رShowers spatial analysis system in the province Mazandaran in Geographic information system (GIS)

Introduction Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal and spatial distribution of rainfall is a useful tool for understanding how non-uniform distribution of water resources and vegetation in each region.Precipitation occurs when the wet weather and the climb factor exist both in the region.In other words, the wet air must rise to a certain height so that it can reach the saturation point due to the subsequent cooling down, and in the next, the cloud produces precipitation.The absence of any of these two factors prevents the occurrence of precipitation. Rainfall variation is considered as a key factor in the structure and functioning of ecosystems, but its impact on scale and magnitude is much less than its spatial variation.The climatic element, especially precipitation, has significant changes in time periods.Therefore, the recognition of the element of precipitation as one of the two elements of the climate and its changes in different times and places allows the optimal utilization of the natural environment.The amount and spatial distribution of rainfall is a fundamental factor for decision making, design and evaluation of hydrological models as well as water management and planning.Temporal spatial variations have diverse and varied impacts on the management and planning of water resources along a water basin and nationally.Climate change is one of the factors affecting the change of water resources.Precipitation, as a highly variable element, has always been a concern for climatologists and waterologists as a fundamental factor in the blue balance. The extreme variability of rainfall along the time-space has a variety of study approaches.The purpose of this research is to identify the conditions of rainfall in the Mazandaran province. Therefore, the location of rainfall in this province was investigated.In this regard, identification of the effective factors of the occurrence of these rainfall in different seasons and their role in the province has been addressed and its results will be available as a scientific and practical solution. Materials In this study, for the purpose of identifying the rainfall in the province of Mazandaran, five years rainfall from 2006-2010 has been used from a total of 12 synoptic stations.Using extracted data from precipitation graphs, rainfall of more than 10 mm was extracted in the studied area.Then the data were categorized into four parts: spring, summer, autumn, winter and year; to create the database, they entered the SPSS and ARC GIS10 software.In the spatial analysis of the data, the semi-modification of these models has been used, which was calculated using ARC GIS10 software.The methods used in the zoning of Kriging and IDW models for fitting include: IDW with three potentials of 1,2,3, and the Kriging method with spherical, circular, exponential, Gaussian, and spherical models, which is performed with conventional Kriging technique.Also, for statistical comparison of models, root mean square error of RMSE, MAE, RMSE and their correlation coefficient were used.Then, optimal mapping based on multivariate regression was fitted based on the simulation method and the recursive method of six variables in rainfall generation including latitude and longitude, number of rainfall days, elevation, relative humidity and dew point temperature. The effects of these factors on rainfall in the province will be evaluated in different seasons and annual. Results&Discussion The results of the spring survey show that there were 5 stations out of 12 stations without rainy rainfall.These stations are located in the plain and in the mountain range of the region.The analysis showed that the correlation coefficient between variables is R^2= 967, which indicates a strong relationship between the set of independent variables and the dependent variable.85.8% of rainfall in the spring season in Mazandaran province depends on these variables. In the summer, only 2 stations in the province did not experience rainfall ranges, both of which were at high altitudes and include the station Alasht and Kyasar.Variables show a very strong relationship in summer with a correlation coefficientofR^2=0.995.Which is 0.99% of rainfall in the Mazandaran province depends on these six variables.The fall season is one of the high seasons in the province of Mazandaran province. Only one station (siahbisheh) has been registered from 12 storm rainfall stations.Estimates show that the six variables analyzed in this chapter with a correlation coefficient of R^2 = 0.983 represent a strong correlation.The results of the winter season show that all stations in Mazandaran province have rainfall, although it includes fewer days than the autumn season.All stations experience at least one day at Alasht Station for up to 7 days in Ramsar.The results of the analysis show that in winter, the correlation coefficient is R^2 = 0.996. Conclusion The method used in the zoning kriging and IDW models for fitting include: IDW with three possible 1,2,3, kriging with spherical model is a model of, circular, exponential, Gaussian and spherical - is the ordinary kriging technique was conducted to assess the accuracy of each map generated by determining the best model calculated health. Also, statistical models for comparing the square root error RMS, MAE, RMSE and correlation coefficient was used for the best model for zonation model IDW and ordinary kriging with the 1.3 was a circle. After extracting the optimal plan based on multivariate regression models and ENTER precipitation method Backward method retrograded create six variables, including the length and breadth of geography, number of days of rainfall, altitude, relative humidity and dew point temperature were fitted. Results show that the six-variable correlation of 0.97 in spring, 0.99 in summer, 0.98 in autumn, winter is 0.99 and about 0.99 per year., Which indicates a strong relationship between these six variables Showers Mazandaran province.