منصور حلیمی

Climate changes in Iran, which have mainly manifested in the form of intensifying periods of drought and increasing temperature, can have profound effects on the forest ecosystems of Zagros in the west of Iran. In this study, the drought trend (with PDSI index) and its effects on the spatial-temporal dynamics of EVI greenness index from MODIS sensor were investigated through correlational relationship in the statistical period from 2000 to 2020 in Zagros oak forests in Kohgiluyeh and Boyer-Ahmad province. To determine the greenness index threshold value, sampling at 20 points in the forests of the province and EVI index values in these areas were used. The results showed that the greenness threshold value of EVI in these forests was at the maximum level in June. The greenness index value and the area of the oak forests of the province have decreased during the study period. The peak greenness of the oak forests of the province had a significant sensitivity to changes in rainfall and drought at the end of the cold period of the year and in the spring season. The negative phases of the drought indices have been completely consistent with the periods of greenness loss. The EVI, as an index of forest greenness, has shown a high sensitivity to the PDSI drought index. The PDSI index has also been able to explain 0.74 of the spatial changes of EVI in the three months of April, May, and June.

the climate factors are main determinant of vegetation spatiotemporal dynamics. A visual comparison of climate and vegetation on a global scale immediately reveals a strong association between climate and vegetation.  The main object of this study is to reveal the spatiotemporal association between climatic factors and Modis derived NDVI in Charmahal & Bakhtiary province of Iran.

In this study, we use MOD13A3 of MODIS product as NDVI layer for study area. MOD11A2 as land surface temperature and mean monthly accumulative rainfall of synoptic station for study area during 2008 to 2018. We used the correlation analysis in 0.95 confident level (P_value =0.5) to reveal the spatiotemporal association between the NDVI and climatic factors.

The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months, the pixels which have the high value of NDVI are spatially associated with the pixels which have highest value of LST (6 to 12C°). In winter the spatial correlation between NDVI and LST is so high which is statistical significant in 0.95 confident level. In transient months such as May, October and November, the spatial correlation between NDVI and LST is falling to 0.30 to 0.35, which is not statistical, significant in 0.95 confident level. Finally, in summer season or warm months including Jun to September, we found the minimum spatial association between the NDVI and LST.

we found that the maximum correlation between NDVI and LST simultaneously appears and no lag time has been observed. The spatial correlation of NDVI and monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1-month lag time but in other months we do not find any significant correlation between NDVI and rainfall.

In many developing countries, including Iran, urban development is one of the most important factors in land use change. Most cities, in their turn, have caused the destruction of agricultural land and have been covered by urban expansion. This is a matter of economic and agricultural activity, especially in province like Gilan, due to temperate climate and fertile soil, which is one of the things that demands that appropriate planning prevent the uncontrolled expansion of these cities. In this regard, we aimed to study the land suitability analysis as a tool for planning and predicting an optimal urban development model in cities under the multi criteria decision making (WLC and TOPSIS models) in the GIS and based on various indicators at Foman and Shafts in western Gilan. The results showed that the output classification of the two models used was different, but suitable areas for urban development were overlapping in two methods. The most suitable areas for urban development are found in northern and eastern parts. Also, land use has the highest importance in zoning. Based on the results, the inner development of the cities is recommended as the first priority.

Atmospheric boundary layer (ABL), is the lowest part of the atmosphere. Its behavior is directly influenced by its contact with earth surface. On earth it usually responds to changes in surface radiative forcing in an hour or less. In this layer physical quantities such as flow velocity, temperature, moisture, etc., display rapid fluctuations (turbulence) and vertical mixing is strong. Above the ABL is the "free atmosphere" where the wind is approximately geostrophic while within the ABL the wind is affected by surface drag and turns across the isobars. The land use/cover changes affecting the surface radiative forces lead to ABL spatio-temporal variation. The main object of this study is to analysis the association among ABL height and built-up spatial growth in Kermanshah city. Data and methods: Multi-temporal satellite images from Landsat imagery data for 1990 to 2015 series of sensors TM, and OLI (Landsat 5 and 8) were taken from USGS database. Data of the Atmospheric Boundary layer height (ABL height) for the city of Kermanshah also were taken during 1990- 2015 from ECMWF – Eran-Intrim website at 0.0125 ° spatial resolution. Firstly, we analysis the temporal trends of ABL height of Kermanshah in summer and winter using linear regression in 0.95 confidence level (P_value = 0.05). The built up area of Kermanshah has been extracted from TM and OLI images using supervised classification method and maximum likelihood classification(MLC) algorithm in GIS image analysis. The Pearson correlation analysis has been used to reveal the relationship between annual ABL height variation and built-up growth of Kermanshah. The results of long term trend of Built up growth of Kermanshah that extracted using MLC algorithm as can be seen in figure 1 indicated that the built up area in Kermanshah has been growth by 1.02 square kilometer annually.According the figure 2, The results of annual trend of ABL height in summer and winter also reveals that in summer there is no significant trends in ABL height while in winter the significant increasing long term trend has been observed in ABL height.

Global warming and climate change are terms for the observed century-scale rise in the average temperature of the Earth's climate system and its related effects. Multiple lines of scientific evidence show that the climate system is warming. Many of the observed changes since the 1950s are unprecedented over tens to thousands of years. In 2014, the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report concluded that "It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. The largest human influence has been emission of greenhouse gases such as carbon dioxide, methane and nitrous oxide. Human activities have led to carbon dioxide concentrations above levels not seen in hundreds of thousands of years. Climate model projections summarized in the report indicated that during the 21st century, the global surface temperature is likely to rise a further 0.3 to 1.7 °C (0.5 to 3.1 °F) for the lowest emissions scenario and 2.6 to 4.8 °C (4.7 to 8.6 °F) in the highest emissions scenario. These findings have been recognized by the national science academies of the major industrialized nations and are not disputed by any scientific body of national or international standing.
Climate change is one of the main challenges that human being has faced since the 19th century. Anthropogenic changes in climate which leads to global warming and various side effects occurred and affected human life. The global warming leads to some significant changes in environmental, ecological and economic conditions. The spatiotemporal dynamics of vegetation colony and various biodiversity dynamics are also related to global warming. One of the main signal of global warming is the significant trends and changes in some climatic factors such as monthly, daily and annual temperature and rainfall. The spatial dynamics of climatic factors such as temperature and rainfall could also be related to global warming. In this study, we aimed to investigate the rainfall variations in different altitude ranges in Iran.
Precipitation varies from year to year and over decades, and changes in amount, intensity, frequency, and type (e.g. snow vs. rain) affect the environment and society. Steady moderate rains soak into the soil and benefit plants, while the same amounts of rainfall in a short period of time may cause local flooding and runoff, leaving soils much drier at the end of the day. Snow may remain on the ground for some months before it melts and runs off. Even with identical amounts, the climate can be very different if the frequency and intensity of precipitation differ, as illustrated, and in general the climate is changing from being more like that at Station (Stn) to that at Stn A. These examples highlight the fact that the characteristics of precipitation are just as vital as the amount, in terms of the effects on the soil moisture and stream flow. Hydrological extreme events are typically defined as floods and droughts. Floods are associated with extremes in rainfall (from tropical storms, thunderstorms, orographic rainfall, widespread extra-tropical cyclones, etc.), while droughts are associated with a lack of precipitation and often extremely high temperatures that contribute to drying. Floods are often fairly local and develop on short time scales, while droughts are extensive and develop over months or years. Both can be mitigated; floods by good drainage systems and drought by irrigation, for instance. Nonetheless, daily newspaper headlines of floods and droughts reflect the critical importance of the water cycle, in particular precipitation, in human affairs. World flood damage estimates are in the billions of U.S. dollars annually, with 1000s of lives lost; while drought costs are of similar magnitude and often lead to devastating wildfires and heat waves. The loss of life and property from extreme hydrological events has therefore caused society to focus on the causes and predictability of these events. Tropical cyclones typically have the highest property damage loss of any extreme event, and are therefore of great interest to state and local disaster preparedness organizations, as well as to the insurance industry.
The data of annual rainfall of 22 synoptic stations has been investigated during 1992 to 2012. First, we sorted these stations based on the altitude ranges into 4 classes, namely: Less than 500 meter, 500 to 1000 meters, 1000 to 1500 and more than 1500 meter above sea level. We used Man-Kendal’s nonparametric trend analysis test to detect any significant trend at 95 and 99 confidence levels (P value= 0.05 and 0.01, respectively).
Discussion and The results indicated that the highest rainfall decrease was observed at the elevations below 500 meters, especially in March and in the annual scale. The highest precipitation at the elevations of 500 to 1000 meters was observed in the months of March, May and October, with the highest drop in rainfall at 1000 to 1500 meters in February and June. On the annual scale, all stations showed a negative trend in rainfall. Many stations, including Maragheh, Maku, Mahabad, Urmia and Birjand, showed a significant decrease in annual scale. The results of this study showed that elevations above 1000 meters have a higher relative stability in rainfall, while rainfall at stations below 500 meter elevations have a more time variability.
Based on the findings of this research, it can be concluded that the monthly and annual rainfall of stations located at elevations below 1000 meters have had greater and more significant changes than the rest of the stations. Thus, it can be said that the climate change has been more noticeable in the stations of this class.

The leishmaniases are among the most important emerging and resurging vector-borne protozoal diseases, second only to malaria in terms of the number of affected people. Like malaria, the leishmaniases can be caused by infection with any of several species of parasites belonging to the genus Leishmania and transmitted by sand flies.The purpose of thisinvestigation was to determinethe atmospheric-oceanic Southern Oscillationpattern, asa factorinclimatic conditionsof the country, theincidence ofleishmaniasis, in the city ofQasr-e Shirin. Inthisregard, theSouthern Oscillationpatterndata was recieved for theperiod1369 to1391fromthe University ofEast Angelia.The data of annual CL incidence in Qasr-e Shirin were collected from the national and international reports of CL incidence in Iran. To evaluate the effect of hot and cold phases of the macro-scale weather pattern, the outbreak data based was separatednbased on the monthly phases and the significance of difference between warm and cold ENSO phases of outbreak data were studied by independent with doublesided t_studen test. The results showed that the annual incidence of leishmaniasis in the city of Qasr-e Shirin data, is significant only inApril and March months. The running of Independent twosided t test on two series of cases based on the separation of cold and warm phases of ENSO Was showed that in April and March in the years since phases with ENSO (La Nina) are The average of outbreaks are the average is higher than the years in April and March are warm phase of ENSO (El Nino) is dominant in Qasr-e Shirin city.

Ambient air quality is a major concern in highly urbanized and industrialized regions such as Tehran.
In this paper, the spatial distribution of 4 air pollutants in Tehran atmosphere was analyzed. The analyzed air pullutants were Carbon monoxide (CO), Nitrogen Dioxide (NO2), Ozone (O3) and atmospheric particulate matters less than 10 micrometers in diameter (PM10). For this purpose, 4 common geostatistical interpolation methods namely: Ordinary Kriging (OK), Universal Kriging (UK), Sample Kriging (SK), and Ordinary Cokriging (COK) with Gaussian modeled semivariogram, were used to estimate the continuous surface for the 4 mentioned air pollutants. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during a 2-year period from 2012 to 2013. The Kriging interpolation schemes are stochastic, local, gradual and exact interpolators. After preprocessing the collected data, they were imported to GIS by using metric coordinate system (UTM Zone 39). Finally, the Kriging predicted map was evaluate using 3 statistical indices of validation namely: Mean Absolute Error (MBE), Mean Bias Error (MAE), and Root Mean Square Error (RMSE) that can be divide into systematic and unsystematic errors (RMSEs, RMSEu).
Findings: The results indicated that, using 2 auxiliary variables, the OCK is the optimum schema for spatial estimation of CO and NO2 pollutants in Tehran. Moreover, SK is found out as the best model for spatial estimation of NO2 and CO. According to optimal model, the highest concentrations of ozone (O3) and particulate matters greater than 10 microns (PM10) are observed in the marginal areas of Tehran, while the highest concentrations of CO, NO2 are observed in the central and northern districts of Tehran such as districts 1 to 4.
The developed optimized model can be used for real time estimation of any pollutants in Tehran atmosphere by updating the observed data.

Cutaneous leishmaniosis (CL) is a neglected tropical vector-borne disease. As other vector-transmitted diseases, its transmission is sensitive to the physical environment. The purpose of this study was to investigate the relationship between El Niño-Southern Oscillation (ENSO) and annual incidence of CL in Iran.
The data of ENSO were obtained from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center for the period 1977-2011(34 years), and the annual data on CL incidence in Iran from WHO. To determine the association between the ENSO annual fluctuations and annual CL incidence we used crosstab analysis and tow-tailed independent t-test to find any possible differences among the annual CL incidence according to different phases of the Southern Oscillation Index (SOI: El Niño, warming phase; La Niña, cooling phase; neutral phase).
Analysis of the data showed that, as compared with the years when El Niño and La Niña were dominant, the annual incidence of CL in Iran was lower in the years when neutral phases of SOI were dominant. The highest incidence was found to have occurred during the years with an intermediate La Niña dominance. During the years when La Nina was dominant the incidence of CL was, on the average, 15-20% higher than that in the years with a neutral phase, while the difference between the annual CL incidence in neutral years and El Niño years was not statistically significant.
The findings reveal that in general the mean incidence of cutaneous leishmaniosis during the years with a neutral ENSO index is lower than that during the years with dominant El Nino or La Nina, although there is no difference between incidence in the neutral years and the El Nino-dominant years.

Malaria is a disease affecting 300–500 million people in tropical and subtropical regions and causes approximately 2.7 million deaths annually. Currently, no vaccine protects against malaria and resistance to anti-malaria drugs such as chloroquine is increasing and spreading geographically. Moreover, anti-malarial drugs are expensive and often unaffordable to low-income populations. A better understanding of the relationship between the El Niño Southern Oscillation (ENSO), the climatic anomalies it engenders, and malaria epidemic could help mitigate the world-wide increase in incidence of this mosquito transmitted disease. There is evidence of a relationship between climate variability and the transmission of mosquito-transmitted diseases. Therefore, in this study we intended to analyze the relationship between ENSO events and annual malaria occurrence (AMO) in Iran to assess the possibility of using ENSO forecasts for improving malaria control.
Martials and Two types of data were used: The data of annual malaria incidence in Iran were collected from the national and international reports of malaria occurrence in Iran; the annual malaria occurrence data for 38 years (1974-2013) were collected from related sources. ENSO is a cyclic phenomenon, which its frequency is 2 to 7 years (i.e., irregular) and is the second strongest natural driver of climate variability, the first being normal seasonal variability. This oscillation has two different phases: a warm episode known as El Niño; and a cold episode called La Niña, where warm and cold refer to the direction of departure from average of the equatorial Pacific Sea surface temperature (SST), a fundamental indicator of the ENSO state. The Pearson correlation analysis at 0.95 confident level (P-value=0.05) on monthly timescale was used in order to understand the relationship between ENSO and annual malaria occurrence in Iran. The 2-tailed independent parametric T-test was used knowing that whether there is a significant difference between the La Nina years occurrence and El Nino years occurrence.
The results indicated that a slight negative association could be detected between ENSO and annual Malaria occurrence in Iran. Our finding showed that the detected correlation between monthly ENSO and annual malaria occurrence is statistically significant only in months October and May while no significant relationship between ENSO and AMO at 0.95 confident level was found for other months.. The inverse relationship between ENSO and AMO means that the years having higher malaria occurrence coincides with the warm ENSO phases or EL Nino episode while the years having lower malaria occurrence coincides with the cool ENSO phases or La Nina episode. In El Niño episode, the annual occurrence of malaria is about 0.2 to 0.3 higher than La Niña episode years.
The positive association between El Niño and rainfall of Iran resulted in higher occurrence of malaria by proliferation of Anopheles mosquito especially in southern and southeastern region of Iran.

One of the most important components of the extent of pollutants mixing and air quality at near the Earth''s surface is the height of boundary layer. Many variables involved in determining the height of the boundary layer of atmosphere. Researchers have used of these variables to estimate and determine of the height and the roof of the boundary layer. The primary aim of this study is to approximate the height or the ceiling of boundary layer on Mehrabad station by vertical sounding of atmosphere. The analysis of vertical profiles of the potential temperature and the inversion Critical have used. In this respect, the vertical sounding data of the atmosphere of Tehran’s Mehrabad station from the database of Wyoming University was used. By drawing and analysis of Skew-T graphs based on Heffer critical inversion, one day per month was chosen as a day with the critical inversion. So the inversion roofing of those days was considered as the roof of the boundary layer of atmosphere. The results show that the cold air subsidence is the permanent type of the critical inversions in both February and December. Also the lapse rates of the potential temperature are 0.024 and 0.026 Kelvin respectively in meters. In these months the height of boundary layer (1482 and 1267 meters) is in the lowest situation. The maximum height of the boundary layer has seen in July and August which is equal to 3060 and 3312 meters, respectively, and the kind of these critical inversions is air subsidence. The study also showed that the most frequent type of the critical inversions is radiation inversions and also in days with these types of inversions the lapse rate of the potential temperature are lower than days with subsidence and frontal inversions. Also the results shows that the thickness of inversion layer in the radiation inversions is higher than others inversions.

One of the most important components of the extent of pollutants mixing and air quality at near the Earth's surface is the height of boundary layer. Many variables involved in determining the height of the boundary layer of atmosphere. Although all of the troposphere (the lower ~10km of the atmosphere) is affected by surface conditions, most of it has a relatively slow response time. The lower part of the troposphere that is affected on a shorter time scale is commonly defined as the Planetary Boundary Layer (PBL). The depth of the mixed layer has a significant effect on the concentration of air pollution, which itself is dependent on the intensity and duration of solar radiation and wind speed. According to Stull, one can describe the planetary boundary layer as “that part of the troposphere that is directly influenced by the presence of the earth’s surface, and responds to surface forcing with a timescale of about an hour or less.” Surface temperature has a strong relationship with height of the PBL. As the surface cycles between daytime radiation and nighttime cooling the amount of convection taking place changes. When the temperature gradient is steep, more convection takes place to dissipate thermal energy in the most efficient manner. In other words, the greater the temperature difference between the surface and the lower troposphere, the higher convective eddies must reach to alleviate the gradient. Relating this to Stull’s definition of turbulence, it can be concluded that the height of the PBL varies with surface temperature. In fact, the spatial range of the PBL can vary from less than one hundred meters to several kilometers. The strong relationship between convective turbulence and height of the PBL is sometimes used to define the boundary layer and call it the Convective Boundary Layer (CBL). Analogous to the Stull’s definition but focusing on turbulence, Lloyd et all describe CBL as “a layer of air typically of order 1km in depth, well mixed by turbulence maintained by buoyancy due to heating at the ground. It is bounded above by stably stratified, no turbulent air and grows through the day. In this study we aimed to analysis the status of ABL in 3 dust period days in Khuzestan province of Iran.
The Data were used in this study includes: The Daily data of dust concentration during 27Jan to 1 Feb 2015, the daily height of ABL also were used. The daily data of ABL were given from ECMWF with 1/8 degree spatial resolution. We used the Pearson correlation and synoptic analysis to assessment the condition of boundary layer at the mentioned days. For analysis the characteristic of ABL the climatic data of Wyoming University were used to assessment the thermodynamics of atmosphere. The spatial distribution of ABL height at the dusty day also were used for 12 UTC.
The results indicated there is the direct relationship between the ABL height and the concentration of dust in the mentioned days. So that in the days that the concentration of dust reaches maximum we fund that the height of ABL reaches maximum simultaneously and vis versa. The spatial distribution of ABL height shown that the height of ABL in the 29Jan reaches maximum that the maximum concentration of dust related to this day. And also the minimum concentration of dust observed in 27Jan and 1Feb that the in this day the height of ABL was minimum. The synoptic analysis also reveals that locating the low pressure system at the 500hp level that the Khuzestan province has been locating at the front of this system lead to transport the dust to study area.
In this study we reveal that the height of ABL in the dust days of Khuzestan has a totally revers behavior in compare to the air pollution days in Tehran. In the pollution days in Tehran the lowing of ABL height and inversion lead to intensify the concentration of pollution while in the dust days of Khuzestan the height of ABL were increased in compared with non-dusty days.

Nowadays with regard to the patterns of the energy consumption, identify the factors and components that affect it, has been the most important approach of the geosciences issue. The cooling and heating degree days (CDD & HDD) are the Indices for potential of energy consumption in any region. Therefore identify Iran’s different region from scope of this climatic indices can be very useful in planning of energy consumption. Spatial interpolation methods are frequently used to estimate values of climatic data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in data of CDD & HDD in Iran. Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for climatic data, especially in Iran that have inadequate number of meteorological station. Therefore the main objective of this paper is comparison of three spatial interpolation methods for estimating CDD and HDD as a climatic variable in not measured location.
Material and This paper focuses on determination the relative performance of 3 different spatial interpolation methods for estimating CDD and HDD of Iran and then select optimum method for estimation this variable as a climatic index of potential of energy consumption. HDD and CDD provides a simple metric for quantifying the amount of heating and cooling that buildings in a particular location need over a certain period (e.g. a particular month or year) it calculated over a period of time (typically a month) by adding up the differences between each day's mean daily temperature to base temperature (65 and 70˚F For HDD and CDD, respectively). The mean seasonal observed data of cooling and heating needs, are collected from 30 meteorological stations for the period 1965- 2005. Inverse distance weighting (IDW), ordinary kriging (OK) with different Semivariogram and tension Spiline, are selected as interpolated methods. Moreover, cross-validation (CV) is used to evaluate the performance of different spatial interpolation methods. 4 statistical criteria named: Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), an acceptability index (d-Willmote) and finally coefficient of determine (R2), are used to analyze and compare the performance of these interpolation methods.
The results indicated that all models have taken overestimate error to prediction of HDD and underestimate error to prediction CDD. Also we found that the average of observed data is 345.5 for HDD and 157 for CDD and the average of estimated data from IDW, Kriging and Spiline for HDD and CDD is 363, 363, 346 and 138,142, 148, respectively. And can dedicate the best model for estimating the general and total attitude such as average is tension Spiline. But for prediction of spatial detail and spatial construction that is most important in this paper, the Krigin model with the spherical Semivariogram has minimum MAE, MBE, RMSE (89, 17, 128) for HDD and (69, -15, 90) for CDD and is considered as the optimum model for interpolating this climatic variable.
This paper results indicated that the Kriging model with the spherical Semivariogram is more appropriate to estimate the CDD and HDD. The output of this paper can help decision maker to have more clear identification of the climatologically potential for the energy consumption in Iran. And could be useful for climate adapted architecting.

Leishmaniases are among the most important emerging and resurging vector-borne diseases, second only to malaria in terms of the number of affected people. Leishmaniases are endemic in 98 countries and three territories worldwide and threaten about 350 million people. It is estimated that 14 million people are infected worldwide with about two million new cases occurring each year. The parasite is then transmitted to human hosts by the bite of a Phlebotomine sand fly that was previously fed by biting an infected reservoir. We examine the monthly dynamics of the incidence of this zoonotic cutaneous leishmaniasis (ZCL) as a function of surface climatic and environmental indicators in region which covering a large area of the semi-arid of southeastern portion of Iran Kerman province. in order to we give the climatic and enviornmental data and use the correlation analysis to investigate the assosiation between this variable. we use cross correlation in lag 4 to 6 month to detect the lag effect of climate factors in ZCL incidence. We find the negative correlation between the climatic factors and number of cases of ZCL incidence in Kerman simultaneously. but with 4 to 3 month lags we finde the strong and significant positive correlation among this variable.

E Precipitation is a vital component in the hydrological cycle. Its spatio-temporal variations has great environmental an socioeconomic impacts. The spatial variation of rainfall is depending upon many factors. Some of this variation is due to synaptic systems and some others is formed by local physiographical characteristic of station such as elevation from sea level، slope، windward and leeward slopes، land cover and land use and etc…. if the rainfall is formed by widespread and pervasive synoptic system it can be exist a significant spatial similarity and homogeneity in amount of given rainfall in all over the region which is affected by synoptic system. But if the rainfall is dominated by the local factors the higher heterogeneity of given amount rainfall can be expected. In this study، we used the 20-years monthly average precipitation (1990-2010) for 42 synoptic stations، in the west and north western portion of iran which include 6 province namly: the East and West Azerbaijan، Kurdistan، Ilam، Kermanshah، Hamadan and Zanjan. We preparation this data as an long term average of monthly precipitation for each station and then import them to GIS by metric Orojected coordinate system (PCS). We used Moran،s Index as an Spatial statistic approach to investigate the spatial relations of monthly precipitation. This tool measures spatial autocorrelation (feature similarity) based on both feature locations and feature values simultaneously. Given a set of features and an associated attribute، it evaluates whether the pattern expressed is clustered، dispersed، or random. The tool calculates the Moran''s I Index value and both a Z score and p-value evaluating the significance of that index. In general، a Moran''s Index value near +1. 0 indicates clustering while an index value near -1. 0 indicates dispersion. However، without looking at statistical significance you have no basis for knowing if the observed pattern is just one of many، many possible versions of random. In the case of the Spatial Autocorrelation tool، the null hypothesis states that «there is no spatial clustering of the values associated with the geographic features in the study area». When the p-value is small and the absolute value of the Z score is large enough that it falls outside of the desired confidence level، the null hypothsis can be rejected. If the index value is greater than 0، the set of features exhibits a clustered pattern. If the value is less than 0، the set of features exhibits a dispersed pattern. The morans I Statisic for spatial autocorrelation is given as 1) Moran index 2) Where Zi is the diviation of an attribute for feature I frome its mean، wij is the spatial weight between feature i and j، n is total number of object and S0 is aggregate of al spatial weight. We found the amount of monthly given rainfall in the study region in cool season (November to February) reveal a significant positive autocorrelation. and on the other hand the spatial variation coefficient of rainfall in these month is smaller than other remaining month. the revealed Moran’s I indicated in the 4 mentioned months so strong significance that somebody cannot Assigning this spatial homogeneity to chance and randomness. In the cool season the study area which located in west and northwestern of Iran is dominated by westerly and following them the atmospheric synoptic systems entrance to country and affecting all of the country area then the rainfall is formed by widespread and pervasive synoptic system has significantly spatial similarity and homogeneity in all over the region and the strong positive autocorrelation is revealed in these months. In the warm season (July، September، August، October، and May) we find inverse condition. The Moran’s index in these months was very small and near to zero. We couldn’t detect any significant spatial autocorrelation in these months. In our study region the warm season especially summer season (July to September) is the dry period of year. The occurred rainfall in these months is usually sporadic and non-comprehensive. These rainfalls usually characterized by being showery which is formed by local atmospheric convective a cells. In this type of rainfall the different local physiographical characteristics such as elevation from sea level، slope، windward and leeward slopes، land cover and land use and etc… have a substantial roll in formation and spatial distribution of this rainfall. So that the difference physiographical characteristic of each region this local formed precipitation is not too similar. In the warm season absenting westerly in this region، the local physiographical characteristics determinant the occurred rainfall and due to this physiographical dissimilarity in the region، heterogeneity of given amount rainfall can be rise. The spatial variation coefficient of rainfall in warm season is very higher than col season. The revealed Moran’s I was not significant in 0. 95 confident level and there are no spatial pattern in this warm. Our finding indicated that only the cool season months including November، December، January and February reveal a significant spatial autocorrelation in 0. 95 and 0. 91 confident level.

Environmental and ecological factors especially climatic conditions have a significant impact on the prevalence of parasitic diseases. These factors has is more importance in Malaria Incidence and Epidemiology. Malaria as the most important parasitic disease, considered as one of most important public health problem. The climatic factors not only affect the growth and proliferation of the Anopheles mosquito but also affect in Plasmodium activity. In present study we surveyed the malaria situation during 1972 to 2005. In order to the five climatic parameters including mean temperature, total precipitation, number of days with precipitation over 0mm, rainfall distribution index and relative humidity were used as annual average during 1971 to 2005. we calculate average of annual of climatic parameters for the 31 synoptic stations of Iran Using GIS software. First, using the ordinary Kriging model with spherical a Semi variogram was traced maps of climatic parameters and these factors mean was calculated for each year. Finally, to investigate the relationship between climatic parameters with the prevalence index the Pearson correlation coefficient at 0.9 confidence level (p = 0.10). was used. A multivariate linear regression model was applied to estimate the prevalence of disease based on the mentioned climatic factors. Study of malaria prevalence in 1971 to 2005 showed cases of disease with has a decreasing trend with slope 0.0142 in year that this trend has been accelerated from 1375. Many cases of disease have been reported one year after increased precipitation. The result showed the weather humidity factors is important than temperature factors in the prevalence of malaria. I general, the developed model is explained 0.48 of changes prevalence time. In order to be successful Anti-malaria campaign in Malaria-prone areas in addition different influencing factors climatic factors should be considered.

Malaria as a mosquito-borne disease is largely dependent on climatic conditions. Temperature, rainfall and relative humidity are considered as climatic factors affecting the geographical distribution of this disease. These climatic factors have definite roles not only in the growth and proliferation of the mosquito Anopheles but also in the parasite Plasmodium activity. The purpose of this study was to find in which regions of Iran climatic conditions favour spread of malaria. Data on 3 climatic factors, including the mean monthly temperature, rainfall and mixed ratio of humidity, obtained from 31 synoptic meteorological stations during the 30-year period 1975-2005 were used. By running the informative filters through map algebra tools in the Geographical Information System (GIS), the synoptic meteorological stations were classified into 4 groups in terms of climatic conditions favouring activity and proliferation of the mosquito Anopheles and the parasite Plasmodium. Then the regions were interpolated in terms of climate risk of malaria incidence using the tension Spiline interpolation method. The maps of climatic potential malaria risk indicated that the southern provinces including Hormozgan, Bushehr and Khuzestan, southern parts of Sistan-Balouchistan province, such as Chabahar and Nikshahr, as well as Northern provinces of the country, including Mazandaran and Gilan, have the highest climatic potential for risk of malaria spread. On the other hand, provinces in the North Western region, including West and East Azarbaijan, Ardebil, Kurdistan and Zanjan, have the lowest climatic potential risk of malaria spread. Further analysis of the data showed that a large segment of the population is living in regions with medium- to high-risk zones. The findings of this study can be used when designing malaria control programs to identify different regions in terms of climate-based malaria risk. The presented risk map of malaria in this study is completely based on climatic factors. The disconformity between these presented climate-based maps and the observed high-risk map is due to such factors as socioeconomic and lifestyle changes, as well as border problems (foreign subjects entering the country).