatic-hydrological parameters, has a significant role in providing the world's water resources for industrial, agricultural and drinking purposes. At the same time, the dangerous consequences of heavy snowfall, avalanches, destruction of rural housing, disruption ...
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IntroductionSnow, as one of the important climatic-hydrological parameters, has a significant role in providing the world's water resources for industrial, agricultural and drinking purposes. At the same time, the dangerous consequences of heavy snowfall, avalanches, destruction of rural housing, disruption of road transport and communication and numerous other consequences that it has on the natural and human environment are significant for environmental scientists (Shakiba et al, 2015: 88). However, heavy snowfall, especially in the lowlands and lowlands of the middle latitudes, is unexpected and somewhat surprising. So that its continuation for a few days in these areas will have negative effects on practically all living standards of the residents of these areas (Hosseini, 2014: 101). In recent years, the use of satellite data in natural, hydrological and water resource management has grown significantly, and in this regard, MODIS sensor images due to acceptable spatial resolution and fast temporal retrieval power with a variety of bands. Spectral has put it in a good position. Also, due to the very high albedo of snow, it is possible to measure the level of snow cover using satellite data. MethodologyIn the present study, the environmental approach to circulation was used to investigate the relationship between circulation patterns and heavy snowfall. Thus; first, the days of heavy snowfall in the studied stations were identified and then the synoptic patterns and atmosphere of the representative days were analyzed. In this regard, after receiving snow altitude data from the Meteorological Organization, heavy and widespread rainfall events were identified in three western provinces of the country, including Hamadan, Kurdistan and Kermanshah in the form of 16 synoptic stations, during the years 2000 to 2019. In order to study and analyze the synoptic patterns of days with heavy snowfall, by referring to the website of the National Center for Environmental Forecasting / Atmospheric Sciences (NCEP / NCAR), daily data on Sea Level Pressure (SLP), High Geopotential (HGT), zonal wind (UWND) and meridianal wind (VWND), air temperature (Air) and instability index (Omega) were extracted at the intersection of 2.5 * 2.5 and the relevant maps were drawn using GRADS software. Also, the area covered by snow was obtained from MODIS satellite images. MODIS data are of level1b type, which was calculated based on the parameters in the header, radiance and reflectivity. Reflective and thermal parameters for bands 4 and 6 were also used to apply the NDSI (Normalized Difference Snow Index). Results and DiscussionAfter 20 years of study, 8 days were identified that heavy and heavy snow had fallen in the area. On February 4, 2011, in the middle of the atmosphere, a deep trough formed in the western Mediterranean and North Africa, with a strong positive vorticity. This situation has affected the study area.The location of this trough in the Mediterranean provides the moisture needed for snowfall from the Mediterranean Sea. ConclusionsThe results showed in the ground formed a powerful cyclone on Iraq and turbulent weather caused chaos for the region. This condition causes the air to cause accelerated the rise of the package and water vapor in the atmosphere with his quick ascent to the seed quickly convert hexagonal snow. Creates a pressure gradient that causes more than 12 HPa in the region was to create a strong front will be formed in the region. In the high latitudes of cold air and warm air in front of it is the lower latitude. Has caused more than 60 to 70 percent of the study area are covered by snow. A deep trough of cold air loss in middle levels at depths greater than 25 degrees latitude has been. With extreme vorticity and air along rapid ascent has been closed. NDSI index showed the results of actions by deploying the most weather systems has gone down snow-covered forests of western Iran.
Climatology
Seyyed mahmoud Hosseini seddigh; masoud jalali; Hossein Asakereh
Abstract
IntroductionThe results of the study showed that the correlation headley cell and subtropical jet on the atmosphere Iran at the level 200 hPa has a positive correlation with a value of 0.4-0.7 to 35 ° latitude and also regression analysis showed that in latitudes between 15 35 degrees north of the ...
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IntroductionThe results of the study showed that the correlation headley cell and subtropical jet on the atmosphere Iran at the level 200 hPa has a positive correlation with a value of 0.4-0.7 to 35 ° latitude and also regression analysis showed that in latitudes between 15 35 degrees north of the subtropical jet 1(m/s) is higher than normal, although in 2017 up to latitudes 30 degrees north showed an increase of 2(m/s), which had a negative effect on rainfall.Data and MethodThe relationship between Hadley cell and olr in the southern, southwestern and southeastern regions of Iran with a value of 0.4 and the Zagros and northwestern heights of Iran with a value of 0.7 and regression with a value of (w/m2) 0.01 more than normal.Results and DiscussionIt acts as a tangible source of heat in the middle Wordspehr and the heat is added directly to the middle Wordspehr and causes heating of the upper half of the Wordspehr.ConclusionRegression 2 to 1 is shown. Low relative humidity along with the dried air mass is located below the descending branches of the headley cell, which has ruled the drought conditions (-0/7) showed that it creates conditions for lack of rainfall and drought.
Climatology
Behrooz Sarisarraf; Hashem Rostamzadeh; Mohamad Darand; Omid Eskandari
Abstract
Precipitation is one of the most important and variable climatic elements that changes in time and place. Critical rainfall at various time scales, especially daily, causes severe damage to human communities in densely populated urban areas and natural ecosystems and affects many arid economies. Earth ...
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Precipitation is one of the most important and variable climatic elements that changes in time and place. Critical rainfall at various time scales, especially daily, causes severe damage to human communities in densely populated urban areas and natural ecosystems and affects many arid economies. Earth outgoing long-wave radiation is studied as a significant parameter to detect clouds and estimate this type of precipitation. The current study aims to examine the relationship and analysis of outgoing long-wave radiation variables and precipitation values in Arc GIS software environment for the four cold months 17 statistical years in Iran using AIRS sensor products of Aqua satellite and GPM satellite. Correlation and regression models and confidence interval estimation were used to measure the correlation of long-wave radiation output in predicting precipitation patterns and their changes. According to the results obtained in all months studied, In the whole country, except Caspian Sea basin in January, parts of the central and eastern plateau of eastern Iran, there is a negative correlation of 10 to 92%, Which indicates that the country's atmosphere is humid and prevents the release of outgoing long-wave radiation. In the western rainfall areas of the Zagros Mountains, negative correlations above 70% and outgoing long-wave radiation is less than 260 W⋅m−2 which is due to cloudy and humid atmosphere with precipitation.In December and February, the rainfall areas north of the Caspian Sea basin range have negative correlations of above 50% and OLR less than 235 W⋅m−2 of rainfall and the reason for the lower numerical value north of the Alborz mountain range to predict is the existence of high relative humidity in the region, which is the cause of less outgoing long-wave radiation output of the earth.
Climatology
younes nikookhesal; Ali Akbar Rasouli; Davod Mokhtari; Khalil Valizadeh Kamran
Abstract
IntroductionThe water cycle in nature is directly related to the climate of that region. Reasonable and correct use of water resources requires accurate quantitative and qualitative knowledge and collection of appropriate climate data and information. Depletion of groundwater reservoirs, drying of canals ...
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IntroductionThe water cycle in nature is directly related to the climate of that region. Reasonable and correct use of water resources requires accurate quantitative and qualitative knowledge and collection of appropriate climate data and information. Depletion of groundwater reservoirs, drying of canals and springs and even semi-deep wells and reduction of deep well discharge, change of groundwater flow direction, salinization of aquifers, salinization of soil due to irrigation with saline water, barren The emergence of fields, soil erosion, etc. has put most of the plains of the country at risk of further desertification (Tavousi, 2009: 14).Atmospheric precipitation is the main source of surface and groundwater and the study area is poor in terms of atmospheric precipitation and its amount is between 150 to 450 mm per year, which varies in plain and mountainous areas. The climate of the region is semi-arid and cold and is mostly influenced by the Mediterranean climate. Due to the fact that groundwater is the most important source of water consumption in the study area, the impact of climate change, especially precipitation on the water table of wells in the area was investigated in this study.Materials and methodsTo study the trend of groundwater level changes in Marand plain, water table data of 23 piezometric wells and data of 8 rain gauge stations during the last 16 years of 1395-1395 were used. After using the correlation matrix method to select rainfall stations and considering the complete statistical data and appropriate coverage of the area by these stations, 4 stations were selected for the study and for each station, a piezometric well was selected within the station. This research was first calculated using precipitation data and water table of piezometric wells SPI and SWI values and then NRMC values for each index, respectively, in each method are briefly referred to:Calculate SPI and plot seasonal SPI variations of selected stationsThe standardized rainfall index was provided by McKay et al. (1993, 1995) to provide a warning and help assess drought severity and is calculated by the following formula: Relation 1: SPI = (X_ij-X_im) / σIn the above relation, X_ij is the seasonal rainfall at rainfall station i, with j number of observations, X_im is the long-term average rainfall and σ is the standard deviation.Calculate SWI and plot the seasonal SWI of selected wells The standard water level index was presented in 2004 by Bui Yan et al. (2006) to monitor fluctuations in groundwater aquifers in the study of hydrological droughts, which is calculated by the following formula:Relation 2: SWI = (W_ij-W_im) / σWhere W_ij is the seasonal average of the water table of observation wells i to j, W_im is the long-term seasonal average and σ is the standard deviation.Calculate the NRMC values of each indicator and plot the normalized distribution curveIn this method, seasonal normalized distribution curves were adjusted for both SPI and SWI indices. Cumulative normalized curve is a kind of condensation diagram of a climatic or hydrological variable (such as precipitation and water table) that is extracted from the subtraction of each observation in the statistical series of the long-term average and its division by the average according to the following formula. (Rasooli, 1994)Relation 3: NRMC xi = ( (Xi-X m) / ({(Xi-X ̅m) / X ̅m}) ) * 100 In the above formula, Xi represents the amount of each rainfall observation or the amount of water table and X ̅m is the long-term average in the series of observations.Results and DiscussionInvestigation of normalized distribution curves showed a correlation between precipitation changes and groundwater level in Marand plain. This correlation has a higher significance with a delay season. Shamsipoor (2003) in Hamedan plain achieved a 9-month delay between precipitation and water table. Mohammadi et al. (2012) in Arak plain expressed the impact of groundwater resources from drought with a delay of two months. The results of the study (Rudel and Lee 2014) in the study of groundwater drought index in the United States showed that the SPI drought index with a delay of 12 and 24 months had the highest correlation with the SWI index.ConclusionConsidering the more fluctuations of the water table than the fluctuations of the rainfall, it can be concluded that human factors such as uncontrolled harvesting is an effective factor on the water level of wells. Komasi et al. (2016) stated the effect of human factors on the decrease of groundwater level before the factor of climate change in Silakhor plain. Calculations showed that the value of correlation for both SPI and SWI indices in the nonlinear multivariate equation is higher than the value of the linear equation, which indicates the effect of several other factors in addition to precipitation fluctuations on the groundwater level. According to the results of the study, it seems that the groundwater level in addition to precipitation depends on other factors such as geology, lithology, tectonic morphology, the shape of the aquifer, the distance of aquifers to the feeding site and .... And to achieve more complete results, it seems necessary to address these factors in future research.
Climatology
naser pouyanfar; Gholam Ali Mozafari; Kamal Omidvar; Ahmad Mazidi
Abstract
IntroductionPistachio, like many subtropical fruit trees, need a cold period in their annual cycle to allow the buds to bloom naturally after the right conditions are in place. There are several models to calculate the chilling needs of pistachio, of which the chilling hours model, Utah and Utah positive ...
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IntroductionPistachio, like many subtropical fruit trees, need a cold period in their annual cycle to allow the buds to bloom naturally after the right conditions are in place. There are several models to calculate the chilling needs of pistachio, of which the chilling hours model, Utah and Utah positive are the most important of these models. The studied geographical area is Yazd-Ardakan plain located in Yazd province.Materials and methodsIn this study, according to statistical tests based on meteorological variables, the chilling hours model was selected for modeling. To conduct this research, three-hour temperature data of Yazd Synoptic Station during the statistical period of 1367-1396 were used to model and estimate the total monthly chilling hours of pistachio and The daily temperature data of this station during the statistical period of 1961-2005 were used for the SDSM model and the monthly temperature statistics of the years 1385-1397 were used to evaluate the downscaling data of the CanESM2 model under different RCP scenarios and finally modeling for the years 1400 -1429 was done.Result and discussionResults indicate that there is a significant correlation between monthly cumulative hours of temperatures between zero and 7.2 ° C and monthly temperature parameters such as mean minimum temperature, mean maximum temperature and mean monthly temperature, which in the absence of data hourly temperature can be used to model and determine monthly cumulative hours.ConclusionFindings show that the chilling needs of Kalle-Ghuchi, Owhadi and Ahmad- Aghaei species will be met in the coming years and Akbari and Fandoghi species will not be met.
Climatology
Monir Shirzad; Hajar Feyzi; Majid Rezaei Banafsheh
Abstract
Introduction Reference evaporation and transpiration is one of the important elements of the hydrological cycle, which plays an important role in agricultural studies, water resource management plans, irrigation and drainage network design and water structures (Nuri et al., 2013, Volume twenty, ...
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Introduction Reference evaporation and transpiration is one of the important elements of the hydrological cycle, which plays an important role in agricultural studies, water resource management plans, irrigation and drainage network design and water structures (Nuri et al., 2013, Volume twenty, number five, page 12). Due to the small amount of precipitation and the limitation of water resources in Iran, the correct management of water resources is very important and it is necessary to be careful in using water.Data and MethodIn order to carry out this research, daily climatic data during the years 2014 to 2015 of East Azerbaijan (four stations of Maragheh, Midane, Jolfa and Ahar) were prepared from the regional meteorological organization. After normalization and determination of correlation, the data were used in MATLAB software with artificial neural network method with Lunberg-Marquardt training to 70-30 combination for training and simulation. The input data for the simulation of evaporation and transpiration (temperature, sunshine hours, humidity, wind speed) and the work evaluation criteria are RMSE, R2 and MAE, which we gave priority to the data with less error. Results and DiscussionIn this research, the method based on artificial intelligence (ANN) and three experimental models (Penman Monteith Fau (PMF56), Blaney Kridel (B-C) and Kimberly Penman (K-P) were used to model the non-linear transpiration evaporation system of the reference plant. The results showed that the artificial intelligence method has better accuracy and speed in estimating ET0 compared to experimental methodsConclusionThe results showed that the artificial intelligence method has better accuracy and speed. Also, comparing the method of artificial neural networks with classical methods, the results indicate the appropriateness of the performance of artificial neural networks.
Climatology
Younes Nikookhesal; ali akbar rasoli; Davod Mokhtari; Khalil valizadeh kamran
Volume 26, Issue 80 , August 2022, , Pages 327-317
Abstract
IntroductionInvestigating the effect of drought on water resources of countries plain is high important at optimal management of water resources in the agriculture and natural resources part. The phenomenon of climate change, affects the amount of water existence in aquifer by changing amount of precipitation. ...
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IntroductionInvestigating the effect of drought on water resources of countries plain is high important at optimal management of water resources in the agriculture and natural resources part. The phenomenon of climate change, affects the amount of water existence in aquifer by changing amount of precipitation. The occurrence of consecutive climate droughts affects ground water resources. Knowing and awareness of the effect of time between two phenomenon of drought and hydrological drought, can help managers and planners of the water sector. Over the years, the effect of drought on ground water resources less attention has been paid. In order to understand the state of groundwater resources and optimum management, it is necessary to carry out a thorough study of groundwater fluctuations. In this research, Marand plain is the purpose of this study. Marand Plain is poor in rainfall and has a rainfall of 450 mm / year and at least 150 mm / year which varies in the plains and mountainous regions. In this research, we have tried to investigate the effect of atmospheric drops, including rainfall, on ground water level in the Marand watershed.MethodologyThe Marand plain with 45 °, 15 to 50 minutes east longitude and 37 ° 7 'to 38 ° 56' north latitude and with an area of 42.517 square kilometer is one of the vast plains in the northwest of East Azarbaijan province. Which is selected as the study area. In this study, in order to study the trend of ground water level changes in the Marand Plain, the static surface data of 23 piezometric wells was used during the 2000 to 2016. First, a common statistical period was chosen to analyze the data series (2000 to 2016). Then in order to reconstruct the statistical defects, the correlation between stations and piezometric wells and linear regression method was used. The IDW method was used to calculate the average rainfall of the plain. Finally, the standard water level index (SWI) and the SPI index for the studied basin were calculated and analyzed. Discussion The aim of this study was to investigate the effects of climate drought on the fell of groundwater level in the Marand plain using SPI and SWI indices. Meteorological drought conditions in the Marand plain were calculated using the SPI index on a 12-month time scale. The results and drought accuracy of the rain gauge stations in the studied basin showed that during the study period, the first period of drought since 2005 started gradually with decreasing atmospheric precipitation and continued until 2007 and after a period of humidity short-term, again, a short period of drought from 2008 to 2009 has been on the ruling area. The SWI index was used to survey the status of groundwater level. This indicator also showed that in terms of time and place, the drought based on this index corresponded to the drought caused by the SPI index.Conclusion Using the SPI index, the drought trend was studied in the region. The results showed that during the study period (2000-2016) three drought periods from winter 2005 to beginning of 2009, summer of 2011 to the end of 2012 and winter of 2015 to summer of 2016 occurred. Drought affected areas included the east and center of the study area and the west of the region witnessed more atmospheric precipitation. The SWI index was used to survey the status of groundwater level. The index showed that in terms of time and place, the drought based on this index corresponded to the drought caused by the SPI index. Data analysis showed that these two indices with a time interval of one season had a correlation of 1%. This means that the hydrological drought after a season has a direct impact on the surface of the water. In general, we can conclude from the results of this study that the trend of ground water surface changes has been consistent with the drought and weathering changes in the region. Therefore, the fall of the ground water level of Marand plain can be largely influenced by weathered droughts.
Climatology
Ebrahim Ahmadzadeh; Khalil Valizadeh Kamran; Davod Mokhtari; ali akbar rasoli
Abstract
IntroductionRecently, high extreme and frequency distribution of higher sequence of precipitation have been attended more. Through this, because of geographical characteristics of each area, diverse and different thresholds have been presented and utilized for the mentioned precipitation’s characteristics. ...
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IntroductionRecently, high extreme and frequency distribution of higher sequence of precipitation have been attended more. Through this, because of geographical characteristics of each area, diverse and different thresholds have been presented and utilized for the mentioned precipitation’s characteristics. Through the present research, for exploring and analyzing the extreme precipitation event in Tehran through the 1983-2016 statistical periods, some of the indexes presented by World Meteorological Organization Committee were utilized.Data and MethodThe study area in the present study is Tehran province. Tehran province is located in the center of Tehran, with an area of about 12981 square kilometers, between 34 to 36.5 degrees north latitude and 50 to 53 degrees east longitude. Data from Abali and Mehrabad synoptic stations were selected daily for use in the present study during the statistical period of 2016-1983. Before analysis, the data were subjected to quality control and homogeneity test. In cases where for any reason there were incomplete data in the data series of each station, they were reconstructed and supplemented.Analysis of non-parametric I-Kendall trend and age slope estimatorIn the present study, in order to study and analyze the trend of limit events, the indexes provided by the National Climate Committee of the World Meteorological Organization and the Acceptable Research and Climate Prediction Research Program, abbreviated as ETCCDMI, are used. These indexes are part of a set of indexes presented by the World Meteorological Organization's Working Group on Climate Change (Peterson et al., 200: 341), which are used by numerous researchers for analysis in different parts of the world.Model of peak values Above the threshold (POT)The POT first fits the set limit and then one above this threshold with the generalized parity distribution. In the present study, the ninety-fifth percentile was considered as the initial threshold (Coelho et al., 2008: 120; Friederichs, 2010, 211). The test threshold was then set to determine whether it was appropriate or inappropriate. In recent years, two visual methods have been developed to select the threshold. In the present study, methods were used to validate the selected threshold. The first method is the description of residual life, also known as conditional excess (Lechner et al., 1992: 229). In the MRL method, the excess rate is plotted from the threshold to the threshold .How to estimate GDP distribution parameters using the maximum likelihood methodFor different estimates, there are several methods such as torques, possible weighted moments, the existence of correct representation, and so on. However, the most efficient performance method is evaluated as the most complete method (Rao and Hamed, 2000: 21). Therefore, in the present study, the correct method of displaying the work was used.Results and DiscussionThe results of man-condensate precipitation statistics at the studied stations. The results obtained from Mann-Kendall test showed that no significant trend in success level was experienced in the studied stations in the statistical period of 1983-2016. Except that in Abali station, the reduction of the number of consecutive dry days and in Mehrabad station, the reduction of the one-day rate (PX1day) at the level of 90% is significant. One day exhibition at Mehrabad station is a downward trend in the level of 90% confidence with the rate of 1.9 days in the last decade.During the statistical period of 2016-1983, no significant trend was experienced in relation to the index of the number of values for 5 consecutive days. The annual show on other days does not make sense. The number of days with more than 10 mm (R10) and the number of days with more than 20 mm (R20) and the number of days with threshold (Rnn) in the two study stations are not significant.In this study, using the Mann-Kendall non-parameter test and sen slope estimator, the final rainfall trend analysis was performed at Abali and Mehrabad stations. According to the results of the Mann-Kendall test, the display of consecutive dry days (CDD) showed a decrease of 8.5 days per decade at Abali station. But on consecutive wet days (CWD) the upward trends are not significant. The Daily Intensity Index (SDII) is also significant without trend. One day exhibition at Mehrabad station is a downward trend in the level of 90% confidence with the rate of 1.9 days in the last decade. In Abali station with confidence intervals (-0.08, -0.11) and Mehrabad station with confidence intervals (-0.09), the figure is zero. Therefore, in these stations, it has a thin sequence with finite torque that is close to producing a show.The study of growth curves showed that in the 34-year statistical period (1983-2016), most events in stations have a return period of 1 to 10 years. In higher return periods, fewer observations are consistent. The confidence bands of the growth curves also showed to some extent that the deviation of the POT model is less even in the return periods. But as the return period increases, the confidence interval increases. This indicates that as the period increases, the uncertainty in the results increases that the extrapolation of the data is in the range beyond the time frame of the statistical period under study (34 years). Reliable bands have shown that return periods of 1000 years are too unreliable to use in practical applications.ConclusionThe aim of this study is to investigate the changes in the intensity and frequency in Tehran province during the period 1983-1916. In this regard, the study of the initial trend of rainfall showed that in relation to the marginal rainfall, most of the backgrounds had a downward trend in the region. The study of the sequence behavior of events and the frequency and intensity of these events, using them, are higher than the thresholds that have increased in frequency in the study areas. The results of this part of the study are highly consistent with the work of Rahimzadeh et al. (2009) who reported negative trends for cold-bounded appearances and thresholds for precipitation and positive trends for warm-range indices in 27 synoptic stations in Iran. . Rahimzadeh and Hedayat Dezfuli (2011) also showed intensification of heating and decrease along with extreme fluctuations and temperature limit power in Hormozgan province and Mohammadi and Taghavi research (2005) increased the frequency of hot limit indices and cold limit index indices in the city. Has stated Tehran. Maroufi et al. (2011) have achieved similar results in studying the trend of borderline events in the northern and southern coasts of Iran. Also, the estimates and severity of precipitation boundary events using the mean time intervals between events (ARIs) indicate return periods of 1 to 10 years for boundary precipitation. Finally, the resulting Q - Q diagrams and Chi - square test (χ 2) showed that the POT model has great potential for modeling precipitation limit events in the study area.
Climatology
saeid Jahanbakhshasl; Behrouz Sari Sarraf; Hossein asakereh; soheila shirmohamadi
Abstract
Introduction Climate extreme events have expanded and intensified during the 21st century. Extreme precipitation event annually leads to severe damage in agriculture, environment, infrastructures and even the human loss. Therefore, identification of the behavior of such events is one of the pivotal aspects ...
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Introduction Climate extreme events have expanded and intensified during the 21st century. Extreme precipitation event annually leads to severe damage in agriculture, environment, infrastructures and even the human loss. Therefore, identification of the behavior of such events is one of the pivotal aspects of climatic change and the increase of information about extreme precipitation is tangibly necessary for the society especially with regard to those, living in the areas with high risk of flood. extreme precipitation events can be defined as significant deviations from the precipitation mean. As a result, to identify such precipitations, a criterion was needed to evaluate the rate of precipitation values’ deviation from mean. Importantly, given the different types of indicators and thresholds proposed for extracting extreme precipitation, choosing an appropriate threshold with climatology conditions of the study region which could also be capable of identifying extreme precipitation optimally in terms of amount and frequency, requires high precision. The present study aimed at identifying the extreme precipitation events in the west of Iran through introducing the appropriate threshold and spatial scale for the extraction and investigation of these events.Data and MethodsThe west of Iran with the areaof 230760 square kilometers includes about 14% of total area of Iran. Zagros Mountains, stretching from northwest to southeast, are the most important feature of the west of Iran. Two databases have been used in this study. The first database regardsthe precipitation data of 1129 synoptic stations, climatology and rain gauge in the west of Iran. The stations statistics have been checked in terms of existence of any outlier. Ultimately 823 stations out of 1129, were used for producing gridded data. The gridded data, are the results from the interpolation of daily precipitation observations since January 1st 1965 to December 31st 2016, using Kriging interpolation method and spatial separation of 6*6 kilometers. the final base, a matrix possessing the dimensions of18993*6410 (representing time on the rows and place on the columns) was developed. The second database referred to the Sea-level pressure patterns (Hectopascal).To identify such precipitations, in addition to the main threshold that includesthe mean of precipitation more than 75th percentile for each pixel per day of a year, a second threshold including the standard deviation of these precipitations (with the values of one, two, and three times more) has been also added to the mean. Accordingly, three groups of extreme precipitation were identified in the region which were separated according to the spatial zone that had been covered. Moreover, the sea-level pressure patterns were extracted with regard to these precipitations for each zone andthen classified using clustering analysis technique.Results and Discussionthree groups of precipitations with different coverage zoneswere identified: 1- 83 days with equal to or more precipitation than the mean of precipitations more than 75th percentile plus one time standard deviation which cover more than 40% of the region. 2- 144 days with equal to or more precipitation than the mean of precipitations more than 75th percentile plus two times standard deviation which cover more than 20% of the region. 3- 82 days with equal to or more precipitation than the mean of precipitations more than 75th percentile plus three times standard deviation which cover more than 20%The maps of 7 participation groups of the first type in comparison with 6 precipitation groups of the second and third type contain common and repetitive patterns. Each precipitation maps of the second and third types explains a type of patternand there is minimum overlapping in the maps. Therefore, the precipitations are obtained from the most particular and distinct atmospheric patterns. considering the three properties of 1- equality of precipitation groups of type two and three (both include 6 groups of atmospheric patterns). 2- repeating the atmospheric patterns of precipitation of type two prominently in the precipitations of type three. 3- the formation of the most optimum atmospheric modeling for the precipitations of both thresholds in the zones of 20% and higher, in the west of Iran, the extreme precipitations refer to those with higher means of recipitations more than 75th percentile plus two times standard deviations,have mostly occurred in the zone of 20% and higher of the region.
Climatology
Soodabheh Namdari; Ali Hajibaglou; GholamReza Abazari
Abstract
IntroductionAtmospheric mineral dust particles play a key role in the radiation budget of the atmosphere and the hydrological cycle, and have an important effect on public health by disrupting climate systems and air pollution. Due to Iran’s location in the arid and semi-arid belt of the world, ...
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IntroductionAtmospheric mineral dust particles play a key role in the radiation budget of the atmosphere and the hydrological cycle, and have an important effect on public health by disrupting climate systems and air pollution. Due to Iran’s location in the arid and semi-arid belt of the world, Iran is constantly exposed to local and regional dust systems. Considering the importance of the negative effects of dust storms and their increasing trend in some dust sources, the study of these changes in the last two decades show the importance of the dust storms in recent years. Moreover, spatial-temporal identification and analysis of the properties of these dust particles is very important in order to manage this crisis and prevent the harmful effects of dust particles. In Iran, due to desert conditions, the presence of dust hotspots has always caused air pollution and reduced the quality of life of people. In recent years, some dust hotspots have been ambiguous about increasing the intensity of dust emission. In this study, using the AOD product of MODIS, which compute the dust intensity, and based on the annual frequency and averages of dusty days, the location of dust hotspots were identified and then the trend of dust intensity in each hotspots were examine. The results showed that despite the relatively similar climate, the trend of changes in these dust hotspots does not follow the same pattern and complex human activities and natural changes.Data and Method In this study AOD product from MODIS with the resolution of 10 km was used to extract dust information then the frequencies of days with AOD greater than 0.6 per year were extracted. In addition to correctly calculating the average of AODs, calculating the number of days without data is also important in the results. The spatial and temporal distribution of the study period, were identified in three periods, 2000-2006, 2007-2012 and 2013-2018. The percentage of changes in each of the dust sources compared in different periods. The standard deviation was extracted to identify the areas most vulnerable to dust storms. Finally, to detect the quantitative distribution, the trend of AOD changes in the extracted dust hotspots was used to investigate the changes in the dust intensity trends.Results and DiscussionThe map of dust hotspots in the first period shows the main dust sources are in the north of Sistan and Baluchestan (Zabol) and south of Sistan and Baluchestan (Chahbahar), in the southeast of Semnan (Dasht Kavir), Damghan, Garmsar, Jazmourian, southwest of Hormozgan, (Bandar Lengeh area), south and southwest of Khuzestan, southwest of Yazd (Nayer), as well as parts of Qom, Ilam (Mehran), Isfahan, and south of Fars provinces. In the second period of study, many dust centers have become more intense and extensive. According to the map of dust centers in the third period of studies, compared to the first and second periods, the area of dust centers has decreased.According to the results, about half of the areas without emission has been turned into areas with dust with different frequencies in second period, and also about half of the area of very high-frequency hotspots has been turned into other dust sources with less intensity in the third period. Also, the most fluctuations in dust intensity have occurred in Sistan, Jazmorian, southeast of Semnan, East Azerbaijan, Zanjan and Khuzestan provinces. The results of trend analysis of dust intensity in different dust hotspots show that despite the relatively uniform climate, the dust sources trends in different dust sources do not follow the same pattern.ConclusionDue to the geographical location of Iran and the existence of vast deserts, the wethear has always affected by dust sources of inside and outside of the country. In this study, using satellite data with appropriate resolution, the location of dust sources in three time periods were extracted. The changes of each dust intensity class in the second and third periods were compared with the first period so that regardless of location, changes in dust intensity can be evaluated in general. Then, using the standard deviation method, the dust hotspots with the highest percentage of changes were identified. Finally, the trend of changes was calculated by examining the trends of changes in 24 main dust centers. According to the results of the present study, many changes have been observed in some dust sources and the intensity of dust in many dust sources has decreased. While some sources such as Isfahan, and Khuzestan province due to the role of human factors such as agricultural activities as well as the reduction of surface and ground water and as a result of drought and changes in soil texture have an increasing in trend of dust intensity. Since a decreasing trend is observed in most of dust sources, eastern and southern parts of Iran, the results of this study indicate the key role of climatic factors in changes and fluctuations in dust emission in Iran. Because climatic factor can be the only factor which has a relatively uniform effect on the dust emission on a large scale of Iran.
Climatology
Naser Jafarbegloo; Ali Mohammad khorshiddoust; majid rezaei banafsheh; Hashem Rostamzadeh
Abstract
INTRODUCTION
Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital ...
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INTRODUCTION
Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital determining factor in the distribution of plant species on the planet, can be both a limiting factor and a factor in the spread and species distribution (Rodrigo, 2000: 155). Therefore, in this study, we examined the changes in minimum temperatures in the statistical period (1980-2010) and predicted these changes in the 2050s (2065-2046) in the Northwest of the country using the LARS-WG microscale method and model output. Atmospheric pairings of HadCM3 and MPEH5 were addressed. The prediction of minimum temperature variations to determine the extent of its future changes and considering the necessary measures to minimize the adverse effects of climate change on agricultural products were of great importance. In this regard, general atmospheric circulation models (GCMs) are designed that can simulate climatic parameters.
DATA AND METHODS
In the present study, the output data of two HadCM3 and MPEH5 general circulation models based on two scenarios A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in the Northwest of the country. The results were monthly and periodic on the base period (1980-1999) and the 2050s (2046-2065), thereby the minimum temperature was evaluated and analyzed. In assessing the LARS-WG model, the observational and simulation error data were evaluated using MSE, RMSE, MAE and R2, and the model was evaluated for the appropriate region. The results showed that the minimum temperature in the future period will increase compared to the base period in the study area. This increase in air temperature at the study area is based on the HadCM3 and MPEH5 models, on average, 1.9 and 1.7 degrees Celsius to 2065 horizons compared to the base period. The north-eastern part of the northwestern region of Iran will have higher temperatures than the semi-southern regions. In fact, the cooler regions of the high latitudes will face more incremental changes in the amount of minimum temperatures. The results and achievements of this research are important for long-term plans for adaptive measures in the management of fruit gardens, agricultural products and water resources management. In order to calibrate and ensure the accuracy of the LARS-WG microscale model, the model was first implemented for the basic statistical period (1980-2010); then the minimum temperature output and its standard deviation were compared with the observational data of the studied stations, which indicated a small difference between the observed and simulated values and also deviated from their criteria.
RESULTS AND DISCUSSION
The results of evaluation of observational and simulated data by LARS-WG microscale model using RMSE, MSE and MAE error measurement indices for the studied stations indicate that there is a significant difference between the simulated values and the values of the observed observations. There is no critical 0.05 significance levels, and Pearson correlation values between simulated and real data are acceptable at the significance level of 0.01. The obtained results show that the accuracy of the model varies in different stations. In general, the results of error measurement indices indicate that the LARS-WG model is of good accuracy for micro-scaling the parameters under study. In order to better represent and ensure the accuracy of the prediction as well as to investigate the uncertainties in the studied models, the simulated values were compared and observations were made on a long-term average during the base period in the studied stations using comparative graphs. As can be seen, the observed and generated values in the base period at all stations are very close to each other and the LARS-WG model has been successful in simulating the studied parameter. After evaluating the LARS-WG model and ensuring its suitability, the data predicted by the model for two scenarios A2 and B1 using HadCM3 and MPEH5 models and were examined on a monthly and long-term basis. The study of the status of minimum temperature changes of the studied stations in the future period (2065-2056) shows that the minimum temperature is based on both scenarios and in all months and stations compared to the period, the base has increased. Due to the large number of study stations, only stations located in provincial centers of this study are listed.
CONCLUSION
Cold and frost are one of the most significant climatic hazards on fruit trees. This type of climate risk affects different parts every year, especially the cold regions of the northwest of the country. Studies show that in recent years, the rate of economic damage to fruit trees in this region has increased, so in this study, the outlook for changes in minimum temperatures in this region using the LARS-WG statistical microscale model and output two HadCM3 global model and MPEH5 were introduced in the 2050s (2065-2046). For accuracy and precision of the models, error measurement indices and coefficients of determination and correlation were used. The results showed that the LARS-WG model has a good ability to simulate the studied variables in the study area. The results of long-term prediction of the studied models show that the minimum temperature values will increase in all study stations, which is based on HadCM3 and MPEH5 models on average. In the 2050s, and it will be 1.9 and 1.7 respectively, compared to the base period. The results of the studies of Kayo et al. (2016), Sharma et al. (2017), Khalil Aghdam et al. (2012), Qaderzadeh (2015), Sobhani et al. (2015) and Khalili et al. (2015) were confirmed. In general, based on the studied scenarios and models, the minimum temperatures are expected to increase in the study area in the future. By increasing it, the yield of some crops that need cold during the growing and productive period would decrease. It can also reduce snowfall, followed by frost on crops and lack of water in dry seasons. Therefore, due to the fact that following the climate changes, the conditions of the agricultural climatology are also changing, it is necessary for the relevant officials and planners in the agricultural sectors to adopt the necessary strategies to reduce the consequences and adapt to the new climate.
Climatology
Parichehr mesri alamdari; seyed Hassan rasouli
Abstract
Introduction
With the beginning of the Industrial Revolution in 1830 and the growing growth of human knowledge, various changes have taken place in human life and human needs for energy and consumption of fossil fuels such as coal, oil and natural gas have led to a sharp increase in materials such as ...
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Introduction
With the beginning of the Industrial Revolution in 1830 and the growing growth of human knowledge, various changes have taken place in human life and human needs for energy and consumption of fossil fuels such as coal, oil and natural gas have led to a sharp increase in materials such as Carbon dioxide has been released into the atmosphere. Increasing its population exacerbates this phenomenon. All of these changes have caused the weather to change. The phenomenon of climate change, which is mainly related to the increase of greenhouse gases in the atmosphere, is a clear example in this field. This phenomenon causes many current problems such as gradual warming of the climate, melting of ice, rising sea levels, torrential rains, increasing drought, acid rain and threats to human health. And wildlife species in different regions of the earth (Atabi et al., 2007: 146). The development of urbanization and migration of rural residents to cities to enjoy the benefits of civilization, especially in the second half of the twentieth century led to the overdevelopment of cities (Alijani et al., 2010: 541). The desirability and quality of urban areas make a difference in the value of land use. Knowledge of how urban temperature patterns are distributed allows planners to manage the construction of urban green space to adjust the temperature. Also, by studying the relationship between user patterns and the distribution of thermal patterns, it is possible to provide programs to change and relocate these uses to improve environmental conditions. Despite the year-on-year changes in the average temperature due to the natural variability of the climate, increasing trends in the average annual temperature are evident in most parts of Iran, including the city of Sari. These increasing trends are mainly due to the increase of greenhouse gases in the atmosphere (due to the burning of fossil fuels and changes in the surface characteristics of urban areas (Alizadeh Chobari et al., 2016: 571 and 572). In this regard, the city Sari is located in a dense area of activity and residential centers and with its various capabilities has been able to enjoy a special position in the province.This city due to its strategic location and suitable climate and location Tourism and unique agricultural capabilities are facing population growth and increased migration. Considering the challenges such as the increasing growth of the urban population, the uneven expansion of cities, the destruction of the environment, etc., which has reduced the quality of life and created heterogeneous uses in different urban areas; As a result, the climatic parameters of the region are also subject to change. In this regard, the effect of these changes on the city of Sari and solutions to deal with it have been studied.
Methodology
The present research is applied-developmental for the purpose and is descriptive-analytical according to the method of work. In this study, in order to measure the spatial distribution of population in the eleven districts of Sari, data under the Geographic Information System (GIS) has been used. In order to investigate the spatial distribution of population in each area of Sari, the Shannon relative entropy model has been used and to calculate the maximum thermal island intensity, the Oke numerical-theoretical equation has been used. Sari, the capital of Mazandaran province in northern Iran, is one of the largest and most populous cities in Mazandaran province and the north of the country, which is located at 53 degrees and 37 minutes east longitude and 34 degrees and 36 minutes north latitude. In terms of natural location, this city is located in the south of the Caspian Sea and in the plains of Sari city and only its southern and southwestern parts lead to mountains and low satellite hills. The height of the city from the sea level is 18.5 meters and the difference in its area to the coast of the Caspian Sea is 24 kilometers. The general slope of the city is from south to north and is very gentle (Sari Master Plan Studies, Mazand Tarh Consulting Engineers, 2015).
Results and Discussion
In the present study, the relationship between the spatial distribution of the population and the creation of thermal islands in the city of Sari has been investigated. After examining the spatial distribution of population and the intensity of changes in thermal islands, it is concluded that there is a relative relationship between the two indicators of spatial distribution of population and the intensity of changes in thermal islands in Sari. In region 2 of region 3 of Sari city, which had the lowest equilibrium in the spatial distribution of population, the intensity of changes in thermal islands was also low, and in areas where the spatial distribution of population was semi-balanced (region one of region one, regions 1 and 2 from region 2, and region 1 from region 3 of Sari city), the intensity of thermal island changes was low. Also, in the areas where the spatial distribution of the population was balanced (areas 2, 3 and 4 of area one, areas 3 and 4 of area 2 and area one of area 4), the intensity of thermal island changes was low and moderate.
The results indicate the fact that there is a direct relationship between net residential density and the intensity of changes in thermal islands in the city of Sari. As the net residential density increases, the intensity of changes in thermal islands in Sari city increases, and as the net residential density decreases, the intensity of thermal island changes decreases. Based on the findings of the survey of Sari city areas and analysis of the spatial distribution of population and the maximum intensity of thermal island changes, it is concluded that there is a relative relationship between these two indicators in Sari city areas. In the areas that had the lowest equilibrium in the spatial distribution of the population, more intensity changes were observed in the thermal islands and in the areas where the spatial distribution of the population was semi-balanced and balanced, the intensity of changes was less in the heat islands. On the other hand, according to the results of Spearman correlation coefficient, it can be said that the most important effective factor in the maximum intensity of thermal island changes, which is inversely related to this phenomenon, is the net residential density. Areas in Sari that have the highest intensity of thermal island changes
They also had the lowest net residential density. Therefore, it is necessary to apply appropriate policies such as revising and improving management in the way of population loading in various urban development plans and planning for the management and organization of urban structures in relation to the intensity of changes in thermal islands. Can be effective. It can also provide favorable grounds for guiding the development of population policies in various urban development plans to create a balance with sustainability in the city of Sari.
Climatology
Seyed Hossein Mirmousavi; Zahara Taran
Abstract
Introduction
Dust is one of the most common climatic phenomena in arid and semi-arid regions of the world The phenomenon of dust is a natural occurrence and occurs in areas with vast areas of arid and desert areas, Lack vegetation and other surface coatings. Due to its presence in the arid and semi-arid ...
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Introduction
Dust is one of the most common climatic phenomena in arid and semi-arid regions of the world The phenomenon of dust is a natural occurrence and occurs in areas with vast areas of arid and desert areas, Lack vegetation and other surface coatings. Due to its presence in the arid and semi-arid belt of the world, Iran is constantly exposed to local and synoptic dust and dust systems. In recent years, the phenomenon of dust in the Middle East has been increasing, Because it is one of the five regions of the world that has the highest dust production . Long periods of drought and inappropriate interventions in nature can increase the likelihood of this phenomenon.
In recent years, the trend of dust events in the west and south of Iran, especially in the spring and summer, has increased dramatically .This phenomenon is affected by certain atmospheric conditions and its distribution can affect the temperature, temperature, precipitation and atmospheric circulation conditions of the area during the months of the year.
Materials and methods
In this study, data of 56 years old (during 1961-2016) precipitation, temperature and dust on daily scale from 30 synoptic stations in the west and southwest of Iran were obtained from the country's meteorological organization. In line with this study, MATLAB, ArcGIS and SURFER softwares have been used. In order to analyze the information, recognition of fluctuations and the relationship between dust, temperature and precipitation have been used.
Results and discussion
Recognition of fluctuations and the relationship between dust, temperature and precipitation are investigated using regression, spectral analysis and Pearson correlation coefficient. Then it is represented by trend maps, cycles, and correlation tables. The results for the West and Southwest of Iran have been obtained and explained in detail.
Conclusion
The study of the spatial distribution of the trend shows that most of the stations studied in the dust and rainfall have an increasing trend and have been in a decreasing trend temperature. Spectral analysis of dust, dry days, and temperature showed that short-cycle cycles in addition to the most frequent distribution, showed a higher probability of occurrence than long-term periods. In most of the stations studied, the correlation of dust with temperature and dry days has a positive and direct, relationship with the rainfall has a negative and inverse relationship. The local mororan analysis for the spatial autocorrelation of dust with dry days in the western, northwest, northern and parts of the east of the study area has shown a high value cluster pattern (positive spatial autocorrelation). The spatial autocorrelation of dust with precipitation in the northeastern, eastern, and small parts of the southeast and west of the study area has a high cluster pattern (positive spatial autocorrelation). The spatial autocorrelation of dust with temperature in the eastern, western, and small parts of the south of the range has a high cluster pattern (positive spatial autocorrelation).
Climatology
mehdi asadi; Ali mohammad khorshiddoust; Hassan Haji Mohamadi
Abstract
Introduction
Data and information from the Meteorological Department of India and the Joint Hurricane Warning Center (JHWC) were used to investigate the structural nature of Ashuba tropical storm in the Arabian Sea from June 7 to June 12, 2015. To study the atmospheric structure, the analyzed digital ...
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Introduction
Data and information from the Meteorological Department of India and the Joint Hurricane Warning Center (JHWC) were used to investigate the structural nature of Ashuba tropical storm in the Arabian Sea from June 7 to June 12, 2015. To study the atmospheric structure, the analyzed digital data were taken from the European Center for Medium-Term Forecasts and the Center for Environmental/Atmospheric Forecasts (NCEP/NCAR) for the Arabian Sea and beyond. The study area was the Arabian Sea, located between the Indian subcontinent (eastern part) and the Arabian Peninsula (western part) and northwest of the Indian Ocean. On average, 1-2 tropical cyclones form on the Arabian Sea each year. Even in some tropical regions, strong cyclonic cycles occur at the synoptic scale (Evan & Camargo, 2001: 145). Therefore, from previous years, climatologists have studied the types of storms, due to the increase in tropical cyclones in the last decade; and thereby, this issue is followed with more sensitivity. Consequently, the main purpose of this study was to explore the structural nature of Ashuba tropical storm on the Arabian Sea in order to identify one of the region's main moisture sources.
Materials and Methods
Storm data statistics were obtained from the Meteorological Department of India and the Hawaii Hurricane Warning Center. Analyzed digital data, including; Geopotential altitude (Hgt), orbital (u), meridional wind (v), sea surface pressure (SLP), air temperature and sea water temperature (SST) for standard levels at 17 compression levels with a resolution of average daily geographic degree belonged to the National Center for Environmental Prediction/Atmospheric Science and precipitated networked data were obtained from the European Center for Medium-Term Atmospheric Forecasting (ECMWF) with a resolution of 0.125 degrees Celsius for the Arabian Sea. NASA and MODIS satellite imagery were also used for the visible band for every six days. The CAPE index was applied to evaluate the energy required by the storm supplier.
Findings and Discussion
The results of study displayed that in the middle level of the atmosphere, while forming a low-altitude nucleus with very strong positive rotation, the conditions for the production of tropical storms in the region have been provided. On the other hand, on the surface, low pressure has formed in the southeast of the Arabian Sea with a central pressure of 995 hPa and has started moving westwards towards the coasts of Oman and northern Yemen. Creating a very strong convergence current on the surface and upper divergence caused the storm to reach its maximum strength in the region on June 9. However, the anomalous temperature of the water surface in the range where the storm reached its maximum intensity reaches to over than 5 degrees Celsius. The increase in water surface temperature and the transfer of heat and moisture into the storm has strengthened and, by its nature, caused heavy rainfall in the region. Finally, on June 12, as it approached the east coast of Oman, it began to disappear due to lack of moisture for its dynamic movements, and changed from a tropical storm to a tropical hurricane. Also examining the prepared maps for the amount of precipitation and the flow of the lower levels of the atmosphere, it was determined that on the first day of the storm, a cyclonic current occurred in the east of the Arabian Sea, resulting in the maximum amount of precipitation in the west of the system, which reaches more than 240 mm. On the second day, moving north of the system, the amount of precipitation was concentrated in the south, so that the southern coast of India was not unaffected by precipitation and had about 120 mm of rainfall. On the third day, with the placement of this tropical storm in the north of the Arabian Sea, the maximum precipitation was created in the east of the system, which was more than 160 mm. On the fourth day, the western half of the Indian coast was faced with a rainfall of nearly 110 mm, which was due to its location in the east of the cyclone, which in turn caused the rise of air and the transfer of moisture to the air parcel, floods in the region. On the fifth day, the maximum rainfall was close to the eye of the storm, which was close to 100 mm, and the coastal areas of the Indian subcontinent were still experiencing heavy rainfall. Examination of the 850 hPa pressure system revealed that on the first day, the maximum relative pressure system nucleus formed in the southeastern parts of the Arabian Sea. These conditions have led to very strong convergence in the lower levels. The presence of such strong convergence and amplification of rotation has caused this anomaly to reach its maximum in the region. The strong rotating nucleus then extended to the west coast of India and then moved westward on the third day to the central regions of the Arabian Sea, with a very strong rotating current extending from latitudes 10 to 30 degrees north. As the storm/hurricane approached the west coast of the Arabian Sea, it intensified to more than five pressure system units on the fourth day. On the fifth day, the positive nucleus became independent and formed a very strong rotating closed cell. On the sixth day, with the cyclone remaining on the eastern coast of the Arabian Peninsula, its power had gradually diminished.
Considering the water temperature in the region, which is an average of 6 days, it showed that the water temperature in most parts of the Arabian Sea was high, so that these conditions reached more than 32 degrees Celsius in the coasts of India and the center of the Arabian Sea. These conditions were less only in the northern regions of the sea than in other regions. To understand the water surface temperature, its anomaly was also calculated for six days with the storm. Its output indicated that the eastern, northern, western and southwestern regions of the Arabian Sea were associated with a positive anomaly of 2 to 3° C. Negative anomalies only reached -1.5 degrees Celsius in the north and south of the sea. Occurrence of maximum positive anomalies in the region was one of the main reasons for the intensification of cyclones in the region, so that the western regions of the Arabian Sea had the maximum positive anomalies and on the other hand the maximum area of tropical cyclone activity.
The 12-hour reports from the Indian Meteorological Agency and the Hawaii Hurricane Warning Center were used to route the tropical storm. In these two centers, there were several data methods for routing and the origin of the storm. Geographical coordinate data with a 12-hour separation was used, which from the beginning of the storm to its decline, its characteristics and longitude and latitude were recorded as a text file. The onset of the storm was from the eastern part of the Arabian Sea, which migrated northward to higher elevations and deviated in its path due to the dominance of the Coriolis to the west of the region and disappeared off the coast of Oman.
Conclusion
Ashuba tropical storm/hurricane formed on June 7, 2015 in the Arabian Sea and disappeared on June 12, 2015. This investigation revealed that on the first day, a low-lying cell was formed in the eastern part of the Arabian Sea, during which a positive rotating nucleus or vortex was formed in the mentioned area and strengthened in the following days. The role of the Arabian Sea and abnormal changes in its water surface temperature in the occurrence of hurricanes has been mentioned in the researches of Ghavidel Rahimi (2015: 31) and Lashkari and Kaykhosravi (2010: 19). On June 9, as the subtropical anticyclone expanded further east, the Arabian Sea's low-pressure cell became oval in a circle, contributing to the deepening of the system, creating another bond at the heart of the closed cell with a height of 5,810 geopotential meters. In the last days, as the coasts of Oman and Yemen approach, the intensity of this cell decreases and its extinction stage was reached. On the surface, in parallel with the mentioned period, a low-pressure core with a central pressure of 995 hPa formed on the southeast of the Arabian Sea and the creation of a very strong positive rotation indicates the occurrence of hurricanes in the region. The central pressure of the storm reached less than 993 hPa on days 9 and 10, which was the peak of the storm. As it approached the shores, the intensity of this cyclone was greatly reduced, turning it from a tropical storm into a tropical turbulence. Examination of the water surface temperature showed that the average water surface temperature in these 6 days in most parts of the Arabian Sea was more than 29 degrees Celsius. Inspection of water surface temperature anomalies also disclosed that the maximum positive anomalies corresponded to several places in the sea, including the southern coasts of Pakistan to western India, eastern Oman and a very strong core corresponding to the southwest of the Arabian Sea with an average temperature of more than 5° C. The maximum rainfall inside the cyclone indicated that on the first day of the storm, the maximum rainfall in the southwest was 240 mm. In the following days, with the transfer of this core to the south, southeast and finally to the east, the maximum rainfall would be on the west side of the Indian coast. Only in the last days it was observed that while the maximum rainfall occured in India near the eastern part of the eye of the storm, a maximum precipitation center with an average of 100 mm has been created. In this study, two indicators, CAPE and SWEAT, were used to assess the location of storm formation. The results showed that these two indicators well showed the formation and severity and weakness of the storm during different stages. Thus, on the first day in the south of the Arabian Sea, the amount of CAPE was more than 5000 Jules/kg, which indicates the amount of convective energy available. On the other hand, the values of the SWEAT index have reached more than 380, which specify that the probability of a hurricane in this region is very high. Also, with the increase of water surface temperature in the region and the increase of anomalies in it, the necessary energy is provided for the production of cyclones in the region, which with the increase of energy within the air mass system and the presence of buoyancy energy in it, and on the other hand, instability indicators in monitoring and tracking these types of storms showed that they are a suitable tool for tracking and are able to navigate it while being aware of the intensity of the storm.
Climatology
Zeynab Jawanshir; Khalil Valizadeh Kamran; Aliakbar Rasouly; Hashem Rostamzadeh
Abstract
Introduction
For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included ...
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Introduction
For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included the selection of variables, bandwidth and spatial correlation errors. Using the GWR, Franklin analyzed the spatial characteristics of the rainfall along with the elevation changes. Elvi also used this model to study the spatial factors that affect land prices. The GWR produces spatial information that expresses spatial variations between variables' relationships. Therefore, the maps produced from these analyzes play a key role in the spatial non-static description and interpretation of variables (Mennis 2006) and an equation Generates a separate regression for each observation instead of calibrating an equation, so it allows the parameter values to be continuously changed in the geographic space. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away.
Data and Method
The Surface Energy Balance Algorithm for Land (SEBAL) calculates the surface heat flux instantaneously as well as 24-hour. The latent heat flux shows the energy required for true evapotranspiration and is calculated as the remainder of the equilibrium energy equation (Mobasheri, 2005). In remote sensing estimates of surface Albedo, surface temperature and surface leakage in the thermal infrared region, reflectance is used to calculate spatial variations in short-wave radiation and long-wave radiation emitted from the surface of the earth. A combination of short-wave and long-wave radiation combines the ability to calculate the pure absorbed surface radiation for each image pixel. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away. In other words, the GWR only uses geographically close observations to estimate local coefficients. This method of weighting is based on the idea that the use of geographically close observations is the best way to estimate local coefficients. The GWR method not only does not consider the effects of self-variables on the independent variable, but also the effects of neighboring situations. The values of the geographic weighting model can be used to describe the spatial correlation of the factors used. Therefore, we extend the study area to several sections We divide the values of the geographic weight coefficients in each of the sections in relation to each of the environmental parameters. Unlike regular regression models, they provide an equation for describing general relationships between variables. GWR allows the parameter values to be changed continuously in the geographic space. Each of the equations is obtained using a different weight of the observations contained in the total data.
Results and Discussion
The analysis of the relationships between selected indices by geographic weighted regression model and the classification of output values through the normalization of data in seven categories. The values obtained vary between 1 and 1, and the smaller the index, the spatial disjunction is variable, and the larger it shows the presence of spatial clusters. It was found that all three indexes of evapotranspiration, surface temperature and vegetation index have cluster spatial pattern. Therefore, the null hypothesis is based on the spatial correlation itself, and as a result, three of the above indicators can be used for spatial analysis of the actual evaporation. Based on the correlation between the factors affecting the macroeconomic factors, the factor of vegetation index has the most effect on the magnitude of the spatial distribution in the studied area (53% with an area of 471782864 square meters). However, as the results are clear, this number is an overall number and covers the overall situation in the area. And does not refer to spatial features of the area. In the results of weighted regression, the effect of elements can be observed spatially. Accordingly, according to the geographic weighted regression method, the relationship between evapotranspiration and surface temperature was negatively affected and negatively affected. The relationship between dehiscence and vegetation index was studied in different years. The highest digit on the seventh floor is 13/99 and in the area of 266611500, which shows a high positive effect. The relationship between evapotranspiration and the Albedo shows the highest value in the first and second classes. The values of 18 and 10 in the area of 490428000 and 1170753300 m 2, respectively, show a very negative impact and a significant negative effect.
Conclusion
Geographic weighted regression method is a statistical method that is adapted to study local patterns. This method is, in fact, a technical technique that analyzes the relationship between spatial variables in a hypothetical unpopular space. In this research, we tried to express the effect of several indicators on actual evaporation. These indicators are not all indicators that have had an impact on actual evapotranspiration Because actual evapotranspiration is closely related to other climatic factors. Because of the unique ability of spatial weighted regression to identify and analyze the relationships between variables, it is recommended to use it in quantitative analyzes. The Z classes resulting from the GWR analysis of the actual evapotranspiration in different years have different states that indicate the spatial effect of the surface temperature in different conditions.
Climatology
Mohammad Hossein Aalinejad; Saeed jahanbakhsh; Ali Mohammad Khorshiddoust
Abstract
Introduction
Determining the temporal change of snowmelt or agriculture water equivalent of snow, predicting flood, and managing the reservoirs of a region is of utmost importance. Some major parts of the western sections of the country are located in the mountainous region and most of the precipitations ...
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Introduction
Determining the temporal change of snowmelt or agriculture water equivalent of snow, predicting flood, and managing the reservoirs of a region is of utmost importance. Some major parts of the western sections of the country are located in the mountainous region and most of the precipitations of this region occur in the form of snow in winter. The runoff resulting from snowmelt has an important role in feeding the rivers of this region and it has a significant share in developing agriculture and the economy.
Scientific studies have shown that climate change phenomena have significant effects on precipitations, evaporation, perspiration, runoff, and finally water supply. As the demand increases, climate changes, greatness, frequency, and the damage resulting from extreme weather events, as well as the costs of having access to water increase, as well. Therefore, evaluating the runoff resulting from snowmelt and the effect of climate change seems necessary for managing water resources.
Methodology
Gamasiab basin is located in the northeast part of the Karkheh basin originating from the springs in the vicinity of Nahavand. Its basin has an area almost equal to 11040 square kilometers that have been located in the east part having 47 degrees and 7 minutes to 49 degrees and 10 minutes geographical longitude and from the north part, it has 33 degrees and 48 minutes to 34 degrees and 54 minutes geographical latitude. This basin has an altitude between 1275 to 3680 meters.
In this study, snow-related data required for simulation were derived from the daily images of the MODIS sensor. To this end, first, the snow-covered area of the Gamasiab basin was measured during the 2016-2017 water years using the process of satellite images obtained from the MODIS sensor in the google earth engine system. All geometric justifications and calibration processes of images were applied precisely in the mentioned system. In the next step, the output of the GCM model scenarios was utilized for calculating temperature and precipitation changes in future periods. These CMIP5 kind models were under the control of two RCP45 and RCP85 scenarios and were downscaled with LARS-WG statistical model.
Moreover, to investigate the uncertainty of models and scenarios, the best models and scenarios were selected for producing temperature and precipitation data of future periods; accordingly, the outputs of the models for future periods (2021-2040) having the basis period of (1980-2010) were compared using statistical indexes of coefficient of determination (R2) and Root Mean Square Error (RMSE). The results were entered into the SRM model as the inputs. In addition, temperature and precipitation data of meteorological station of the studied region as well as the daily discharge of the river flow of hydrometric station of Chehr Bridge (as located in the output part of Gamasiab basin) were used during the statistical period of October 2016 to May 2018.
Discussion
Using Digital Elevation Model (DEM) of the region and the appendage of Hec-GeoHMS in GIS software, firstly, flow direction map, flow accumulation map, and stream maps were drawn and the output point (hydrometric station of Chehr Bridge) was introduced to the border program of the identified basin and the basin was classified based on the three elevation regions.
Producing temperature and precipitation data of future periods requires a long-term statistical period; accordingly, the meteorological station of Kermanshahd was selected since it was in the vicinity of the studied region. To be confident in the ability of the model in producing data in future periods, the calculated data had to be compared with the observed model and data in the studied stations. The capabilities of the LARS-WG model in modeling the mentioned parameters of this station confirmed the observed data. Moreover, the ability of the model in modeling precipitation was very good and acceptable; however, the most modeling error was related to the precipitation in Mars.
In the next phase and compared to the basic periods, the mean of changes in average precipitation and temperature was measured in the studied stations during January and Juan of 2015 to 2017(for which simulation had occurred); as an index of changing the climate, this was entered into the SRM model under climate change conditions. During the simulation period (January to Juan), it had been predicted that the precipitation parameter would decrease and the temperature parameter would increase.
Conclusion
The results of this study indicated that using the MODIS sensor could provide an acceptable estimation of the snow cover level of the Gamasiab basin, which lacked snow gauge data. Moreover, the results of simulation with the SRM model showed that the model could simulate the snow runoff in the studied region. As the main purpose of the study, the effect of temperature and precipitation in future periods was well stated considering the uncertainty of CMP15 series models and scenarios. The results of temperature changes indicated an average increase of 1.8 C. the results of precipitation also indicated an average decrease of more than 5%. However, decreasing precipitation in the cold months of the years had been predicted severely so that the reduction of precipitation in February was of utmost importance for feeding the snow cover and rivers, which had been estimated to be 20%. This happened while increasing precipitation was mainly related to the hot months of the year whose amount was insignificant and didn`t have that much effect on the runoff. Accordingly, due to the increases in temperature and decreases in precipitation in cold seasons, the results of runoff simulation have indicated a 24% reduction for 2016-2017 and a 29% reduction for 2017-2018 water years.
Climatology
Hossein asakereh; Seyed Abolfazl Masoodian; Fatemeh Tarkarani
Abstract
Introduction
According to previous investigation and examining climatic elements, the hypotheses of global warming and consequently, global climate change is confirmed by majority of climatologists society around the world. The global changes probably continue for the next decades. The changes in climatic ...
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Introduction
According to previous investigation and examining climatic elements, the hypotheses of global warming and consequently, global climate change is confirmed by majority of climatologists society around the world. The global changes probably continue for the next decades. The changes in climatic elements, by and large, categorized into two types; trends and variation. The trends refer to long term changes, whiles variations indicate vary time interval changes including oscillation, phase, jump (sift), and persistence.
Precipitation is one of climatic elements which can properly reflect chaotic behavior of climate system, and illustrate the nature of changes in the system. Trends, Oscillation, and persistence in this element are investigated in national and international scale, whilst the decadal variations as an index of climate variation can contribute to the current literature. In current study we attempted to illustrate an objective feature of precipitation characteristics and its anomalies over four recent decades by using Asfezari National Dataset (AND).
Data and Methods
In the present study, the gridded precipitation data of the third version of AND with spatial resolution of 10×10 km during the time period of 1970/3/21 to 2016/3/19 (46 years including 16801 days) is used. This dataset adopted from 2188 synoptic, climatology, and rain gauge stations and subjected to interpolation by using Kriging interpolation method. The dataset covers an area from N and E. Accordingly, a pixels cover the area for 16203 days. Consequently, the dataset includes dimensions.
General spatial features of Iran precipitation for the whole under investigation period was studied based on climatological annual precipitation. Next, the same characteristics calculated for four decades ending up to 2016/3/19. Finally, for every decade the anomalies of precipitation in compare with the whole understudy period and its previous decades calculated in order to discover the spatial pattern of decadal fluctuation in precipitation.
Discussion
General characteristics of annual precipitation
Annual mean of precipitation over Iran is 250.5 mm. Due to decline in temperature contrast and strength of fronts in the Mediterranean cyclones, as a main source of precipitation in Iran, the annual precipitation over Iran decreases from west to east, and from north to south.
The annual precipitation in 63.2% of Iran is lower than the climatic annual mean. The annual mean of precipitation in this area which generally located in east and south of the country is approximately 150.5 mm. Thus, the total precipitation in this area is equal to the total precipitation in the rest 36.8% of the country which its annual precipitation is more than the annual precipitation in the country, 422 mm. The spatial variation of precipitation is confirm by other statistics, for instance, skewness, kurtosis, the extreme threshold indices. For instance, a large part of Iran (26.73%) includes 100-150 mm annual precipitation, whiles the precipitation in 15.8% of the country reaches to 150-200 mm. Parts of northeast of Iran, and the coast of Persian Gulf and Oman Sea in the south, in addition to southern slops of Alborz mountain chain experience a precipitation amount of lower than 100 mm. In contrast to the above-mentioned dry regions, the (approximately) wet regions include limited areas for each precipitation class. For example, only 9.1% of the country characterized with 500 mm of precipitation, while the classes of 200-300, 300-400, and 400-500 comprise 20.62, 12.64, and 6.11 percents of the country, respectively.
Decadal variation of precipitation
In current section the spatial distribution and statistical features of precipitation in each decades was illustrated. The following list includes our finding of statistical - graphical analysis of precipitation in four successive decades:
1) The difference between spatial mean and median of annual precipitation increased from the first to the last decades. The increasing in this characteristic refers to increase in spatial asymmetrical distribution of precipitation over the country.
2) A comparison between spatial distribution of precipitation maps showed that generally, the areas experienced precipitation above the decadal and whole period average are decreased from the first and last decades.
3) The increase in spatial skewness from the first decade to the last decade is another evidence of increasing in precipitation spatial differences.
4) The last but not the least finding is the changes in the extreme threshold indices. From the first to the last decade, the range of 10th and 90th percentiles have increased.
Conclusion
Previous studies depicted that the amount of Iran precipitation has decreased over recent decades. In order to investigate the role of each decade in the decreasing values, the gridded precipitation data of the third version of AND with spatial resolution of 10×10 km during the time period of 1970/3/21 to 2016/3/19 (16801 days) is used. General spatial features of Iran precipitation for the whole under investigation period was investigated based on climatological annual precipitation. Next, the same characteristics calculated for four decades ending up to 2016/3/19. Finally, anomalies of precipitation in compare with the whole understudy period and previous decades calculated in order to discover the spatial pattern of decadal fluctuation in precipitation.
Our finding showed that by and large, precipitation has decreased over recent decades. The changes has been more pronounced in southern and northern coastal area, western slopes of Zagros mountain chain, and northern slopes of Alborz mountain chains. Previous researchers attribute these changes to changes in humidity advections in recent years.
Climatology
mehran fatemi
Abstract
Introduction One of the climatic factors that occur during the cold period of the year in most parts of the country is the phenomenon of cold and glacial. Glacial begins when the temperature decreases and falls to a certain critical threshold, and with the effects it has on the earth's surface, it affects ...
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Introduction One of the climatic factors that occur during the cold period of the year in most parts of the country is the phenomenon of cold and glacial. Glacial begins when the temperature decreases and falls to a certain critical threshold, and with the effects it has on the earth's surface, it affects human life as well as construction activities and the yield of horticultural crops. This complication occurs on fruit trees in winter or early spring and causes a lot of damage. The glacial phenomenon not only endangers the natural life of all living things but also plays an important and decisive role in economic, environmental, and development matters such as roads, dams, and bridges. Glacial is very important in different stages of growth of agricultural and horticultural crops. Because if happen, it leads to production constraints. Glacial means zero temperatures or less than zero. Likewise in terms of technology for agriculture, in the event of thin ice crystals formation on the surfaces with sub-zero temperatures, the temperature of the surface air layer is reached above the dew point. In terms of farming meteorology, glacial is related to the low-temperature alteration which causes damage to the tissues of the plant. Glacials can be classified based on the severity, duration, and timing of occurrence. The classification based on the severity is the power of energy distribution components, which usually are measured based on average temperature, minimum, and average of zero and sub-zero and the lowest temperature of the minimums. The beginning and end dates of the glacial period are important from an agricultural point of view. The first glacial that occurs at the beginning of the glacial age is called early autumn glacial. In the autumn, glacial earlier than normal damage to actively growing branches. The last glacial that occurs at the end of the glacial period is called the late spring glacial. Fruit trees are increasingly susceptible to glacial damage from the time flower buds open, during flowering to the stage of small green fruit. To minimize glacial damage in susceptible areas, full knowledge of the frequency, persistence, and timing of glacial events is essential. To measure the risk of glacial, the recorded data of the minimum air temperature in meteorological stations are used. From a meteorological point of view, glacial occurs when the surface temperature and vegetation on it decrease to less than zero degrees Celsius. Materials and Methods In the current study, the minimum daily temperature statistics of 10 meteorological stations during a period of 17 years (2001-2018) have been used. To analyze the frequency of glacial occurrences for each year, the time of occurrence of the first early autumn glacial and the last late spring glacial was obtained. To convert the data into processable numbers based on the Julian days, each date is assigned a number. Based on this, the September 23 (1st of Mehr) was considered No. 1 and August 23 (31st of Shahrivar) in normal crop years was considered 365, and based on this, the number of the first glacial (early autumn cold) and the last glacial (late spring cold) were identified separately based on the stations during each crop year. Days, when the temperature was less than zero degrees Celsius, were extracted as glacial day and glacial at 5 weak temperature thresholds (temperature between zero to -1.9 degrees Celsius), mild (temperature between -1.9 to -3.9 ° C), moderate (temperature -4 to -5.9 ° C), severe (temperature between -6 to -9.9 ° C) and very severe (temperature -10 ° C and Less) was studied (adapted from Qalehri, 2018: 16). Using SPSS software, the best statistical sequence was obtained to calculate the start and end dates of glacial at different probability levels. The results indicated that most of the selected statistical series have a normal distribution. ArcGIS software was used to zoning the time of onset and end of glacial and to prepare many maps of glacial occurrence. Result and discussion The spatial distribution of the beginning of the glacial in the province follows the topographic state of the region and begins earlier in the southern and southeastern parts of the province. In some parts of the southern and southeastern regions, due to the high altitude of the region and being located in the mountainous areas, early autumn glacial occurs earlier, such as Garizat station, and occurs from November 6 to 12 (Aban 15 to 21). At Bafgh station, the beginning of autumn glacial occurs from November 13 to 19 (Aban 22 to 28). At Marvast, Meybod, and Abarkooh stations, the starting date of glacial is from November 20 to 25 (Aban 29 to Azar 4). The date of occurrence of early autumn glacial in Herat and Robat stations is November 26 to December 2 (5 to 11 Azar). The beginning date of glacial in Mehriz, Yazd, and Aqda stations is from December 3 to 9 (12-18 Azar). The beginning date of glacial based on different probabilities in Garizat stations with a probability of 30%, is November 3 (12 Aban), with a probability of 50% is November 6 (15 Aban), with a probability of 70%, November 9 (18 Aban), and with a probability of 90%, November 14 (Aban 23), as the earliest start date of autumn glacial. At Yazd station, with a probability of 30%, the first glacial has occurred on November 23 (2 Azar), with a probability of 50%, December 4 (Azar 13), with a probability of 70%, December 8 (Azar 17) and with a probability of 90% on December 24[Ma1] (3 Dey). The glacial at Bafgh station will end sooner on January 8 -17 (18-27 Bahman). Glacial in central and southern areas such as Mehriz, Yazd, Aqda, and Herat will end on February 18 to February 26 (Bahman 28 to Esfand 7). At Meybod, Abarkooh, and Robat Posht Badam stations, the end date of the glacial is February 27 to March 9 (Esfand 8-18). At Marvast station, the end of the glacial occurred on March 9-19 (Esfand 18-28). In the highlands, including Garizat station, the glacial starts earlier and ends later, so the glacial season is longer in these areas and the growing season is shorter, March 20-30 (Esfand 29 to Farvardin 10). The end date of glacial at Bafgh station with a probability of 30%, occurs at January 23 (Bahman 3), with a probability of 50%, February 12 (Bahman 23), with a probability of 70%, February 25 (Esfand 6) and with a probability of 90%, March 5 (Esfand 14). At Garizat station, the last glacial occurs with a probability of 30% on March 26, (Farvardin 6), with a probability of 50%, on March 29 (Farvardin 9), with a probability of 70% on March 31 (Farvardin 11), and with a probability of 90% on April 8 (Farvardin 19). The spatial distribution of the number of glacial days on the threshold zero shows that southeast areas including Garizat station have the most frosty days (1685 days) and Bafgh (483 days), Mehriz (484 days), Robate Posht Badam (518 days), Yazd (463 days) and Aqda (362 days) have the lowest number of glacial days during the statistical period (2001-2018). Spatial distribution of glacial occurrence at temperature thresholds of (0 and -1.9) have the highest number of glacials and the central and northern regions have the lowest number of glacials. Therefore, the Garizat station (467 days) has the highest amount of glacial, and Bafgh and Aqda stations have the lowest amount of glacial at this threshold. Likewise, on the threshold (-2 to -3.9), the southeastern and northwestern regions have the highest number of glacial and the northern and central regions have the lowest number of glacial. So, Garizat, Abarkooh, and Meybod stations have the highest amount of glacial and Mehriz, Yazd, Bafgh, Robat-e Posht Badam and Aqda stations have the lowest amount of glacial at this threshold. Conclusion Studies conducted between the start and end dates of glacial and the height of selected stations showed that there is a significant relationship between altitude and the date of occurrence of early autumn glacials. As altitude increases, glacial begins sooner. This fact designates that early autumn glacials happen earlier in the mountains than in the plains. The glacial onset map shows that in the plains of the province, the time of the first glacial is about a month later than the highlands of the province. In late spring glacials, the relationship between altitude and the end of the glacial is direct and by increasing the altitude, the date of the last spring glacial is delayed. This indicates that in the plains, the glacial period begins later and ends earlier, in other words, the glacial season in these areas is shorter and the growing season is longer. Conversely, in the highlands, the length of the glacial increases, and the length of growth decreases. This is significant from an agricultural point of view. Besides, the frequency of glacial in the southern and southeastern regions is higher than in the northern and northeastern regions, which has a significant relationship with altitude. The results of the analyzes showed that the Garizat station has the most glacial at all thresholds in the studied period. The lowest amount of glacial days is related to Bafgh, Aqda, and Mehriz stations in the temperature threshold (less than -10). The spatial distribution of the occurrence of glacial at different temperature thresholds also showed that in general, the southern and southeastern regions of the province have the highest frequency of this phenomenon, and as we move to the north of the province, the frequency of glacial decreases.
