فهرست مطالب bijan ghahreman

The development of water resources in many cases has led to increased economic welfare, improved living and health standards, food production, etc. However, in some cases due to the insufficient attention to all aspects of these projects, the irreparable environmental effects and subsequent social and economic effects have been imposed on society. Paying attention to environmental water requirements is one of the most important issues in decision making in water resources development plans. The objective of this study is to assess river environmental water requirements in upstream and downstream of Beheshtabad Dam. Beheshtabad Dam has designed to build on the Karun River for water transfer from Karun to Zayanderood basin. But it has not been implemented due to the various problems and challenges.

Protecting and restoring river flow regimes and hence, the ecosystems they support by providing environmental flows has become a major aspect of river basin management. Environmental flows describe the quantity, timing, and quality of water flows required to sustain freshwater,estuarine ecosystems,the human livelihoods, and well-being that depend on these ecosystems. Over 200 approaches for determining environmental flows now exist and used or proposed for use in more than 50 countries worldwide. In the present study, hydrological methods have been used. These methodes include Tennant and modified Tennant, Flow Duration Curve (FDC) and FDC shifting (for different environmental management classes). For this purpose, four hydrometric stations (three stations upstream and one station downstream of the dam) have been selected.

The results of the study showed that the river water flow had not been sufficient to meet environmental water requirements in several cases, especially in years when the region was experiencing mild to moderate drought conditions. According to the Tennant method, the minimum environmental flow requirement averages based on Beheshtabad, DezakAbad, Kaj, and Armand stations data were 3.80, 5.06, 6.99, 22.01 m3/s, respectively. Using the mentioned stations data, , the minimum environmental flow requirement averages were 3.62, 6.07, 7.91, 23.67 m3/s based on the modified Tennant method. According to the flow duration curve method, minimum environmental flow requirements (Q95) were 1.96, 5.1, 8.32, 30.62 m3/s, using data collected from Beheshtabad, DezakAbad, Kaj, and Armand stations, respectively. The results of the flow duration curve shifting method indicated that the river water flow did not meet the river environmental water requirements in different environmental management classes in some months and years. Comparative results of different methods revealed that the minimum environmental flow requirement of Beheshtabad River upstream of Beheshtabad Dam was 1.22-16.75 m3/s from September to April (based on FDC shifting method, class C). The estimated minimum environmental flow for Koohrang River was 3.69-16.81 m3/s from September to April. The downstream of the dam, Karun River requires a minimum flow rate of 20.8-73.29 m3/s from September and October to April (based on FDC shifting method, class E).

According to the results of various methods used in this study, the Karun River flow is not enough to meet the minimum river environmental water requirements in some years and months. Therefore, decision-makers must pay attention to the environmental water requirements in decisions related to the development plans and water transfer from this river. It should be noted that the river environmental water requirements have not been met completely when the region has experienced moderate or mild drought, which would be more acute in cases of more severe drought conditions. Therefore, the current surplus water of this basin may not be a sustainable source to transfer to another basin.

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.

The process of spatially allocate the water demand is a potential source of errors that should be considered when building the hydraulic simulation model. There are several methods for this purpose and each has limitations and advantages. In the case of existing accurate recording of coordinates for water meters in GIS, there are several base demand automated allocation strategies for modeling the spatial distribution of water consumption in water networks. Where the spatial data for water meters are not available, conventional methods such as Thiessen and LDM (Length Dependent Method) can be used. And if this data is incomplete mixed practices can be used. This study proposed a mixed method named SAWDSL (Spatial Allocation of the Water Demand based on Data of Consumers) that divides the total consumption in subgroups related with corresponding water meters’ recorded. This method assigning the demand of each subgroup into the nearest pipe. The simulation results of WaterGEMS software using SAWDSL, Thiessen and LDM methods, after calibration, were compared to field measurements in zone I1 of Mashhad city. RMSE between observed and simulated pressure with the SAWDSL, Thiessen and LDM methods were determined to be 0.9, 2.3 and 1.4 respectively. That shows if the incomplete data of water meters would have existed and have enough time to use them, SAWDSL method is more accurate than others. Otherwise LDM method is more accurate than Thiessen method.

Drought is one of the most complex and dangerous natural disasters that changes both in space and time. Global warming has intensified such extreme events in recent years. Thus, the use of drought indices that consider both the effects of precipitation and temperature, as well as the use of joint spatio-temporal methods, which are the extensions of spatial statistics, can probably lead to better drought monitoring and thereby increasing the accuracy of predictions. The data correlation structure is determined by the spatio-temporal covariance functions in these methods. The aim of this study is to use and compare a number of spatio-temporal variograms for predicting and spatio-temporal mapping of drought by using the 12- month SPEI index.

In this research, the monthly rainfall and temperature data of 48 stations in the northeast of Iran during the statistical period of 1981-2012 were used to calculate the SPEI index in a 12-month time scale. The exploratory analysis of the data was studied in terms of stationarity and isotropy assumptions. The data were divided into two groups of training and experimental data of 2012. The separable, metric, sum-metric and product-sum spatio-temporal covariance functions were fitted to determine the best combination of spherical, linear and exponential variograms for each of the spatial and temporal variograms on training data. The best model was selected using the MSE and MSPE statistical criteria, and the required parameters were estimated. Finally, using spatio-temporal kriging, the experimental data were predicted, mapped, and compared with the map of the observed values. Cross-validation of spatio-temporal and purely spatial models was done via COR, ME, MAE and RMSE statistical criteria by using 25 and 47 neighborhoods.

The test of the stationarity of spatio-temporal data showed the spatial stationary. Drawing of the average time series data showed a decreasing trend, which was modeled by a simple regression with the use of SPEI index values as dependent variable and time as an explanatory variable, and the data were detrended. The spatial variogram in four directions of 0°, 45°, 90° and 135° did not show a significant difference between the four variograms and the assumption of isotropy was therefore accepted. The separable, metric, sum-metric and product-sum models were used to determine the correlation structure of data. The comparison of models by means of MSE criteria showed that product-sum and sum-metric models have less error as compared with the other two models. Comparison of these two models in the prediction of unobserved values selected the product-sum model as the better model with the linear variogram for both the space and time via the MSPE criteria. After estimating the model parameters and using spatio-temporal kriging, the SPEI values were predicted for the experimental data and their spatio-temporal maps were plotted. The similarity of the map of the predicted values and that of observed values indicated the good performance of the model in predicting the unobserved values. Cross-validation of spatio-temporal and purely spatial models also showed that the performances of various models were very close to each other.

The results of this study showed that the product-sum spatio-temporal covariance model has a good ability to predict the unobserved values as compared to other models, and with the aid of these models, the values of the desired variable can be predicted in any spatial location and at any time scale. Also, cross-validation of the models showed that the different spatio-temporal and purely spatial models do not differ significantly from one another, and the precision of the models have not increased as compared to the purely spatial state.

Spatial and temporal infiltration, hydrological studies at large levels such as catchment areas and at small levels, such as managing irrigation systems, are costly, time consuming and complicated. One of the proper methods for determining the general relationship of penetration is the scaling of relationships, which is a practical solution to the problem of soil variability. In this paper, With the aid of scaling and using penetration testing data, a binomial relation was obtained for water penetration in three soils. Maximum MAE and RMSE values for calibration of the proposed relationship were calculated to be 0.48 and 0.55 respectively. These values are highly accurate to the depths of penetration (10 to 25 cm). Although the evaluation of the empirical relationship between the three-dimensional influence that was obtained in previous studies has less error rates, the simplicity of the binomial relationship and the shorter computing of the empirical relationship of reasoning infiltration on Its superiority.

In this research, by presenting a new method using the generalized linear model, the long term effects of climate change on rainfall changes in the Shoor basin under B1, A2 and A1B scenarios were investigated in three periods of 30 years up to 2090 horizons Using the data of daily precipitation and monthly temperature in the base period (1971-2000) in 13 synoptic stations, using generalized linear models, the incident model and the amount of precipitation simulated and used for fine Exponential scale of GCM model data was used. Of the 20 models provided by the CCCSN database, a wide range of climate change changes were introduced. The generalized linear models, by linking the climate and rainfall variables, provide a fine-scale scaling-up of the outputs of the GCM models. The results of the implementation of the generalized linear model show that only two models of INGV-SXG, BCM2.0 are suitable for producing future scenario scenarios. The results also indicate a decrease in rainfall and a rise in temperature in the coming periods. The highest percentage of monthly rainfall reduction has taken place in the summer season than the base period. Accordingly, the maximum average monthly precipitation during the period 2030-2001, 25.22%, during the 2031-2060 period, 37.5% and 2061-2061, between 35.58% and the base period of the forecast It will be.

The dynamical simulation model of WOFOST is widely used for yield estimation at farm and regional scales as well as different climate conditions. In modelling processes, there are lots of parameters which have to be estimated (calibrated) and also in the other hand there are limitations for providing enough observational data. Therefor it is required to choose sensitive parameters for model calibration. In this study, a global sensitivity analysis has presented for maize and wheat simulation in WOFOST model. Global sensitivity analysis methods are useful tools to rank the model parameters based on their influence on model outputs and considering the entire range of parameters. In other words, these methods consider the influence of a unique parameter as well as the influence of its combinations with the other parameters. In this paper, Regional Sensitivity Analysis (RSA) method is applied as a global method and its results are discussed. The variations of sensitivity index for the two crops are obtained from minimum 0.006 (insensitive) to 0.37 (high sensitive). Furthermore, Results for maize crop showed that the parameters which are related to temperature process (TSUMAM, TSUMEA) and absorbed radiation (SLA, AMAX, EFF) are among the most influential parameters in simulation of maize crop yield. In case of wheat crop, only the parameters which are related to absorbed radiation process (SLA, RGRLAI, AMAX, and EFF) are identified as most influential parameters.

Kashafrud Watershed Area is the part of the Great Plains Gharehghom in the northeastern province of Khorasan Razavi with area of 8996 square kilometers and considered the main drain Mashhad plain. to determine flooding potential of Kashafrud watershed used of modified SCS Method, the GIS system according to classification of map watershed and Tamab grade 4 in the digital elevation model, mapping the distribution of precipitation, precipitation losses and land use. Meteorological data, including precipitation statistics and rainstorms (28 stations) related to the period received of the relevant organizations and reviewed. After calibration data based on a common time base for 25 years (1989-1988 to2013-2012) and remove of suspicious data, in some cases rebuild the lengthening period. Layers of information is used such as maps: weather stations location, topography, land use, soil, vegetation, Political divisions of province and divisions watershed of Tamab. receiving DEM and making any necessary modifications basin boundary and physiographic characteristics calculated such as area, perimeter, height, slope, time of concentration. value of CN calculated by using the land use map and soil hydrology groups performed. Analysis Storm determined by using the software SMADA and standard distribution function (Pearson type 3) and value of of rainfall and storm return periods of 2 to 100 years. After calculation of weighted average height of the runoff in each subwatershed (sub 86), to determine the flood zones watershed, estimates maximum flooding (Qmax) caused by rainfall effective and drawn flooding potential map of watershed. Of the total area of watershed is, 68.25% of the normal flooding potential, 25.5% of of critical and is 6.25%.supercritical flooding potential. Cities Chenaran and Mashhad are flooding potential than other cities. From a total of 658 villages and population center recognized in watershed, 78% of the villages due to their placement in plain areas and low-slope are in the zone of potential flooding normal, 17% of the villages in the area with the potential flooding of critical and 5% of the villages due to the inclusion in the Highlands and steep with the potential flooding zone supercritical.

Infiltration is one of the most important physical parameters of the soil, which plays an important role in the hydrological cycle. The locaton variability in analyzing issues of water flow in the soil, at large levels such as the catchment area, is very difficult and costly. The use of scaling methods is a practical solution to the problems of soil variability. After presenting the theory of similar environments, scaling methods were proposed to overcome the problem of soil variability. In this research, the Richards equation was solved in a wide range of moisture (saturated moisture up to the remaining moisture content) for sandy soil, and the scale of accumulated penetration values was presented using the scaling of the results of solving this equation. These values were approximated with the triple-form of the Philip equation and, using regression models, for each sentence of this equation, an empirical relation was established for water penetration in soil. Then, using two sandy soils and clay in a specific moisture, the experimental effect was measured for both scaled time scales of 0.1 and 0.01 which included short and long periods. Then, using two sandy soils and clay in a specific moisture, the experimental Efficiency was measured for both scaled time scales of 0.1 and 0.01 which included short and long periods evaluated. Of the four available scenarios, the highest mean square error value was obtained at 0.0053 for clay and for long periods of time. cuase to the fact that this criterion was high in relation to other scenarios, the effect of gravity was calculated in long periods of time. The condition of applying the scaling method used in the research is not the effect of gravity on the capillary force. Also, the influence of the proposed relationship with the comparison of field data measured by Barry et al. (1995) on a sandy soil with a fixed height of water on the soil surface of Boehne et al. (1993) using the Rain simulator , Conducted penetration tests on two clay soils under cultured and uncoated conditions was evaluated. The results showed that the aforementioned relationship can provide an acceptable estimate (with the highest root mean square error of 3%) compared to the measured values of water penetration in the soil.

Sufficient measurements of effective parameters with suitable temporal and spatial distributions for all phenomena are needed. However, providing the data and informations are difficult and costly. So, temporal and/or spatial downscaling is of prime importance. By downscaling, one can generate data from available ones. There are diffent downscaling methods in literature but they are not compared with each other yet. In this research, the performance of three different downscaling methods of self similar fractal, piecewise self similar fractal, and periodic regression are compared. Three-hour temperature data of Mashhad synoptic station were considered for 1992-2009 period. Modeling was done for daily data and was downscaled to three-hour perid, the results were compared with measured data. Similar fractal method was superior over the other two methods, based on two criteria of Root Mean Square Error, and Akaike Information Criteria.

Evapotranspiration is the main component of hydrologic cycle and has an important role in crop water requirement estimations, water balances studies, and water resource management. There are a lot of direct and indirect methods to estimate reference crop evapotranspiration, but each has some limitations. For example, limitations that can be mentioned for direct measuring are the insufficient precision in measuring devices and the scale problems. An indirect method like Penman-Monteith on the other hand needs a lot of daily climatic parameters. This research tried to use self-organizing maps as an unsupervised artificial neural network method to predict evapotranspiration by minimum meteorological data input. Based on fuzzy clustering indices, evapotranspiration values in the study area, Mashhad plain, are divided into two clusters with low and high ETo coincided with the climate of the area. Also, in order to validate the model, statistical indices containing root mean square error, determination coefficient, and Nash–Sutcliffe model efficiency coefficient are used and the results are compared with the experimental models output. The results showed that even the simplest SOM model which employs mean temperature and maximum sunshine duration as input have less errors compared to the experimental equations.

Field and purposeAccording to huge utilization of domestic and urban wastewaters in planting and agricultural fields around big cities, now a days local researches about this field and observing different outcomes from irrigation by waste water, has got a great importance.
ProcedureIn this research we have used discharging waste water of filtration plant of Parkand Abad of Mashhad (BOD=169.5 & TSS=138 & SAR=14.6) and water of the well (EC=740 micro M per second) as samples. Irrigation and experiments were performed in fields of 4 square meters, which were surrounded by the soil of farm. With waste water and water of the well, fields were irrigated one to five times and every time the amount of irrigation was 150 mm. After that, by using concentric cylinders, absorption potentiality and full hydraulic conduction was estimated. The other chemical and physical parameters of the soil in the fields are presented in standard book of measuring methods.
FindingsIrrigation with wastewater effects density of the soil and decreases the amount of it. So much so that with 1, 2, 3, 4 and 5 times increase of wastewater irrigation, relatively we had 2, 6.6, 4, 15.7 and 17.6 percent decrease in density of the soil. Also with increase of irrigation times by wastewater, ions of sodium, phosphorus, nitrates and nickel were increased as well. The maximum increase was seen in nitrates ions (38%) and sodium (84%). The amount of absorption potentiality in irrigated fields with water and wastewater was less than the fields which were not irrigated at all. Besides this absorption was even less in the fields which were irrigated by wastewater. Also from the second irrigation on, because of formation a seleh layer, absorption was suddenly reached to its maximum rate (0.34 mm per minute), which absorption was controlled by this seleh layer. One of the most important reasons of seleh layer formation was existence of sodium particles in wastewater which were absorbed in the soil. It seems that sodium ion causes scattering particles of soil, so due to these fact small particles fills the pores and prevent absorption of water in the soil.

Water shortage in nature، especially in arid and semi-arid regions، often related to a decrease in water quality. This research is applied in loamy gravely field area which is located in 25 km away from city center of Mashhad in 1390-1390 and 1391-1392 cultivate years. The followed experimental design was randomized in split- split plot complete block in which four different levels of salinity (0. 5، 5، 8 and 11 dS/m، are respectively in S1، S2، S3 and S4 levels) as the main plots، four irrigation levels as subplots (supply 125، 100، 80 and 50% water necessity، I1، I2، I3 and I4 Respectively) and two cultivars of canola (Hyola401 and RGS003) as the sub-sub plots in three places. Log -linear function of the entre salinity in I4 treatment yield higher is estimated grains application more than reality. Based on the results of quadratic functions and the higher order estimates are better than the other two functions. The coefficient of R2 determination values is shown that the rate of Hayola cultivars and RGS، quadratic function with R2=0/99 and R2 = 0/8 would be first level. In contrast، the minimum numbers of Hayola and RGS logarithmic and linear functions were respectively in the next ranking. The RMSE indices show that the total higher amount of Hayola cultivar and RGS are 12/5 and 8/17 percentage yield less (according to the CRM -positive) estimates of the real values while the quadratic function total amount of 11/7 and 6/4 % performance for less (due to the positive CRM) are have been estimated the actual values. Thus، according to the occurrence comparison these indices for different functions and a final ranking is assigned to a quadratic function، this function is optimized as a function of the combined conditions of low salinity and irrigation Cultivars of Hayola and RGS which would be introduced in Mashhad.