Feasibility Assessment for Use of Wind Energy in Ardebil and Zanjan Provinces
Majid
Rezaee Banafsheh
Associate Prof., Dep. of Physical Geography, Faculty of Geography and Planning, University of Tabriz, Iran
author
Saeed
Jahanbakhsh
Prof. in Physical Geography, Faculty of Geography and Planning, University of Tabriz, Iran
author
Yagob
Dinpashoh
Associate Prof., Dep. of Water Engineering, Faculty of Agriculture, University of Tabriz, Iran
author
Marziyeh
Esmaeilpour
Ph.D. in Physical Geography (Climatology in Environmental Planning), Faculty of Geography and Planning, University of Tabriz, Iran
author
text
article
2014
per
IntroductionIn comparison with fossil fuels which pollute the lower layer of the atmosphere, use of windenergy has many environmental and economic advantages. Wind is a clean and renewableenergy resource and use of the energy in the recent decades has been welcomed so much in theworld. Energy plays an important role in the development of each society. All differentactivities including housing, transport, industry and agriculture may be dependent on this sourceof energy. The use of clean and renewable energy such as wind has environmental advantagescompared with fossil fuels. The increase of population and demand for energy has caused thatwind energy potential is considered as an alternative source of energy. Wind speed is the mostimportant parameter of the wind energy. This is used in the analyses relating to this energybecause wind power has a cubic relation with wind speed.MethodologyThe parameters of Weibull distribution is used to estimate the parameters related to wind energyand determine the sites which have wind energy potential. Thus, it is important to use propermethods in the estimation. In this paper, 6 distinct methods for estimating parameters of Weibulldistribution have been considered. For this purpose, 5 synoptic station which have adequate 3hours wind speed data from 1987 to 2009 (23 years) have been selected. These stations are:Ardebil, Parsabad, Khalkhal, Zanjan and Khoramadare. The method of moments, empirical,graphical, energy pattern factor and maximum likelihood methods and probably weightedmoments have been employed to estimate scale (m/s) and shape parameters (dimensionless) ofWeibull distribution. For determining the best parameters estimating method using cumulativedistribution function of the Weibull distribution (F(v)), expected values have been generated.The Chi square test has been used to select the appropriate method. Cumulative distributionfunction has also been used in order to calculate the probability that wind speed is smaller thanor equal to 5 m/s. In addition to 10 m height, Weibull distribution parameters and parametersrelated to wind energy potential namely, wind power density (Wm-2), wind energy density(Kwh-1m-2), the most probable wind speed (ms-1) and the maximum energy carrying wind speed(ms-1) have been computed in 20 and 40 m.Results and DiscussionAmong considered methods, method of moment because of having higher significance level andlower chi square compared with other methods has been selected as the best one to estimateWeibull distribution parameters. Using this method, scale and shape parameters of Weibulldistribution at 20 and 40 m height has also been estimated. Then, wind energy characteristics,namely, wind power density (Wm-2), wind energy density (Kwh-1m-2), the most probable windspeed (ms-1) and the maximum energy carrying wind speed (ms-1) have been computed. Theresults have showed that at monthly time scale and in 10 m height, the maximum value of k wasobserved in Zanjan and Khalkhal stations on June and the lowest value of k in Khalkhal stationin January. The maximum value of c was observed at Ardebil in Februrary and the lowest valueof c at Parsabad in November. Using the wind power density all the stations are ordered asfollowing: Ardebil, Khoramdareh, Khalkhal, Parsabad and Zanjan. This order shows thatArdebil has high potential of wind energy and Zanjan has the minimum potential.ConclusionWe can summarize main conclusions drawn from this investigation as following:• In this study, among the considered methods, the method of moment is specified as theproper one and using parameters obtained from this method, features relating to windenergy in studied stations were estimated.• Among the studied stations, the maximum of wind power density is observed atArdebil. In this station, in 40 meter height the largest value of wind power densitywhich was equal to 491w/m2 is also observed. The minimum value of wind powerdensity is observed in Zanjan which is equal to 107.2 w/m2.• The probability that wind speed is smaller than or equal to 5 m/s was calculated usingPhysical Geography Research Quarterly, 46 (3), Fall 2014 3cumulative distribution function for the studied stations in 3 heights namely 10, 20 and40 meter. The results showed that in 10 m height, among studied stations the highestprobability belongs to Ardebil. After Ardebil station, the highest probability wasobserved in Khoramdare.• In 10 meter height, at Parsabad, Khalkhal and Zanjan in all the year the probability islower than Ardebil and Khoramdareh. So, in these stations the operating possibility ofwind turbines will be low.
Physical Geography Research
University of Tehran
2008-630X
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3
no.
2014
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https://jphgr.ut.ac.ir/article_52131_8b18fb57ae2f00bfe7dc165b37364974.pdf
dx.doi.org/10.22059/jphgr.2014.52131
Identification of Synoptic Patterns Causing Heavy Rainfall in Northern Coast of Persian Gulf
Ismael
Ahmadi
Ph.D. Candidate in Climatology, Dep. of Geography Sciences, Kharazmi University, Tehran, Iran
author
Bohloul
Alijani
Prof. in Climatology and Director of Center of Excellence for Spatial Analysis of
Environmental Hazards, Dep. of Geography Sciences, Kharazmi University, Tehran, Iran
author
text
article
2014
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IntroductionSometimes, the showers of the northern coast of Persian Gulf are very heavy and disastrous andhazardous. They cause heavy damages to the people and the infrastructures of the region.Therefore the economic development of the area is highly dependent upon the identification ofthe cause and management of these hazardous phenomena. The main factor controlling thesurface climate is the pressure patterns of the atmosphere. Therefore, the main objective of thisstudy is to identify the synoptic patterns of these showers. Thus, we can predict their occurrenceand mitigate their damages. The successful achievement is dependent on two major factors:(a) the methodology of pattern recognition and (b) identification of actual patterns. Most of themodels of pattern analysis are linear while the atmospheric processes are non-linear in nature.Any methodology that neglects the nonlinear nature of atmospheric phenomena would result ininadequate classification of atmospheric circulation. For this reason, this research has used thenonlinear models of classification algorithms to identify the pressure patterns of the heavy rainsof the area.MethodologyThe study was based on the hypothesis that the daily atmospheric circulation can be explainedby the geo-potential height of 500 hPa level, precipitable water, and the velocity of vertical∗E-mail: ahmadi.ism@gmail.com Tel: +98 9129376680Physical Geography Research Quarterly, 46 (3), Fall 2014 5patterns and heavy rainfall, the data have been collected through 15340 days (1966-2007) forthese three variables of 289 grid-points, with a resolution of 2.5 degrees, from NCEP database.Daily rainfall data for the same period have also been gathered for Bandar-e-Abbas, Bandar-e-Lengeh, Boushehr, and Abadan stations from Meteorological Organization of Iran. First, thedaily circulations as micro-patterns have been classified using self-organizing map (SOM)algorithm, a type of unsupervised neural network. This algorithm begins to calculate theEuclidian Distance between an input vector and all of the weight vectors to find the 'winner' unit(BMU) with the weight vector closest to the input vector. The calculation continues to update allthe weight vectors, especially those within neighbouring radius .The iterative calculationproceeds towards the projection of similar data samples in the high dimensional, complex inputdata space to an identical unit area in the map. As a result, the neighbouring units in the map aresimilar to each other while distant units are dissimilar. Then, the U*-matrix, as a suitablemethod for two-dimensional visualization of the trained SOM that enabled us to recognize thedegree of the similarity among adjacent units in the two-dimensional map, was employed toidentify boundaries among clusters and to extract the actual number of meso-patterns. Finally,K-means method was utilized to cluster these meso- patterns into distinguished macro patterns.Results and DiscussionThe results revealed that SOM, by classifying the micro-patterns into 289 meso-patterns, coulddiscriminate the days of warm and cold periods with an accuracy of more than 99 percent.These patterns were classified into 11 macro patterns through the U*-matrix and K-meansmodels. Through displaying the number of heavy rainfall events in each station on each unit ofSOM, it was specified that four macro-patterns explained up to 83 % of heavy rainfall events ofthe region. These patterns are named as follows: Pattern No. 4 as Syria trough becomes deeper,Siberian high pressure moves towards west, and the moisture of Arabian and Oman Seas moveto PG. Similarly, the identification of pattern 6 is possible by Sudan low, subtropical jet streamvelocity increase, and its base decreases. Pattern 7 is identified by cut-off low system, very lowpressure, and closed low up to upper troposphere. Pattern 9 is specified by two characteristics:(a): the simultaneous presence of warm and cold season components of atmosphere during theseasonal change, and (b) dense isobars over PG.ConclusionOn the basis of the results, we concluded that the combination of SOM classification method,U*- matrix and K-means clustering methods can be employed as an appropriate instrument toclassify nonlinear atmospheric variables, in one hand, and to resolve the problem of extractingthe actual synoptic patterns, on the other. Of the four synoptic patterns of heavy rainfall, cut-offlow and seasonal transition patterns should be taken into account more seriously because of thepersistence and startling nature of their heavy rainfall as well as the vulnerability of society forthe probable damage.
Physical Geography Research
University of Tehran
2008-630X
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3
no.
2014
275
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https://jphgr.ut.ac.ir/article_52132_bec45b978afb3b126e73b3eb33fba946.pdf
dx.doi.org/10.22059/jphgr.2014.52132
The Relationship between Circulation Pattern Types in Sea Level Pressure and Precipitation in Iran
Ghasem
Azizi
Associate Prof. in Climatology, Faculty of Geography, University of Tehran, Iran
author
Teimour
Alizadeh
PhD Candidate in Climatology, Faculty of Geography, University of Tehran, Iran
author
text
article
2014
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IntroductionThe recent developments in computer sciences have considerably affected the application ofnew methods in climatology. Especially these new technologies have increased the usage of thenew methods in climatic classification. The previous classifications were calculated only basedon insufficient number of climatic factors. For example, the well-known classification ofKoppen was based on precipitation and temperature. In contrary to such threshold-basedclassifications, the implementation of multivariate statistical techniques has allowed to classifyclimate without predefined thresholds by grouping individual objects by Jacobeit (2010)methodology. Application of multivariate analysis in climatology is conducted by Yarnal et al(2001). The aim of this paper is to use the classification technique and recognize the circulationpatterns at the sea level and their connection to variability of precipitation in Iran. To obtain acomprehensive view of the precipitation in Iran and its effective factors a number of theresearches are conducted. Many papers have investigated the main circulation and air masses aseffective factors on Iran precipitation. However, there is not an agreement among them and themain disagreement seems to be about the methodology. Khoshhal (1997) using synopticanalysis studied the greater than 100 mm precipitation in coastal area of Caspian Sea. Heshowed that in contrast to the previous studies, the cold advection of the Siberian anticycloneover Caspian Sea is not the main reason for forming the heavy precipitations and these eventsare connected to the entrance and settlement of anticyclone and cyclonic systems. Applying thePhysical Geography Research Quarterly, 46 (3), Fall 2014 7vorticity calculation, Alijani (2003) identified the rainy air masses in Tehran.He concluded thatthe effect of 500 hPa level is stronger than other levels and the cyclonic circulation type createthe heavier precipitations.MethodologyThere are two main approaches in synoptic climatology: the environment to circulation andcirculation to environment approaches. Because of the high variability of precipitation,researchers used the environment to circulation in their studies (Yarnal, 1993). As a result, theenvironment to circulation approach is used in this paper as well. The mean daily precipitationsof synoptic stations of Iran were collected for time period of 1980 - 2009. The distributions ofthese stations are shown in Fig 1. Then the point data were interpolated with cell size of0.057° grid point (5.95.9 Km). Totally a number of 46939 cells were calculated and an n pmatrix was created. Where n refers to the days (10958 days) and p refers to the spaces (46939).Using this matrix in daily basis, the Percent area, Mean and maximum precipitation for all areaof Iran were calculated. To eliminate the local precipitation and considering only the extensiveprecipitations, two conditions were defined: the average precipitation of Iran must exceed 1mm, and over 40 percent of Iran area must receive precipitation. Accordingly, a number of 1548days of extensive precipitation in the course of study area were recognized. For explanation ofthe circulation patterns of these events, mean sea level pressure, in a scale of 2.5°2.5° gridpoint, NCEP/NCAR reanalysis data from 0° to 100° eastern longitude and 10° to 80° northlatitude were collected and a 15481189 matrix was created. The Principal ComponentAnalysis (PCA) was used in order to reduce the volume of the matrix. Many researchers haveused the PCA and its application in multivariate analysis.Results and DiscussionThe results of PCA over the extensive rain matrix of Iran are shown at table 1. As it can be seenin the table, a number of 48 eigenvalues greater than one which explained 92% of the totalvariance was obtained. Among these, 15 factors that explained more than 1% of whole thevariance were selected. These factors explained 88% of the total variance. Load factor matrixscore is the matrix that has a 154815 dimension.ConclusionIn this paper, the connection between circulation patterns on sea level and Iran precipitation wasanalyzed by applying environment to circulation approach. For this purpose, the daily grid pointprecipitation with 5.9*5.9 Km dimension obtained 1548 days, with considering a condition thatat least precipitation in Iran must be 1 mm and also 40 percent of Iran area must receive theprecipitation. Sea level pressures of these days were selected for identification of the main typeof the circulation patterns. The (PCA) technique was used for reduction data and with clusteranalysis it obtained 5 main circulation pattern types.The investigation of the relationship between the circulation patterns and the precipitation8 Physical Geography Research Quarterly, 46 (3), Fall 2014events revealed that there are five distinctive precipitation patterns in Iran. These types areincluding:Type 1: Interaction between Sudan low pressure and Siberian anticyclone;Type 2: combination of Mediterranean low pressure -Sudan low pressure and interactionwith Azores and European anticyclone;Type 3: interaction between Sudan low pressure and European high pressure tongue;Type 4: Interaction among Tibet high pressure, Azores high pressure, and polar lowpressure;Type 5: Thermal low pressures and Indian monsoon system.
Physical Geography Research
University of Tehran
2008-630X
46
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3
no.
2014
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https://jphgr.ut.ac.ir/article_52133_0727096e2b8ea2e6d1a3114177e1817f.pdf
dx.doi.org/10.22059/jphgr.2014.52133
Modelling of Climatic Parameters in Province of Southern Khorasan
Behrooz
Sobhany
Associate Prof. in Climatology, University of Mohaghegh Ardebily, Ardebil, Iran
author
Fakhry Sadat
Fateminiya
PhD Candidate of Agricultural Climatology, University of Mohaghegh Ardebily, Ardebil, Iran
author
text
article
2014
per
IntroductionClimate change has played an important role in all aspects of human life. Therefore, climatepredictions in atmospheric general circulation models (GCM) will have particular importance.In this study, downscaling by A1B scenarios, IPCM4 and BCM2 of atmosphere generalcirculation models in LARS-WG model are used for analysis of climate change impacts onmaximum and minimum temperature, solar radiation and precipitation. The data have beengathered from 7 Synoptic stations in province of Southern Khorasan. For this purpose,assessment process of simulation and observation data are conducted by three steps, includingcalibration, validation and modelling. To evaluate the agreement between the observed andsimulated data, two indexes were used; Root Mean Square Error (RMSE) and Coefficient ofDetermination (CD). The results of analysis in Makesens, Sin’s Estimator and Mann-Kendallshowed that minimum temperature, maximum temperature and evapotranspiration in all stationswill be increased in years of 2011-2060. The results have shown that rainfall in all stations(except Ghaen station) will be decreased and solar radiation in all stations (except Ghaen andFerdous station) will also be increased. Increasing trend in temperature in Birjand and Ghaenstation will be lower and in Tabas Station will be higher. It may be appeared that weak changesin climatic parameters in some stations are related to specific geographical conditions andtopography of this region.Climate change in the past and today would change the pattern of human life and it seemsthat humans and their activities are causing the global climate change. Uncontrolled growth ofpopulation, transportation and other human activities, particularly pollution resulted fromindustries lead to major changes in climate. After the industrial revolution changes in globalclimate such as increases in extreme climatic events have appeared due to the excessive use offossil fuels and land use change. At present, this variability has become a major concern ofclimatologists and weathermen. Therefore, attention to researcher long term forecast aboutclimate parameters for change value help decrease the effects of ill climate change.Atmospheric general circulation models to assess future climate is one of the common methods.Meanwhile, LARS-WG model as one of the general circulation models of the atmosphere isimportant for future climate change and has led to some efforts by many scholars. The highaccuracy of climate data modelling in different climatic stations has been confirmed by manyresearchers.MethodologyIn this study at first, daily statistics including minimum temperature, maximum temperature,rainfall and radiation related to 7 Synoptic stations in province of Southern Khorasan wereobtained from meteorological organization of Iran (Table: 1).Table 1: Geographical characteristics of the sample stationsStations LON LAT ElEBirjand 59 12 32 52 1491Boshruyeh 57 27 33 54 885Ferdous 58 10 34 10 1293Ghaen 55 05 33 47 845Khour 58 28 32 56 1117Nehbandan 58 48 36 16 1213Tabas 56 55 33 36 711In Second step, some weather data have been produced by using parameters listed in Larsmodels. Two BCM2 IPCM4 models for each synoptic station in South Khorasan are used in thisstudy to arrive target modeling data under scenario A1B. After entering data into the modelLars-wg and getting the trends in the observed time series data, we have attempted to reproducethe data at the stations during 2011- 2060.. Finally, the simulated data are compared withobserved data using statistical analysis and graphing. They have also examined the ability of themodel to simulate meteorological data in stations. The Root Mean Square Error (RMSE) andcoefficient of determination (CD)have also been applied. If the RMSE values are closer to zero,this indicates the observed and simulated values are closer to each other and to be more preciseanswers in each step.Results and DiscussionThe results of the research showed that BCM2-A1B models in Birjand, Boshruyeh, Ferdous,Nehbandan and Tabas and IPCM4-A1B models in Ghaen, have the lowest simulated values.Distribution of annual minimum temperature simulated for the period 2011-2060 shows that thePhysical Geography Research Quarterly, 46 (3), Fall 2014 11minimum temperature in southern Khorasan province is 0.02 per year and the coefficient ofdetermination of 0.09 is increasing and that the minimum temperature during the five decadescan be changed between 11.4 to 12.9 degrees. The simulations conducted by LARS-WG modelsover the next fifty years will change in the average of minimum temperature among theSynoptic stations of provinces, from 7.5 degrees in Ghaen to 17 degrees in Tabas. The averageof maximum temperature will also change from 23 degrees in Ghaen to 30 degrees in Tabas. Inprovince of South Khorasan, the average of minimum temperature is 12 degrees and the averageof maximum temperature is 26.6 degrees. Increasing trend of temperature in Birjand and Ghaenstations will be lower and in Tabas Station higher. Obviously, due to the lower temperature, thecities of Ghaen and Birjand will experience more precipitation. Nehbandan, Tabas and Khorwill have the highest mean radiation in cities and Birjand will receive the lowestevapotranspiration and radiation and Tabas the highest rates of evapotranspiration.ConclusionFrom the results this can be concluded that BCM2-A1B models in Birjand Boshruyeh, Ferdous,Nehbandan and Tabas and IPCM4-A1B models in Ghaen have the lowest differences imulatedvalues with observed values. Results of LARS model simulations for the next fifty yearsshowed an average low temperature in Ghaen and Birjand cities. The Birjand has the lowestrates of evapotranspiration and Tabas the highest temperature and the least amount ofprecipitation. Ghaen had not seen much rain, but relative to other stations it will have weakincreasing trend. The results of analysis of Makesens, Sin’s Estimator and Mann-Kendallshowed that in the years from 2011 to 2060 we will experience an increase in the minimumtemperature, maximum temperature and evapotranspiration in all the stations and also adecrease in all (except Ghaen station). It will also observe an increasing trend in radiation in allstations except for Ghaen and Ferdous.
Physical Geography Research
University of Tehran
2008-630X
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no.
2014
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https://jphgr.ut.ac.ir/article_52134_deaf99b683cd781b01e7a78ebb7e71fc.pdf
dx.doi.org/10.22059/jphgr.2014.52134
Estimation of Surface Temperature and Cropping Intensity in Hamedan Province Using Remote Sensing Data
Samira
Amini Bazyani
MSc in Irrigation and Drainage Eng., Agriculture Faculty Bu-Ali Sina University, Hamedan, Iran
author
Hamid
Zare Abyaneh
Associate Prof., Irrigation and Drainage Eng., Agriculture Faculty
Bu-Ali Sina University, Hamedan, Iran
author
Mehdi
Akbari
Associate Prof., Agricultural Engineering Research Institute, Alborz, Iran
author
text
article
2014
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IntroductionSurface temperature and cropping intensity maps are the most important components of thewater requirements in basin scale and are also the most difficult to measure. Conventionalmethods are very local, ranging from region to field scales. Estimates of the Surface temperatureand crop density over the entire area, especially for irrigated areas, are essential. Today, surfacetemperature, actual cropped area, crop pattern and cropping intensity under different conditionscan be estimated by using satellite data and Remote Sensing (RS) techniques. In order to obtainthe surface temperature and cropping intensity, a set of satellite images have been used.Estimated temperatures have been compared with measured values at 5 cm soil depth inmeteorological stations.MethodologyThe study area is Hamedan Province, in west of Iran and at latitudes between 33O and 33' to 35Oand 38' north and longitude 47O 45' to 49O and 36' east. The area of this province is 19546 Km2.According to Climatic diagram of Emberger its Climate is cold semi- arid with the minimumand maximum temperature of 2/8 and 19/2, respectively.In this paper, we have used data of five meteorological stations in Hamedan and Kordestanprovinces. A set of 12 Landsat 7 images during the 1998-2002 have also been used. Geometricand radiometric corrections have been performed on all the images. Normalized DifferenceVegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were established. Basedon these indicators the surface temperature (Ts) has been estimated using the SEBAL (SurfaceEnergy Balance Algorithm for Land) algorithm and compared by the measured data reported bymeteorological stations of Hamedan province.Six statistical parameters including coefficient of determination (R2), Root Mean SquareError (RMSE), Modeling Efficiency (EF), Mean Error (ME), Coefficient of Residual Mass(CRM) and Mean Absolute Error (MAE) (Equation 7 to 12) have been used to compare surfacetemperature of satellite images and the temperature reported by meteorological stations.Results and DiscussionResults of Normalized Difference Vegetation Index (NDVI) and surface temperature imply thatthere is high and reversed correlation between these indices Results of comparison of surfacetemperatures in the dense vegetation surrounding meteorology stations with recorded weathertemperature in passing time of satellite show that there is not a striking difference between theseparameters.Results show that Root Mean Square Error between surface temperature of SEBALalgorithm and the temperature reported by meteorological stations for different stations isdifferent from 4/4 to 6/6 degree. Results of modeling of Efficiency index show that all stationswith efficiency over 10% are acceptable. CRM index for all data show -0/02 and imply thatestimated values have a good precision. The results of Mean Absolute Error index and MeanError imply that the model with 4/2 error and -0/7 deviation degree from surface temperatureare estimated and has acceptable precision. Generally, algorithm of assessment index aboutestimating surface temperature shows that this algorithm has a relative high precision andcoefficient correlation.ConclusionResults indicated that there is no significant difference between surface temperature usingremote sensing data and the statistics reported by meteorological stations. Primary resultsshowed that there was a significant relationship between measured and estimated surfacetemperature. The results of correlation coefficient were 0.75 and Root Mean Square Error(RMSE) and Mean Absolute Error (MAE) were 5.4C and 4.2C, respectively.Results of the present and performed researches indicate that remote sensing can play aeffective role to determine timely maps of plant cover, air temperature and surface temperatureand optimizing usage of irrigation resources. By remote sensing and geographical informationsystem can be used as suitable and confident tool to study dispersion and intensity of plantcover, air temperature, and plant level faced with environmental pressure.
Physical Geography Research
University of Tehran
2008-630X
46
v.
3
no.
2014
333
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https://jphgr.ut.ac.ir/article_52135_76c1b5b1b394aee3d180f0072587bd7f.pdf
dx.doi.org/10.22059/jphgr.2014.52135
Detection of Geo-potential Height Changes, Vorticity and Sea Level Pressure of Prevailing Circulation Atmospheric Patterns Impacting Iran Climate
Mohammad
Darand
Assistant Prof. in Climatology, University of Kurdistan, Iran
author
text
article
2014
per
IntroductionThe changes in behaviour of climate and meteorological parameters are closely related tochanges in atmospheric circulation. The analysis of historical atmospheric tropospherecirculation is critically important to global and regional climate change and extremes withregard to its dynamical features. The circulation changes are manifested by a gradual reductionin high-latitude sea-level pressure, and an increase in mid-latitude sea-level pressure associatedwith one phase of the Arctic Oscillation (a hemisphere-scale version of the North AtlanticOscillation. Recent observations have found that the tropical belt running around the equator hasgrown wider, and has expanded by around 2° to 5° latitude and into the adjacent subtropicalregions since 1979. Global greenhouse gas emissions contribute to expansion of the tropics(about 0.05° per decade)in the northern hemisphere tropics. The effect of black carbon andtroposphere ozone emissions are about twice the size of those due to greenhouse gases alone(about 0.07° to 0.12° per decade). The aim of this study is investigation of variability in theintensity of the monthly geo-potential height, vorticity and sea level pressure over synopticcirculation patterns impacting Iran.MethodologyIn order to accomplish this research, daily grid data with spatial resolution of 2.52.5 degreeduring 1/1/1960 to 31/12/2012 have been extracted from NCEP/NCAR database. The monthlyaverage sea level pressure and geo-potential height in level of 1000 hectopascal is calculated todetect the action of spatial kernel of each synoptic system. The for this investigation is includingdaily zonal and meridian wind components (Uwnd and Vwnd) data, geopotential height data foreach levels of 1000, 850, 700 and 500 hectopascal levels, and sea level pressure. Vorticity iscalculated by Uwnd and Vwnd components. Then monthly mean is also calculated for eachlevels separately. Although some circulation patterns can operate in special months and seasonsof year but trend analysis has been evaluated for each circulation patterns in whole months andseasons. To detect time series trend, nonparametric Mann Kendal statistic test has been applied.The trend has been tested in 95% confidence level. Sen Estimator is used to calculate trendslope rate.Results and DiscussionSiberian anticyclone, Sudan cyclone, Mediterranean region cyclones (East Mediterranean,Mediterranean and Black sea) and Monsoon and Persian Gulf cyclones have been selectedaccording to the maps of sea level pressure and geopotential monthly means. In total vorticity,geopotential height in different levels and sea level pressure have significant trend in 95%confidence level. The highest variation is observed for high levels at 700 and 500 hectopascal.In winter season, sea level pressure, vorticity and geopotential in 1000, 850, 700 and 500hectopascal levels don’t show significant trend. While in spring and summer the trend ofvorticity in four selected levels is significant and negative, geopotential height and sea levelpressure have positive significant trend. Over cyclonic systems in Mediterranean region, the sealevel pressure and geopotential height show increasing trend especially in winter season. Theslope positive trend on the Sudan is significant in winter. The trend of geopotential height infour selected levels in winter is positive and significant. In other seasons, the trend is positiveexcept in 1000 hectopascal level. Over Monsoon cyclone system in all seasons, geopotentialheight is increasing from 1000 to 500 hectopascal levels. Vorticity trend in low levels at 1000and 850 hectopascal is positive. The increasing geopotential height over Persian Gulf is smallerthan other atmospheric circulation patterns especially in low levels. Vorticity rate contrary toother systems is increasing.ConclusionIncreasing sea level pressure and geopotential height over cyclonic circulation patterns of Sudanand Mediterranean regions especially in cold seasons of year (winter and autumn) result in adecrease in systems cyclonic action, sea level gradient pressure, instability and precipitation inregion. In warm seasons of year (spring and summer) negative trend of vorticity and positivetrend of sea level pressure and geopotential height result in decrease in warm seasonsprecipitation and increase in stability. The results of this study agree with findings of some otherresearchers about increase in warming of troposphere, changes in synoptic systems intensity,instability increase and negative vorticity increase in north hemisphere.
Physical Geography Research
University of Tehran
2008-630X
46
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3
no.
2014
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https://jphgr.ut.ac.ir/article_52136_1cc4008741fe94f4649963e723b1e7a1.pdf
dx.doi.org/10.22059/jphgr.2014.52136
Spatial Analysis for Production of Climate Classification Maps, West Part of Urmia Lake
Ali
Nasiri
Assistant Prof., Dep. of Geography, Payamnoor University, Tehran, Iran
author
text
article
2014
per
IntroductionIn the modern era of communication, by increasing population the resources would be relativelyscarce. Therefore, in order to deal with environmental serious problems and complex humanclimaterelationship in all dimensions of spatiotemporal and land use planning and programmingpractices, the climatic zone map was a sustainable developmental tool in the study area.MethodologyThe climate zones are recognized by investigating the analysis of various climatic factors,different empirical methods and spatial and nonspatial quantitative methods. The naturalenvironmental areas have differential climate zones. Accordingly, different climate zones ofIran especially climate factors and local variables are neither studied nor recognized. Hence, themain purpose of the present study is to produce climate zones map of west part of Urmia Lakeby the simultaneous analysis of spatial and nonspatial climate data. West area of Lake Urmia isstudied in the present paper as a region of environmental problems; it is the main part of UrmiaTownship that contains Urmia city. This is the largest and capital city of West AzerbaijanProvince. Various climate factors whether of local or global influences affect formation ofclimate types in the area. Inherent factors are (or genetical) global wind systems like westerlies,polar cell systems and complex local natural circumstances, vegetation cover, superficial waterresources, elevation, geomorphology and topographic conditions, geographical directions, andgeographical latitude and longitude. Climate producer factors have different properties.Accordingly, analyses of the obtained data are very difficult, so the spatial analysis methods areproposed as powerful tools for simultaneous analysis of the different data. In this research,diverse climate data and factors from various resources in different stations of the studied areasuch as Urmia, Naghadeh, Salmas, Oshnavieyeh, and kahriz together with height andgeographical directions data have been analyzed to produce map of different climate zone.Hereby, analysis of different types of data such as spatial and nonspatial data is one of the mostdifficult challenges in climatic researches. In order to solve this challenge, GIS spatial analysistechniques, spatial and multivariate analysis algorithms such as Maximum Likelihood analysis(MLS), Principal Component Analysis (PCA) and Iterative Self Organizing Data AnalysisTechnique (ISO data) have been used to analyze different types of data. Structure of variableshas been verified by application of the multivariate analysis of PCA method. The number andnature of the factors have also been analyzed to specify the rate and find out how they areaffected by climate properties of the study area. By using PCA methods the effective factorshave been employed to determine contribution rate of each factor in development of climatearea.Results and DiscussionThe results show that there are 4 different climate types: mountainous cold, wet, semi – wet andsemi – arid climates in the west part of Urmia Lake area. It is also shown that the local factors(such as height, geomorphologic features including aspect, slope, spatial arrangement ofmountains and etc. under the control of external factors such as west, north and southwest windsystems entering into the area, play an important role in the formation of climate types. Theyalso act as a reinforcement or adjustment of climatic conditions. Here, in survey of the presentstudy, the term of named as morpho–climatic subject is as a new phase in studies ofclimatology.ConclusionInvestigation of the results shows that the local condition and factors like geomorphology andtopographic characteristics of a region in different scales under the control of external factorssuch as west, north and southwest wind systems play an important role in development ofdifferent climate types such as different patterns of the micro regional and local climates. Thiscan be concluded that climate properties are developed by the influence of both geomorphologicand weather conditions of a specific region.
Physical Geography Research
University of Tehran
2008-630X
46
v.
3
no.
2014
375
388
https://jphgr.ut.ac.ir/article_52137_ce7cfe1eaf63c55227f5f17f467b401a.pdf
dx.doi.org/10.22059/jphgr.2014.52137
Determining Planting Dates for Spring Safflower by Temperature and Digital Elevation Model in Esfahan Province
Talat
Yasari
Assistant Prof., Department of Physics, Zabol University, Iran
author
text
article
2014
per
IntroductionPlanting date plays main role in crop performance. Planting date through correspondence withthe climatic elements affect vegetative and reproductive growth and ultimately affect the qualityand quantity of crops. Among the climatic elements, temperature and day length are moreimportant under irrigated condition. It is necessary to mention that the majority of cropscultivated in Iran are indifferent to day length. The temperature is the most important element incontrolling their growth period. By using long-term weather data and related software such asArc map we can determine the suitable planting dates for a wide area. Therefore, by eliminatingfield experiment and avoiding large amount of time and cost, much can be saved. The purposeof this study is to determine the best planting dates for spring safflower in different parts ofEsfahan province in order to gain the maximum performance in any climatic zone.MethodologyThe minimum, maximum and mean temperature of 51 synoptic and climatic stations of Esfahanprovince and other neighbouring provinces from 1961 to 2011 have been used to determine theappropriate planting dates of spring safflower in Esfahan province. Using the mean temperatureand Kriging method, Esfahan province is divided into three zones including zone 1 (warm),zone 2 (moderate) and zone 3 (cold). For determining the planting dates of spring safflower indifferent part of Esfahan province daily mean and minimum temperature from January toOctober as average of 15 days have been calculated and related maps were plotted in GISenvironment. Interpolation of temperature was done by Digital Elevation Model (DEM) and∗E-mail: yasari85@yahoo.com Tel: +98 9133278087Physical Geography Research Quarterly, 46 (3), Fall 2014 19regression analysis between temperature and height in the GIS environment. Beginning ofplanting dates in warm, moderate and cold regions were considered to reach mean oftemperature to 7, 9 and 12°C, respectively. For determining the growth inhibitory of hightemperatures, average of the 15-days mean and maximum temperature calculated from June toSeptember and related maps were plotted in GIS environment. The daily mean temperature of30°C and the maximum temperature of 37°C are considered as high-temperature inhibition.Results and DiscussionDelay in spring planting of safflower accelerates the development stages, decrease vegetativegrowth and yield components, and ultimately cause safflower yield reduction. Early plantingdates due to production of higher seed yield are recommended. Thus, if the thermalrequirements of safflower provide the safflower cultivation, earlier and higher yield will beachieved.In the first thermal zone, information layers of the regions were combined that in themmean temperature is reached to 7°C and the minimum temperature to above 0°C. Therefore, inmid-January the eastern and northern half of the province is appropriate for safflowercultivation. In this zone, in east and north parts of the province the planting dates start atJanuary 19 and end in March 6. Khorobiabanak and Biazehbiabanak are stations that located inthis region. In the second thermal zone, information layers of the regions were combined that inthem mean temperature reached to 9°C and the minimum temperature reached above 0°C.Therefore, in mid-March the areas of south-eastern and central provinces were added to theprevious range. In this zone, on some parts in south of the province the planting dates start atMarch 7 and end at April 4. Esfahan, Kabootarabad, Palayeshgahe Esfahan, Najafabad, andBalan stations are located in this region. In the third thermal zone, information layers of theregions were combined that in them mean temperature reached to 12°C and minimumtemperature reached above 0°C. Therefore, in mid-April, additional narrow strip of the northwestto the south of the province was added to the previous range. In this zone, in the other partsof the province the planting dates start at April 5 and end at May 21. Golpaygan, Meymeh,Abyaneh, Daran, Singerd, Chadegan, Emam Gheys, Mehrgerd and Hamgin, Damaneh Freydan,Freydoon Shahr, Badijan, Hana and Khonsar stations are located in this zone. It is noteworthythat in the west and north western part of the province some regions with 2338 to 4405 m heightare not suitable for safflower planting due to low temperature.ConclusionBased on the results in the first, second and third thermal zones, planting dates in the province isgenerally started from January and continue to May.. By considering temperature requirementsof safflower the suitable planting date must be considered. Cultivation and planting shall notface to limited temperature and in every zone the first planting date is the best time for planting.
Physical Geography Research
University of Tehran
2008-630X
46
v.
3
no.
2014
389
405
https://jphgr.ut.ac.ir/article_52138_5f7442a7ef05009a1cabfba2394f23f3.pdf
dx.doi.org/10.22059/jphgr.2014.52138
English Abstracts
text
article
2014
per
Physical Geography Research
University of Tehran
2008-630X
46
v.
3
no.
2014
1
19
https://jphgr.ut.ac.ir/article_52139_2bca124d3ab12bfec3d4cad8a3a904bd.pdf
dx.doi.org/10.22059/jphgr.2014.52139