stepwise regression

Grapes, as the most important and economically significant horticultural product in the Sistan region, play a pivotal role in the region's economic structure. Given the dominance of grape cultivation and its considerable impacts on the economic, social, and environmental aspects of the region—key components of sustainability—assessing sustainability levels and identifying factors threatening it is an effective step towards strengthening the production model of this crop. The main approach of this study is a survey, and in order to evaluate the sustainability of grape orchards in the region and identify the factors affecting it, data from the 2019–2020 crop year was utilized. These data were collected through 86 questionnaires, randomly distributed among the region’s farmers. In the first stage, after identifying and developing sustainability measurement indicators, the indicators were scaled using the method of scale discrepancy resolution and then weighted through Principal Component Analysis (PCA). The findings show that more than half of the grape orchards in the region are in a relatively unsustainable condition. Moreover, the results of stepwise regression analysis indicate that factors such as production, orchard area, number of trees, labor, animal manure usage, farming experience, and education level are among the most significant factors influencing the sustainability index. In contrast, inputs such as chemical fertilizers and pesticides had minimal impact, and receiving loans had a negative effect on sustainability.

Rain water harvesting is an appropriate option for storing surface runoff for subsequent uses during periods with limited access to water. The most important step in the application of rainwater harvesting systems (RWH) is the site selection suitable areas. Therefore, by identifying suitable sites for this purpose, time and cost will be saved . In this research, multivariate regression model and GIS were used to site selection in situ (RWH) in Tajare watershed. For this purpose, layers such as crown cover, litter, rock and stones, soil, curve number, rainfall, slope and depth of field as independent variable and infiltration were considered as the dependent variable. Then, according to the maps, their values were calculated in average for each of the 27 sub-basins. Also, to investigate the relationship between these variables and weighting, each of the effective layers of multi-variable regression was used by the stepwise method The results showed that the linear multivariate regression model with an explanation coefficient of 0.993 was able to estimate the penetration factor values well In terms of grade of importance, the curve number variables with a coefficient of -2.433, depth of soil with a coefficient of 0.3488, and rubble and gravel percent with a coefficient of 0.057, were the most important, and other factors were not significant. Comparison of the map from the site selection of multivariate regression in this research with some recommended criteria of various research studies showed that the predicted classes with good in the central parts of the basin and very good in the upstream areas of the basin which in the eastern and southeastern part of the basin fit have a good overlap with the recommended areas with these criteria.

Water erosion is the result of interactions between various environmental factors such as topography, soil characteristics, climate characteristics, runoff and land use and management. Sediment production is highly dependent on runoff, so doubling the speed of runoff increases its leaching capacity and transportability up to five and six times. Knowing the effective factors in sediment production plays an important role in determining the amount of sedimentation of a watershed and understanding the phenomenon of erosion and its consequences, and it can be used in prioritizing the sub-basins of a watershed. The purpose of this research is to investigate the relationship between morphological characteristics and the amount of sediment production in the catchment area of the Gharasu River in Ardabil using principal component analysis and multivariate regression, in this regard, using 16 independent variables and 1 dependent variable in 13 sub-basins of the Gharasu River.

As one of the sub-basins of the Aras catchment basin, Gharasu catchment is located in the geographical coordinates of 47°31' to 48°47' east longitude and 37°47' to 38°52' north latitude. In this study, 1:100,000 scale geological maps, 1:25,000 scale topographic maps, and discharge and suspended sediment statistics and information of 13 hydrometric stations of Gharasu River sub-basin were used in the 50-year period from 1350 to 1399. In order to check the correlation between independent and dependent variables and to test the normality and normal distribution of data, Shapiro-Wilk and Kolmogorov-Smirnov tests were used in SPSS software. Also, in this research, the OLI Landsat 8 satellite images were used to extract the vegetation cover index (NDVI). For this purpose, step-by-step linear regression is used to determine the most effective variables and to determine the most appropriate statistical relationship between suspended sedimentation and the variables used, and principal component analysis is used to determine the most effective factors of suspended sediment production in the watershed.

In this study, the average annual suspended sediment of the basin was considered as dependent variable and other parameters as independent variables, and Pearson correlation method was used to check the correlation between independent variables and dependent variable. The variables of time of concentration, length of the main waterway, area, Slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basin than other variables. Based on the models obtained from sediment correlation analysis, the amount of sediment produced with the concentration time variable had a positive correlation and was significant at the 5% level. The results of the analysis of the principal components show that the four factors of area, length of the main waterway, concentration time and slenderness ratio of the basin have an eigenvalue greater than 1, and the number of extracted factors is 4 factors. The first factor (area) has been able to explain 37.230 percent of the variance of all research variables. This value is 24.735, 16.950 and 9.849 percent for the second factor (length of the main waterway), the third factor (concentration time) and the fourth factor (slenderness ratio) respectively.

The present research was conducted with the aim of investigating the relationship between geomorphic parameters, vegetation and erodible formations of selected sub-basins of Gharasu basin with the average annual sediment. For this purpose, the statistics and information of 17 variables (16 independent variables and 1 dependent variable) were obtained for 13 sub-basins of Gharasu basin from the regional water of Ardabil province. The relationship between geomorphic parameters and annual precipitation was determined using stepwise multivariate regression method. The research results indicate that geomorphic parameters have a high correlation with the amount of annual sediment production and can be used in sediment prediction. Meanwhile, the variables of concentration time, length of the main waterway, area, slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basins than other variables. Among these variables, the basin concentration time variable was used in the final step-by-step regression model and was selected as the sediment predictor variable. This variable alone can predict 98% of annual precipitation. In order to ensure the appropriateness of the data, the KMO coefficient was used to analyze the main components. The value of KMO = 0.78, as a result, the data will be suitable for factor analysis. The results showed that the four factors of area, main waterway length, concentration time and elongation factor could explain 88.754% of the variance of all research variables.

The purpose of the current study is to analyze the frequency of peak flood discharge with a 100-year return period in 206 Iran watersheds and to quantify it based on the most important factors. In this regard, flood frequency analysis was performed based on annual maximum discharge data and fitting of conventional continuous distributions in hydrology and fitting statistical tests. Then, for modeling, 8 parameters affecting the flood peak discharge including heavy daily rainfall, average vegetation, area, perimeter, average slope, average elevation, length of the main river, and the slope of the main river at the catchment area leading to the extraction of selected hydrometric stations. Also, the stepwise regression analysis technique was used to determine the factors affecting the production of flood peak discharge in the selected stations. The results of the study showed that the southwestern, southern, and southeastern basins of Iran with peak discharges of more than 4000 m3/s had the highest 100-year peak discharges among the study basins. The results of the stepwise regression model indicated that five parameters of area, heavy rainfall, elevation, vegetation, and slope of the basin with an adjusted coefficient of determination of 0.72, standard error of estimation of 132.7, Akaike's information criterion of 1.62, and variance inflation factor of 0.62 had the best performance in estimating the flood peak discharge. The results of this study, considering its large spatial scale, which includes the whole of Iran, can be used as a practical guide by the hydrologists and decision-makers in estimating the 100-year flood peak discharge in ungauged watersheds based on the most important factors affecting its generations.

Soil salinity is one of the most important environmental problems, and the identification and zoningof saline soils is difficult due to the need for sampling and laboratory analysis, as well as havingtemporal and spatial variability. In recent years, the use of satellite imagery has always been ofinterest to experts due to its ease of use and ability to detect phenomena. Remote sensing informationgreatly aids the study of soil salinity and can be helpful in identifying salinity values. In this study,220 soil samples were collected from Meymeh area of Dehloran, south of Ilam province, according tothe type of study and physiographic types and soil units. Then, pH and EC values were measuredusing standard methods. Soil salinity values were evaluated using correlations between EC electricalconductivity values obtained from ground data and variables obtained from Landsat 8 satellite imagesincluding bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinityestimation model was obtained using stepwise regression method. This method involves the automaticselection of independent variables, and with the availability of statistical software packages, it ispossible to do so even in models with hundreds of variables. In previous studies, indicators and bandshave been used separately and in a limited way, but in this study, an attempt has been made to use acombination of different indicators more widely, and finally to achieve the best relationship byeliminating the indicators that have the least impact on soil salinity estimation. Using significant levelanalysis and correlation between the output of models and ground data, the best model with a value of(R2 = 0.882) was selected and a soil salinity map was prepared based on it. In the study area, thehighest area belonged to non-saline class which comprises 75% of the total study area and about 1%of the soils belong to the saline class.

Today, soil erosion and sediment production are considered a danger to humans and their lives, so in areas where soil erosion is not controlled, soils gradually erode and lose their fertility. The aim of this study is to model the relationship between the amount of suspended sediment load and the geomorphic features of the Nirchai River Basin.

In this study, GIS was used to extract geomorphic features and its relationship with sedimentation. Stepwise multivariate regression analysis was used to determine the relationship between geomorphic characteristics and sedimentation of each sub-basin. The results of investigating the relationship between geomorphic characteristics and sediment in sub-basins showed that the amount of sediment produced had a positive correlation with the volume of flow and the form factor of the basin and was significant at the level of 5 percent. Also, in order to identify the factors affecting the amount of sediment in the basin among the existing variables, the principal component analysis method was used.

The results show that among 25 variables affecting sediment production, four factors of area, basin coefficient, environment and discharge were 37/78, 25/47, 18/5 and 11/22 percent of variance of all variables of research.

Therefore, it is concluded that in total, all the effective factors of the four final extracted factors were able to explain 92.9 percent of the variance of all research variables.

Reference evapotranspiration (ET0) is a climatic parameter and can be computed from weather data. It is one of the most important hydrological parameters for calculating crop water demand, scheduling irrigation systems, preparing input data to hydrological water-balance models, regional water resources assessment, and planning and management for a region and/or a basin. The climatic data from synoptic stations with more than 20 years continues record in West Azarbaijan province was used. The well-known FAO-PM56 method was used to calculate the ET0. Then Multiple linear Regression (MLR) was used to estimate the ET0. The RMSE, MEA, NSH, and R2 were used to evaluate the performance of the MLR model. Then, the correlation coefficient (r) between ET0 and each of the meteorological parameters was obtained. And finally, with using Path analysis method, the influence of direct (P) and indirect effects of the meteorological parameters on ET0 was calculated. In the studied synoptic stations, NSH between 0.91 and 0.99,   R2 between 0.91 and 0.99, RMSE between 0.05 and 0.15, and MEA between 0.04 and 0.12 were obtained. Also, it was found that the wind speed at all of stations had a significant correlation (at the 0.01% level) with ET0. The path analysis results showed that the maximum value of P (direct effect of meteorological parameters on ET0) in all of the stations belongs to wind speed. The P value of wind speed in Urmia equal to 0.85, Piranshahr equal to 0.99, Takab equal to 0.97, Khoy equal to 0.90, Sardasht equal to 1.06, and Mahabad equal to 0.78 are obtained.

In recent years, the phenomenon of climate change and drought has become a global problem in the arid and semi-arid regions of the world. Climate change as a problem in the annual bio-farming cycle causes extinction of plant and animal species, reduced vegetation richness, impaired and reduced fertility severity in animals, changes in the pattern of migration of birds and animals (due to new habitats or food sources New) and changes in the spawning pattern of fish. Droughts and floods are one of the most severe climatic events that are likely to change faster than the average climate of any region. Today, access to freshwater resources is a very important issue in most countries, including the Middle East and Iran, according to FAO statistics, while the Middle East accounts for 14 percent of the Earth's surface, accounting for only 2 percent of water resources. The drying up of internationally valuable lakes and wetlands, the lowering of rivers to crisis levels, and the exposure of people in 12 provinces to drinking water shortages are among the consequences of a nationwide drought. Droughts have been particularly prevalent in the tropical and subtropical regions since the 1970s. Reduced ground precipitation and increased temperatures, which increase evaporation and decrease soil moisture, are important factors that have led to more drought zones. Recent droughts have emphasized the need for more research into the causes and effects of droughts and the need for additional planning to help reduce the potential consequences of future droughts. On the other hand, some studies consider the increase in greenhouse gases and disruption of sunlight transfer to and from the earth to the atmosphere as a reason for the recent drought. In the present study, monthly changes of atmospheric carbon dioxide and monthly changes of total water storage in the period 2003-2015 in Iran were investigated. Combined data with the Obsm4MIPs algorithm of GOSAT satellite and SCIAMACHY sensor were used to obtain the trend of changes in carbon dioxide concentration and GRACE satellite data for changes in total water storage from 2003 to 2015. The results of the canonical correlation show a strong relationship between carbon dioxide concentration and changes in total water storage. Stepwise regression model was used to model the relationship between changes in total water storage with CO2, discharge rate and groundwater consumption. The results of regression model showed that carbon dioxide with R2 = 0.91 had the highest relationship with total water reservoir changes in the model. It is noteworthy that the identification of these relationships on a large scale is tangible and at the local scale management practices are more influential in changing water resources, especially groundwater.

Introduction:

 Soil degradation by water is the most serious form of land degradation in many parts of the world, especially in arid and semi-arid areas, where soil formation rates are usually less than soil degradation due to rapid soil erosion, the impact of human abuses And incorrect use of soils. For this reason, crushing land control strategies such as agricultural agriculture, mulch, environmental improvement or land expansion are necessary to avoid drought in agricultural land. Awareness of the process of soil erosion and sediment transport as an effective factor in reducing soil fertility and soil loss, filling dams, catching and blocking irrigation channels, polluting water from rivers, and reducing water quality have long been considered by geoscience experts. Understanding the factors affecting sediment production plays an important role in determining the amount of sediment yield of a basin and understanding the phenomenon of erosion and its consequences and can be used to prioritize sub basins in a watershed. Areal characteristics encompass morphological characteristics such as drainage density, stream frequency and watershed shape parameters. Ease access to Digital Elevation Models, remote sensing data as sediment yield predictors, simplify the calculation of the watershed geomorphic characteristics. The purpose of this study was to use the geographic information system to extract the watershed geomorphic characteristics and determine their relationship with sedimentation in the Gharanghoo basin.

Methodology:

This study was conducted in 19 subwatersheds in Gharanghoo basin. In order to select appropriate subwatersheds, the hydrometric and rainfall data for hydrometric and meteorological stations were obtained from East Azarbaijan Regional Water authority for the selected watersheds. Annual sediment load was calculated using sediment rating curve method. Physiographic and geomorphic characteristics including 25 geomorphic parameters were calculated for each sub watershed using digital elevation model with spatial resolution of 30 m. In order to determine the relationship between geomorphic characteristics and sediment yield of the subwatersheds, a multivariate regression stepwise analysis was used. In the multivariate regression, the important geomorphic characteristics which affect watershed sedimentation are identified and based on those parameters, the best annual sediment yield and geomorphic characteristics equation were presented.

 Results:

 The annual amount of sediment varies from 63500 tons per year in the Kalghan sub basin (Kalghan dam) to 4636762.6 tons per year in the gharanghoo area at the intersection with Ghezel Ozan. Basin sedimentation weight as dependent variable and other parameters were considered as independent variables. The variables of flow volume, area, environment, equivalent rectangular length, equivalent rectangular width, drainage density, branching index, minimum height, coefficient of elongation and roughness of the basin were compared. Other variables have higher correlation with sediment yield. The result of the study of the relationship between geomorphic characteristics and sediment of sub-basins showed that the amount of sediment produced with flow volume and basin coefficient was positively correlated and was significant at 5% level. The principal components analysis (PCA) method was used to identify the factors affecting sediment yield of the existing variables. The results show that the four factors of area, area, length and coefficient of form of basin are 50, 20.9, 13.6 and 7.7 percent of the variance of all variables, respectively. In total, the four finalized factors have been able to explain 92.2% of the variance of all research variables.The results show that the four factors of area, area, length and coefficient of form of basin are 50, 20.9, 13.6 and 7.7 percent of the variance of all variables, respectively. In total, the four finalized factors have been able to explain 92.2% of the variance of all research variables.

Discussion & Conclusions:

 The results of this study indicate that there is a significant relationship between the geomorphic characteristics of the studied watersheds and annual sediment yield. Watershed Form factor is a dimensionless index for flood flow and movement, erosion severity and sediment transport capacity of watersheds. This factor is a function of watershed area and length. Run off and amount of flood peak in bigger watersheds will increase sediment yield. Many researches have reported high correlation between rainfall and sediment yield. Arid climate and poor vegetation cover in selected watersheds is the main reason for high correlation of rainfall and sediment yield. Soil erosion and sediment yield will increase due to high intensity and low duration of rainfall along with scarcity of vegetation cover and erodible soils in this region. Overall, study results indicated that with the development of new technologies and the possibility of extracting different physiographic and geomorphic parameters of watersheds from a digital elevation model, it is possible to present regional equations for prediction of sediment yield using geomorphic characteristics that can be used in sediment control and Watershed Management Programs.

Introduction Reference potential evapotranspiration (ET0) is one of the main elements of hydrologic cycle which can be estimated from weather data. This element can be used in calculating crop water requirements, scheduling irrigation systems, preparing input data to hydrological water-balance models, regional water resources assessment, and planning and management of water in a region and/or basin. The use of ET0 permits a physically realistic characterization of the effect of the microclimate of a field on the evaporative transfer of water from the soil-plant system to the atmosphere. It provides a measure of the integrated effect of radiation, wind speed, temperature and humidity on evapotranspiration. The long-term mean ET0 value in a certain time scale (month, season or year) can be changed during the recent decades in a given station. By decreasing ET0, crop water demand decreases, too. Conversely, by increasing ET0 the crop water requirements increases accordingly. Therefore, it can be suggest that change in the rates of ET0 due to climate change would have great importance to agriculturalists and water decision makers. On the other hand, accurate estimation of ET0 is crucial in improving the irrigation efficiency in a region. Many climatic parameters impacted the ET0 value in a single site. On the other hand these parameters are correlated to each other. Materials & Methods The climatic data from the synoptic stations with at least 20 years of continues records in East Azarbaijan province gathered from the Islamic Republic of Iran Meteorological Organization (IRIMO). The obtained data include maximum air temperature (Tmax), minimum air temperature (Tmin), wind speed in 10 m height (U), maximum relative humidity (RHmax), minimum relative humidity (RHmin), and duration of sunshine hours (n). The well-known FAO-PM56 method was used to calculate the ET0. There are many methods for ET0 estimation. The Penman–Monteith (PM) method is recommended as the standard by the United Nations Food and Agriculture Organization (UNFAO) and has gained worldwide acceptance and received much research interests. The PM equation has been widely used in ET0 estimation. However, this method needs more meteorological data which is not available in many regions. This led scientists to use other methods which do not need more parameters. Among the empirical methods which estimate ET0 using less climatic parameters are Hargreaves, Tornth-Wait, Belaney-Criddle and Priestley–Taylor. Unfortunately, output of these models are not accurate in all the sites. Therefore for using these simple empirical models the calibration process should be done as well. Therefore, the following issues need urgent study: (1) selection of as few dominant meteorological variables as possible meteorological parameters affecting ET0, and (2) universal application of an established model in more regions. The alternative method namely multiple linear regression (MLR) can be used to estimate the ET0. In order to evaluate the performance of the MLR method some measures calculated by comparing the results of MLR with FAO56-PM method. These measures are the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R2). Then, the correlation coefficients (ryxi) calculated between the ET0 time series (y) and each of the meteorological parameters (xi). Then, the partial correlation coefficients (rij) calculated between the explanatory variables (xi and xj) as well. Both of the direct and indirect effects of each climatic parameter on ET0 evaluated by path analysis. These effects are denoted by P and Rdc, respectively. By solving the Eq. 6 the elements of P (direct effect of xi on y or ET0 ) are obtained. By multiplying the obtained P vector (direct effects) on r_(x_i x_j ) the indirect effect of xi through the xj on ET0) were calculated. This process repeated for all the selected sites. Path analysis was first proposed in 1921 as a mathematical and statistical method by the geneticist Sewell Wright. Nowadays, the method is broadly used in agriculture and energy demands, revealing direct or indirect relationships between some morphological characters. However, little information is available on the use of this technique to evaluate the affecting factors of ET0. Given the fact that all the meteorological variables are strongly correlated and ultimately lead to multi-collinearity, traditional trend and correlation analyses cannot quantify the interactions among the meteorological factors when filtering the suitable parameters. Path analysis is a type of multivariate statistical analysis for studying relationships among variables, and it can reveal the strength of effect of independent variables on a dependent variable. Path analysis can determine direct and indirect effects of independent variables on the dependent variable, multi-collinear independent variables resulting from their own strong correlations, and optimal regression equations without unnecessary independent variables. The path coefficient is a type of standard partial regression coefficient (without units) that expresses causalities among related variables, and is also a directional correlation coefficient between independent and dependent variables. This analysis conducted for each of the selected stations in East Azarbaijan province, Iran. To do this firstly correlation coefficients between each of the climatic parameters and ET0 time series calculated. Similarly, correlation coefficients matrix between the climatic parameters which affect ET0 obtained for each of the stations. Results and discussion Results showed that the values of MAPE obtained for the stations were between 0.433 and 0.874. However, the R2 values were between 0.972 and 0.9953. Similarly, the RMSE were between 0.042 (mm/day) and 0.982 (mm/day), and the obtained MAE values were between 0.033 and 0.057, respectively. Also, it was found that the wind speed at the stations namely Tabriz, Jolfa, Sarab, Sahand, Maragheh and Mianeh had significant correlation (at the 0.01% level) with ET0. The strongest correlation detected in the station Ahar, which was between ET0 and the wind speed (at the 0.01% level). The results of path analysis showed that the maximum value of P (direct effect of meteorological parameters on ET0 belonged to the wind speed. The P values of wind speed in the stations Tabriz, Julfa, Sahand, Sarab, Maragheh, and Mianeh were equal to 0.637, 0.787, 0.877, 0.578, 0.850, and 0.780, respectively. In the station Ahar, the highest value of the P observed, which belonged to the Tmax (equal to 0.398).

Accurate estimation of ET0 is very important from the view of optimal water management in any region. Wind speed was found to be the dominant direct climatic parameter due to having the largest value of the P. In general, it can be concluded that the causal analysis method can be considered as an effective way to investigate the direct and indirect effects of meteorological parameters on ET0. Overall, it is more reasonable and scientific to apply path analysis method to evaluating dominant meteorological parameters which affect the ET0 in direct and indirect manners. The further research can be oriented in analysis on why dominant factors vary with meteorological stations. Development of other soft computing techniques which calculate ET0 using the climatic methods (such as firefly algorithm, artificial neural networks, support vector regression, and genetic expression programming) and comparing their accuracy with that of the MLR recommended for further studies.

Knowledge of the amount, spatial and temporal distribution of precipitation days is essential to plan.as a strategic axis for the future planning should be considered. The vast territories of Iran between Siberia in the north, the Mediterranean in the West, the deserts of Africa in Saudi Arabian Sea and the largest country in the South West and the East India factor for the interaction of different weather systems on Iran; and one each in the range of years according to a system to bring Iranian, Iran affect climate. Deep interaction, complex and continuous of precipitation caused by climate change and other elements of diversity in space and time was this element is; However, climate studies will be valuable when researchers have provided real time data; weather stations valuable information about the amount and frequency of rainfall courses are available to researchers, however, measurement stations rainfall is usually in population centers or research centers have been special. The aim of this study is knowing what Analyze number of days of rain before Iran, with output in the range of 56 database-annual Aphrodite (01/01/1951 to 31/12/2007 AD) is
In this study, data from the database to Analyze rainy day Iran - is Aphrodite, the Middle East (APHRO_ME) of the final product of this database as v1101, by resolution 25/0 × 25/0 by 56-year period (1 / 1/1951 to 31/12/2007 AD) is used. To divide the country into zones rainfall zone stepwise regression methods as well as for zoning rainy days rainy days kriging method has been optimized.
Has in every area of rainfalls and the time is different. Skewness provided shows that the spatial distribution of precipitation is skewed to the right, the low-rainfall areas than in areas with high rainfall. According to the terms dynamic and thermodynamic systems, causing precipitation and depending on your geographic location, in dealing with local conditions can cause precipitation regions differently. Therefore, the amount of precipitation has statistical parameters will be different. The difference in median, mean and deviation indicates that the data does not follow a normal distribution.
The number of rainy days between 9 to 147 days. Iran is the average number of rainy days is 38 days, while the number of rainy days is 36/62% of the area of the country less than 38 days. The region's rainy day in terms of number of rainy days 147 South West of the Caspian Sea (32 km south of West synoptic Bandar Anzali) is located. Similarly, the lowest number of rainy days with 9 days in South East Iran Iran is located 116 kilometers East of Khash synoptic stations.
The results showed that the average of rainy days is 38 days, however, the number of rainy days is 36/62% of the area of the country less than 38 days. The maximum rainfall Iran on the Caspian Sea is located at 147 days in the South West, on the other hand a minimum of 9 days rainy days in the South East is Iran. Iran was divided into four zones Likewise, Iran's biggest drawback is divided into four zones rainy days of the entire north coast, the northern part of North Khorasan, North West and West Highlands in a group. Finally, the division offered the best division's zone of rainy days, divide the country into six zones were detected. The six zones are zones Khzrry with rainy days 126 days, across the mountainous regions of West, North West and Northeast with rainy days 77 days across mountainous 57 days, zone bar between the highlands and lowlands of leeward of the 38 Days of Rain, Finally, the relationship between the number of rainy days with latitude and height above sea level for the entire zone between Iran and six zones was rainy. Relationship provided for the entire region of Iran was presented by a factor of 0.57 determine the most important factor in the equation, longitude were identified. Although differences between the average and maximum number of rainy days Iran with other research such as (Alijani, 1389) and Masoodian (1390), which it named difference with this study is the research database, but acknowledged evaluate.

Recognition of climate and doing research about climatic requirements of farm plants can be regarded as main factors in the production process. According to the importance of the climatic fluctuations in rainfed productions, in this study effect of spatial distribution and temporal variation of climatic elements on wheat yield was studied in dry land farms of Kaleibar and Khodaafarin counties. For this, distribution of precipitation parameters was mapped in GIS environment and year-to-year variety of temperature and rainfall analyzed by drawing of charts. And thus with using wheat growing requirements (including desired climatic conditions), ability to meet the climatic needs of this valuable plant were examined. In addition by running of stepwise multivariate regression model between 16 meteorological parameters and average wheat yield, the role of climatic fluctuations in dry land wheat yield identified and the best predictive model determined. Final results of this study show the fact that the importance of each of temperature and precipitation factors differ according to different phonologic stages of wheat at the study region. Although it seems that the mean annual rainfall in study area appears desirable but large fluctuation from year to year as well as small amount of rain in Khordad(June) are the main limitations of rain fed wheat cultivation. Temperature in germination and grain filling stages are created ideal condition but at the flowering stage temperatures are cooler than optimal physiological threshold. The stepwise regression presented a model with 7 meteorological parameters that can be explain a significant impact of climate parameters on dry land farming wheat yield at 95% confidence level on the study region.