دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Fast Shoreline Changes in Sefidrud Delta Using Transects Analyses Methodبررسی تغییرات سریع خط ساحلی قاعدۀ دلتای سفیدرود بهروش تحلیل نیمرخهای متساویالبعد1203514110.22059/jphgr.2013.35141FAمجتبییمانیدانشیار دانشکدۀ جغرافیا، دانشگاه تهرانابراهیممقیمیاستاد دانشکدۀ جغرافیا، دانشگاه تهراناحمدمعتمداستاد دانشکدۀ علوم، دانشگاه تهرانمنصورجعفر بیگلواستادیار دانشکدۀ جغرافیا، دانشگاه تهرانقاسملرستانیاستادیار دانشکدۀ علوم انسانی و اجتماعی، دانشگاه مازندرانJournal Article20120916Sustainable management requires knowledge and a good understanding about processes of shoreline changes. Shoreline change detection can ensure health of beach areas. The study area is located in the coastline of the Caspian Sea in an approximate length about 15 km in Sefidrud Delta. Delta Sefidrud have been formed in different periods that experienced symmetrical or semi-symmetric and asymmetric morphologies on the shoreline. General currents of the Caspian Sea have a west to east direction. Sea dynamics in the direction and sediment movement have the greatest impact on the shoreline. Hence, the shoreline should always be oriented towards the estuary of East River Delta, while aerial photography and satellite imagery shows something contrary. Thus, in addition of waves and the coastal currents dynamics no changes have occurred along this direction during the Holocene, so, some other factors may contribute to forming of the delta with periodical differences in geometry. Therefore, contribution of these factors should be characterized in the present curvature of the shoreline. The purpose of this
study is to evaluate variables influencing rapid changes and dynamics of coastline over the last sixty years.
Methodology
To achieve the purpose of this research, some data including sediment discharge from rivers, wind and sea level change statistics, aerial photos, topographic maps and satellite images in several times have been used as the material of the research. Methodology of the work is based on transects in three separate portions. To do this, after atmospheric and geometric corrections of Landsat images and aerial photos in ENVI software, the images were processed for better visualization and interpretations. The images, then, have been imported into ArcGIS 9.3 software. The shoreline positions have been separately extracted from each of the images as distinct layer files. Given longitudinal and transverse movement of the shoreline, Kiashahr main road position, without any changes during this period, has been taken as constant milestone in a separate layer to measure rate of progress and retreat in the shoreline. The changes have been calculated relative to the main road. The entire shoreline is divided into three regions in the delta basal. With overlay of the images on the coastline, erosion analysis and sedimentation in the Sefidrud Delta base during the period 2011-1955, the contributing factors have been measured for each of the three regions of shoreline.
Results and Discussion
The results show that in the first period there has been a decrease in sea level changes. With the displacement of river along the course from the east to the west of Sefidrud Delta basal, drastic changes have occurred in the curve of the coastline. The changes have occurred in the two periods before and after the construction of SefidRood Dam. Shoreline changes in the second period with an increasing trend of sea level are dominated by the Shas operations. With creation of transects in this term, it is specified that coastline compared to the previous period has increased in the first and second zones and is reduced in the third. With reduction in sea level during the third period, Shas operations have been stopped and the top part of the delta has been eroded under the influence of waves.
Conclusion
The results indicate that changes in the rate of sediment output from Sefidrud Dam with Shas operations have the greatest impact on the delta basal to account for the rapid changes in shoreline. Aggradation during Shas operations has been recorded with average speed of 26 m per year (1998-1981). This shows a significant difference in acceleration of shoreline change in 19 m per year before the Shas operations and 9 m per year after the operations. Hurricanes can also play an important role in the changing shoreline occasionally. In the time periods, river mouth has more changes within the delta base. More changes have been related to the changes and irregularities in the shape of the coastline. With a fixed location in the mouth for a long time, sand bandwidth will be increased in the delta base. In aerial photos of 1955 and 1966 in addition of satellite images of the 1978, 1987 and 1998, when the changes have occurred in direction and movement from East to West of river estuary delta base, the changes is measured to be very high in the shoreline. It should be noted that in some parts of the shoreline there appears to be some critical points. The addition of new and old river estuary of Sefidrud, tributaries in the river estuary are also clearly visible. These areas as critical points can be viewed with a special look.مدیریت پایدار سواحل نیازمند آگاهی از روند تغییرات خط ساحلی است و آشکارسازی تغییرات خط ساحلی، میتواند سلامت ساحل را تضمین کند. پهنۀ مورد مطالعۀ این پژوهش، بخش غربی خط ساحلی دریای خزر، در محدودۀ قاعدۀ دلتای سفیدرود، بهطول تقریبی 15 کیلومتر است. هدف این مطالعه، بررسی متغیرهای مؤثر بر تغییرات سریع خط ساحلی طی شصت سال گذشته است. برای دستیابی به این هدف از اطلاعات دِبی و رسوب سفیدرود، باد و نوسانهای تراز آب بههمراه عکسهای هوایی، نقشههای توپوگرافی و تصاویر ماهوارهای چند زمانه بهعنوان ابزارها و دادههای اصلی پژوهش استفاده شده است. روش کار برپایۀ استفاده از نیمرخهای متساویالبعد (ترانسکت) در سه بازۀ مجزا، برای ثبت میزان تغییرات خط ساحلی است که با رویهماندازی تصاویر موجود در نرمافزارهای جغرافیایی و استخراج خطوط ساحلی تاریخی، دلایل فرسایش و رسوبگذاری در قاعدۀ دلتای سفیدرود، طی دورۀ زمانی 1390-1334، بررسی و مشارکت عوامل مؤثر بر تغییر خط ساحلی در هر یک از بازههای سهگانه مورد سنجش قرار گرفت. نتایج نشان میدهد که تغییر در میزان بده رسوب خروجی از سد سفیدرود با انجام عملیات شاس، بیشترین تأثیر را در تغییر سریع خط ساحلی قاعدۀ دلتا داشته است. طی عملیات شاس، میانگین دلتاسازی با سرعت 26 متر در سال (1998-1981) به ثبت رسیده است که نسبت به سرعت تغییر خط ساحلی در دورۀ قبل از عملیات با 19 متر در سال (1980- 1955) و 9 متر در سال پس از عملیات شاس (2011-1999)، تفاوت چشمگیری را نشان میدهد. همچنین طوفانهای دریایی نیز، بهصورت مقطعی میتوانند نقش مهمی در تغییر خط ساحلی ایفا کنند.دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Impact of Wind and Atmospheric Patterns in Location and Direction of Dasht-e Kavir Ergsنقش باد و الگوهای جوی در مکان¬گزینی و جهت ریگزارهای دشت کویر21383514210.22059/jphgr.2013.35142FAمهرانمقصودیدانشیار دانشکدۀ جغرافیامجتبییمانیدانشیار دانشکدۀ جغرافیافرامرزخوشاخلاقاستادیار دانشکدۀ جغرافیاعلیشهریاراستادیار ژئومورفولوژی، دانشگاه یزدJournal Article20130128Introduction
In climate, Iran is a part of the Afro-Asian belt of deserts. This climate is almost rainless and has very arid climatic condition. The desert soils are mainly covered with sand and pebbles. These materials are largely carried by the wind. Dasht-e Kavir is a large desert lying in the middle of the Iranian plateau (at longitudes from 58 ̊ to 53 and Latitudes from 36 ̊ to 32́). As the desert is surrounded by Alborz and Zagros Mountains, the moisture cannot penetrate into the desert area. In summer, Establishment of the Azores high pressure account for the dry conditions in the Dasht-eKavir. These conditions reduce the total amount of rainfall and the lack of sufficient vegetation in the Desert. Therefore, Wind has a high potential for Erosion, Transport and accumulation of Sediment. As a result, the conditions for the formation of Ergs and Sand sheets are provided in the desert. Furthermore, Dasht-e Kavir was surrounded by various high lands so that they are the most effective factor in the deposition of sand and switching in location of Ergs. The temperature difference in Desert mountain ranges is always a generator for different daily local wind. The local winds can play important role in morphological changes of the Desert Surface.
Methodology
Formation of dune areas is determined by the production of sediment by a range of suitable particle sizes, the availability of the sediment for transport by wind, and the transport capacity of the wind. In this Study, satellite images were first obtained to reconnaissance the location of the study area. Thus, the location of highlands and the scattering of Khartouran, Chah Jam, Sargardan And Jen Ergs around the dasht-e Kavir was examined by observation DEM, ETM+ ,MSS and Google Earth Images through ArcGIS Software. By this, the role of highlands of the concentration and accumulation of Sand is observed.
Second, Ergs morphology is detected on the satellite images. Prevailing wind direction was determined based on Ergs Landforms. Prevailing winds around the desert, Wind Rose, was identified via Wind data from meteorological stations by WRPLOT Software. In addition, U-wind and V-wind in Dasht-e Kavir was examined by wind dynamics data from 925 HP leveland PANOPLY Software.
Third, the vorticity of summer in Dasht-e Kavir has been Examined in relationship to Ergs morphology by dynamics data 925 HP level via PANOPLY Software.
Results and Discussion
The results of morphological effects on satellite images and their relationship with local wind regime have illustrated that Wind regime in the Dasht-e Kavir was coincident with Ergs Morphology so that Wind direction and Ergs Morphological, follows from a process of convergence. Sand roses for summer around Dasht-e Kavir have been shown to be in east to west in the half Northing, while in the southern part of the Desert they have been west to eastern winds. Desert U- winds are more oriented to the East-west while the most of V- winds in the desert have a North - South.
Vorticity of the desert in summer also represents a trend in the direction of rotation. The Vorticity of the desert in summer also represents a trend in the direction of rotation in Anti- clockwise.
Conclusion
Ergs Morphology is corroborated in common systematic morphogenesis in the Dasht-e Kavir. This common Systematic morphogenesis around the desert represents a spatial route, Sothat the morphology of the north- east of Dasht-e Kavir, sand Khartouran, is North East - South West. This trend is changing with the movement to the west so that vary to North – South direction in Chah Jam Erg. These winds continue to Northern Rig-e Jen and in the southern parts of the Rig-e Jen divert towards North West- South East and West – East direction in Choupanan Town.
Concurrent review Vorticity and Erg morphology around the Dasht-e Kavir, represents the interaction between Ergs morphology and weather patterns so that Ergs Morphology and Patterns of weather have a convergent path. Thermal low pressure system is generated in Dasht-e Kavir in Summer so that direction of rotation in thermal low coincides with the Erg Morphology around Dasht-e Kavir. Therefore, the topography has influenced the situation of Ergs, Vorticity and Wind direction has been effective in Ergs morphology.بهدلیل شرایط اقلیمی خشک دشت کویر، پهنههای ماسهای عمدهای در قسمتهای مختلف آن پراکنده شده است. در مکانگزینی و جهتگیری این پهنههای ماسهای، عوامل مختلفی نقش دارند که از مهمترین آنها میتوان به توپوگرافی و جهت باد غالب اشاره کرد. از آنجا که باد یکی از عوامل مؤثر در تولید تپههای ماسهای و استقرار آنها است، میتوان با استفاده از آمار باد ایستگاههای هواشناسی و رسم گلباد، جهت و سرعت باد را تعیین کرد. با توجه به اینکه در محدودهی دشت کویر، بهدلیل شرایط نامساعد طبیعی، ایستگاههای هواشناسی محدود است، بنابراین از دادههای دینامیک جو برای مطالعهی جهت باد در سطح دشت کویر و ریگزارهای مورد مطالعه استفاده شد. برای این امر با توجه به میانگین ارتفاع دشت کویر (درحدود 700 متر)، دادههای مربوط به سطح فشار 925 هکتوپاسکال، برای تعیین نوع وزش بادهای فصل تابستان، بهمنزلۀ دورۀ خشک دشت کویر مورد استفاده واقع شد. بادهای مداری غالب شرقی ـ غربی و بادهای نصفالنهاری شمالی ـ جنوبی در سطح دشت کویر بههمراه توپوگرافی قسمتهای جنوبی و جنوب غربی، سبب تراکم بیشتر ریگزارها در این قسمتهای دشت کویر شده است. با استفاده از دادههای حاصل از بادهای مداری و نصفالنهاری، جهت بادهای تابستانی در قسمتهای مختلف دشت کویر مشخص شد که تا حدود زیادی با جهت گلبادهای ایستگاههای هواشناسی و نوع مورفولوژی عوارض ماسهای در تصاویر ماهوارهای منطبق است. مطالعۀ جهت بادهای تابستانی، نشاندهندۀ وجود دو سامانۀ فشار متفاوت مؤثر در جهتگیری ریگزارهای واقع در نیمۀ شرقی و غربی دشت کویر است.دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Evaluation of Appropriate Climatic Conditions for Swimming Activities in the Beach of Gilan Provinceارزیابی شرایط مناسب برای فعالیت شنا در سواحل استان گیلان39543514310.22059/jphgr.2013.35143FAمهنازعزیز ابراهیمکارشناس ارشد آبوهوا شناسی در برنامه¬ریزی محیطی، دانشگاه خوارزمیبهلولعلیجانیاستاد آبوهوا شناسی و مدیر قطب تحلیل فضایی مخاطرات محیطی، دانشگاه خوارزمی0000-0002-8620-6733Journal Article20121208Introduction
Ecotourism as a shortened term of Ecological Tourism is the possible leisure activities of people in the nature. It is based on purposeful trips for visiting nature and cultural and spiritual perceptions of natural attractions and also for enjoying a variety of natural phenomena (Rezvani, 1380). Generally, visiting nature makes ecotourism different from other kinds of tourisms (Zahedi, 1382).
Dynamic nature and a variety of leisure activities available in coastal areas, has made the areas highly favorable for tourists. This has transformed coastal areas into one of the most influential regions for local and national economies in the world. Climate can also affect attractiveness of places for tourists and may have a major role in selection of tourist sites. Based on past evidence, climate can be a key factor in vacation planning and satisfaction of vacation experience so it is known as a central stimulus for vacation planning. One piece of information that tourists need to know for vacation is the climate of destination cities. Most of the tourists take this factor into account for choosing their destination. However, climate has a low presence in tourism literature, while it is highly important in vacation planning of tourists. Considering above-mentioned issues, no comprehensive and detailed work is carried out on standard conditions of swimming in coast areas of Caspian Sea. This paper has studied the subject as the first.
Methodology
In this research we have tried to reveal one of these tourism potentials, that is, swimming activity in the southern coast of the Caspian Sea in Gilan Province. For this purpose the hourly data of temperature, wind speed, relative humidity, and sun shine duration were obtained from the Meteorological Organization of Iran for the period 2005-2008, the period when the sea surface data such as wave height and temperature were available. The data have been analyzed for the stations of Astara, Anzali, and Lahijan.
It is worth noting that, a survey from a sample of fifty people of Tehran citizens had a major role in form of the tourism. Tourist activities have been examined in this study and this make it possible to find out the demands of tourists according to the survey. Among the four options of: 1) the beach and the sea, 2) forest and mountains, 3) ancient monuments and culture of the area –customs, and 4) souvenirs, about 46% of the tourists introduced the beach and sea as a main tourist attraction in Gilan. From the four activities of 1) surfing on the beach, 2) swimming, 3) boating, and 4) fishing, the swimming was the activity 50% of the respondents in both cases had the highest demand among all other options. Selection of beach, sea and swimming activity by the tourists helped choose the type of tourism and the specific tourist activity in accordance with the following model. Marine tourism» Beach tourism» Beach sport» Aqua» Swimming
According to the data a suitable indicator of environmental conditions was first created for swimming in the lake. This index includes the suitable temperatures for environment, the suitable temperatures for water, maximum wave height that is permitted to swim, maximum wind cooling does not cause discomfort in people, no phenomenon of lightning, and no rain. This index is called the standards of swimming. Then the factors have been taken from above-mentioned stations filtering the days when the facts were consisted of suitable days for each station according to the annual, monthly, weekly, and weekend intervals. Referring to this stage it was clear that, which month, which week and which weekend were the best times for swimming activities in each station in the Caspian Sea.
Results and Discussion
The best days have been viewed and compared in two hours of 9:30 am and 15:30 pm at three stations studied during timescales annually, monthly, weekly and for weekends. It was found that the rate of good days for swimming activities in the coast of Gilan at 15:30 pm is more than those in 9:30 am.
The results of this study indicated that, in addition to other factors involved in standard conditions for swimming, solar radiation is the most important factor in choosing the appropriate time for swimming ashore. Because it has a direct effect on factors determining the appropriate time of swimming such as air temperature, water temperature, wind speed and cool and etc., the sun is, indeed, neutralizing their negative effects.
Conclusion
The results have indicated that the best months for swimming, in order, are August, July, June and September. Astara is the most suitable sea side for swimming during week days and weekends. Anzali and Lahijan are the second and third best places in order.موقعیت جغرافیایی ویژه و تنوع پدیدههای طبیعی باعث شده تا ایران پنجمین کشور دارای جاذبۀ گردشگری در جهان شناخته شود. در حالیکه بررسیهای انجام شده براساس آمار سازمان جهانی جهانگردی، حاکی از درصد کم جذب گردشگر در ایران است. با توجه به روند رو به توسعه و سودآوری این صنعت در کشورهایی که در زمینۀ طبیعتگردی سرمایهگذاری کردهاند، میتوان این صنعت را صنعتی همسو با محیط زیست و با بهرهدهی بالا درنظر داشت. در این پژوهش آزمونی برای مشخص کردن یکی از پتانسیلهای این نوع گردشگری انجام گرفته است. این پتانسیل مورد نظر، فعالیت شنا در سواحل دریای خزر واقع در استان گیلان است. برای این امر دادههای ساعتی معیارهای دما، سرعت باد، رطوبت نسبی و طول مدت ساعات آفتابی و ... از سازمان هواشناسی، برای یک دورۀ چهار ساله از سال 2005 تا 2008 میلادی دریافت شد، دورهای که در آن دادههای سطح دریا مثل ارتفاع امواج و دما قابل دسترسی بودند. ایستگاههای انتخابی عبارتند از: آستارا، بندرانزلی و لاهیجان که دادههای مربوط به آنها مورد تجزیه و تحلیل قرار گرفتند. پس از انجام آزمون و بهدست آوردن تقویم روزهای مساعد شنا، نتایج نشان داد که بهترین ماهها برای شنا بهترتیب، آگوست، جولای، جون و سپتامبر هستند. ایستگاه آستارا بهترین مکان برای شنا در طول روزهای هفته و آخر هفتهها است. انزلی و لاهیجان، بهترتیب دومین و سومین مکان مناسب برای شنا هستند. براساس نتایج پژوهش، انرژی تابشی خورشید مهمترین عامل در انتخاب زمان مناسب شنا در ساحل است؛ چرا که اثر مستقیمی بر شاخصهای دیگر تعیین اوقات مناسب شنا، از جمله دمای محیط، دمای آب، سرعت و برودت باد و ... دارد و در واقع اثرات منفی آنها را خنثی میکند و از این طریق بر انتخاب بهترین ساعت انجام شنا و مناسبترین ماه برای این فعالیت اثر میگذارد.
دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Floodwater Spreading Site Selection by FAHP and GCA and Comparison of Model Performance
(Case Study: Garabaygan Catchment, Fasa Plain, Shiraz)بررسی و مقایسۀ کارایی روش¬های FAHP و GCA برای مکان¬یابی پخش سیلاب در محیط GIS (مطالعۀ موردی: حوضۀ آبریز گربایگان)55763514410.22059/jphgr.2013.35144FAحسنعلیفرجی سبکباردانشیار دانشکدۀ جغرافیا، دانشگاه تهرانسیروسحسن پوردانشجوی کارشناسی ارشد سنجش از دور و GIS، دانشگاه تهرانعلیعزیزیدانشجوی کارشناسی ارشد مدیریت و برنامهریزی محیط زیست، دانشگاه تهرانآرشملکیاناستادیار دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه تهران0000-0001-8174-6784سیدکاظمعلوی پناهاستاد دانشکدۀ جغرافیا، دانشگاه تهرانJournal Article20121017Introduction
One of the main principles in the process of spreading floodwater is use of the water in arid and semi arid areas for an efficient utilization of both the water and the soil. Executing more than one decade research plans on floodwater spreading in the realm of Iran aquifers have proved that the plains and deserts have got a great potentiality in order to supply water and to prevent the irreparable damages of flood and desertification. The first and the most important step in executing a floodwater spreading project is a suitable zonation for water spreading and to penetrate it into underground water tables. It is impossible to use Geographical Information Systems (GIS) in order to site select potential zones for floodwater spreading without using Multi-criteria Decision Making system (MCDM). Floodwater spreading plan except to gather water and transfer waste water to nourish the aquifers by the purpose of reducing soil erosion and improving the vegetation is studied with a multi-purpose attitude. One of the most appropriate tools in site selection for certain zones is the application of computerized conceptual models in the Geographic Information System (GIS) environment. Because there are a variety of models in this field, identifying and introducing the best model is one of the most essential actions in executing these operations or plans. We have tried in this research to observe the important factors and criteria such as: geocentric factors (geology, geomorphology and soil), hydrology, geohydrology, slope and physiographic characteristics of basin and also discussing certainty or uncertainty of effective locative data in site selection of the potential zones to spread floodwater. On the other hand we have attempted to identify and introduce the most suitable model in site selection of the potential zones to spread floodwater in the Garabaygan aquifer basin in Fars, Iran. FAHP model and GCA with some of their operators are the selective models in this research.
Methodology
<em>Study area: </em>
Garabaygan region in the Fasa is located in 190 Km away from southeast Shiraz in lat. from 28° 41' to 21° 41' N and long. from 53° 53' to 45° 57' E. Also it's located at 1120 to 1160 above sea level.
<em>Methodology </em>
Firstly in this research we calculated nine effective factors including geomorphology, geology, slope, height, land use, alluvium thickness, drainage density and electrical conductivity in floodwater site selection by using FAHP and GCA models and then we provided and classified the information layers of these nine factors by using Arc GIS 9.3. Considering the weights of every factor and the scores that they have been assigned, we made the final map of zonation based on these models by classifying them into five classes: very unsuitable, unsuitable, average, suitable, very suitable.
<em>FAHP Method: </em>
The analytic hierarchy process (AHP) is one of the extensively used multi-criteria decision-making methods. One of the main advantages of this method is the relative ease with which it handles multiple criteria. The use of AHP does not involve cumbersome mathematics. AHP involves the principles of decomposition, pairwise comparisons, and priority vector generation and synthesis.
A major contribution of fuzzy set theory is its capability of representing vague data. The theory also allows mathematical operators and programming to apply for the fuzzy domain. A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function, which assigns to each object a grade of membership ranging from zero to one. Essentially, the uncertainty in the preference judgments gives rise to uncertainty in the ranking of alternatives as well as difficulty in determining consistency of preferences. These applications are performed with many different perspectives
and proposed methods for fuzzy-AHP. In this study, Chang’s (1992) extent analysis on fuzzy-AHP is formulated for a selection problem.
In the fuzzy-AHP procedure, the pairwise comparisons in the judgment matrix are fuzzy numbers that are modified by the designer’s emphasis.
To deal with vagueness of human thought, Zadeh first introduced the fuzzy set theory, which was oriented to the rationality of uncertainty due to imprecision or vagueness.
A triangular fuzzy number (TFN) is shown in Fig. 1. A TFN is denoted simply as( The parameters and respectively denote the smallest, possible and the largest promising value, and the largest possible value that describe a fuzzy event.
Fig. 1. A triangular fuzzy number
<em> </em>
<em>GCA Method:</em>
The most important function of the theory (GCA) is proposing a modern method to study and survey systems in the uncertainty situation which is based on the gray sequence, creation of a collection of gray numbers provided that values of gray numbers are not known, but the area in which those values lie is given. Gray systems are named after colors of the concerned topics. With the purpose of clarity, in this theory information and data are displayed as indicators of the degree of darkness of the colors (color sequences from white to black). The word "black" is assigned to the information and data which their inner structure and relations are totally unknown and hardly possible to be encoded. GTS is one of the mathematical which helps much in solving problems in the three following situations: 1. Uncertainty 2. Discontinuous data 3. Insufficient data.
Results and Discussion
In this research we have used nine effective factors including geomorphology, geology, slope, height, land applying, alluvium thickness, drainage density and electrical conductivity in floodwater site selection. In this study some criteria (i.e., geology, slope, drainage density and alluvium thickness,) have maximal effects whereas some others (i.e., elevation, landuse, and geomorphology) have minimal effects.
Final map of both methods are supplied in 5 classes from completely suitable to unsuitable. Completely suitable class in FAHP model has an area of 17.101 hectares and in GCA model has an area of 12.195 hectares of total area (7946 hectares) of the province. The table 1 shows the results.
Table 1. findings of functional models
capa coefficient
Area of a region in ha
Accuracy of the model
functional models
-.0897
17.101
%47.37
FAHP
.0943
12.195
%52.63
GCA
30.296
%100
total
Conclusion
In this study, FAHP and GCA were used in combinative approach with GIS in order to determinate appropriate areas for flood spreading in Garbaigan plain. The findings show that susceptible regions for flood spreading are in quaternary units like: Qc2, Mm-1, Qb, Qgsc, Qscg, and PLQb. Also according to geomorphology and land uses, cone carters, plains and low density pastures are the totally appropriate zones for flood water spreading. These zones are in correspondence with the location of the Kosar floodwater spreading station. They have the special characteristics for spreading floodwater. On the other hand, according to this, our obtained results is the best reason for choosing the Fuzzy model and Gray System Theory for evaluating the quality of data in comparison with other applied models. Also comparison of finding obtained from this two models show that GCA model is more accurate than FAHP model to find susceptible regions for flood spreading.اولین و مهمترین گام در انجام پروژۀ طرح پخش سیلاب، مکانیابی مناطق مستعد برای پخش آب و نفوذ دادن آن به داخل سفرههای زیرزمینی است. در این راستا استفاده از سامانههای اطلاعات مکانی (GIS)، برای تعیین مناطق مستعد پخش سیلاب بدون استفاده از سامانۀ تصمیمگیری چندمعیاره (MCDM) مقدور نیست. در این پژوهش ابتدا نُه شاخص شامل شیب، ارتفاع، هدایت الکتریکی، قابلیت انتقال، ژئومورفولوژی، کاربری اراضی، تراکم شبکۀ زهکشی، زمینشناسی و ضخامت آبرفت که در مکانیابی پخش سیلاب مؤثرند با استفاده از نرمافزار Arc GIS تبدیل به لایههای اطلاعاتی شده و پس از آن کلاسبندی شدند. سپس با روش خوشهبندی خاکستری (GCA)، تمام دادههای ناقص یا گسسته به اعداد خاکستری برای بالا بردن کیفیت تحلیل و ارزشگذاری (وزندهی) اطلاعات و دادهها تبدیل شدند. همچنین برای بالا بردن دقت پهنهبندی و تحلیل مقایسهای، از دو روش تحلیل سلسلهمراتبی ـ فازی (AHP) و روش خوشهبندی خاکستری (GCA) استفاده شد. درنهایت بر اساس هر روش، نقشۀ نهایی حوضه تهیه و به پنج کلاس کاملاً مناسب، مناسب، متوسط، نامناسب، کاملاً نامناسب پهنهبندی شد. نتایج حاصله نشان میدهد که روش خوشهبندی خاکستری در مورد پهنهبندی مناطق مستعد پخش سیلاب، دقیقتر از روش تحلیل سلسلهمراتبی ـ فازی (FAHP ( بوده و همچنین نتایج حاصل از کاربرد این دو روش، نشاندهندۀ قرارگیری مناطق مستعد در واحدهای کواترنری PLQb, Qscg, Qgsc, Qb, <br /> Mm-1, Qc2 است.دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Using Neural Fuzzy Inference System to Estimate Sediment Load and a Comparison with MLR and SRC Models in Ghranghu River Basinاستفاده از سیستم استنتاجی فازی عصبی در تخمین بار رسوبی و مقایسۀ آن با مدلهای MLR وSRC در حوضۀ رودخانۀ قرانقو77903514510.22059/jphgr.2013.35145FAمجیدرضایی بنفشهدانشیار گروه جغرافیای طبیعی، دانشگاه تبریزمهدیفیضا.. پوراستادیار گروه جغرافیا، دانشکده علوم انسانی، دانشگاه زنجانسحرصدر افشاریکارشناس ارشد اقلیمشناسی، دانشگاه تبریزJournal Article20110731Introduction
Prediction of sediment load is used in a wide range of topics to estimate volume of dams, sediment transport in rivers and etc. In recent years, artificial neural network was used in rainfall-runoff modeling, prediction of discharge intensity and estimation of sediment load. Sediments are sources of pollutions such as chemical compounds. The results of the many researches indicated the effectiveness of modeling in hydrological predictions.
Jin (2001) used Artificial Neural Network (ANN) method to assess the relationship between discharge and sediment load and stated that the ANN model can achieve better results than the sediment rating curves. Tayfor (2002) used the neural network model in sediment transport and concluded that this model was more predictive than the physical models. In this paper, Neural Fuzzy Inference System (ANFIS) is used as a non-linear model to estimate the suspended sediment load. The comparisons showed that the ANFIS method has achieved better results in predicting the daily suspended sediment load than MLR models and SRC models. Dogan et al (2005) also used Artificial Neural Network model (ANN) and fuzzy logic (FL) to predict monthly suspended sediment load in the Sakarya River Basin in Turkey.
Methodology
In this study, to determine the amount of suspended sediment load, average daily discharge, rainfall and Gharnghu river basin sediment data (1387 to 1388) have been used as the material. Thus, the above data first have been entered in fuzzy neural models (ANFIS), multivariable regression (MLR) and the sediment rating curve (SRC). Then a comparison between them has been made to determine the ability of each model. Observed data and predicted data replaced with R<sup>2 </sup>and RMSE and according to these values the best model has been determined.
Results and Discussion
The purpose of the suspended sediment modeling studies is establishing significant relationships between discharge and sediment data. For this purpose several methods have been used. In this paper, daily discharge, current and the previous day rainfalls and suspended sediment load data have been used as the inputs for the model. The amount of sediment has been predicted by the neural fuzzy inference system, multiple regression equations and sediment rating curves. Then, a comparison was made between the results and the ability of each model.
Table1. Performance of ANFIS, MLR and SRC models
R<sup>2</sup>
RMSE
Models
0.9668
190
ANFIS
0.8946
381
MLR
0.8384
454
SRC
The comparisons have showed that the ANFIS model with R<sup>2</sup> value about 0.9668 and RMSE about 190 has achieved the best result. Table 2 shows that the ANFIS model performs better than the MLR and SRC models. The ANFIS and MLR models have given better estimates of the maximum sediment load than the SRC model. The ANFIS, MLR and SRC models have predicted the maximum amount of the sediment load up to 6549, 5982 and 5329, respectively. These values have been estimated 11, 19, and 28% lower than the observed value. ANFIS models in comparison with the MLR and SRC models have high potential in establishing relationship between discharge and suspended sediment load. Sediment rating curve models establish the linear regression relations between the logarithm of the sediment and discharge values. Thus, these models require a normal distribution of the data and this is one of the main weaknesses of the models. The main characteristic of the ANFIS model is its flexibility and ability in making nonlinear relationships.
Conclusion
sediment load. The inputs of these models are rainfall, discharge and sediment data. In the first part of this research, regression equations have been set between discharge and rainfall data. In the second stage, discharge, rainfall and sediment variables set as the ANFIS model inputs and have been used in estimating suspended sediment load. Then in the third phase, the ANFIS model is compared with SRC and MLR models. The value about 0.9668 has been obtained for ANFIS model by using R<sup>2</sup> factor and it shows that the ANFIS model has better performance than the other models. Besides, the MLR model has achieved better results than the SRC model. To estimate suspended sediment load in SRC model, the discharge factor has been applied. Conducted researches indicate that rainfall and sediment data must also be used beside discharge data. The main advantage of the ANFIS model relative to other models is their capabilities in modeling nonlinear relationships. Overall, the ANFIS model achieves better results than other models.انتقال رسوبها در رودخانهها با توجه به نقش آنها در مباحث هیدرولوژیکی، از اهمیت ویژهای برخوردار است. این رسوبها به روشهای گوناگون اندازهگیری میشوند. اندازهگیری مستقیم بار معلق رسوبی در رودخانه، هزینهبر بوده و امکان احداث ایستگاههای اندازهگیری در تمام طول رودخانه وجود ندارد. همچنین معادلههای مورد استفاده در تخمین بار رسوبی، برای تمام مناطق قابل استفاده نبوده و علاوهبر آن، نیازمند دیدهبانیهای بلندمدت است. با این حال، برخی از روشها در تخمین بار معلق رسوبی به نتایج مطلوبی دست یافتهاند. در این مطالعه، سیستم استنتاجی فازی عصبی (ANFIS) با بهرهگیری از ترکیبهای ورودی مختلف برای تخمین بار معلق رسوبی روزانه بهکار گرفته شد. به این منظور در اولین بخش از پژوهش، مدل ANFIS با استفاده از دادههای دِبی روزانه و بار معلق رسوبی روزهای پیشین، تعلیم داده شده و برای تخمین بار معلق رسوبی رودخانۀ قرانقو مورد استفاده قرار گرفت. در دومین بخش از پژوهش، مدل ANFIS با استفاده از شاخصهای ضریب تبیین (R<sup>2</sup>) و خطای مجذور میانگین مربعات (RMSE) با مدلهای منحنی سنجه رسوبی (SRC) و رگرسیون چندمتغیره (MLR) مقایسه شد. نتایج نشان داد که مدل ANFIS با برخورداری از مقادیر ضریب تبیین (R<sup>2</sup>) برابر 9668/0، RMSE برابر 190، در مقایسه با سایر روشها از قابلیت بهتری در تخمین بار معلق رسوبی برخوردار است. در این بین، مدل SRC با برخورداری از مقادیر R<sup>2</sup> و RMSE که بهترتیب معادل 8384/0 و 454 تخمینزده شده است، به ضعیفترین تحلیل در پیشبینی بار معلق رسوبی دست یافته است.دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Comparison of Stochastic and Artificial Neural Networks Models in Modeling and Forecasting the Standardized Precipitation Index Values and Classesمقایسۀ مهارت مدل¬های استوکاستیک و شبکه¬ها¬ی عصبی مصنوعی در مدلسازی و پیشبینی مقادیر و طبقات شاخص بارندگی استاندارد شده911083515010.22059/jphgr.2013.35150FAسمیهحجابیدانشجوی کارشناسی ¬ارشد هواشناسی¬کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهرانجوادبذرافشاناستادیار گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهراننوذرقهرماناستادیار گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران0000-0002-9442-8870Journal Article20120421Introduction
Drought is a temporary and recurring meteorological event which results from the lack of precipitation over an unusual extended period of time. Early indication of possible droughts can help set out drought mitigation strategies and measures, in advance. Therefore, the drought forecasting plays an important role in the planning and management of water resource systems.
Stochastic models have been extensively used for forecasting hydrologic variables such as annual and monthly stream flow, precipitation, and etc. in the past. But they are basically linear models assuming that data are stationary, and have a limited ability to capture non-stationarities and nonlinearities in the hydrologic data. However, it is necessary to consider alternative models when nonlinearity and non-stationarity play a significant role in the forecasting. In the recent decades, artificial neural networks have shown great ability in modeling and forecasting nonlinear and non-stationary time series due to their innate nonlinear property and flexibility for modeling.
The aim of this study is to compare the stochastic and artificial neural network models in forecasting the standardized precipitation index (SPI) in some stations of Iran. This is because of the multiplicity of drought occurrences in Iran and the necessity to determine the best forecasting model.
Methodology
The monthly total precipitation data (1973-2007) related to four synoptic stations of Iran including Bandar Anzali (with very wet climate), Hamedan Nojeh (with semi arid climate), and Bushehr (with arid climate) and Zahedan (with hyper arid climate) have been used after the homogeneity and adequacy of data have been confirmed by statistical tests.
In the present study standardized precipitation index (SPI) time series (at 3-, 6- and 12-month timescales) have been calculated for the period of 1973-2007. The most suitable distribution function for precipitation at 3- , 6- and 12- month timescales has been determined by Easyfit software on the basis of kolmogorov-Smirnov statistic. This is performed separately for each month. Then, each cumulative probability density function is transformed into a cumulative standardized normal distribution. The SPI values for the period of 1973-2000 are used to calibrate the models and the rest of the data to be tested.
Development of stochastic model consists of three stages of identification, estimation, and diagnostic checking (Box and Jenkins, 1976, 19). During the identification stage the candidate forms of the models are determined using the autoregressive function (ACF) and partial autoregressive function (PACF) and general forms of the models are determined on the basis of Schwarz Bayesian information criterion (Schwartz, 1978, 461–464) and Akaike information criterion (Akaike, 1974, 716–723). In the estimation stage the model parameters were calculated using Minitab14 software. Finally, diagnostic checks of the model are performed using kolmogorov-Smirnov (K-S) and Portmanteau test (Makridakis et al., 2003, 185) to reveal possible model inadequacies and to assist in selecting the best model.
In the present paper two different approaches of neural networks including recursive multi-step neural network approach (RMSNN) and direct multi-step neural network approach (DMSNN) are used for forecasting several time steps ahead. The RMSNN approach based on one output node forecasts a single step ahead, and the network is applied recursively, using the previous predictions as inputs for the subsequent forecasts. DMSNN is based on the multiple outputs, when several nodes are included in the output layer, and each output node represents one time step to be forecasted.
The models are evaluated with statistical tests, correlation coefficient, and error index for 1- to 12-lead time ahead forecasting over the period of 2001- 2007. Also, the capability of the models in forecasting the SPI classes is investigated using Cohen’s Kappa statistic (Cohen, 1960, 37–46).
Results and Discussion
The results of stochastic modeling of SPI time series showed that the null hypothesis related to the normality of residuals is accepted for 3- and 6- month time scales but rejected for 12-month time scales at 1% significant level in all stations. The results of Portmanteau test signify that the chosen stochastic models are adequate on the available data at 1% significant level.
The results of artificial neural networks (RMSNN and DMSNN) modeling of each SPI time series are presented as optimal architectures of the best number of input and hidden neurons.
The significance lead times of drought forecasting are determined based on correlation coefficient and Kappa statistic between the observed and forecasted values of the SPI time series in the stations of interest. Accordingly, the most appropriate models for SPI values and classes have been determined by a comparison of three models for each time series.
Conclusion
The results have revealed that generally, for 3-, 6- and 12-month time scales, stochastic models (with average error of 0.678, 0569 and 0.344 and average correlation coefficient of 0.682, 0.777 and 0.919, respectively) are more accurate than artificial neural network models to forecast SPI values. The comparison of models in forecasting SPI classes also showed that the most accurate model for forecasting SPI classes for 3-, 6- and 12-month time scales is DMSNN, RMSNN and stochastic model (with average Kappa of 0.397, 0530 and 0.750) in sequence.هدف از پژوهش پیش رو، مقایسۀ کارایی مدلهای استوکاستیک و شبکههای عصبی مصنوعی در پیشبینی کمّی شاخص بارندگی استاندارد شده (SPI) در اقلیمهای خشک و مرطوب ایران است. برای این امر، محاسبۀ SPI، در مقیاسهای زمانی سهماهه، ششماهه و دوازدهماهه در چهار ایستگاه سینوپتیک کشور طی دورۀ 2007-1973 انجام شد. در گام بعد، مدلسازی سریهای زمانی SPI برای پیشبینیهای یک تا دوازده گام به جلو، به سه روش مدلسازی استوکاستیک، شبکۀ عصبی بازگشتی (RMSNN) و شبکۀ عصبی مستقیم (DMSNN) انجام گرفت. مقادیر SPI مربوط به دورۀ 1973 تا 2000، برای توسعۀ مدلها و مابقی برای صحتسنجی مدلها مورد استفاده قرار گرفت. در مرحلۀ صحتسنجی، مقایسۀ مقادیر مشاهدهشده و پیشبینیشده SPI با استفاده از آزمونهای آماری، ضریب همبستگی و شاخص خطا انجام شد. همچنین برای بررسی قابلیت مدلها در پیشبینی طبقات SPI، از آماره کاپای کوهن استفاده شد. در نهایت، اولویت دقت مدلها از دیدگاههایی چون، افق زمانی پیشبینی و مقیاس زمانی بررسی خشکسالی تعیین شد. نتایج بهدست آمده نشان داد: 1) در مقیاس زمانی سه، شش و دوازدهماهه، بهطور کلی مدلهای استوکاستیک (بهترتیب با میانگین خطای 678/0، 569/0 و 344/0 و میانگین ضریب همبستگی 682/0، 777/0 و 919/0) از نظر مهارت پیشبینی مقادیر SPI در اولویت کاربرد قرار دارند. 2) در مقیاس زمانی سه، شش و دوازدهماهه بهترتیب، مدلهای DMSNN ، RMSNN و استوکاستیک (با میانگین کاپای 397/0، 530/0 و 750/0) از نظر مهارت پیشبینی طبقات SPI در اولویت کاربرد قرار دارند.دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Estimation of Nitrogen Content in Soybean Using Remote Sensingتخمین میزان نیتروژن در گیاه سویا با استفاده از سنجش از دور1091243515310.22059/jphgr.2013.35153FAفاطمهشکیکارشناس ارشد فیزیولوژی گیاهی، دانشگاه شهید بهشتیفرانسوازبرنارددانشیار گروه علوم زیستی، دانشگاه شهید بهشتیروشنکدرویشزادهاستادیار گروه سنجش از دور و GIS، دانشکدۀ جغرافیا، دانشگاه تهرانعبدالحمیددشتی آهنگرکارشناس ارشد سنجش از دور و GIS، دانشگاه شهید بهشتیJournal Article20121031Introduction
Chemical concentration of plants is indicator of their biologic status. Among the many foliar chemicals in plants, nitrogen (N) is an important indicator of photosynthetic rate and overall nutritional status. Plants usually take up nitrogen in the nitrate form (NO<sub>3</sub><sup>-</sup>) and one major source of nitrate leaching is fertilizer applied to the crops. Supplying inadequate N may decrease crop yields and increase the N fertilizer (more than the needs of plants). In addition to economic loss, nitrate ions may move into surface and ground water and contribute to eutrophication of lakes and streams and raise health problem (Liaghat and Balasundram 2010). Thus estimation of nitrogen content is important in many agricultural studies.
Traditionally leaf nitrogen content is measured in the lab using different chemical methods. Nitrogen analysis either by the Kjeldahl or Dumas method is expensive and requires specialized equipments. An alternative method for N determination is the digestion of potassium persulfate (K<sub>2</sub>S<sub>2</sub>O<sub>8</sub>). Persulfate digestion requires only a modest initial investment and has few environmental risks. The common problems of all above mentioned approaches are the facts that they are time consuming, expensive and destructive approaches. The advent of remote sensing has proved its usefulness as an alternative measure to these traditional approaches.
The aim of this study is to estimate canopy nitrogen content in vast area in northern part of Iran, Gorgan, using remote sensing vegetation indices. Later it was used in calibration of different vegetation indices and for estimation of CNC of a vast area in Gorgan, Iran.
Methodology
LANDSAT TM imagery simultaneous to the field campaign was acquired. The field campaign was conducted in the latter half of August 2009 in northern part of Iran, Gorgan (36° 54' N, 54° 53' E). Fifty sample plots of 30 m× 30m were randomly chosen. In each sample plot, 4 to 7 subplots were selected and in each subplot 30 leaves form different parts of Soybean crops were cut and transferred to lab. Then using persulfate digestion, nitrogen content of the leaves was determined. In the field, canopy percentage was measured and multiplied by the leaf nitrogen content to calculate the canopy nitrogen content (CNC). The regression line between different vegetation indices (NDVI, GI, SAVI2, GRI) and CNC was calculated and the results validated using cross validation approach.
Results and Discussion
Our study showed that the Persulfate digestion is an accurate method for determination of total N in soybean plant when measured in lab. Persulfate digestion does not produce a large quantities of toxic waste associated with Kjeldahl digestion. Additionally, persulfate digestion requires a minimum of specialized equipment: large screw topped culture tubes, an autoclave or a large pressure cooker and test tube racks. The method facilitates the determination of a large number of samples with the use of simple equipments.
The relation of measured nitrogen at leaf and canopy level against indices is shown in figure 2. Clearly the relation at canopy level shows a better behavior than at leaf level. Results showed that GI has close relationship with CNC and can be used to retrieve crop vegetation nitrogen. This index uses green band of the electromagnetic spectrum which is appropriate for chlorophyll estimation and has a direct relationship with nitrogen. The most commonly used vegetation index is NDVI. The NDVI has been used for many years to measure and monitor plant growth, biomass production and vegetation cover from multispectral satellite data. Although in our study NDVI was not chosen as the best index, this index is generally considered a good indicator of the amount of vegetation and, hence, is useful in distinguishing vegetation from soil ( Svotwa et al. 2012).
Conclusion
Our study showed that persulfate digestion does not produce the large quantities of toxic waste associated with Kjeldahl digestion and it requires a minimum of specialized equipments. In comparison to the used indices in this study (NDVI, GI, SAVI2, GRI), the GI index demonstrated a better correlation with canopy nitrogen content. This good relationship is not surprising as GI has been developed for chlorophyll estimation which has a direct relationship with nitrogen. Although, the amount of N is only 26% of leaf dry weight but surprisingly it has strong effect on reflected radiation.غلظت شیمیایی مواد در برگ گیاهان، مهمترین عامل آشکار کنندۀ شرایط زیستشناختی آنها است. از بین عناصر شیمیایی برگی مختلف، نیتروژن یکی از عناصر مهم و اصلی در فتوسنتز و وضعیت تغذیهای گیاه است. بهطور سنتی، مقدار نیتروژن برگ در آزمایشگاه با استفاده از روشهای شیمیایی تعیین میشود. مطالعات نشان داده که فناوری سنجش از دور، روش نوینی را برای جایگزینی روشهای شیمیایی پیچیده، زمانبَر و هزینهبَر در برآورد نیتروژنِ گیاهانِ مناطق جغرافیایی گسترده پیشنهاد میکند. هدف این پژوهش، برآورد مقدار نیتروژن تاج پوشش گیاه سویا در منطقۀ جغرافیایی گسترده و با استفاده از روشهای سنجش از دور است. در این مطالعه از تصویر سنجندۀ TM ماهوارۀ LANDSAT استفاده شده است که این تصاویر همزمان با تاریخ عملیات میدانی دریافت شد. عملیات میدانی در روزهای پانزدهم تا نوزدهم مرداد ماه سال 1389 در ناحیۀ شمال ایران ـ گرگان انجام گرفت. پنجاه پلات 30×30 مترمربعی بهصورت تصادفی انتخاب شد و در هریک، چهار تا هفت زیرپلات یک متر مربعی با توجه به همگنی محصول برگزیده شد. از هر زیرپلات سی برگ از قسمتهای مختلف تاج پوشش بریده و پس از انتقال به آزمایشگاه با استفاده از روش پرسولفات، غلظت نیتروژن گیاه اندازهگیری شد. در هر زیرپلات درصد پوشش گیاه نیز اندازهگیری شد. درصد پوشش حاصل در میزان نیتروژن اندازهگیری شده در سطح برگ ضرب و در نتیجه مقدار نیتروژن تاج پوشش (CNC) گیاه بهدست آمد. رگرسیون مقدار نیتروژن تاج پوشش در مقابل شاخص گیاهی اختلاف نرمال شده (NDVI)، شاخص سبزینگی (GI)، شاخص گیاهی تعدیل شده با خاک (SAVI2) و شاخص (GRI) ترسیم شد و با استفاده از روش اعتبارسنجی مورد ارزیابی قرارگرفت. نتایج نشان داد که شاخص GI رابطۀ نزدیکی با CNC دارد (022/1 = و 6488/0 = ) و از آن میتوان در تخمین مقادیر نیتروژن در گیاهان استفاده کرد.دانشگاه تهرانپژوهش های جغرافیای طبیعی2008-630X45220130823Evaluation of Pressure Synoptic Regions Affecting Climate of Iran
in the Cold Half of the Yearشناخت پهنه¬های همدید فشار مؤثر بر اقلیم ایران در نیمۀ سرد سال1251443515510.22059/jphgr.2013.35155FAبختیارمحمدیاستادیار گروه اقلیم شناسی، دانشکده منابع طبیعی، دانشگاه کردستانامیدیزدانیکارشناس ارشد جغرافیا طبیعی، دانشگاه کردستانJournal Article20121031Introduction
Air pressure, also known as the atmospheric pressure, is the magnitude of force exerted by the atmosphere on a certain extent of surface area. The average of atmospheric pressure is about 1013 hpa at the sea level. The air pressure is considered as mean, maximum and minimum sea level pressure. The sea level pressure is often investigated as the first step in the study of meteorological events. Lots of researches have been conducted about sea level pressure, map patterns of the pressure in various regions and their linkage to some of indices or different climatic elements. A number of these investigations will be mentioned as the following.
Jones and Simmonds (1993) analyzed the spatial and temporal anomaly of sea level pressure and the center of cyclogenesis in the northern hemisphere. Their findings indicated the significant difference between cyclogenesis centers and the maximum anomaly of the sea level pressure in high latitudes. The highest anomaly of sea level pressure has also been indicated to be in latitudes from 30 N to 40 N and cyclogenesis centers have been seen around 5 to 7 degrees in the north of the region.
Knaff(1997) studied the anomaly effects of sea level pressure on tropical cyclones in Atlantic ocean. The results showed that tropical cyclones of Atlantic Ocean are often developed in the condition of extreme negative anomaly of the sea level pressure and the existence of a deep trough in the upper layer of the troposphere. When the anomaly of sea level pressure is high, the mid layers are drier and Adiabatic cooling in the mid layers of atmosphere is enhanced subsequently. The deep trough of upper layer develops severe baro-clinicity leading to the formation of tropical cyclones.
Yi Yu and Tae Kim (2011) examined the relationship between oscillation of extra tropical sea level pressure and ENSO position in the center and east of the Pacific Ocean. The results showed that the oscillation of sea level pressure in extra tropical region of Pacific Ocean has a great role in ENSO excitement and its movement.
Mohammadi (1388) investigated the sea level pressure of this kind of precipitation in order to synoptically analyze Iran's extreme rainfalls. Three major sea level pressure patterns have been identified to have a role in the occurrence of the rainfalls. In the first pattern Arabian low pressure/Siberian high pressure dominates over Iran and 28 percent of the heavy and widespread rainfall is resulted from this pattern. In the second pattern, Siberian high pressure in northeastern of Africa and Arabian low pressure account for 53 percent of heavy and widespread rainfall. In the third pattern western Siberian pressure- Iraqi low pressure are the dominant patterns accounting for 19 percent of heavy and widespread rainfall in Iran. So it can be concluded that Arabian/Iraqi low pressure are the main factor in providing indispensible condition at the ground for the occurrence of super heavy rainfall in the country.
Methodology
In the present paper synoptic patterns of sea level pressure have been studied in latitudes from 0 to 80 E and in the longitudes form 0 to 60 N. The 6 hourly pressure data of sea have been applied for fall and winter seasons for the period from 1948 to 2010. The spatial resolution of the data was on a 1°x1° lat/lon grid. Therefore, two individual databases are developed for both fall and winter seasons. A cluster analysis by the method of Ward is applied on the data of each season.
In Cartesian coordinates, if and are the vectors, so their distances are calculated as followed.
The position of a point in a Euclidean n-space is a Euclidean vector. So, p and q are Euclidean vectors, starting from the original space, and their tips indicate two goals. Ward merging method was used to the linkage of observations.
Results and Discussion
Appling a cluster analysis on 6 hourly sea level pressure data in both winter and fall in the period from 1948 to 2010 have indicated that there are seven major pressure regions in the study area. With regard to the location of the grids chosen to be the representative of each region a name was attributed to them. The area and position of major sea level pressure regions are also depicted.
Conclusion
The investigations indicated that there are seven major sea level pressure regions for each fall and winter seasons. It can be supposed that the major pressure regions identified in this paper are indeed the major pressure systems (sometimes with different titles) that climatologists often refer to them. To better understanding of theses major pressure regions some of their statistical attributes are presented.فشار هوا که یک متغیر جوی است، در قالب میانگین، بیشینه و کمینه فشار تراز دریا و تراز ایستگاه بررسی میشود. فشار تراز دریا، اغلب اولین گامی است که در مطالعات همدید رویدادهای هواشناسی تحلیل میشود. در این پژوهش الگوهای همدید فشار تراز دریا در نیمۀ سرد سال در بخشی از نیمکرۀ شمالی (مختصات جغرافیایی صفر تا 80 درجه طول شرقی و صفر تا 60 درجه عرض شمالی) بررسی شد. برای این امر از دادههای شش ساعتۀ فشار تراز دریا، در فصلهای پاییز و زمستان، طی 63 سال (سالهای2010-1948) استفاده شده است. این دادهها بهصورت شبکهبندی منظمی با ابعاد 5/2 در 5/2 درجه جغرافیایی بودند. بنابراین دو پایگاه داده جداگانه برای فصل پاییز و زمستان ایجاد شد. روی دادههای مربوط به هر فصل تحلیل خوشهای با فواصل اقلیدوسی بهروش ادغام وارد انجام گرفت. نتایج نشان داد که در هر فصل، هفت پهنۀ اصلی فشار تراز دریا وجود دارد. پهنههای اصلی فشار در فصل پاییز شامل: کمفشار دریای سرخ، کمفشار عمان، پُرفشار قزاقستان، پُرفشار اروپا، پُرفشار غرب روسیه(شمال دریای خزر)، کمفشار اسکاندیناوی و پُرفشار سیبری و همچنین فصل زمستان نیز شامل: کمفشار دریای سرخ، پُرفشار شمال آفریقا، پُرفشار شمالغرب ایران، پُرفشار اروپا، پُرفشار قزاقستان، پُرفشار غرب روسیه(شمال دریای خزر) و کمفشار اسکاندیناوی بودند.