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Rainfall is the most variable climatic parameter. These changes occur both in space and time scale which identify the climate of the region. The aim of this study is to evaluate the spatial autocorrelation of precipitation in the Khorasan Razavi province. For this purpose, precipitation data were obtained from12 synoptic stations in Khorasan Razavi. To acquire the variations in seasonal precipitationin Khorasan Razavi, modern statistical methods were applied such as Spatial Autocorrelation, Global Moran's Index, Local Moran's I index, Hotspots and cluster analysis (Clusters/Outliers). These methods were applied using Grads, MATLAB and ArcGIS Software Packages. The results showed that rainfall in the Khorasan Razavi province has a high clustering pattern. According to the local Moran index and Hot Spots, rainfall has positive spatial autocorrelation patterns (clusters of high rainfall value) in the central, northwest and northern parts. This pattern of rainfall, at confidence levels of 90%, 95% and 99%, covers totally 25% of the province areas. The southern regions have negative spatial autocorrelation (clusters of low rainfall value) which totally cover an area equal to18 percent of the province. In the other areas of the province which cover 57 percent of the entire area,no significant pattern has been observed. This indicates heterogeneity of rainfall over the province. The present study shows that altitude which is one of the local factors is the most important factor which influences the distribution of rainfall pattern over the province.

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Abstract
Flood spreading in mountainous regions is very different as compared with plains. Flood spreading in Koohdasht, Roomeshkan and keshmahour plains includs gabion structures, stilling basins, diversion dikes, banquets, and channels. An average of 25 million cubic meters per year infiltrate into the plain. Planting 170000 tree species results in an increase in forage production and a reduction in flood damage. Statistics indicate that the plain Roomeshkan has not been faced with reduced groundwater levels and discharge rates of the wells are normal.
 

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It is important to study moisture in the atmosphere for irrigation management and determining long-term plans for water resource management. In this study, data on climatic parameters of rainfall, minimum, maximum, and average temperature were investigated to determine the moisture index, effective rain, potential evapotranspiration and soil moisture requirements at 12 meteorological stations in northeastern Iran.In the present study, the experimental method of Papadakis was used for estimating potential evapotranspiration and the USDA method was used to determine the effective rainfall in CropWat. 8 environment. Monthly rainfall was determined using the ratio between effective rain, evapotranspiration and rainfall.The results of the processes at the station level were determined based on the IDW interpolation method in ArcGIS 10.2. The results showed that North Khorasan and South Khorasan provinces have high and low humidity conditions, respectively. Most effective rainfall occurs in March and April. Effective rain decreases from north to south. Evapotranspiration potential increases from north to south. The northeastern region has poor moisture conditions. Except for cold regions of North Khorasan, other areas are severely exposed to aridity, and moisture stress is observed for most crops during the hot months of the year. Analyzing the moisture index showed that the moisture index decreased significantly from north to south of the study area. The region has a high potential for evapotranspiration, and in fact, the input of moisture through precipitation is less than the amount lost due to evapotranspiration. South Khorasan faces one of the most critical conditions of potential evapotranspiration. The length of the wet season in the region, except for the North Khorasan province, is short. In areas of South Khorasan, there is almost no wet month. The spatial distribution of moisture indices follows the latitude and regional ripple patterns. The results and findings of this research are important in the planning and management of water resources, agricultural programs and operations.
 

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One of the landscape management approaches is the construction of bridges along the rivers. On the other hand, the bridge scouring is a serious damage to river engineering as the main source of water and sustaining planet life. Accordingly, in this research, using field data, the accuracy of empirical methods, genetic algorithm (GA), least squares support vector machine (LSSVM), and combined method were compared in estimating scour depth of simple bridge piers. In the GA method, a number of empirical relationships were modified and the results of these modified relationships were compared with the measured scour values. In the LSSVM method, through the input of different independent parameters, model training was performed, and scour depth was predicted. In the combined method, using the LSSVM model from combining the results of different individual relations, the scour depth of the bridge piers was estimated. The results showed that modified relationships by genetic algorithm and LSSVM model have higher accuracy than empirical methods. Also, if only the parameters used in the empirical relationships are included as input parameters to the LSSVM model, the modified relationships have less error than the LSSVM model. The statistical evaluation criteria of RMSE, E, R², and NSE for the best state of the combined method were 0.4 m, 49%, 0.88, and 0.58 respectively in the training stage and 0.52 m, 50%, 0.7, and 0.38 respectively in the test stage. In general, the combined method estimates scouring depth with higher accuracy than other methods.