حسین ثنائی نژاد

Present study estimated the large-scale actual evapotranspiration in multiple land use landscape (e.g., Irrigated cultivation, rainfed and pastures) by employing the remote sensing approaches. Measurement of actual evapotranspiration is a difficult process which was calculated as a function of the various climatic and topographical factors for the studied area. Thus, using of models which can estimate the amount of actual evapotranspiration owing to a large and high reliability factor is one of applicable solutions. In recent decades, some methods have been proposed by researchers to determine and measure the actual evapotranspiration. These methods had some limitations and they mainly require a large number of ground-based measurements. Employing the methods based on meteorological stations data from the past has been used as a reference for the estimation of actual evapotranspiration. There are a number of disadvantages of traditional measurement operation such as the lack of sufficient number of measurement points, incomplete meteorological data in many areas, high cost and time consuming of collecting terrestrial information. On the other hand, these measurements are usually based on the weather stations (point based). So, the techniques based on remote sensing data have been developed to tackle these problems. Remote sensing technology has the ability to collect data in large spatial ranges and in short time ranges and the best method to estimate the actual evapotranspiration for areas with difficulty data access or lack of data.

In this study, actual evapotranspiration values were measured by using Surface Energy Balance Algorithm for Land (SEBAL) and Remote Sensing Technique in Fariman area of Khorasan Razavi province. The ground data were used in this study which are included as wind speed, dry temperature, relative humidity, minimum temperature, maximum temperature, sun hours, radiation and evaporation. These parameters were extracted from the automatic and synoptic stations of Fariman. Furthermore, Landsat 8 satellite images were employed to estimate the actual evapotranspiration, that were downloaded from the USGS website in geotiff format. In order to calculate the required parameters, eight days of landsat-8 satellite images (in 2015 and 2016) were used to enhance the proper estimation. In the SEBAL algorithm, the actual evapotranspiration is calculated as the latent heat flux which were determined by using the energy balance equation at the time of the satellite overpass. Because of the large area of study and the impossibility of using accurate evapotranspiration instruments such as lysimeter, eddy covariance and etc., for validation of the results of SEBAL algorithm, the FAO Penman-Monteith standard method was used as the reference. Comparisons among results were considered based on some indexes like R2 and RMSE.

The values of different parameters of the SEBAL algorithm were calculated parametrically. These parameters included as the normalized different vegetation index (NDVI), net radiation flux, land surface temperature, difference between air and land surface temperature, soil heat flux, sensible heat flux and finally actual instant and daily evapotranspiration. Statistical comparison of the SEBAL outcomes with FAO Penman-Monteith method shows that the coefficient of determination is 0.96 and the mean squared error is 0.5 mm per day. These results indicate the high accuracy of SEBAL algorithm in estimating actual evapotranspiration in semi-arid climates with multiple land use landscapes.

In this study, the actual evapotranspiration was estimated for part of the Fariman region with irrigated cultivation, rainfed and pasture lands by using the SEBAL algorithm. This model is a remote sensing method based on physical equations. The developed model which was based on the remote sensing technique estimates the amount of evapotranspiration owing to large - scale areas and areas where meteorological data are not available. The most important limitation of this study was the absence of measured actual evapotranspiration owing to which it was finally tried and examined by the best possible method to validating the estimated values . Therefore, the results obtained from the SEBAL algorithm were compared with the results of the FAO-Penman-Monteith method. Results show that the drift of the values obtained from the SEBAL algorithm was slightly higher than the reference method in some days, and some days it was lower. In total, the accuracy of the final results for the entire study area can be deduced from the accuracy of the results obtained in the considered part of the images and the integrity of the parameters used throughout the images. Finally, by considering the obtained results, it can be concluded that the SEBAL algorithm has reliable outputs for different land use areas and can be used.

Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale.

The behavior of daily changes of relative humidity is quite variable. We first draw the curve of this variable on a normal day. And it can be seen that the distribution of this variable is not normal. The curve of this variable is a skewed curve to the right. Therefore, the equal coefficients could be used only as an approximation for estimating the daily average of relative humidity. Climatic conditions of the meteorological stations are also another parameter to be considered. This research presents a new method for estimating the daily average of relative humidity in three climatic regions of Iran. The patterns for the sample stations in each climatic region were presented separately.

E. Eccel (2012) developed an algorithm to simulate the relative humidity of the minimum daily temperature in 23 weather stations in the ALP region of Italy. In this research, the base pattern was calibrated by temperature and precipitation measurement. Ephrath, et al. (1996) developed a method for the calculation of diurnal patterns of air temperature, wind speed, global radiation, and relative humidity from available daily data. During the day, the air temperature was calculated by: (1) (2) Where S(t): Dimensionless function of time, DL: Day Length h, LSH: the time of maximum solar high h, ta: current air temperature, P: the delay in air Tmax with respect to LSH h. Farzandi, et al. (2012) presented more accurate patterns for estimating daily relative humidity from the humidity of Iranian local standard hours and daily precipitation variables, the minimum, maximum, and average daily temperature in coastal regions. The purpose was to present linear and nonlinear patterns of daily relative humidity separately for different months (12 patterns) and annually in coastal regions (the Caspian Sea, the Persian Gulf, and the Oman Sea).Mirkamandar, et al. (2020) modified new patterns of diurnal temperature based on climatically clustering in Iran. The final pattern has an interception and new coefficients to estimate the daily average of temperature. Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating the daily average temperature in arid and semiarid regions of Iran. The final pattern has an interception and new coefficients to estimate the daily average of temperature. (3) Veleva, et al. (1996) showed that the atmospheric temperature-humidity complex (T-HC) of sites located in a tropical humid climate cannot be well characterized by annual average values. Better information is given by the systematic study of daily changes of temperature (T) and relative humidity (RH), which can be modeled by linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters. Which were used in the present work. First, a classification that provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation, and three indices of Demartonne, Ivanov, and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range, arid and semi-arid zone. Several clustering methods were used and the around method was found to be the best. Cophenetic correlation coefficient and Silhouette width were validation indices. Homogeneity and Heterogeneity tests for each cluster were done by L-moments. The “R”, software packages were used for clustering and validation tests. Finally, a clustering map of Iran was prepared using “GIS”. The data of 149 synoptic stations were used for this analysis. Systematic sampling was done to select sample stations. The linear regression model was fitted after screening and data preparation. A model was presented for estimating the daily average temperature in each climatic region and sampling stations in each cluster. The best models were presented by reviewing the required statistics and analyzing the residuals. The calibration and comparison of the presented patterns in this paper with commonly applied models were undertaken to calculate the mean squared error. “SPSS.22” software was used for analysis.

The coefficient of determination (R2) and the Fisher statistics showed that the patterns had a good ability to estimate the daily average of relative humidity. The daily average of relative humidity patterns confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The mean squares errors were a measure of the applicability of patterns. The accuracy of the estimating daily average of relative humidity recommended models in three climates was confirmed by calculating the mean squared errors. The proposed patterns of this study had less error than the common patterns.

The relative humidity at 3 pm was more effective in estimating the daily average. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The averages of the residuals in each pattern was zero. According to the graphs, stabilization of variance can be seen based on the residual on each pattern in each cluster. Proposed patterns were calculated according to mathematical principles. But the common patterns did not observe these mathematical principles. The mean squares errors (MSE) of proposed patterns were less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns.

One of the issues in the Meteorological Organization is the lack of documentation of errors in the process of producing, measuring, recording and preparing data in the atmospheric observation system.  Also, the poor data quality, missing  data, inappropriate spatial density  of meteorological stations are the major problems faced by climatology researchers in Iran. Nonstandard distance of stations, inappropriate co-locating of stations, human errors in reading and recording data, errors in measuring equipment, different methods of measurment, Non maintenance and calibration of station, constructions around the stations, changes in the type of instruments and sensors for atmospheric parameters measurement and station relocation during the statistical period are other problems that affect the accuracy of the meteorological data.

The aim of this study is to investigate and document the problems in the system of monitoring and interpretation of atmospheric data in Iran with a pathological approach. To that end, articles, specialized books, high-end documents such as World Meteorological Organization documents, programs, plans and documents of the Meteorological Organization, guidelines and directives, were reviewed. In addition, field assessments and case studies were conducted using  methods such as preparing a questionnaire and interviewing experts.

The results of the questionnaire and interviews with experts showed:1-  Synoptic experts have between 70% to 90 % of the required skills.2-   Respondents knew between 70% to 90 % of the standards and guidelines.3-   Between 50% to 70% of the standards are adhered in the network of meteorological stations.4-  Between 50% to 70 % of the required periodic calibrations on equipment are performed at standard intervals.5-  Between 50% to 70 % of the equipment used at the stations are accurate and standard.6- Not calibrating and maintaining  stations  reduce  accuracy of data between  10% to 30%.7- Only 10% to 30% of the stations have complete metadata.8-Upper air facilities supply between 50% to 70% of the meteorological requirements.9-  Sanctions have reduced our access to upper air, radar and satellite data between 50% to 70%.10- Station automation policy, due to reduced human interference in the data process, if implemented correctly, can increase data accuracy between 30% to 50% on average.11- If the station automation policy, is implemented regardless of regular training of experts, periodic calibration on equipment and continious quality control of data, it can reduce data quality and accuracy between 30% to 50%.12- The mental condition of the synoptic experts, the working and management issues can affect between 70% to 90% of the observer's performance and data accuracy.13-  Between 30% to 50% of meteorological station data need to be homogenized.14- For estimating temperature and precipitation parameters in non-station locations, using interpolation methods  were found appropriate between 50% to 70%.15-  Remote sensing methods were found appropriate for estimating temperature and precipitation parameters in non-station location  between 70% to 90%.16-  Between 30% to 50% of meteorological satellite data are available online to our researchers.17-  Available meteorological radar facilities only provide 30% to 50% of the  radar data required.18-   Due to restrictions, only about 50% to 70% of radar’s locating were appropriate.19-For about 30% to 50% of radars, studies on evaluating and improving the uncertainty in radar data based on the calibration and correction coefficients were taken . 20-   Allocate appropriate government funding to the Meteorological Organization can reduce over 90% of the problems caused by inadequate access to remote sensing data (such as satellites and radars). The following are some of the major  probems  at the station during field observation. 1- Nonstandard conditions of station and natural climate of the area. 2. Nonstandard methods for observing and adjusting the equipment. 3. Differences in  observing methods. 4- Inaccuracy of the observer. 5. Lack of adequate supervision. 6. Imprecision and inconsistency of data. 7. Lack of support and supply of new meteorological components and equipment.

Based on the results of the questionnaire and field observation, the most problems that affect on the accuracy of  data are as follows.1- Insufficient knowledge and skills of synoptic experts in the data process, lack of complete knowledge the relevant standards and rules, lack of compliance with the standards and established rules, lack of precision in the work process and insufficient supervision of the correct process of work by the authorities.2. Construction of stations in non-standard conditions, Non maintaining and non calibrating of station over time and finally station relocation during the statistical period.3. Not performing periodic and regular calibrations of equipment at standard time intervals, using of different types of equipment with different brands in the meteorological stations and imprecision and inconsistent data of these brands with each other at standard level, insufficient infrastructure for automatic calibration. Lack of spare parts and support and software problems with these systems 4. Organizational and managerial issues that can directly affect the motivation, mental condition and performance of the observer and  therefore the accuracy of the data.5. Sanctions have a significant impact on the provision and support of new meteorological equipment and radars.  As well as, a lack of sufficient financial resources has contributed to the organization's inadequate access to new meteorological instruments and so to this type of data.

Drought is a creeping and gradual phenomenon that can cause irreparable damage in many fields such as agriculture, food security, water resources management and the economy. It is essential to accessing a tool that can measure agricultural and hydrological droughts based on the dynamics of the relationship between land surface-atmosphere -not necessarily precipitation and air temperature- and provide early awareness for managerial decision-making. What makes drought monitoring important to us is the impacts on the agricultural sector (agricultural productivity, access to food security, agricultural and livestock insurance), water supply management and economic and social impacts. The concept of evaporative demand drought index (EDDI) reflects thirst for the atmosphere and is easily and realistically available in near-real-time based on physical climatic drivers of air temperature, wind speed, solar radiation and humidity, providing comprehensive information on drought dynamics.  The use of an indicator that can indicate the dynamics of drought in the shortest possible time will help to make managerial decisions and different levels of policy making to announce early operational warnings in the field of agriculture and reduce the social and economic consequences of this phenomenon. In this regard, there is a need for gridded data that can provide the required data set and compensate for non-uniform network data gaps or satellite data limitations. near-real-time networked data such as ERA-Interim is also useful in regional and extensive drought monitoring.

In this study, the ERA-Interim reanalysis data from ECMWF database was used to estimate the evaporative demand drought index in different climatic conditions of Iran and its ability in drought monitoring was investigated. Using probabilistic methods, the ASCI-PM method to estimate atmospheric evaporation demand, the EDDI index was calculated as the index of hydrological drought in Iran in different time scales during 1979- 2017. Also, the EDDI index was evaluated against the SPI and SPEI common drought indices.

The results of evaporation demand estimates in the country show that seasonal variation and climate variability are factors that change the rate of evaporation demand. As a result of the interaction of the governing climatic factors, different climatic zones are created throughout the country and each region experiences different evaporation rates throughout the year. The EDDI index compensates for the gap between the theory of drought and operational drought management in determining and monitoring persistent drought as soon as possible between the occurrence of the phenomenon and the available data available. Significant correlation coefficients at monthly, seasonal and annual scales between EDDI and SPEI index indicate the important role of evapotranspiration in drought monitoring at arid and semi-arid regions so can offset the weakness of SPI in low rainfall areas. It has the ability to monitor short, medium and long term droughts earlier than other common indices, such as SPI and SPEI. The EDDI is capable of reporting a variety of persistent droughts without the need for precipitation data. Rapid response to environmental drying and humidification, processes caused by interactions between the atmosphere and the Earth's surface, making this index more flexible and advanced than other common indices. The longer the cumulative period of drought, the greater the time the indicator progresses.

The EDDI indicator is an easy tool for operational early warning, fire hazards, seasonal to seasonal drought prediction and long-term hydrological drought Monitoring at any time scale (e.g. seasonally). It can also be used to predict longer periods (annual or multi-year), so compensates the gap between sub-seasonal to seasonal forecasts. An important advantage of using networked data in calculating the EDDI index is that applicable at all times of the year - on cloudy days or for areas with snow cover, to complete the data due to satellite transit times and Delay in data access - no restrictions. Since atmospheric evaporation demand in the EDDI index is considered to be the cause of the drought, when combined with satellite data, EDDI and ESI (Green Water Index) combine to show real drought stress. Give. It is also recommended that MODIS, LANDSAT and ALEXI products be used to evaluate transpiration values and compare the EDDI index with satellite data and compare the results. The EDDI index is capable of decomposing to atmospheric governing factors such as radiation and advection components. By sensitivity analysis, it is possible to determine the main governing factors such as temperature, wind, short wavelength radiation and specific humidity on drought in each region, Determining and provided additional understanding of the dynamics and assessment of drought.

The aim of this study was to investigate cloud’s characteristics and some effective physical indices on rainfall in Mashhad. We initially studied cloudy sky in Mashhad by consulting meteorological yearbook of 1986-2010. Density and prevalence for medium and low clouds as well as density, prevalence and average for vertical clouds (type 2 cumulus and cumulonimbus) were calculated for every month. This study shows that in Mashhad the highest frequency of low and medium clouds occurs in cold and rainy months during winter and spring, while most of the clouds with vertical development occur in spring. We also studied freezing level in clouds in both rainfall and no-rainfall cases, using skew-T diagrams and radiosonde data during 1992 to 2011. The condition considered here was overcast sky. We obtained a regression equation in order to specify role of physical indices (CAPE، SI, K، PWT) in precipitation. At first, rainfall days of Mashhad were extracted from Khorasan Razavi meteorological department archive (1992 to 2011). On these days, six hour rainfall (00 to 06 Z) were determined. Then, instability and physical indices were obtained from radiosonde data of Mashhad weather station. A regression equation was estimated by analyzing the relationship between natural logarithm of six-hour rainfall as dependent variable and instability indices as independent variable. This research determines which relationship between some indices with natural logarithm of six-hour rainfall is nonlinear. Analysis of regression variance shows that regression was significant at 99% level. This means that at least one of the indices has a linear relationship with logarithm of six-hour rainfall. In this equation, due to significant level, CAPE and PWT were removed. Most relationships were found between SI and natural logarithm of six-hour rainfall.

Rainfall modeling is essential in water resources and agriculture management, especially in arid and semi-arid climates. Problems in generalizing precipitation from a point to a region are the reasons for alternative methods to estimate precipitation, and one of these methods is the use of remote sensing. Across the globe, research is being conducted to evaluate the accuracy of remote sensing precipitation data. Accordingly, the aim of this study is to model the prediction of TRMM Multi-satellite Precipitation Analysis (TMPA-3B43) data in arid and semi-arid climates of Iran, using the SARIMA simulation model for the period of 1998-2017 in seasonal scale.  

For this study, monthly precipitation data from TMPA 3B43-v7 were selected from two different climates. To ensure the reliability of the results, a completely randomized selection of four regions was made from different provinces and topographic conditions. The TMPA satellite data (3B43-V7) was prepared by the NASA databases during the period of 1998-2017. These data have a time-stamping time resolution and spatial resolution of 0.25 degrees, covering from 50 degrees south to 50 degrees north of latitudes. Satellite pixel data was corrected using the corresponding synoptic station data. Monthly data after correction were aggregated to seasonally data. Spring data from April, May and June, the summer data from July, August, and September, autumn data from October, November, and December, and winter data were obtained from January, February, and March. Using the autocorrelation function (ACF) and partial autocorrelation function (PACF), time series models were determined. The normality of the residuals, the independence of the residuals and the constant of the variance of the residuals was checked out. A general model was fitted to the data to check the accuracy of the selected model. The result showed that the selected model has the least error compared to other models. After calibration and evaluation of the selected models, seasonal precipitation data was predicted based on the monthly scale for the period (2018-2021) and compared with the base period (1998-2017). The evaluation was measured using some statistical indices including AIC, R, MBE, MAE, and RMSE.  

The correlation coefficient based on monthly data changes from 0.81 to 0.9 in the studied areas which confirms the high accuracy of modeling based on SARIMA technique in monthly data for seasonal prediction. Best model for corresponding pixel of Behbahan station was SARIMA(0,0,1)(1,1,1)12 and SARIMA(0,0,0)(2,1,1)4, Zarghan station was SARIMA(2,0,0)(1,1,1)12 and SARIMA(0,0,0)(2,1,2)4, Kashan station SARIMA(0,0,0)(1,1,1)12 and SARIMA(0,0,1)(2,1,1)4 and Sirjan station SARIMA(1,0,1)(1,1,1)12 and SARIMA(0,0,0)(2,1,1)4 for monthly and seasonal modeling respectively. The mean bias error in the monthly and seasonal periods of the Kashan station corresponding pixel (0.2 and 0.5 respectively) and the monthly period of the Sirjan station corresponding pixel (-0.3) was obtained. Seasonal modeling based on monthly data was compared with seasonal prediction data. In the autumn, the same as the spring season, seasonal precipitation increases in three regions and decreases in one station. In the summer, it was observed relative stability in seasonal precipitation at all points. In winter, unlike the spring and autumn seasons, seasonal precipitation was decreased in three areas.  

The results showed that if the predicted monthly data turned into seasonal data, they would be more accurate for seasonal forecasting. Generally, based on annual precipitation, the studied areas will have a relative precipitation reduction of 1.8 mm per year. Regarding the average annual rainfall in the four studied areas, the average annual precipitation is estimated to be decreased by about 1 percent in the study period. Separately, annual precipitation changes are -1.3%, 1.1%, 1%, and 1.8% in Kashan, Sirjan, Behbahan, and Zarghan areas respectively.

Precipitation as one of the most important parameters of meteorology and climate, is basic factor in water resource management. This factor has a direct relation with the regional climate. The accuracy of simulating this parameter is very important due to its wide variation. Observation data at Iran's first synoptic stations from 1330 (1951) is available at the Iranian Meteorological Organization website. Old and long-term temperature and monthly precipitation data in five cities of Iran Including Mashhad, measured by the Embassy of the United States and Britain from the Qajar period (around 1880) and recorded in World Weather records. Unfortunately, these data have missing. Monthly missing data are during World War II (1949-1949) and sporadically during the statistical period. Stations from neighboring countries due to the Parity criterion, solidarity and completeness of data in missing periods selected as base stations. Monthly precipitation of Ashgabat Station from Tajikistan and monthly rainfall of Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as independent variable in the making of Missing Rainfall in Mashhad. Three factors of distance to Mashhad station, correlation and existence of data in missing months were effective in selecting these stations. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. Ant colony optimization algorithm (ACO) is probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. The repair of the monthly precipitation of Mashhad with these stations has been done with ten regression linear, semi-logarithmic and logarithmic regression models as follows. This was done with programming in the R-Studio environment. The parameters of the five selected patterns were optimized by evolutionary methods (genetic algorithm and anion colony algorithm). Simulation of these methods has been done with the help of MATLAB software 2017. The results showed that the genetic algorithm and Ant Colony methods Ratio of regression methods , dramatically increase the accuracy of estimating missing rain data. The lowest RMSE regression pattern is 9.79, which is optimized by genetic algorithm to 2.66 and by Ant Colony algorithm to 2.659. Precipitation as one of the most important parameters of meteorology and climate, is basic factor in water resource management. This factor has a direct relation with the regional climate. The accuracy of simulating this parameter is very important due to its wide variation. Observation data at Iran's first synoptic stations from 1330 (1951) is available at the Iranian Meteorological Organization website. Old and long-term temperature and monthly precipitation data in five cities of Iran Including Mashhad, measured by the Embassy of the United States and Britain from the Qajar period (around 1880) and recorded in World Weather records. Unfortunately, these data have missing. Monthly missing data are during World War II (1949-1949) and sporadically during the statistical period. Stations from neighboring countries due to the Parity criterion, solidarity and completeness of data in missing periods selected as base stations. Monthly precipitation of Ashgabat Station from Tajikistan and monthly rainfall of Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as independent variable in the making of Missing Rainfall in Mashhad. Three factors of distance to Mashhad station, correlation and existence of data in missing months were effective in selecting these stations. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. Ant colony optimization algorithm (ACO) is probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. The repair of the monthly precipitation of Mashhad with these stations has been done with ten regression linear, semi-logarithmic and logarithmic regression models as follows. This was done with programming in the R-Studio environment. The parameters of the five selected patterns were optimized by evolutionary methods (genetic algorithm and anion colony algorithm). Simulation of these methods has been done with the help of MATLAB software 2017. The results showed that the genetic algorithm and Ant Colony methods Ratio of regression methods , dramatically increase the accuracy of estimating missing rain data. The lowest RMSE regression pattern is 9.79, which is optimized by genetic algorithm to 2.66 and by Ant Colony algorithm to 2.659.

Climate change increases the repetition and frequency of droughts and this event has undergone significant changes. Water resources and agriculture in Iran has not been spared from the harm of these changes. In this study, because of the importance of Kashafrood basin in the North-East of Iran, the meteorological droughts in the region over the next three years (2027-2018), (2037-2028) and (2047-2038) will be discussed. Since the Standardized Precipitation Index (SPI) is one of the most useful indices of drought, this index was calculated in this study. Numerical calculations and dynamic models are the most accurate sources for prediction of meteorological variables. In this research, the precipitation outputs of the EC-EARTH model under the RCP4.5 scenario that has been downscaled with RCA4 dynamic model were used. According to the resolution of the RCA4 dynamical model, the Kashafrood basin was divided into 6 pixels (0.44 at 0.44 degrees) and then assessment values were determined on each pixel. The results showed that the EC-EARTH model is competent in the prediction of precipitation at a confidence level of 99% with a correlation coefficient averaged 64%. The average number of dry and very dry months was calculated on the whole basin. Analysis of the results in the next three years compared to the observation period (2016-1987) showed that the number of droughts will increase, but the severity of future droughts will be reduced

Todays, in addition to the optimal hydraulic design of water distribution networks, the optimization of energy consumption in pumping station is more important for researchers. Due to the energy costs include the major part of operation cost at a water network, in this paper study the optimization of daily energy cost of pumping station with five parallel pumps, using by Darwing scheduler module at WaterGEMS V8i software based on Simple Genetic Algorithm (SGA) and Fast Messy Genetic Algorithm (FMGA). The hydraulic constraints include the minimum pressure and maximum pressure of junctions, maximum velocity of pipes and maximum number of economic off and on of pumps. The results showed that the energy costs reduced 15 and 10 percent using by Simple Genetic Algorithm (SGA) and Fast Messy Genetic Algorithm (FMGA), respectively, with regard to electricity tariffs in To optimize the performance was achieved without pumps.so we achive 10 and15persent reduce energy cost.

In this study, trends in the monthly, seasonal and annual river discharge and rainfall by use of nonparametric test such as Kendall, Mann-Kendall, sen,spearman and pearson parametric correlation in Dargaz Daroongar basin were studied. For this purpose, discharge data from hydrometric stations Mohammad Taqi Beyk,sang surakh and Golkhandan and precipitation data from Dargaz, Mohammad Taqi Beyk, sang surakh and shamkhal stations were used. The results showed that the seasonal and annual trends were decrised in stations Mohammad Taqi Beyk and golkhandan. Pearson parametric and non-parametric Spearman were used to illustrate the relation between rainfall and river flows in a region and a significant positive correlation between summer rainfall and river flows have been approved by both tests

In order to grow plants in any region، suitable weather and climatic condition are required. Climatic parameters such as precipitation، temperature and humidity have significant roles in the growth and yield in a region; therefore، understanding the climate and analyzing the ecophysiological characteristics of plants are the most important factors in the yield of the plant. Saffron is one of the profitable plants in South Khorasan. The yield of saffron from this region has caused Iran to be the targets producer of this plant in the world. The significance of planting saffron in Iran is related to many factors such as the high efficiency of water usage and employment of villagers. The analysis of the effect of climatic parameters on the saffron yield and determining the suitable areas for planting this plant according to these parameters are important for agriculture and economy. The statistics and data of 20 years taken from the Weather Station in the region and the 10 years saffron yield were used. Regression analysis and create of equation using precipitation، relative humidity، and the relation between these parameters was accomplished by the use of JMP 4 software. Digitized climate zonation maps were made by Arc GIS 9. 2 software. The results showed that precipitation was most effect on the saffron yield during the months of December، Feburay، March and April compared with the other months and considering humidity، the most affected months are October، November، Decmber، January and February. Also، after analyzing the equation and the climate zonation maps and the final map، it became obvious that the most of the areas of the Province were able to be ranked as suitable. The North and North-Eastern regions were the best areas regarding the parameters discussed in order to grow saffron. The center of province were considered average region to grow saffron and the Southern and South-Western areas were determined the least suitable for growing saffron.

Remote sensing is one of the techniques recently used for the purpose of monitoring and predicting crop yield. The objective of this study was predicting wheat yield in Mashhad during 2009 and 2010 by Landsat (TM) and (ETM+) images with ground based data. For this purpose, three images dated 6, 14 and 22 Jun 2009 and 8 and 24 May and 9 Jun 2010 were analysed and vegetation indices were computed. Field data included wheat growth stage, yield and geographical position. Results showed that the best relation between yield and indices were obtained in 14 Jun 2009 and 14 May 2010. These dates coincided with milky and maturity stages of wheat. Cumulative indices estimated yield prediction more precisely than others indices. Red band had the highest correlation (r2=0.76) with yield among the other band. Through the other indices, PD311 and PD312 had the best correlation with the yield. Correlation between NDVI and yield was not significant. The best correlation with the yield was obtained by applying the multi linear regression method (r2=0.83).

Precipitation is a key input to different crop and hydrological models. Usually، the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network، this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However، direct application of these techniques may produce inaccurate results. In the last years، applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper، CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale، but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0. 31 in daily scale. The highest correlation in monthly scale was equal to 0. 62 for Barzoo، Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0. 63. Also، the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days، mean annual precipitation and the number of non-rainy days.

Some studies that investigate the climate change and hydrologic balance relationships utilize reference evapotranspiration to either calculate the changes in trends and magnitude of actual ET or to determine changes in atmospheric demand. Some studies utilize temperature or radiation-based empirical equation. Since many climate variables that affect ETo rates have been changing, single-variable equation for estimating the trend and magnitude of ETo should be avoided. The present and future temporal characteristics of ETo are examined in this paper. ETo are calculated during 1961-2005 by the Penman-Monteith recommended by FAO with historical weather data while ETo during 2011-2099 are downscaled from HadCM3 outputs under two emission scenarios(A2 and B2) by SDSM and under A2 by LARS-WG. Results showed that Downscaled ETo by SDSM will increasing during 2011-2099 at a Rate of 1.7 and 0.96 mmyr-1 under A2 and B2 scenario respectively. Averaged over the two emission scenarios, the projected increase of downscaled ETo by SDSM are 4.2%, 7.1% and 12.5% for the three periods 2020s, 2050s and 2080s respectively. Another model has projected 3.4%, 7.8% and 14.9% increases in ETo for these three periods respectively.

The Intergovernmental Panel on Climate Change (IPCC) stated that there is high confidence that recent climate changes have had discernible impacts on physical and biological systems. Impacts of climate change are felt most strongly through changes in extreme climate events, which are responsible for a major part of climate-related economic losses (Jiang, et. al. 2012). The state-of-the art General Circulation Models (GCMs) can reproduce important processes in global and continental scale of atmosphere and predict future climate under different emission scenarios. Since spatial resolutions of GCMs are often coarse (hundreds of kilometer), there is a mismatch of scale between GCMs and the scale of interest for regional impacts. Therefore, a range of downscaling methods have been developed to bridge the gap between the coarse resolution of the climate model outputs and the need for surface weather variables at finer spatial resolution (Wang et. al. 2011). Downscaling methods can be divided into two classes: dynamical downscaling (DD) and statistical (empirical) downscaling (SD). In this study, SD Model was evaluated by downscaling precipitation in the Kashafroud Basin. The statistical downscaling model (SDSM) used in our study here is a hybrid of a stochastic weather generator and regression methods (Wilby et al. 2001). This method includes a built-in transform functions in order to obtain secondary data series of the predictand and/or the predictor that have stronger correlations than the original data series (Wilby et al. 2004). Study area The KashafRoud basin, located between 58° 2´ and 60° 8´ E and 35° 40´ and 40° 36 ´N, totally has an area of about 16500 km2. To the north east of the catchment is the HezarMasjed Mountain, to the south west is the Binaloud Mountain and in the center of the catchment is the Mashhad plain. The climate of KashafRoud river basin ranges from severe semiarid to arid climate. The multi-year average precipitation and air temperature of the basin is about 220 mm and 12/2 °C respectively (Sayari et. al., 2011). Data: The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data during 1961-2001 and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model during 1961-2099. Areal average daily precipitation data of the KashafRoud basin (Mean of four weather stations daily precipitation data) during 1969-2001 was used for downscaling. Modeling of four extreme precipitation In which R means the local predictand, L (l1, l2,..., ln) represents n large scale atmospheric predictors, and F is the built quantitative statistical relationship. SDSM uses large-scale atmospheric variables to condition the rain occurrence as well as the rainfall amount in wet days. It can be expressed as follows (Wetterhall et al. 2009; Wilby et al. 2004): in which i is time (days), ωi is the conditional possibility of rain occurrence on day i, i uˆ is the normalized predictor, αj is the regression parameter and ωi−1 and αi−1 are the conditional probabilities of rain occurrence on day i−1 and lag-1 day regression parameters, respectively. These two parameters are optional, depending on the study region and predictand. We used a uniformly distributed random number ri (0≤ri≤1) to determine the rain occurrence and supposed that rain would happen if ωi≤ri. On a wet day, rainfall can be expressed by a z-score as:In which Zi is the z-score on day i, βj is the calculated regression parameter, and βi−1 and Zi−1 are the regression parameter and the z-score on day i−1, respectively. As mentioned above, they are also optional; ε is a random error term represented by the normal distribution.Downscaling precipitation Calibration and validation of SDSM First, all of the 26 atmospheric variables in the region were taken as potential predictors, then most sensitive predictors for the region were analyzed month by month. The analysis results were integrated; and finally, 3 predictors were selected for predictand. The results showed that the pattern of change and numerical value of precipitation can be reasonably simulated. Although some differences existed between values of observed and simulated indices but the pattern of change in most of months were good. In the next 30 years, total annual precipitation would decrease by about 3.3 % in A2 scenario and 3.6% in B2 scenario and summer might be the most distinct season among all the changes in extreme precipitation indices.

Determination of mean slope of a watershed area is an essential parameter in most of the water resources projects. There are many methods for determining this parameter. The methods frequently used can be listed as Justin، Networking، Horton، Eight points، Average slope curve and a Geographic Information System (GIS). Generally experts based on their experiences use one of the mentioned methods. Investigations in some watershed areas showed that the results of applying the various methods are considerable different in a given area. In order to obtain the most accurate method، it is necessary to compare the results of experimental methods with the mean slope which is directly measured in the field، namely direct method. In this study، three regions were selected in different parts of Khorasan Province. For these regions topographic maps with large scales of 1:1000 to 1:3000 were prepared. Afterward seven mountain slopes were chosen in the regions and mean slope was computed for the slopes using the above mentioned methods. The mean slope was directly measured in the field for all seven mountain slopes as well. The obtained results from the various methods have been compared with the direct method by Paired-Samples T test. Analysis showed that the Horton method is the most accurate method with respect to the others. Justin and GIS methods are in next order of accuracy in comparison with the direct method.

This research is aimed to predict erosion and sedimentation of Tang-e-Kenesht basin in Kermanshah province using MPSIAC and EPM models in GIS software. This basin has about 14348 hectare area. This region has various vegetation, geology and soil texture and land use types. The basin has divided into 9 sub-basins and its maximum and minimum elevations are 3300 and 1400 m, respectively. Needed data were collected in part through published reports, while the remainings were derived by field survey. Necessary maps in MPSIAC and EPM models were prepared in Autocad-2000 medium and were transported to Arc-Info, after some revisions to them. After constructing topologies for all polygons, we entered all layers weights in Arc-View software. Combinations of all layers were managed thereafter. Nine layers for MPSIAC model and three layers for EPM model were combined to result the final layer of erosion and sedimentation. Basin erosion was calculated as 1002.7 and 1739.2 m3/Km2 by MPSIAC and EPM models, respectively. The result for basin sediment was 521.7 and 307.8 m3/Km2, respectively. Thereafter, medium and high erosion classes were found for the two models under study, respectively. Due to not fully compatible tables for EPM model and its subjective nature, one can conclude that MPSIAC model may have better performance.