نشریه پژوهش های اقلیم شناسی
پیاپی 5-6 (بهار و تابستان 1390)

Between different factors, atmosphere conditions are most important natural factors which affect the agricultural crops production. One can observe these effects as frost, sunstroke, outbreak of pest and etc. At the moment, agriculture is one of the most important sections in the economy of a country. In fact, one can say that economic growth is impossible without agriculture. Agricultural crops growth is widely dependent to the atmosphere conditions. Light, temperature, co2, water and nutrients are controlled by the atmosphere. Cultivation of crops in sites with compatible conditions for them has two results. First, it produces the maximum profit and efficiency for farmers. Second, it causes the least damage to the agricultural resources during the long time. Now, as a result of new inventions, cotton includes 48 percent of loom productions. Assessments indicate that producing of cotton in the country provides only half of the country needs at the present time. Therefore, imports of cotton bring out a lot of money from the country each year. The main objective of this study is assessment of environmental and climatic conditions of Khuzestan province according to the cotton needs in order to investigation of the existence and non-existence of potential of cotton planting as a significant product at this province. First, beginning and end of cotton cultivation date was determined regarding to the climate of Khuzestan province and necessary condition for cotton, on the basis of 90% possibility. To do so, the daily precipitation and temperature data from synoptic station of Khuzestan province which had long-term data (20 years) were used. Then, the important environmental and climatic conditions of cotton cultivation were identified according to the scientific references. Next, for studying the compatibility rate of each factor, a map was prepared separately in Geographical Information System. So, 11 factors were used for cotton planting and their GIS maps were provided. For providing climatic maps, data from 6 well-scattered synoptic stations were used. Finally, with run of Query at GIS by using eternal Boolean Model, suitable areas for cotton planting across the Khuzestan province were exploited and at the next stage, the Topsis Model was used in order to privileging selected areas. According to the analysis, 31 March and end of September and October were determined as the beginning and end of cotton planting date, respectively. From the analysis of the factors which were applied following conclusions were drawn:Suitable degree-day for cotton planting is accessible in whole of the province. Temperature of germination is between 26-28 C in this province and so provides the cotton thermal need in this period. Temperature of flowering is only provided in North-West of province. Also, temperature of harvest month has no limitation for cotton planting. Cloudiness during the planting period is less than 2.5 Octa which indicates the suitability of this factor for cotton planting. Sunshine hours are completely provided, too. Humidity of harvest month is a limitation for cotton planting in the province, since it is only provided in the West of province. Precipitation of harvest month is zero which produces appropriate conditions for cultivation. Slope factor is suitable only in the Central and West of province. All of places in the province have suitable height for cotton planting except some regions in the North and North-East of province. Between the necessary factors for complete the cotton planting period in the Khuzestan province only two factors cause limitation: humidity of harvest month and temperature of germination but other factors are appropriate for cotton planting. The results revealed that Khuzestan province with 280000 hectare of apt lands for cotton planting is one of cotton cultivation poles in Iran and can help to remove the inner needs to cotton. These lands are located in the North-West of province in the Safi Abad and Bostan Townships. Also, the privileging of the selected areas indicated that southern regions are more appropriate for cotton planting in comparison with northern regions. So attention to cultivation of cotton is urgent in this province, regarding to needs of Iran and potencies of Khuzestan province in field of cotton cultivation.

The main aim of this research is the assessment of change trend in agro climatological parameters that influence the growth of citrus in the north of Iran. Thereby the daily data of min and max temperature, sunshine and precipitation for 6 number stations of the area under study provided from Iranian meteorological organization. At the next stage, the other parameters including temperature range, mean temperature, Growing Degree Day (GDD), Heliothermal Units (HTU) and Photothermal Units (PTU) has been calculated in the monthly, seasonal and annually scales. Then, Man Kendal trend test and linear method have been used for trend analysis in the above mentioned scales. The results of this research show the significant positive trend in min, max and mean temperature, GDD, HTU and PTU. The temperature range had significant negative trend. Precipitation has positive trend in 5% level of significant in a few stations in the monthly scale and it hasnt significant trend in the seasonal and annually scales. If the variables such as minimum temperature, max temperature, mean temperature, Heliothermal Units, Photothermal Units and temperature difference continue theirs increased and decreased rations respectively, would have harmful effect in the Citrus growth.

Intriduction: Air pollution in megacities is influenced by many factors such as the topography, meteorology, industrial growth, transportation systems, and expanding populations. Urban/industrial emissions from the developed world, and increasingly from the megacities of the developing world, change the chemical content of the downwind troposphere in a number of fundamental ways. Atmospheric pollution is becoming an increasingly critical problem to human health and welfare especially in megapolises. In fact, many factors affect air pollution and concentration of pollutants. Variations of meteorological conditions can play a vital role by influencing level of air pollutants. Variations in the physical and dynamic properties of the atmosphere on time scales from hours to days can play a major role in influencing the level of air pollutants. The surface wind field is important for pollution dispersion.Vertical thermal gradients can determine the extent to which pollutants are diffused through the atmospheric column. A large number of studies have conducted on the relationship between air pollution concentrations and meteorological conditions (e.g., Alijani 2004; Adamopoulos et al. 1996; Makra et al. 2007; McGregor and Bamzelis 1995; Davis and Kalkstein 1990b). This article investigates large-scale weather conditions that have caused severe air pollution episodes over Tehran area during the last decade (1999-2008). Air pollution episodes in urban areas follow certain pre-determined patterns, being associated with certain local meteorological conditions and emission of primary pollutants. In this article, the synoptic and local scale atmospheric circulation that prevails during air pollution episodes in a megacity, Tehran, is examined for a period of 10 years (1999-2008). This study investigates large-scale weather conditions that caused severe air pollution episodes over Tehran area during the last decade (1999-2008). Using 00UTC of Final analysis data set (FNL), daily meteorological parameters and Pollutant Standard Index (PSI) were investigated to relate synoptic characteristics of pressure patterns to the high levels of air pollutants. The pollutants considered in this study were only in the gaseous form which SPI values were more than 200. Four episodes with high pollution concentrations (PSI>200) were occurred in the city in the period and in spite of occurrence in different seasons, their pressure patterns have similar characteristics. High concentrations of air pollution occur exclusively, during dominance of high pressure over Zagros chains and thermal low pressure over the Caspian Sea, accompanied with an anticyclonic ridge in the midlevel atmosphere. Results for the study period have clearly shown that anticyclonic conditions are associated with a higher frequency of severe air pollution episodes than synoptic conditions associated with cyclonic flow. This confirms results from elsewhere, which have shown a close relationship between anticyclonic conditions and high pollution loads (McGregor and Bamzelis, 1995, Kalkstein and Corrigan, 1986; Davis and Gay, 1993) often associated with anticyclonic conditions are weak winds, which limit ventilation and thus transport and dispersion of pollutants away from an area. Although the severe air pollution episodes have occurred in different seasons, the pressure patters of them have similar characteristics. The synoptic situations producing the severe air pollution events are typically anticyclone patterns that dominate over Tehran area with a high frequency. Results for the study period have clearly shown that high concentration of air pollutants occurred exclusively during thermal high pressure periods over Zagros chain and south Alborz chain of mountains and thermal low pressure over Caspian Sea along with an anticyclone-ridge in mid level atmosphere. Although exploratory in nature, study results suggest that a synoptic typing method may offer considerable scope for evaluating air pollution potential. Sometimes the large-scale weather conditions are the dominant influences and at others the local conditions are prevalent, although both of them are always present. As a general rule one can state that during strong synoptic pattern, characterized by strong winds, clouds, and, at times, precipitations, local influences are largely suppressed. However, when winds are weak and the sky is clear, the local effects control the lowest layer of the atmosphere (Landsberg, 1980). This is significant, while there has been a decrease in the pressure gradient over Tehran area and the thermal low and thermal high pressure in the two side of Alborz Mountain certainly it appears air pollution potential in Tehran area. While these situations associated with a well developed ridge in the middle atmosphere, they are conducive to severe air pollutant built up in the atmospheric boundary layer due to suppression of vertical mixing heights and poor ventilation regimes. As on poor ventilation and vertical mixing days have the potential to build to considerable levels. The results show, in the severe pollution episodes of Tehran, the weak surface wind was observed over Tehran and west of the area. Boundary layer height limited to 50-200 meters above the surface. It is likely that such a configuration of calm conditions and atmospheric stability account for high concentrations of pollutants. In conclusion, synoptic and mesoscale weather classification is a useful tool for studying the air pollutant concentration and dispersion in a megacity such as Tehran.

Iran is located in the south-west of Asia and is in the arid belt of the world and about 60% of the extent of the country is mountainous and the remaining part (1/3) is desert and arid lands. The climate of the country can be divided into three main categories: -Warm temperate, rainy with dry summer in a narrow strip in the north, -Dry, hot desert in the central plateau, -Dry, hot steppe covering the rest of the country. So, it could be so difficult to predict climate change over whole of country. In this case, using new methods for solving weather equations and having climate prediction because of its long term temporal has so many important rolls for massive management. In climate change studies, the global circulation models (GCMs) are usually used to simulate the past and future global climate. Unfortunately, despite the advancement in GCM research and modern computing technology, the most recent generation of general circulation models still have serious problems due to their low spatial resolutions (with the field variables being represented on grid points 300 km apart). So, because of its serious limitation and resolution, using them in long term forecast couldn’t predict actual weather in station scale or small scale and it is important to assess the accuracy and uncertainty of GCMs in various climatic and geographical regions. To employ output of Global Climate Models and accesses to good resolution, "Downscaling Methods" are used that are divided in two dynamical and statistical groups and some when syncretistic of them. A thorough evaluation of the current generation of GCMs has only started recently and the evaluation of a rich spectrum of indices on extremes is new. In this, calibrating downscaled data is very important as a main parameter to reach best resolution and for analyzing long term forecast. Two different approaches to downscaling have been employed. It has adopted a methodology that exploits mean inter station correlations to correct the statistics of grid-box means. The method, closely related to block-kriging, is demonstrated to remove the sample size sensitivity of statistics in daily grid-point precipitation. It has adopted a direct downscaling by distance and direction weighted average of point observations. At this filed, SDSM is a Statistical down scaling model that distributed both of these aggregation techniques to the consortium. Several data of selected stations have been started applying to a dataset and coding study area. These datasets will provide a valuable reference for model evaluation simulate predictor variables across selected region. The SDSM model run on selected period and reached amount of precipitation, minimum and Maximum of temperature and their standard deviations. By using statistical methods we could evaluate SDSM outputs to reach the best conclusion and selecting best data. With acceptable results, we could use them for climate prediction over region. Materials: In this paper, at the first we tried to select 41 synoptic stations that have 41 years climate data (1961-2001). These stations distributed to whole country with several climates. These data applied our observation dada. At this method we used third version of the coupled global Hadley Centre Climate Model (HadCM3) Outputs as predictor of method and A2 scenario that is one of the most probable emission scenarios. Then we down scaled them by using SDSM model version4.2 that could downscale general circulation models to station scales. Then by using statistical methods and reaching differential coefficients could analyses downscaled data by base data and present suitable correlation of them. Results was shown, there is no significant deference with 0.5 critical errors and correlation of data and accepted at 0.01 significant levels. And there is a good accepted correlation between modeled data and observing minimum and maximum temperature and precipitation data. So, using Downscaled data is acceptable with suitable efficiency to correct future data at station scale. This study should help to fill in the knowledge gap in GCM downscaling research of climate and add an important piece in the global climatic assessment jigsaw puzzle.

Sea Surface Temperature (SST) is a critical factor in humidity providing and climatic structure of the regions mainly surrounded by oceans and seas. Major amount of humidity resources of Iran provides by regional water bodies of Caspian, Oman, Mediterranean and Black Seas, Persian Gulf and North of Indian Ocean (Alijani 1999: 221). Colder than normal of winter time Caspian Sea surface temperature can increase winter precipitation of South-West and South-Central parts of Caspian Sea, Central and Southern parts of Fars province and all regions of the Khuzestan province. Usually above normal sea surface temperature of Caspian Sea accompany by 20% decrease in winter precipitation in Southern beach of Caspian Sea, North of Fars and all regions of Khuzestan provinces. Warm winter SST of Caspian Sea increases spring precipitation of all weather stations located in the Southern beach of the Caspian Sea (Nazemosadat 2004: 1-14). There are other studies that investigated the impact of sea surface temperature over seasonal precipitation of Iran (Moosavi baygi et. al. 2008: 217-224, Nazemosadat and Shirvani 2005: 1-10, Ghasemi and Khalili 2008: 116-133). Relation between sea surface temperature of Pacific Ocean and precipitation over America, Caribbean Sea countries, Southeast Asia, Australia and Africa have been studied by many scientists (Markovsky and North 2003: 856-877, Wear 1987: 2687-2698, Lim et al 2007: 33-39, Li and Zhang 2008: 237-243, Misra 2003, 2408-2418). Different statistical methods of principal component analysis, canonical correlation and empirical orthogonal function are widely used in recent studies for investigation the relation between sea surface temperature and precipitation. Principal component analysis has been used for analysis the relation between large scale weather patterns and winter droughts over Iran (Ghasemi and Khalili 2008: 116-133). Principal component of Persian Gulf SST are extracted for seasonal sea surface temperature prediction (Nazemosadat 2005:1-10).Methodology and Data: Two types of data including sea surface temperature and precipitation are used in this research. We used ERSST v.2 grided sea surface temperature in the period of 1980-2009 with 2*2 latitude and longitude resolution and seasonal precipitation of 141 synoptic stations of Iran. EERSST data is extended reconstructed sea surface temperature data that have been obtained by using various observed in-situ marine data and remote sensing data from satellite observations. Precipitation data have been extracted from I. R. of Iran Meteorological Organization in the same period of 1980-2009. Area of study for sea surface temperature all water bodies in Middle East including Caspian, Black, Mediterranean, Red and Oman Sea, Persian Gulf and north of Indian Ocean. We applied Principal Component Analysis (PCA) to the seasonal Sea Surface Temperature over six main water bodies around Iran. Number of 483 initial SST parameters has been reduced to less than 10 orthogonal SST modes having around 90% of initial SST variance. Discussion and The results of the monthly and seasonal SST PCAs over the period 1980-2009 are presented first. There are 9 significant center of SST change which is located over southern beach of India, Sudan beach, East of Mediterranean-Black Sea, north of Arabian Sea, west of Mediterranean Sea, Bay of Bengal, Caspian Sea, and Yemen beach. Major part of variances is concentrated in the first seasonal modes, varying from 29.9% in autumn over southern beach of India to 37.2% in winter near water bodies around Sudan. The results are summarized in table 1. Table 1. Principal components of the Sea Surface Temperature of the regional water bodiesPCAs Autumn Winter Spring Summer Change Center Variance Change Center Variance Change Center Variance Change Center VariancePCA1 South of India 29.9 Sudan beach 37.2 Sudan beach 34.8 Yemen beach and Sudan 32.8PCA2 Sudan beach 21.6 East of Med. and Black Seas 13.9 South of India 16.4 East of Med., Black and Caspian 20.7PCA3 East of Med., Black and Caspian 15.4 North of Arabian Sea 12.7 North east of Arabian Sea 13.4 South of India 15.5PCA4 North of Arabian Sea 8.1 Bay of Bengal 9.3 East of Med. And Black Sea 13.1 Sudan beach 10.6PCA5 West of Med. Sea 5.5 South of India 6.4 West of Med. 6.3 North of Arabian Sea 3.1PCA6 Bay of Bengal 4.2 West of Med. 5.8 Caspian Sea 2.9 West of Central African beach 3Regarding to the amount of variance presented in table 1, it is clear that the most of SST variability are concentrated over the water bodies around South of India, Sudan and Yemen beaches. Cluster analysis was used to obtain mean seasonal SST patterns. The first and important seasonal patterns of SST are shown in figure 1. In the figure, circles with + and - signs inside show positive and negative anomalies, respectively. Figure 1 shows that the important mode of SST variability in the autumns is bellow normal temperature in all water bodies under study, especially over Caspian Sea. The first mode of SST in winters accompanies by above normal SST over Caspian, Black and East of Mediterranean Sea and bellow normal SST around Sudan beaches and West of Mediterranean Sea. In the sprigs the first mode of SST variability is characterized by more than normal over all water bodies, but the maximum SST increase is located over Caspian Sea. SST variability in summers is same with spring but amount of positive SST anomaly is significant over Red sea as well as Caspian Sea.Seasonal precipitations of all 141 synoptic stations of Iran were correlated with 6 first SST PCAs of regional water bodies. Numbers of stations with significant precipitation correlation with regional SST patterns are shown in figure 2. Maximum and minimum number of stations with significant correlation was found to be in spring and autumn with 105 and 57 stations out of 141, respectively. The thermodynamic interaction between Sea Surface Temperature and precipitation takes place through the process of SST and humidity exchange at the sea-atmosphere-land interface. In this process, SST plays an important role, particularly in providing atmospheric water content and humidity resources of adjacent continental area. In this paper, seasonal precipitation of 141 synoptic stations of Iran are correlated to the SST PCAs patterns of regional water bodies consists of Caspian, Mediterranean, Black, Red, Oman and Arabian seas, Persian Gulf and north of Indian ocean. We found that the most important center of seasonal SST variability is located over water bodies nearing to Sudan beach and western-north part of Indian Ocean (winter and spring), South of India (autumn), East of Arabian sea, from Yemen to Sudan adjacent water bodies (summer). We found that water bodies near North of Indian Ocean and the Caspian Sea have maximum and minimum role in regional SST variability, among all water bodies around Middle East, respectively. Mean seasonal SST patterns were extracted using cluster analysis. The study reveals that correlation between normalized precipitations between 141 synoptic stations of Iran and mean seasonal SST patterns over regional water bodies are significant in large number of weather stations. Numbers of stations with significant correlation out of 141 are 105, 83, 73 and 58 in spring, summer, winter and autumn, respectively. The results concluded that there are significant high correlations between SST of regional water bodies and precipitation of Iran, so, the major amount of precipitation variability over Iran can be explained by SST anomalies of regional water bodies. Linkage between SST and precipitation presented in this paper can be used as one of important components for seasonal precipitation prediction over Iran stations.

Forecasting sea state and its role in marine transportation, shipping, fishery, fishing and professional affairs, is clear. By Using different models and identifying accuracy and precision of them can effectively achieve to these. One of the useful models is SWAN. For evaluating the performance of this model in aspect of executive and scientific both, study on verification of its products is required. In this study, the model outputs of wave height and period in the case of Bushehr and assaluyeh have been verified. With this study it is tried on the accuracy of the model outputs with the corresponding values of Buoy and its performance on predictions 12, 24, 48, 72 and 96 hours studied and assessed. In the verification process by using the adaptive table we want to control climatic data with the model output for both wave height and period parameters. For this purpose, threshold values smaller than or equivalent with 2 seconds and smaller than 5 seconds and the values between two mentioned thresholds for wave period also values h<=25cm،, 25 cm50cm for wave height were calculated. Moreover the skill scores of models for predicting wave height and period were calculated. Mean absolute and relative errors with the SWAN model and Buoy data for 48 hours forecasting were calculated and listed in Table. As a sample graphs of the measured wave period and measured height wave by Buoy, with the predicted value of them are plotted. Summarized findings results of the analysis are obtained. By examining the output values from the model and observation data for two regions of Bushehr and Assaluyeh and setting minimum and maximum values for the two parameters, the mention ranges are defined.

Results And Discussion

In Assaluyeh region more than 60 percent of cases, the wave height was between 25 cmT >5s and less than 22 percent wave’s frequency was 5s T> s2 and more than 72 percent frequency waves was T>5 s.Values obtained from the skill scores for forecasts 12, 24, 48, 72 and 99 hours shows that the model predictions for the both parameters in 48 hours forecasting in any two regions of Bushehr and Assaluyeh has high accuracy. Proportion Correction (PC) Values shows that more than of 94 percent, predicting the occurrence or non-occurrence waves frequency and more than 86 percent for the wave height performed correctly and in Assaluyeh this value for the wave frequency of more than 64 percent for wave height of more than 88 percent of cases have been performed correctly.Overall results of SWAN model verification on wave height and frequency have acceptable accuracy. The mean absolute errors of observation data show that the model accuracy for wave height is greater than its accuracy for the wave frequency.

Keywords: verification, discontinues table, climatic parameters

Drought is a severe dilemma which influences different aspects of mankind’s life. Drought has a negative impact on economy, environment and agricultural sector and cause heavy damage and losses in many parts of the world. Therefore the quantitative estimation and prediction of drought phenomena has become an important issue for policy makers and the scientific community. In the last three decades, remote sensing has provided a useful tool for drought monitoring and a variety of remotely sensed drought indices based on vegetation indices, land surface temperature (LST) and albedo, have been developed. The main objective of this study was drought riskzonation in Sheitoor basin located in Yazd province by using satellite, climatology and environmental data. The data used in this research consist of ALOS (AVNIR) image collected on 18th July 2009, topographical maps (scale: 1/25000), rainfall, temperature and evaporation data which were obtained from meteorological stations.The Sheitoor basin is located in the central part of Iran. It covers a total area of 416 km2. The altitude varies in the region between 1844 and 2989 meters. Average annual rainfall in the study area is 171 mm and average annual temperature is 14 °C. Based on the Dumarten's climate classification method, the climate of study area is cold arid.At first, the ALOS image was processed to obtain the TOA radiance using gains provided in header file. Next the FLAASH algorithm was used to remove the influence of atmosphere and also for conversion of the TOA spectral radiance into ground reflectance. The image was registered to UTM Zone 40 (WGS 84) coordinates using 1:25000 scale digital maps, 17 control points, a polynomial (degree 2) equation and the nearest neighbor resampling method. In the next step, effective parameters on drought including environmental factors (slope, aspect, height, land cover/use, stream density and vegetation fraction) and also climatic data (temperature, rainfall and evaporation) were mapped in GIS environment. The land cover/use map was extracted from satellite data using supervised classification algorithm. Vegetation fraction was also extracted from image using MSAVI1 index. The other parameters such as height, slope and aspect were produced using topographical maps (scale: 1/25000). Data standardization is a basic task in data analysis when several incomparable criteria are involved. To make comparable various data layers, the data layers which effect on drought were standardized using linear fuzzy. For example, drought severity decrease with an increase in altitude and areas having more height are less sensitive to drought, so maximum and minimum altitude were converted to 0 and 1.The AHP method was used to identify the weight of each parameter. Results of weighted layers showed maximum weight for land cover/use parameter due to the human intervention in natural ecosystems. Next, Index overlay and various fuzzy logic operators (Fuzzy Sum, Fuzzy product, Fuzzy OR, Fuzzy and) were used to model the drought risk.Drought change land cover, soil moisture and surface roughness, it also influences the exchange of energy and water between the vegetation, soil and the air. Thus, it may affect surface radiation, heat and water balance by changing surface biophysical factors such as the VI, albedo and LST. In general, with the development of a drought, the NDVI decreases, the albedo and surface temperature increase and the soil moisture decrease, provided that other factors are stable. Combinations of these parameters may provide a useful tool for better understanding of the spatio-temporal patterns of drought. Most of the drought indices presented in the last decades are based on the above-mentioned parameters (especially NDVI, LST and Albedo). The retrieval of the surface albedo and the LST contains uncertainties rooted in the atmospheric correction of satellite data, decomposition of mixed pixel information, bidirectional reflectance distribution function (BRDF) modeling and the spectral remedy by a narrowband to broadband conversion. As a consequence, the final error associated with the extraction and quantifying of drought information would be magnified. On the other hand, calculating these indices need time series of satellite data which increase the time and the cost of processing. In Ghulam et al., 2007, the MPDI as a real time index for drought monitoring based on vegetation fraction and soil moisture is presented. This index only needs one image to be calculated. In the present study, results assessed using MPDI. Final results indicated that the index overlay method can signify high-risk areas more accurately (R2=0.81) than the fuzzy operators.

Precipitation and temperature patterns have especial role in the accuracy of hydrologic models. The future patterns of rainfall and temperature can lead to better hydrological predictions. Hence, according to their importance, we try to derive the future rain and temperature patterns of the Gharehsoo River’s watershed. This watershed has been placed in the northwest of Iran in Ardebil province and it has high amount of agriculture productions. Interpolation schemes are utilized in this study to determine the rain and temperature patterns. The utilized software package is ArcGIS software. These interpolation techniques are included of Inverse Distance Weighting (IDW), Global polynomial, Local polynomial, Radial Basis Functions (RBF), ordinary Kriging and simple Kriging. Firstly, we gather the monthly temperature and precipitation data of 10 synoptic stations in 2004. Then, the interpolation schemes are evaluated in order to determine the best temperature and precipitation patterns. The evaluation criteria in this study were Root Mean Square Error (RMSE) and Mean Error (ME). The results of evaluation of different interpolation schemes demonstrated that IDW and RBF method are the best schemes for the spatial modeling of temperature and precipitation patterns, respectively. Using these patterns, it is straightforward to predict runoff using hydrological models. In this paper, a new algorithm is proposed for the prediction of temperature and precipitation patterns for future (2100). To predict temperature and precipitation pattern, it is necessary to utilize of a predictor model to predict the amount of precipitation and temperature. Then the amount of precipitation and temperature are converted to spatial pattern of precipitation and temperature using the developed algorithm in this study. PRECIS model that is a regional climate model was utilized as predictor model in this study. a) case study: The studied area (Gharehsoo river watershed) is located in the Northwest of Iran, between longitudes coordinates 47°45’ and 48°42’ E, and between latitude coordinates 37°46’ and 38°34’ N. The Gharehsoo river watershed area is approximately 4100 km2 and plays significant agricultural role in Iran. the mountainous areas have been located in the western and southeastern parts of watershed. Furthermore, there are many pasture and agriculture lands in this watershed. Watershed elevation varies from 1170 m in Gharehsoo river outflow to 4732 m in Sabalan mountainous. The precipitation in the watershed is highly related to the topography and wind in the watershed.. The sea fronts and orographic conditions are the main factors for precipitation in the western and eastern parts of watershed. In the winter, the cold front of Mediterranean Sea, coupled with the local effects of Sabalan Mountains lead to orographic rainfalls. In summer, weather conditions are predominant of Caspian Sea front is the major factor for precipitation in the eastern part of catchment. Autumn and spring rainfalls are the results of interaction between African-Mediterranean and Caspian Sea fronts. b) Data: Temperature and precipitation data are two basic climatologically variables, measured at meteorological stations. Monthly precipitation (mm) and temperature data for 2004 was provided through Iran Meteorological Organization. The number of stations in the watershed and near to watershed was 11 stations. c) PRECIS ModelPRECIS (Providing Regional Climates for Impacts Studies) is a regional modeling system that can be run over any area of the globe on a relatively inexpensive, fast PC to provide regional climate information for impacts studies. The idea of constructing a flexible regional modeling system originated from the growing demand of many countries for regional-scale climate projections. Only a few modeling centers in the world have been developed RCMs (Regional Climate Models) and utilize them to generate projections over specific areas, because it needs high amount of computational effort and time. The Hadley Centre has configured the third-generation of Hadley Centre RCM, named PRECIS that is easy to set up. The past (1961-1990) and future climate SRES B2 scenario (2071-2100) were simulated by PRECIS model at a spatial resolution of 50×50 km for Iran. It’s necessary to have a series of precipitation and temperature patterns to produce monthly patterns for future. These series of maps are generated using the precipitation and temperature patterns of 2004. The hyetograph maps are calculated by the ration of total volume of precipitation in January and the area of watershed. The calculated total volume of precipitation in January using the precipitation pattern map was about 490 million m3. The ration of volume and the area of watershed was about 0.117 m. This number shows the average precipitation of January. Similarly, these operations can be performed for the other months of 2004. The unit hyetograph and thermograph maps are generated by dividing the precipitation and temperature patterns in 2004 to their corresponding monthly precipitation and temperature values. The precipitation and temperature data were extracted from the PRECIS model for 2100. The monthly temperature data of 2100 shows an increase of temperature about 2 to 5 degrees in future, but there is no specific trend in precipitation data. If the amount of the monthly temperature and precipitation of 2100 are divided by these amounts in 2004, the amount of B parameters are calculated for precipitation and temperature in different months. Finally, the precipitation and temperature patterns will be obtained by the product value of B parameters and unit hyetograph or thermograph maps in each month, respectively. A new method was developed for reasonable prediction of spatial patterns of precipitation and temperature. This new method uses of the results of a Regional Climate Model (e.g. PRECIS model) coupled with the appropriate spatial modeling techniques (interpolation techniques). The derived precipitation and temperature patterns in 2100 in Gharehsoo River watershed show a reasonable similarity with the topography and the climate of the region, Hence This method can be introduced as an appropriate method for the hydrological forecasts and water resource management.