فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 77 (پاییز 1390)

Introduction Geo-tourism is the best practice tourism that sustains, or even enhances, the geographical character of a place, such as its culture, environment, heritage, and the well-being of its residents. Geo-tourism incorporates sustainability principles, but in addition to the do-no-harm ethic, geo-tourism focuses on the place as a whole. Desserts are now becoming a more and more popular tourist destination. Because of Iran's situation in a high-altitude plateau that surrounded by connected ranges of mountains, desert conditions is dominated in the interior plains. Some of the ecological features of the deserts in Iran are strong sunshine, relatively little humidity, little rainfall and excessive vaporization. This area due to Having special geomorphologic conditions, natural and unique landscapes and. .. that apparent elements and components of desert, are important ecotourism areas in Iran. The goals of this study are identified geomorphologic and Geo-touristic attractions for development and introduction most effective parts of the region for focus tourism facilities. Methodology Maranjab is situated in north of Kashan and the Northwest of the Iranian desert known as Dasht-e-kavir. The area spanning latitude 34 to 34°15'N and longitude 51°05' to 51° 35'E. The average topographical height of the area is approximately 1987(mASL). Since the region falls under the rain shadow of the Zagros Mountain Ranges, average annual precipitation is only 138 mm. From a climactic perspective, the atmosphere is mostly stable and cloud free (especially in spring and summer) due to the influence of the subtropical high pressure belt (sun-shine hours are approximately 2860 per year). The area due to dry weather conditions, have special geomorphologic features such; great sand dunes and fields, polygon shapes, natural and unique landscapes and so on. In the study, used data are including environmental data layers in format of GIS files. Maps of Topography, Land use, Geomorphology, Soil and erosion, Geology and water sources and manmade elements And also satellite images and meteorology data were used in deferent stages of study. Methodology of study is based on combination of systematic review of library resources, field works, and statistical and GIS analytical processes. In field studies, dates collected through direct observation (photographs and film), interviews and questionnaires. In this regard, the number of 10 questionnaires was filled by Delphi method, and obtained weights compiled to GIS layers for determine appropriate locations by AHP model. The Analytic Hierarchy Process (AHP) is a structured technique for dealing with complex decisions. Rather than prescribing a correct decision, the AHP helps decision makers find one that best suits their goal and their understanding of the problem. It is a process of organizing decisions that people are already dealing with, but trying to do in their heads. AHP model including of three steps, once the hierarchy is built, the decision makers systematically evaluate its various elements by comparing them to one another two at a time, with respect to their impact on an element above them in the hierarchy. The AHP converts the evaluations to numerical values that can be processed and compared over the entire range of the problem. In the final step of the process, numerical priorities are calculated for each of the decision alternatives. These numbers represent the alternative's relative ability to achieve the decision goal. In order to create a hierarchical structure and for pair comparisons and evaluation matrix was used Expert Choice software. Then coefficients of criteria convert to data base of GIS layers, so for tourism spatial Prioritization all layers computed with together. Results and Discussion According to prioritization of environment and manmade criteria, geomorphologic features, available water resources and human constructions are more important elements for ecotourism. Calculated map of overlying the data layers was divided into five classes; from high potential to low potential for geo-tourism. Final map and its results showed that between from total area of 32,000 hectare, about 32.1 hectare have high potential, 8.3 hectares had relatively high potential, 8.5 hectare have moderate potential, 46.1 hectare have relatively low potential and 10,7 hectare have low potential. Therefore can attempting to plan for about 7.3% of the area that has moderate to high potential for ecotourism activity. According to final result path of Kashan and Aran-Bidgol town to Maranjab Caravansary has the higher capability to develop ecotourism and geo-tourism. Conclusion According to final result path of Kashan and Aran-Bidgol town to Maranjab Caravansary has the higher capability to develop ecotourism and geo-tourism. Therefore Development of tourism in the study area is depending on the level of management and investment. Development of ecotourism activities in the region can be one of the most important factors in preserving the natural environment and sustainability of the tourism resources of the region.

Introduction Today, we have power to change the nature completely. This relates on the technological advances however in the case when we use the natural resource in non secure way, the environment will fall down soon. So what we called as a distortion of nature and environment are the results and products of the human wrong program and plans upon the land and environments. In the case of Lake Bakhtegan and Lake Tashk, building of many dams on the Kor & Sivan Rivers case to anarchy in the water catchment system. These lakes are the second vast internal lake in Iran which has 2/721/656 h, catchment area, its elevation form the sea level is about 1520m. The main resource of water supplying for the lakes are the Kor & Syvand Rivers. The river Kor’s head spring is the mount Zagros and it joins to the Syvand River in the Marvdasht Plain and makes the River Korsyvand. Other resources for the lakes are permanent springs, rain and winter floods. The dams building mainly related on the urban water supply and cultivation. Evident show that the lakes will change to a salty dessert soon. This conversion will lead to many environmental crises, like desertification, salt, destroying of ecosystem, etc. So we can say that, the environmental changes and crises are the productions of economical and political programs over the land by governments or other organizations. In the case of these two lakes, the building of Droodzan and Syvand Dams, built without any real impact assessment evaluation on the periphery nature. This research’s main challenge is to save the lakes. For doing that we need water. But how much water is suitable for saving the lakes? Thus over research’s question is: How much water do the lakes need for surviving? Having the fact, we explored the pervious researches, for examples; Aghelli & Sadeghi, (2002), emphasized on the environmental destroying trends in Iran. As well, Vafakhah & Rajabi explore the dryness condition in the lakes Tashk, Bakhtegan & Maharloo catchment area. As a result the pervious researches have not dealt with the need water for the lakes. Methodology For the determination of need-water volume in the GIS, we had applied the CUT/FILL function. This function work with two different layers: depth layer; which show the average depth in the lake area and the classification layer which show the existing water classes. The class layer derived via ETM satellite image, for doing this we used Fuzzy C- Means classification function, in the PCI Geomantic software. The function used the (2, 5, 7) bands for the classification, then the classification layer with 7 classes was derived. The depth layer was built via elevation contour and elevation points. For this layer in the Arc GIS, the Topo Grid function applied. Except these layers we used an image from Terra Satellite, to show the lakes condition in the drought seasons. Results and Discussion After the primary process upon the ETM Image, the lakes classified to the 7 different classes. Here water color and the light reflection which the satellite sensors delivered, determine the class. The classes are: 1) extreme deep 2) very deep 3) deep 4) average deep 5) a little shallow 6) shallow 7) very shallow The whole need water of the lakes is 1592 million cubic meter. Albeit, the need water of every class differ. • First class; (extreme deep): average depth of this class is 2/5 meter and the total area of the class is 40889 hectares. The need water of this class is about 1020.32 mm3. • Second class: with 19325 hectares area and 1.8 meter average depth, the need water of this class is 258.42 mm3. • Third class: with the total area of 16641hectares and 1.3 average depth, the need water of this class is 131.91 mm3. • Forth class, the need water of this class is about 80.88 mm3. • Fifth class: the need water of this class is about 97.04 mm3. • Sixth class: the need water of this class is about 2.47 mm3. • Seventh class: the need water of this class is about 0.98 mm3. Having the facts, if we want to save the life of these lakes we need 1592 mm3 water from the Kor-Syvand River. More ever the lakes feed from the rain and other water resource which produce 100 mm3during a year. So with subtraction of this volume from the 1592 mm3, the actual need water that the river must supply is 1492 mm3, during the year. This volume of water must give to the lakes during the year especially in the summer. The research showed that fuzzy c-means method of classification is one of the effective functions for layer classification. And finally the environmental crises in the third worlds directly depended on the economical and political decisions which the governments took. Conclusion What we called as a distortion of nature and environment are the results and products of the human wrong program and plans upon the land and environments. In the case of Lake Bakhtegan and Lake Tashk, building of many dams on the Kor & Sivan Rivers case to anarchy in the water catchment system. These lakes are the second vast internal lake in Iran which has 2/721/656 h, catchment area, its elevation form the sea level is about 1520m. The dams building mainly related on the urban water supply and cultivation. Evident show that the lakes will change to a salty dessert soon. This conversion will lead to many environmental crises, like desertification, salt, destroying of ecosystem, etc. For the determination of need-water volume in the GIS, we had applied the CUT/FILL function. This function work with two different layers: depth layer; which show the average depth in the lake area and the classification layer which show the existing water classes. After the primary process upon the ETM Image, the lakes classified in to the 7 different classes. Here water color and the light reflection which the satellite sensors delivered, determine the class. The classes are: 1- extreme deep, 2- very deep, 3- deep, 4- average deep, 5- a little shallow, 6- shallow, 7- very shallow. The whole need water of the lakes is 1592 million cubic meter. Albeit, the need water of every class differ.

Introduction Iran experienced an unprecedented cold air during January 1964 and 2008. The study of January-temperature’s anomalies during 50 years since 1964 up to 2005 showed that the maximum anomaly occurred during Januaries of two years including January of 1964 and 2008.Negative anomaly causes to become -30 degree Celsius Minimum daily temperature during mention Januaries over Iran such as Saghez and Shahrkord stations. The abnormally cold air masses hassled the irreparable damage of the agricultural, bestial even transport sectors and also People’s life inrural-urban was paralyzed. So that the most parts of the country were affected. It seems necessary to review anomalies’ factors. The fluctuations of planetary circulation are characterized by a relative dominance of meridional or zonal patterns. The meridional pattern of circulation is often attributed to the increased heat exchange between the equatorial region and middle and high latitudes. The turbulence of zonal flow and the intensification of meridional flow caused blocking processes. For investigating the hypothesis the quantitative blocking intensity index (BII) which was calculated by the method of Wiedenmann (2002), is used. Methodology By using Z score index, minimum temperature anomaly was calculated. Each concerned two months was divided into sixfive-day periods (pentagonal). For identifying blocking, we got mean sea level pressure and 500-hpa geopotential height levels maps from NCEP/NCAR. Temperature data and zonal and meridional wind components were used in order to draw maps of streamlines. In the research we have used quantity method to identify Blocking event. Here, the quantity (BII) calculated by applying the method of Wiedenmann (2002) was used. Blocking intensity index. BI Index: BI= 100.0[(MZ/RC) – 1.0] The value was scaled in order that BI values rated from 1 to 10, with increasing values being stronger. The rationale used to stratify by weak (BI < 2.0), moderate (2.0 < BI < 4.3), and strong (BI > 4.3) blocking events (Lupo et al, 2002, 3461 after Wiedenmann, 2002). Results and Discussion After comparing mean minimum temperature in 1964 during last 50 years in Iran, we recognized it had experienced -6.1 degree siliceous. Reviewed stations had the most negative anomaly of mean minimum temperature in the fifth and third petagonals in sequence in 1964. Also we found during 1964 January cold, Middle West regions of the country (Hamadan, Arak, Sanandaj stations) had temperature less than -20 degree siliceous. In 2008 January the most negative anomaly of mean minimum temperature were experienced in the forth and second petagonals in sequence in comparison to other years. Also during cold days of the fifth pentagonal in 1964have been recorded -30 and -26 degree Celsius in Saqez and Shahrkord. Isotherm counter -30 degree from the north, -25 degree from the middle and -10 degree Celsius from the south of the country justify this extreme cold. Eventually the least temperature has been seen during 16th and 20th days. After analysis maps, for identifying blocking, BI Intensity (BII) was calculated. It was found blocking intensity is an essential factor in climate anomalies and atmospheric flows deviation episodes, which is affected by the blocking pattern. The intensity of omega pattern in 1964 January is more than dipole pattern in 2008 January. The fifth and forth petagonals of 1964 and 2008 January had the coldest temperature in the country, so the BI was 3.02 and 1.1 in sequence. In another word this classification includes moderate and weak blocking. On the other hand, blocking situation has had an important role in cold advection in last two months. Appropriate blocking situation can justify these cold advecttions. During the cold days of the fourth petagonals in 2008 January appropriate blocking situation in latitude 42.5 and 40 longitude has influenced weak intensity of the fourth period. So mean minimum temperature has reached -9.26 degree Celsius, that is coordinated severe cold in most stations of the country. Conclusion We gained precise results by using quantity index. Blocking intensity was identified by BI. Also it was found the reason of cold in two considered Januarys was blocking. Although 1964 blocking was more severe, it is not main factor of the cold. In another word blocking situation was another factor in cold episodes. Z scores in 1964 January which have been gained are different from ex-similar research (Azizi and et al, 2008).

Introduction Springs are one of the most important water resources that provide a part of human required water. Springs discharge study and their simulation is very important for water resources protection and planning. Karstification is highly influenced by precipitation and terrain, which can cause large differences in karst spring flow between different regions. Present research has been done to investigate the effective factors on karst springs discharge and to simulate springs discharge in the central Alborz highlands (Mazandaran Province, northern Iran). Methodology Mazandaran province located in northern Iran that includes Caspian southern coasts and central Alborz highlands. The present research has been done on the surface of central Alborz highlands (karst areas). Extensive data were collected from TAMAB, Surveying and Climatology Organizations. In this study, the efficiency of Artificial Neural Network (ANN) was considered for simulating Karst springs discharge in the central Alborz highlands. So, 80 Karst springs were studied. The quantitative values of the spring's discharge factors were estimated such as: porosity (%) of aquifer formation, site elevation, slope, annual mean precipitation and distance from water resources. The purpose of network education is to have a network that can improve the relationship between input and output of the model. Due to the lack of any special value for planning Artificial Neural Network, various structures were investigated. 80% of the data were used in education stage and 20% in testing or validating step. For educating and testing a neural network, the number and type of input parameter in model are very important. 8 input models planned for springs discharge simulation are the following: In the above formulas: the Qspring is spring discharge (Lit/s). P: Porosity (%) of aquifer formation. L: distance from water resources (m). R: annual mean precipitation (mm). H: site elevation (m). S: Land slope (?). The efficiency of Artificial Neural Network (ANN) has been considered through two parameters: Median Root of Square of the Error (RMSE) and co- efficiency between the actual and desirable outputs (R). Obs refers to observed values, calc to values calculated by network and model, and n to the number of data in each step. The Nearer is RMSE to zero, the nearer are the observed and calculated values to each other and the more accurate is the simulation in each step. 9) RMSE= 10) Rsqr = Results and Discussion The results showed that ANN is capable in simulating springs discharge and also, the porosity(%) of aquifer formation, elevation and distance from water resources are the main factors on Karst springs discharge in the central Alborz highlands. The efficiency of Artificial Neural Network with multi-layered perceptron format with LM learning technique has been the finding of this study. Considering the results of efficiency of network for the different models, and comparing the obtained results with real data, it can be said that the second model among the 8 suggested models is the best one. In this study, educating the network through 3 learning equations was investigated and the results indicated that in comparison to CG, GDX learning equations, LM learning equation shows higher learning speed and higher error decrease in all models. Comparison of the results in Tables 1 can be concluded that it is easy to tell the LM algorithm is superior to the GDX and CG algorithm between the used models and phases. Conclusion Results showed clearly that the artificial neural networks are capable for modeling rainfall process. Thus, confirming the general enhancement achieved by using neural networks in many other hydrological fields. Using ANN for Hydrologic parameters prediction has been with good results in the past and in most cases there have been high correlation between simulated and observed hydrographs (Olsson et al., 2004). The results of this study showed that aquifer formation porosity(%), site elevation and distance from water resources have the most correlation with karst springs discharge. The results showed clearly that the artificial neural networks are capable for modeling spring discharge. Thus, confirming the general enhancement achieved by using neural networks in many other hydrological fields. Using ANN for Hydrologic parameters prediction has been with good results in the past and in most cases there have been high correlation between simulated and observed hydrographs (Olsson et al., 2004). ANN can be applied to simulate springs discharge (the sites have not been studied).

Introduction Air transportation systems counted as one of the development ways of any area and more than other transportation systems communicates with weather conditions. Between various aerology phenomenons, fog is one of the main reasons for cancellation, annulment and or flight deviation to other airports that don’t take into consideration so far. In Ardabil airport, according to special topographic situation, necessary factors and condition is prepared for occurrence of fog phenomenon and for this reason, in more days of year and different hours of day, various kinds of dense fog forms. Methodology In this research, for statistical analysis of fog in Ardabil airport, recent five-year statistics (2004-2008) of Ardabil airport synoptic station with reading 24 hours at round- the- clock (night and day) with one-hour time distance are used. After extraction of days with fog and influential and communicating atmospheric parameters in fog formation like mean temperature, relative humidity, dew point temperature, direction and speed of wind, pressure, horizontal visibility and nightly radiation, maps of sea level pressure & 500 hectopascal geopotential heights for same days extracted. With analysis of maps and other effective atmospheric parameters in formation of fog, kinds of fogs separated together and investigated occurrence and duration of either one. Because winds of Ardabil airport are very important in fog formation especially frontal & advection fogs, so, speed & direction of these winds were investigated in seasonal scale. In drawing of wind roses, WRplot 3.5 software was used. To present of suitable model for fog forecast and also for investigation of correlation between fog and other atmospheric parameters, data of mean temperature, dew point temperature, relative humidity, speed of wind, amount of cloud and 24-hours changes of atmosphere pressure for days with fog at hours of 15 & 03 GMT and Pearson correlation coefficient and multiple regression were used. Then, kinds of fog graded to 2 - 4 grades and calculated its correlation with other atmospheric parameters by using of spearman method. Coefficients of multivariable regression to forecasting of visibility at 03 and 15 GMT extracted and equation of fog forecasting model calculated. So, sea level pressure & humidity, 500 hectopascal geopotential heights and vector wind maps for 500 hazy days investigated and synoptic patterns of fog formation were recognized. According to fog effect in decreasing visibility in various month of year, suitable flight hours at various month of year presented. Results and Discussion Results of this research represented that in spring, summer and autumn, prevailing wind in Ardabil airport is east wind that comes from Caspian Sea and mountains of Talesh. In winter, prevailing wind is southwest wind. This research also showed that between examined parameters, relative humidity has more correlation with visibility. This correlation in 03 & 15 GMT is -0.164 & -0.192 and both are significant at the 0.05 level. Correlation between visibility and relative humidity in 03 GMT is more than 15 GMT. In 15 GMT, visibility is more related to cloudiness in compare with 03 GMT, because in this time, advection fog has more frequency and accompany with clouds. By compare with correlation between dry temperature and relative humidity in 03 & 15 GMT, we see that this correlation in 03 GMT is significant but in 15 GMT, this correlation is weak & insignificant. In Ardabil airport, from all of fogs, 41 percent were radiation type, 31 percent were frontal type and 28 percent were advection type. Examination of fog synoptic patterns indicated that 4 effective synoptic patterns have role in formation of fog that mainly in earth surface is consist of influence of Siberia high pressure that spreads from northwest to northeast of Khazar sea, formed high pressure that spread upon eastern Europe and high pressure upon Khazar sea and at up of atmosphere, generally is consist of ascent and descent flows in 500 hectopascal geopotential heights. Number 2, 3, 4 & 1 synoptic pattern has the most important role in fog formation in Ardabil airport respectively. Conclusion The results show that the occurrence of this synoptic pattern provides essentially conditions for fog formation in this airport. Hours 15 & 03 GMT with 118 & 234 case respectively, allocated to own most fog occurrence and hour 09 GMT with 12 cases, allocated to own least fog phenomenon occurrence along of under consideration statistical period. In Ardabil airport, the best time for perform of flight operation at April, May, September & October is from 08 to 14 GMT and at November, December, January, February & march is 09 to 12 GMT and at June & July is from noon to dusk.

Introduction Many researches done during the last twenty years has been detected a strong relationship between urban microclimate and precipitation. Urbanization is increasing in parallel with the increase in population. As a result of urbanization and population increase, the energy consumption has also been increasing due to heating, traffic and industrialization. These cause remarkable changes in the meteorological conditions, and climate of cities and neighboring areas. The most salient impact of urbanization on the climate can be observed on temperature. It is apparent that in all cities located in the tropics and in the Polar Regions, the temperature has increased. However, the impact of urbanization on precipitation varies with time and area. In various investigations, it was found that both, the number of precipitation days and intensity of precipitation, have changed due to urbanization especially in mid-latitude countries. Another important effect of the urbanization is the variation of the precipitation across the days of a week. In different studies, it was observed that the frequency of occurrence of precipitation in inter-week days is more than those of weekends. In this study, the relations between urbanization and precipitation in the warm season were investigated for Tehran. Methodology In order to study and determine the influences of Urbanization on precipitation process in Tehran, first of all long term daily precipitation (1966-2005) from 2 stations including Mehrabad (under urban properties) and Bilaghan (with rural properties) were compiled. Because the effect of urbanization is more effective on warm-season’s precipitation, so in this study daily precipitation data related to June until September for a given stations during 1966-2005 was investigated. Trend in data series were studied by using 2 1) the non-parametric Kruskal-Wallis H test, 2) the tau statistic of Kendall test. The non-parametrical Kruskal-Wallis H test under the null hypothesis "there is no change between precipitation values and the number of precipitation days over the years" was used in order to determine whether there are differences in certain periods in the data series. To determine the trends of the precipitation data, the method of Tau statistic of Kendall test was applied to the number of precipitation days. In this test, null hypothesis imply that data are coincidental, and trend doesnt exist, and exception of one hypothesis (rejection of null hypothesis) imply that trend exist in data series. Finally in order to determine the type and time of changes in data series, statistical graphical method of Mann Kendal was used. Sequential values u (t) and u' (t) from the progressive analysis of the Mann-Kendal test were determined in order to see change of trend with time. This test was found to be useful and widely used for detecting trends in climate and environmental sciences. Results and Discussion The evaluation of statistical test’s during the whole statistical period (1966-2005)showed that the assumptions of the data being accidental in 95% significance level in Mehrabad station is significantly rejected and also increasing trend of the number of precipitation days, especially in the recent years are significant. While in Bilaghan station non-significant trend was found in the 90% 95% confidence level. Another important effect of the urbanization is the variation of the precipitation across the days of a week. In studying of variability of rainy days in all days of week, two statistical sub-periods in June until September were selected from the first and last 15-years statistical period, for both stations, Mehrabad and Bilaghan. The study of the first 15 year data in both stations, Mehrabad and Bilaghan showed that to investigate the distribution of rainy days during a week doesnt obey any main pattern. In the study of the last 15 years, Bilaghan station had no pattern as the first 15 years, but in that period at Mehrabad station, there were seen the number of rainy days in the workdays of week increase and in the last days of week decrease. Conclusion The results presented in this paper are preliminary study on the impacts of urbanization on rain fall in Tehran, Iran. It is clear that rainfall has changed significantly over the study period in the Tehran metropolitan area. The records indicate that the number of precipitation days and light precipitation days in Tehran has a gradual increase around 1975s, which is considered as the start of an accelerating city expansion, hence increase of air pollution. These considerable changes in the trends of the precipitation day classes in station of interest are not just due to climate change, but it seems that these tendencies are related to influences of urban microclimate on precipitation process.

Introduction Extreme weather events are rare events from intensity and frequency perspective and at the time of their occurrence, ecosystem and human societies hardly can adapt themselves to occurred changes. Some examples of these events are Heat waves, Cold waves, Floods, Tropical cyclones, Tornadoes, Wildfires and Dust storms. Since extreme weather events occur when precipitation and temperature have extreme values, studying the extreme climatic data is a matter of vital importance. An important issue in the climatological studies is the identification of regions with the same climatic behavior. The results of regionalization based on extreme climatic events can aid decision-makers and planners in dealing with crises aroused from extreme weather events, especially in the development of management policies. Methodology The Regionalization was implemented for 65 synoptic stations in the Islamic Republic of Iran. Three climatic signals were selected comprising maximum 24 hour monthly precipitation, maximum monthly temperature and minimum monthly temperature for a twenty-year period from 1986 to 2005. The climatic data were extracted from the website of Islamic Republic of Iran Meteorological Organization (IRIMO). The regionalization was implemented by means of Spectral analysis and Clustering methods. The main advantage of the Spectral analysis method is the spatio-temporal analysis of climatic data in the frequency domain instead of the time domain which can significantly decrease the calculation volume. Calculating the spectral characteristics of climatic data was a time-consuming process; therefore, software named “Dadisp” was applied for performing the calculation. The climatic data were imported to the software. Then, linear trend of the climatic data was removed and main spectrums were identified using power spectrum density function (PSD). Four key spectrums of precipitation signals and one key spectrum of temperature signals were identified and selected. Clustering is one of the categorization methods. Members of each cluster have the same characteristics. For clustering, a deterministic approach (K- means) was used. Clustering the results of spectral analysis was performed using “MATLAB” software based on three different scenarios. In scenario one, spectral analysis results and geographical characteristics of stations are applied. In scenario two, spectral analysis results and elevation of stations are applied and finally in scenario three, only spectral analysis results are applied. According to the minimum variance criteria, 14 to 17 clusters were selected. Finally, stations with the same climatic behavior and their spatial distribution in Iran were illustrated in six figures. Results and Discussion One of the most obvious results was the singularity of Ramsar station in all scenarios. Based on spectral analysis results, it was presumable that this station has a singular climatic behavior in the clustering. Among southern stations, Kenarakchabahar station was another singular station. The same climatic behavior of Urmia, Shiraz, Tehran and Abadeh stations in the first scenario was also remarkable. Also similarities between results of second and third scenario, like the same climatic behavior of Mahabad, Shiraz, Sanandaj, Karaj and Zahedan stations were notable. The results of clustering are presented in figures 5 to 10 and table 3. Conclusion The identification of regions with the same climatic behavior is an important issue from different perspectives. Hence, the occurrence of extreme weather events like floods in Iran is so prevalent; the identification of regions with the same climatic behavior based on extreme climatic data can aid planners and decision-makers in the process of disaster management. In this paper, a regionalization is implemented by analyzing the content of climatic data and employing a systematic approach for finding the same climatic patterns. Clustering the stations based on spectral analysis results and geographical situation of stations impose a strong assumption on pattern explorer system. This assumption is the behavior similarities of different stations based on neighborhood. Although this assumption is true in most cases, but implementation of this assumption in a large region like Iran with the significant climatic variation need a more conservative approach. Therefore, another two scenarios (second and third scenario) were developed and the results were compared. The results of second and third scenario had inconsiderable differences but both of these scenarios had totally different results in comparison with first scenario. The final results show that the applied systematic approach in this paper was successful in finding stations with singular climatic behavior like Ramsar and Kenarakchabahar stations and it could specify regional patterns in climatic variation clearly. One of the proposed fields for future studies is the implementation of fuzzy clustering methods for specifying the climatic behavior of different regions.

Introduction Iran has very complicated topography and climate including two mountain chains of Zagros and Elborz, two wide deserts of Kevir-e-lut and Dasht-e-kevir, forest lands and three large water bodies of Caspian Sea, Persian Gulf and Oman Sea. Climatically, Iran has a variable climate. Winters are cold with heavy snowfall in the northwest and below freezing temperatures during December and January. Spring and fall are relatively mild, while summers are dry and hot. In the south, winters are mild and the summers are very hot. On the Khuzestan plain, summer heat is accompanied by high humidity (Alijani, 2003). The intergovernmental Panel on Climate Change (IPCC) reported that the global mean temperature has been increased 0.6o C during 20th century while the atmospheric concentration of carbon dioxide also increased from 280ppm to 370 ppm in third Assessment Report (TAR) published in 2001(Kwon, 2005). Validation of PRECIS regional climate model in Bangladesh is performed with the surface observational data of rainfall and temperature (maximum and minimum) at 26 observational sites throughout the country from 1961-1990. It is found that regional analysis provides overestimation of PRECIS values in Bangladesh whereas data extracted at some particular locations provide better performance of PRECIS. For baseline, the performance of PRECIS is about 90% for rainfall.PRECIS can detect about 96% and 100.3% of maximum and minimum temperature respectively (Islam et. al., 2005). Climate change in the past decade in Jianghuni valley is studied by using statistical techniques. Both frequency and strength of extreme climate events such as hot weather, droughts and floods have increased remarkably since 1990s. Also, the regional climate model of PRECIS is used to provide a prediction of future climate in the valley. The results give an average surface warming of 2.9oC under the SRES B2 emission scenario by the end of this century (2071-2100). Precipitation may increase on the same period (Tian, et. al., 2006). Methodology Under Article of the United Nation Framework Convention on Climate Change (UNFCC), all Parties must study the impact of climate change in their countries using regional climate models. To do this, a PC-based regional climate model named PRECIS has been developed at the Hadley Center of United Kingdom Meteorology Office. PRECIS is based on the atmospheric component of the HadCM3 general circulation model. The atmospheric dynamics module of PRECIS is a hydrostatic version of the full `primitive equations and uses a regular longitude-latitude grid in the horizontal and a hybrid vertical coordinate. In this research monthly to seasonal precipitation of Iran has been modeled using PRECIS regional climate model with HadAM3P boundary condition data. PRECIS has a horizontal resolution of 50 km with 19 levels in the atmosphere (from the surface to 30 km in the stratosphere) and four levels in the soil. The present version of PRECIS has the option to downscale to 25 km horizontal resolution. In addition to a comprehensive representation of the physical processes in the atmosphere and land-surface, it also includes the sulphur cycle. The validation of model has been done by comparing observation data and model output data with two different methods of region to region and station to station. For this study, the PRECIS model domain has been set up with a horizontal resolution of 50 x 50 km. The domain is roughly stretched over the latitude 23 to 45?N and longitude 43 to 68?E. The HadAM3P global data set is used to drive the PRECIS model. The horizontal resolution of the HadAM3P boundary data is 150 km and for the present and future climate, it covers the period 1960-1990 and 2070-2100 respectively (Wilson et al., 2005). For the future climate, both SRES A2 and B2 greenhouse gas emission scenario is selected. Results and Discussion Seasonal to annual error and bias of the model outputs have been calculated using to different approach of region to region and station to station methods. We considered Masoudian's zoning (Masoudian, 1387) approach of the precipitation of Iran for computing regional error and bias of the model. In this approach precipitation region of Iran have been categorized in 12 regimes of South Central, Farsi, Kordi, Sistani, West Khorasan, East-Khorasan, North Central, Khuzi, Hormozi, Azari, Baluchi and Khazari. It is found that overall error of model is 5.3%. Maximum error has occurred over Farsi, Hormozi, Khuzi and Khazari regions with errors of 24.9, 16.9, 12.2 and -10.2 respectively. Minimum errors occurred over Kordi, Azari, and Northern central and eastern Khorasan regions. Maximum monthly errors occurred in September, the transition month between summer and autumn and minimum monthly precipitation has happened in May. It seems that wet bias of simulations in Southern regions can be due to transferring of high amount of humidity from large-scale GCM’s cells into RCM’s fine cells. Also dry bias of simulations in Caspian region is because of low ability of PRECIS in parameterizations of convective precipitations. Results show the regional errors are found in Farsi and Hormozi regions and minimum errors are in Kordi and North-Central regions. Maximum and minimum monthly errors are found in September and December, respectively. Conclusion As a main result, PRECIS skill in modeling regional precipitation, especially over the regions with high amount of convective and local precipitation is low, but it can model well the total precipitation of Iran. Average error of modeling over the country is less than 2%, but maximum regional error of modeling is 10% in Farsi region. Maximum precipitation errors are found in transition months. We found that PRECIS can model overall precipitation of Iran well, but it has some deficiency in modeling convective precipitation in Caspian region and southern part of Iran. There is no significant difference between PRECIS-modeled data and actual data retrieved from weather stations. So, as a powerful regional model, PRECIS can be used for regional climate modeling over Iran and future climate change projections.