mohsen pourreza bilondi

The soil water curve is one of the most critical soil hydraulic characteristics. This characteristic is used to determine soil water in the field capacity point and the permanent wilting point (PWP) beside it has a vital role in the application of soil water models in the study of soil-plant-water relationships. This curve is known as the quality soil index which has an effective role in the explanation of agricultural, ecological, and environmental problems. Impressive and efficient management of soil and water resources, water flow and solute transport survey, soil pollution, and contaminant leakage into water sources are dependent upon the accurate estimation of soil water curve parameters. Moreover, this index has a functional role in applying numerical and hydrological models. On the other hand, to better identify and understand its role, different models were provided to describe this curve mathematically. The efficiency of these models depended on the accuracy of estimated parameters in the model structure that was defined. Soil water curve is known as a non-linear relationship that is used to describe the relation between soil and water content or degree of soil saturation. The soil water curve provides essential information for using irrigation methods and about soil resistance and soil mechanical properties. In this research, the performance trend of two meta-heuristic algorithms, including the differential evolution (DE) and particle swarm optimization (PSO), was studied to estimate hydraulic parameters of soil water curves based on the van Genuchten and the Brooks and Cory models in four soil texture classes; loam, silt loam, sandy loam, and sandy clay loam. Besides, this study evaluated the performance of the meta-heuristic algorithm to RETC software. This software has a non-linear square local algorithm. This study can evaluate the ability of the meta-heuristic algorithms to estimate parameters for exponential relationships and nonlinear models.

At the agricultural farm of the University of Birjand, a study was conducted to analyze soil water content in different texture classes. The research involved the random selection of four soil texture classes and the random sampling of 20 points from each class. The soil water content was measured using a sandbox and pressure plate device, covering a broad suction range of 0-15000 cm. In the first phase, soil water curve parameters were estimated for each soil texture using the van Genuchten model and the Brooks and Cory model in the RETC software. Subsequently, the Matlab desktop environment was utilized to apply meta-heuristic algorithms (DE and PSO) to estimate the soil water curve parameters based on the two models. An objective function was defined to minimize the Root Mean Square Error (RMSE) of the meta-heuristic algorithms' performance. Finally, the study compared the performance of the meta-heuristic algorithms (DE and PSO) with the RETC software in estimating soil water curve parameters based on the van Genuchten and Brooks and Cory models, using statistical indices such as RMSE and R2. The soil texture classes play a crucial role in influencing soil water content and nutrient retention, making them an essential factor in agricultural management and crop suitability. The study's findings can contribute to a better understanding of soil water dynamics and the development of improved agricultural practices.

The obtained results of the statistical indices (RMSE and R2) showed that the least value of RMSE was acquired by the differential evolution algorithm (DE) performance. The values of RMSE during the application of the DE algorithm as an estimated method based on the van Genuchten model were 0.0008, 0.0005,0.0004, and 0.0006 also based on the Brooks and Cory were 0.006, 0.006, 0.005, and 0.0005 in sandy clay loam, sandy loam, loam, and silt loam respectively. Also, the highest value of the R2 index was obtained equal to 0.995, 0.996, 0.994, and 0.994 by the utilization of the DE algorithm based on the van Genuchten model in the sandy clay loam, sandy loam, loam, and silt loam respectively. The values of RMSE by the utilization of the PSO algorithm based on the van Genuchten model were 0.0021, 0.006, 0.0057, and 0.006 in the sandy clay loam, sandy loam, loam, and silt loam classes respectively. The highest and lowest values of the RMSE and R2 indices by the application of RETC software were obtained equal to 0.017 and 0.912 (sandy clay loam), 0.01and 0.963 (sandy loam), 0.085 and 0.972 (loam), and 0.01 and 0.924 (silt loam) based on the van Genuchten model.

It could be concluded that RETC software has poor performance in the estimation of soil water curve parameters in all soil texture classes studied based on the van Genuchten and Brooks and Cory models. This trend represents the weakness of the local algorithms to solve multivariable problems where an exponential relationship exists between the variables and they are influenced by each other. On the other hand, the results show the meta-heuristic algorithms have sufficient ability to estimate parameters in multivariable problems. It could be concluded that the meta-heuristic algorithms have better performance in estimating the parameters of soil hydraulic models. The DE algorithm is the best method to estimate soil hydraulic parameters. The PSO algorithm has the nearest performance to the DE algorithm but the best performance to RETC. Finally, meta-heuristic algorithms are suitable options for estimating soil water curve parameters based on various hydraulic models.

Groundwater becomes an important resource in semi-arid and arid regions and areas where access to surface water is limited. In recent years, due to the growth of industrialization, increase in population and change in lifestyle and consumption patterns, it is globally significant. Currently, groundwater accounts for about 34% of the world's total annual water supply and is one of the most important sources of fresh water. Water collection is one of the most important and vital parts of implementing infrastructure and economic projects, which entails a lot of costs, therefore, the evaluation of this matter is for the management of sustainable systems. Groundwater is very important. In this research, influential factors including land slope, geomorphology, soil, fault, land cover, rainfall, depth of underground water, watercourse density and proximity to surface water are used. A questionnaire with the title of comparing two criteria was designed to obtain the opinions of elites and design. In the following, AHP and ANP models are used to calculate the normal weight of underground water potential. At the end, the final map was prepared in the Arc map 10.8 environment. Using AHP and ANP methods, the area was divided into 5 parts: very good, good, medium, poor and very poor. The results of two methods showed that the rainfall factor is the most important factor in determining the potential of underground water. Finally, the information of 10 wells, 3 aqueducts and 3 springs in Nayband area was used to evaluate the validity of the obtained map of underground water potential.

Exploitation, management and protection of water resources, especially groundwater resources, are very important. The management of water resources requires accurate knowledge of the characteristics of the aquifer, the tension to it and finally the groundwater balance. Simulation models help planning managers to determine the amount of aquifer utilization for long-term and sustainable use. For this purpose, changes in the water level of the Kashan plain aquifer in the statistical period of 1387-1397 were simulated by the GMS model for 125 stress periods. The model was calibrated in two permanent and non-permanent modes for a period of 94 months (1387-1395) and validated for a period of 31 months (1395-1397).The result was a drop of 4.4 meters of groundwater during the 10-year study period. To predict the behavior of the aquifer in the near future, the reservoir level was modeled until 1407. Due to the location of this plain in the climatic dry belt and the occurrence of climate change and the lack of effective rainfall to properly charge the aquifer, in this study the focus was on the management of extraction from the aquifer and according to these three scenarios, extraction with the current trend,15% increase and 15% reduction definition and simulation was done. The simulation results of these three scenarios showed a drop of 9.866, 11.895 and 6.993 meters at the end of 1407 respectively.

The accuracy and efficiency of the analytical and numerical models to describe water flow in soil, in unsaturated environments are affected by input data uncertainty, model structure uncertainty, and hydraulic required parameters by the model. Parameter uncertainty has an impact on the model simulation by displaying uncertainty in the simulation results. Hence, the quantitative assessment of the parameter uncertainty and its influence on the model simulation is important in reducing simulation uncertainty. The Bayesian method is a common method for uncertainty analysis that has widespread application in science and engineering to reconcile the concepts of model structure with data (assimilation of input and model outputs, and inference of the parameters). Therefore, a Markov chain Monte Carlo (MCMC) algorithm based on the Bayesian inference to improve the computational efficiency of the analysis was used. The DREAM algorithm is one of the adaptive methods, the Markov chain sampling method which is known as an effective method in used soil-water models due to searching in vast space and solving complex models with a large number of variables. In addition, one of the main problems in using Bayesian inference for hydrological models is their nonlinear relations and using them in heterogenic conditions, DREAM algorithm has been developed to use Bayesian analysis in soil-water problems. Hence, this study has taken the efficiency of the DREAM algorithm as a global optimization method and convergence parser in sampling chain paths and posterior distribution of parameters. The HYDRUS model is a hydraulic model to study the soil-water processes that include nonlinear equations. In addition, center pivot irrigation is a modern method of water management that need to study using hydraulic models under various conditions. Hence, the main purpose of this article is assessment the role of the management method and environmental prevailing conditions in the uncertainty of hydraulic parameters and model structure in estimating water flow under a center pivot irrigation system in four-year alfalfa cultivation.

The profile was dug at 120 cm depth. The soil profile was divided into three layers and two soil texture classes. The physical-chemical soil properties were studied in each layer. Assessment of soil properties stated that exists a heterogeneous layer in this soil profile. TDR was used to measure soil water content before, after, and during every irrigation period. Soil water content was measured from 10 June to 11 September 2018 consecutively. The van Genuchten-Mualem equation was used to estimate soil hydraulic parameters and describe water flow in the HYDRUS model. The HYDRUS model is coupled with the DREAM algorithm to evaluate parameter uncertainty and the model structure uncertainty based on measured soil water content data using TDR in every three categorized layers. In this article the p-factor, d-factor, and S and T indices were used to evaluate parameter uncertainty, the model structure uncertainty, and model performance.

The qualitative evaluation of soil hydraulic parameters was compiled by the posterior distributions of parameters in every three depths. The parameters had a normal distribution, the model could be recognized the value of parameters, whereas the parameters didn't have a normal distribution and had high uncertainty. The “α” parameter had high uncertainty in every three depths, in other words, in two soil texture classes, this parameter compared to other parameters had high uncertainty. Along heterogeneous soil profiles, the "α", "θs", and "n" parameters were shown high uncertainty to the Hydraulic conductivity parameter of soil saturation. The value of p-factor and d-factor were obtained equal to 83.6 and 0.13 on the soil surface and 10 and 0.14 on the subsurface soil. Reducing the p-factor index in the lower soil layers explained the overlap between measured soil water content points with estimated soil water content. So, along the soil profile could be observed high uncertainty of soil hydraulic parameters under center pivot irrigation. On the other hand, increasing the d-factor index in the sub-surface soil stated increased confidence intervals which indicate the model structure uncertainty and the poor performance of the HYDRUS model in heterogenic conditions. Also, the value of two indices of S and T were obtained 0.3 and 0.76 for the surface layer and 0.88 and 1.4 in the lower soil layers respectively. The values of S and T indices stated the ability of the DREAM algorithm to reduce parameter uncertainty and the model structure uncertainty in soil surface whereas the trend of changes in the two indices explained Asymmetry of the confidence interval with respect to the measured points and the pre-estimation of the model in the lower soil layers. Therefore, the trend of the d-factor, S and T indices showed the influence of the mathematical-physics concepts in the HYDRUS model structure in the heterogenic layer and unsaturated conditions. The research results stated the ability of the HYDRUS model in describing water flow under center pivot irrigation as a novel method of managing water sources, especially in arid and semi-arid areas. Even though, the results of the assessment indices showed decreasing model performance in the lower soil layers.

The results of soil profile indicated the effect of parameter uncertainty and the model structure uncertainty in soil moisture estimation affected by management and environmental conditions. In addition, the results showed the ability of the DREAM algorithm to simultaneously evaluate the uncertainty of the parameters and the model structure in order to increase the accuracy of the HYDRUS model under the applied conditions. Also, in this study, the DREAM algorithm indicated the role of the heterogeneous layer in parameter uncertainty and its effect on the accuracy of the model performance. The DREAM algorithm is a practical and management option to evaluate the HYDRUS model during the application of the center pivot irrigation method at the farm level. So, this is an appropriate option to study the efficiency of the HYDRUS model using modern methods in agricultural practices. Moreover, to survey the efficiency of hydraulic models under the prevailing conditions could be used the ability of the DREAM algorithm based on the Markov chain.

Water is an important element of the economic and social development of any region, and the availability of safe and clean water is one of the most important issues facing for human. As water consumption increases, the need to extract water sources and use new sources increases. Many problems related to the quantity and quality of water originates from the cities development and the increase in the urban population. On the one hand, successive droughts and the declines of groundwater tables and on the other hand, the increase in water demand have caused other approaches to be used to supply water from other basins. In this research, the opportunities and challenges of transferring water from the Oman Sea and the Persian Gulf to provide drinking water and industrial use in the three eastern provinces of Iran (South Khorasan, Razavi Khorasan, Sistan and Baluchestan) have been investigated. The spatial and temporal non-uniform rainfall in the eastern regions of the country and the increase in population growth has caused hydrological and qualitative problems in the region. There is no permanent river in the study area, and these regions need transboundary water from Afghanistan. Rainfall amount in these provinces is between 75 and 200 mm. Therefore, the majority of water supply in these areas is from groundwater resources. Also, in addition to climate change effects, high evaporation in these provinces (more than 3000 mm) is one of the most challenging issues in these three provinces, which has caused a negative water balance and water deficit and a decrease in the quality of groundwater. In current situation, the amount of extra groundwater pumping of these regions is equal to 856 million cubic meters (MCM) per year. Toward overcoming this problem, and assessing all approaches carefully, it can be said that the justification of water need for industry sectors from an economic point of view can only be achieved in the cities near the origin of the transfer (southern border areas close to the Oman Sea) and therefore, a mega project of water transfer can be redefined in such a way that it only needs to supply industrial uses for the cities close to the source.

Climate change affects extreme events such as floods and droughts. This research was conducted in the watershed of Kashmar City in Khorasan Razavi province and the output of general circulation models from the CMIP5 model series was used to check the performance and validity of these models in predicting the climatic parameters of precipitation, average temperature, maximum temperature, and minimum temperature during the period 1989-2005. The best model was selected using the evaluation indices of the determination coefficient, the root means squared error, the absolute mean error, the correlation coefficient, and the mean squared error. Finally, the BCSD method was employed to downscale the data for three time periods near future (2020-2038), middle future (2039-2069), and far future (2070-2100) under the IPSL-CM5A-MR model and future emission scenarios (RCP2.6, RCP4. 5, RCP6, RCP8.5). The Mann-Kendall statistic test was used in order to investigate the trend of changes in monthly, seasonal, and annual rainfall and temperature variables. The results of the model evaluation indices for four climate models indicated the highest performance of the IPSL-CM5A-MR model in simulating precipitation and temperature with a high correlation coefficient and relatively low error indices compared to other models. The results of the investigation of the rainfall trend showed that in the far future, the rainfall component will be completely decreasing and it will have the largest decrease under the RCP8.5 scenario with a value of -41.12 mm. The average temperature is also increasing in all months and the maximum increase in the average temperature in the far future under the RCP8.5 scenario is 7.58 °C. The amount of minimum temperature and maximum temperature increases in all months, and the maximum increase of minimum temperature in the far future interval under the RCP8.5 scenario is 8.35 °C. Furthermore, the maximum increase in the maximum temperature in the far future interval under the RCP8.5 scenario is 7.09 °C.

Groundwater resources are the second largest freshwater source in the world after polar glaciers. Due to the importance of this resource in arid and semiarid regions, investigating changes in groundwater levels has an important role in planning and sustainable water resources management. The steady decline in groundwater levels has been observed in many parts of the world. For optimal use of available water resources comprehensive planning is necessary to make informed and accurate understanding of this important component of water resources. Neyshabour plain is one of the most plains of Khorasan Razavi province, which has an important role in agricultural production. Due to the reduction of the Groundwater recharge factors and Unallowable discharges of the resources, annually 74 cm decline in groundwater table in Neyshabour plain were observed. Hence the objective current study was forecasting the monthy groundwater changes in Neyshabour aquifer using fuzzy regression model. For this propose, the parameters affecting aquifer level including monthly precipitation and discharge detected and fuzzy linear regression approach is employed for to estimate groundwater level of Neyshabour Aquifer. The results represent that the most accuracy predicts was in august with R2, RMSE and MAE of applied model was 0.93, 6.05 and 6.01 respectively. In conclusion the results obtained from this study, based on R2, MAE and RMSE measures, showed the efficiency of newly developed formulas based on fuzzy regression method in estimate the groundwater table with proper accuracy.

Every year, the occurrence of heavy rains in the flood-prone basins of the country leads to the occurrence of floods and the resulting damages. Therefore, accurate prediction of rainfall is of great importance to take preventive measures. Therefore, rainfall forecasting is one of the most important issues in the field of water resources management. So far, various methods have been used to predict rainfall. Advanced hydrological research in short-term weather forecasting is uncertain and its impact is still being investigated and understood. The concept of connecting the hydrological model (WRF-Hydro) with the atmospheric model (WRF) is expected to reduce the uncertainties related to the spatial and temporal distribution of storm events, especially for areas with complex characteristics.In this study, the WRF/WRF-Hydro model was evaluated in order to predict 4 rainfall events that resulted in floods. In precipitation simulations, this model was underestimated and the model provided better results in coupled mode. ERA5 data was used to run the model, and these data had a good performance for the model in the mentioned field. Also, Lin, Thompson, and WSM6 were used to configure the model, and according to the error evaluation criteria of RMSE and NSE, all three of these schemes performed similarly.

Today, the increase in the concentration of greenhouse gases in the atmosphere causes changes in the components of water resources on a global and regional scale. On the other hand, forecasting the river flow in the future period is considered a basic tool in water resources management. The rainfall-runoff process is completely non-linear, and it is completely random in terms of time and place. Therefore, it is not possible to easily describe its mechanism using simple models. The IHACRES hydrological model is used to simulate precipitation and runoff in large basins and most weather conditions, and it is also an integrated and conceptual model. The main advantage of this model compared to other rainfall-runoff simulation models is the acceptable accuracy of the results along with the use of minimal input data and its simple structure. The aim of this research is to compare the performance of IHACRES models and the fuzzy neural inference system in the simulation of the outflow of the catchment of Kurit Dam in Golshan Tabas and its forecast in the near future under the RCP4.5 scenario and the large-scale GISS-E2-R model.

In this research, the performance of IHACRES models and Adaptive Neuro-Fuzzy Inference System (ANFIS) in forecasting the runoff was investigated. Model performance comparison of these models introduced the superior model. To simulate the future climate, the downscaling LARS-WG5.0 model was used under the RCP4.5 scenario. The temperature and precipitation data of the output of the large-scale model of the fifth GISS-E2-R report of the American country were extracted from the site. The precipitation and temperature data from 2022 to 2051 were downscaled and then the discharge values for the mentioned years were estimated with the superior model. The input data to the IHACRES rainfall-runoff simulation models and the ANFIS model include rainfall and temperature on a monthly scale. In this research, 60% of observed precipitation, temperature, and runoff data were used for training and the remaining 40% of data were used for validation of ANFIS and IHACRES models. After calibration of the two models, evaluation was done using RMSE and NS indices.

Investigation of linear correlation between precipitation and temperature with observed runoff showed that there is a significant relationship between precipitation and runoff in low and high monthly precipitation, but changes in observed runoff and temperature do not have a significant correlation (at the 95% confidence level). The evaluation criteria of RMSE and NS in the validation section of the IHACRES model were calculated as 0.1 and 0.95 respectively, and for the ANFIS model as 0.05 and 0.52 respectively, which shows the superiority of the IHACRES model over the fuzzy neural inference system model at this stage. In order to complete this section, as explained in the research method section, the LARS-WG5.0 software was used for downscaling of precipitation and temperature output of the large-scale model GISS-E2-R in the near future (2022-2051) under the RCP4.5 scenario.  Extracting the long-term monthly average showed that the average values of precipitation and temperature in the future horizon are equal to 17.38 mm and 21.7 °C, which, compared to the same values in the base period, shows that an increase of 5.45 mm in the average monthly precipitation and 2.9 °C an increase in the average temperature will occur. It can also be expected that this change in the values of climatic parameters will cause changes in the runoff caused by precipitation in the future. After simulating the monthly rainfall and temperature data from 2022 to 2051 by the LARS-WG5.0 downscaling model, these data were given as input to the IHACRES model to simulate the discharge by this model for the near future time period. Based on the results of flow simulation by the superior model (IHACRES model), the flow rate in the river will increase in the near future horizon.

In this research, In order to evaluate the IHACRES models and the adaptive neuro-fuzzy inference system, the available data (2013-2018) were divided based on the ratio of 60% to 40% for calibration (2013-2016) and validation (2016-2018). The validation result showed that the IHACRES model was more capable than the neural-fuzzy inference system model in simulating the observed runoff at Mavder station. The American GISS-E2-R large-scale model and the RCP4.5 scenario was used as input of hydrological model to simulate the runoff in the near future horizon. The results of statistical downscaling by LARS-WG from 2022 to 2051 showed an increase of 2.9 °C in temperature and an increase of 5.45 mm in total monthly precipitation. The output of the downscaling model was used as the input to the superior IHACRES model to simulate the runoff in the near future horizon for the investigated watershed.

Today, one of the problems with drinking water from groundwater is its quality. Many substances dissolved in water are undesirable. Minerals, gases, and organic matter dissolved in water can cause unpleasant colors, tastes, and odors. Some chemical compounds may be toxic, and some soluble organic components have been shown to be carcinogenic. Of course, it should be noted that not all water-soluble substances are undesirable, but the amount of desirable water-soluble substances is very small. In this study, the water quality of Birjand plain aquifer is measured using an indicator called groundwater pollution index (PIG). This index evaluates the quality of groundwater chemically. The PIG index quantifies the concentration measured in water samples according to the limit in the World Drinking Water Standards (WHO). For this purpose, zoning maps of chemical parameters affecting the PIG index including chlorine, sodium, potassium, calcium, magnesium, TDS, pH, bicarbonate, sulfate were prepared using Arc GIS software (10.3); Then PIG index calculations were performed in GIS software and groundwater pollution index map was drawn.According to the classification, 43.25% of the total aquifer area is low and insignificant pollution, 47.9% of the areas are moderately polluted, 8.22% of the aquifer is highly polluted and 0.63% is highly polluted. This indicates that the pollution of the aquifer of Birjand plain is moderate to chemical elements and failure to observe the optimal use of groundwater can increase pollution in the aquifer.

Given the growing population of the world especially in countries with limited water resources, investigating and controlling the quantity and quality of water resources especially ground water resources which is the main source of drinking water supply, can help these communities in the face of the water crisis which will plague mankind in the near future. In this survey, first, 22 wells were selected from Birjand plain aquifer. Then the aquifer was studied using the Ground water chemical Pollution Index (PIG) and ecological hazard index (ERI) of heavy metals using zoning maps and final maps of PIG and ERI indices were prepared. According to PIG, 56.74% of the aquifer has a lot of Pollution, and according to ERI, 0.91% of the aquifer has a lot of Pollution. Then, new index of Pollution and Environmental Risk of Ground water (PERG) was introduced according to both Ground water chemical Pollution Index and heavy metals Environmental Risk Index. PERG detects chemical pollution and heavy metals in the aquifer, and leads to better summarization and decision making in order to collect water from ground water aquifers. In terms of PERG, aquifer pollution is 1.755% of the total area.

In this study, fuzzy linear and fuzzy least-squres regression approach was employed to estimate the monthly reference evapotranspiration of Neyshabour plain. The data used, including maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), relative humidity (RH), solar radiation (Rs) and wind speed (U2), were obtained from synaptic meteorological station of Neyshabour. Three different scenarios were designed to estimate the evapotranspiration for either fuzzy linear or fuzzy least-squres regression models. Mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance fuzzy regression models and its comparison with FAO-56 Penman-Monteith. Results indicated that the fuzzy linear regression model in January and the fuzzy least squares regression model in October had the highest and lowest accuracy with R2 of 0.903 and 0.502, respectively. Among the new proposed models, the fuzzy linear regression under scenario FLR1 (Inputs included Tmax, Tmin, RH and U2) had the highest accuracy, however, in both regression models, despite having lower input parameters (Tmean, RH and Rs), the second scenario, was comparable with other and therefore it can be used in data deficit conditions as an optimal approach in determining ETo for irrigation planning and water resource management.

Iran has geographically located in an arid and semi-arid climate in most regions. Precipitation and its distribution in such regions cause irreparable damage by creating seasonal floods. This study presents a suitable model for optimizing watershed management and flood control in order to reduce flood risks. To reach to this purpose, the concept of time-area diagram in HEC-HMS hydrological model as well Single Successive Sub-watershed Elimination (SSSE) is employed to simulate the flood hydrograph corresponding to the design precipitation for each sub-watershed. According to SCS model for estimating flood discharge and kinematic wave for flood routing, the curve number and Manning's roughness coefficient were calibrated and identified as the most effective parameters. After evaluating the different search methods and objective functions, the univariate gradient as best search method and the Nash-Sutcliffe as the best objective function was selected due to the highest consistency of the simulated discharge in the three events. Finally, the model was validated for 2 storms and the Nash-Sutcliffe values ​​were calculated as 0.948 and 0.892, respectively. After calculating the peak discharge of each sub-watershed, the effect of each on the output flood production was determined using F and f flood indices. Then, isochronic surfaces of the watershed were extracted using three methods and the spatial distribution of the sub-watersheds in the area was investigated. The results revealed that the level of 0.75-1 located in the middle part of the watershed is posed as the first priority. Also, it is colcluded that the surfaces near the outlet have played a much smaller role in peak discharge. In general, from the outlet to the upstream and middle parts of the watershed, as travel time level increases, the effect of sub-watersheds on peak flow discharge increases.

Nowadays؛ climate change and its effects creates a major crisis on the management of water resources. On the other hand؛ since groundwater is one of the most important sources of water supply؛ analyzing the impact of climate change on groundwater in the upcoming period will be necessary. The goal of current study is investigation of the effects of climate change scenarios for the balance of the groundwater level؛ case study on the subterranean water level in the field of Birjand South Khorasan province. In line with this goal in order to evaluate the effects of climate change؛ 26 parameters from the output of the Canadian General Circulation Models (CGCM3.1 AR5) under the emission scenario RCP4.5 was used as predictors. As well as؛ the SDSM model was applied for downscaling the climate data to generate the regional daily scenarios. The Artificial neural Network Fuzzy Integrated System (ANFIS) was used to investigate the changes in the balance of underground water. The results o؛ optimal and intelligent management of groundwater resources and improving the consumption pattern seems necessary.

Due to the height of the dams, water behind dams have high energy and velocity on the spillway is high. Downstream of the spillway as a result of high energy and velocity of water on the spillway, is at the risk of cavitation and damage. The study of cavitation is crucial in this respect, and indicates the amount of flow degradation in the structure. Various works have been conducted on how to eliminate cavitation. Given the nature of cavitation, caused by high speed and pressure reduction, the use of roughness in parts of the spillway can help reduce or eliminate this phenomenon. This study aimed at investigating the effect of roughness under different conditions on changes in cavitation index in the downstream peak body.

To achieve a better understanding of how roughness functions, this research was carried out by changing its arrangement, number and different heights on the cavitation index using a laboratory model. In order to achieve a more real-life simulation, a spillway was installed at a distance of 5.5 m from the opening of the canal to ensure the flow is expanded and that the turbulence of incoming current is minimized. Peak spillway was considered in the study, designed and constructed according to USBR standards. Eight holes were installed on the spillway which were connected to 8 piezometers located in the channel body through flexible pipes to read the amount of pressure static. Effective parameters were identified with dimensional analysis, and three parameters (roughness arrangement, number of roughnesses, effective height of roughness) were tested. The roughness used in this research is made of PVC. After installing the spillway in the canal, the roughness was installed on the spillway. The piezometers were ventilated before each experiment. The channel slope was adjusted. Then, the pump was turned on, and after adjusting the flow rate, the test began. The depth gauge was calibrated for each aperture to measure and correct the height on the apertures. The equivalent pressure height inside the piezometer tubes was read and adjusted for the piezometer base and channel slope. Finally, according to the data, the roughness performance was evaluated using the cavitation index.

The experiments of the present study were performed with different Froude numbers and slopes. After the control experiments, the experiments were evaluated in three modes: the effect of arrangement, number and effective height.• Evaluation of cavitation index changes in control experiments:Observations show that cavitation index decreases as water falls on the spillway. The minimum cavitation index in the control experiment occurs at point 8. Moreover, the average cavitation index increases compared to the control mode by placing the roughness.• Investigation of roughness arrangement on the cavitation index:Observations show that the placement of roughness with convergent, divergent and row arrangement increase the cavitation index compared to the control. In the Froude number of 1.08, the changes in the cavitation index range from 1 to 8, 3.7, 4, 4.2, 4.4, 4, 3.7, 3.8 and 3.5%, respectively, compared to the case. • The number of roughnesses on the cavitation index:By placing 9, 12, 15 and 18 roughnesses in the row arrangement, the observations showed that in all graphs, the cavitation index increased compared to the control. Moreover, with the number of roughness, the cavitation index has a slight increasing trend. With 18 roughnesses in the flow rate of 1.08, the increase in the percentage of cavitation index (compared to the control) was piezometric points 1 to 8, 11.1, 4.03, 12.3, 17.6, 4.4, 4, 3.7 and 7.1.• Investigation of effective roughness height on the cavitation index:Considering the three effective heights of roughness, the observations showed that in all three cases, there is an increase in the cavitation index compared to the control mode, and the most evident change in the effective height has occurred at 0.017. With 1.08% increase in the flow rate of cavitation index compared to the control condition, 1 to 8, 7.5, 4, 12.7, 26.9, 4.1, 3.8, 7.3 and 6, respectively were obtained.

In general, it can be concluded that the installation of roughness on the spillway, the effective height of roughness, the number of roughnesses and its various arrangements, increase the cavitation index compared to the control. The presence of roughness reduces the flow velocity and increases the pressure on the spillway. This issue has a key role to play in eliminating or mitigating the cavitation phenomenon on the spillway. Compared to the control, the average cavitation index increased by 15.17% in the convergent arrangement, 11.8% in the divergent arrangement and 16.11% in the row arrangement. The results show that the greatest change in row arrangement and effective height occurs at 0.017 and n = 18.

Iran is located in arid and semi-arid belt of the earth and also because of drought, climate changes and mismanagement of water use; its freshwater resources are declining. Therefore, the need to increase water productivity is obviously rational. New methods of irrigation can be mentioned to increase the efficiency of water resources management. Management and application of these new methods of irrigation also requires studying the process of soil moisture changes and its availability to the plant. The purpose of this study was to evaluate the performance of HYDRUS-1D hydrological model at two different depths using inverse solution method in relation to pedo-transfer functions in four-year alfalfa farm which irrigated with center pivot irrigation system. Therefore in this study, the Particle Swarm Optimization (PSO) algorithm (as inverse solution method) as well as three parametric functions including Rosetta, Gorbani Dashtaki and Homaee, and Sepaskhah and Bondar were used to estimate soil hydraulic parameters in simulating soil movement and moisture distribution in HYDRUS-1D hydrological model. So, among the three parametric functions, the best function was selected and then the efficiency of the inverse solution method was compared to the parametric method in the process of simulating the unsaturated flow with the HYDRUS model. The results indicated the acceptable ability of PSO algorithm to estimate soil moisture characteristic curve and its hydraulic parameters. Also by evaluating the statistical indices, it was shown that by linking HYDRUS model with PSO algorithm, this model was efficient to estimate the trend of soil moisture changes. The best model performance was obtained in the soil upper layer with E=0.89, d=0.94 and R2=0.98.

Due to population growth and political development of Birjand city, the status of aquifer vulnerability is important for development. Studies on the DRASTIC index indicate the lack of consideration of land use status and the effect of pollutant transfer on aquifer vulnerability. DRASTIC-LU index has eight parameters and since the incoming weights to this index can be a matter of taste, the model was calibrated according to the Nitrate concentration in the wells of the region. The model was calibrated by using AHP method and the completion of questionnaires was analyzed from the point of view of the importance of each parameter in terms of weight and rank. Questionnaire analysis was performed by couple comparison method and using Expert choice software. The calibrated results indicate an increase in the correlation coefficient between the vulnerability index and the Nitrate concentration at the aquifer. The results showed that land use was positive due to the surface concentration of pollutants in the agricultural and urban sectors, which played an important role in the vulnerability index of aquifers. The results also showed that the accuracy of the vulnerability index could be increased by using the proposed method of this research and calibrate weights and ranks by using the hierarchical analysis method. The correlation between Nitrate concentration and DRASTIC-LU index in the aquifer had increased from 42% to 65%.

One of the main important strategies can be achieved to preserve the water resources is the optimal use of water in the agricultural sector. Water infiltration as a key component of water resource plays a significant role in this challenging problem. Water permeability in soil strongly depends on environmental factors, climatic conditions, latitude and soil characteristics, and has high spatial variability. The various simulation models are used to predict the amount of water infiltration in the soil. there have been a lot of research on the estimation of water infiltration models in the soil, which has been studied in the public only to study the models of infiltration and influence only one of the effective factors on infiltration. The proper prediction of the infiltration and the uncertainty assessment of these models through GLUE (Generalized Likelihood Uncertainty Estimation) algorithm, considering the region conditions and the combined effect of several factors affecting the infiltration process, is main aim of this study.

Their measurement is conducted in one of the farms of the county in Roshtkhar. The required measurements through double cylindrical method were carried out in three iterations and in two different soil textures, sandy loam and clay loam. In this research, the uncertainty assessment was performed by four models of Kostiakov, SCS, Philip and Horton infiltration. Uncertainty prediction of these four models through GLUE (Generalized Likelihood Uncertainty Estimation) algorithm was used in the MATLAB programming environment, with 100,000 iterations. GLUE mapped all sources of uncertainty into parameter uncertainty. Moreover GLUE conceptual simplicity and it flexibility has led to this method being considered as one of the most applied methods in the uncertainty evaluation in other sciences in last decade studies. Here one percent of best simulations were selected to define the 95percent prediction uncertainty. Hence posterior distribution of each model parameters is plotted and assessed.

In order to quantify the results of uncertainty, four indicators "the percentage of data placement measured in the desired confidence ranges, the bandwidth of the simulated data in the desired confidence range, the degree of asymmetry of the simulated data in the desired confidence range", which is briefly depicted in letters" P, d, s, and T ", respectively. The results showed that the parameters of the Kostiakov model in both soil texture with a value of P greater, d less, s between zero and 0.5 And T between zero and one show more certainty, so that the values of these indicators in sandy loam soils equal to 100, 0.378, 0.055 and 0.388 respectively, and in clay loam soils In 100, 0.519, 0.147 and 0.558 respectively.

According to the four P, d, s, and T indices, it was found that the paramteres of Kostiakov model has more certainty than other model parameters, which can be considered as an appropriate model.

Climate change is one of the most important factors that will affect different parts of human life on earth and will be adverse effects on environmental resources, economic, social and especially water resources. Increasing concentration greenhouse gases in recent decades and increased temperature caused by it, has led to significant changes in meteorological elements. One of the strategies to mitigate climate change effects is to assess its effects on precipitation and temperature in each region. So, climate change effects on temperature and precipitation variables in Birjand Plain have been studied in this research using CanESM2 AR5 model under emissions scenarios 2.6, 4.5 and 8.5 RCPs. Due to the low resolution of general circulation models, SDSM downscaling model was used and climate changes in precipitation and temperature were simulated for future periods (2020-2049). Model simulation results indicate a rise in temperature in future period, so that maximum temperature rise (1.89°C) and the lowest rainfall (0.07 mm) was observed in June. Results can be used in planning water resources management and agricultural sector.

 Groundwater has always been considered as one of the main sources of drinking, agriculture, and industrial water, especially in arid and semi-arid regions. Investigating groundwater level changes in any region has an important role in planning sustainable water resources management. Continuous decline of groundwater level has been observed worldwide in the past half-century. Groundwater is the most important and the only source of freshwater in Neyshabour plain. Unallowable discharges of the groundwater resources and the reduction of recharge factors have caused about 200 million cubic meters deficit in Neyshabour aquifer. Therefore, estimating groundwater is vitally important for the management of water resources.

This study was conducted in Neyshabour aquifer in Khorasan Razavi province situated between 58o13' to 59o30' eastern longitude and 35o40' to 36o39' northern latitude. Neyshabour plain has an important role in agricultural productions of Khorasan Razavi. In this study, the fuzzy possibilistic regression and fuzzy least square regression approaches were evaluated in order to forecast the groundwater changes in Neyshabour aquifer. For this propose, the parameters affecting aquifer level, including monthly precipitation, discharge detected, and fuzzy regression approaches were employed to estimate groundwater level of aquifer, and then raster maps were determined by geostatistical methods. Data bank was determined by Arc GIS software from raster maps to train and test fuzzy regression models. 50 percent of data was selected as calibration data and 50 percent of data was selected as validation data in each model. In linear regression, for each series of input variables, only a specific output value is computed, while fuzzy regression models estimate the boundaries of possible values for the output variables. Therefore, unlike the classical regression, which was based on probability theory, the fuzzy regression is based on possibility and fuzzy sets theory. Fuzzy possibilistic regression, introduced by Tanaka et al. (1982), is an approach that provides the best regression equation by minimizing the amount of fuzzy. The general form of this fuzzy regression function is as follows: [ Ỹ =Ã0+Ã1X1+Ã2X2+ Ã3X3+…+ÃnXn (1)where Ã0 and Ã1 are the fuzzy intercept and fuzzy slope coefficients, respectively, and X is the independent variable and the output Ỹ (or dependent variable) is a fuzzy number.Fuzzy least-squares regression (FLSR) method as proposed by Savic and Pedrycz (1991) was adopted for this analysis. For the purpose of current study, the efficiency of the fuzzy possibilistic and fuzzy least square regression models for groundwater prediction in Neyshabour aquifer were compared. Validation and Verification of models were determined based on mean error (ME), root mean square error (RMSE), and coefficient of determination (R2).

 The results of the comparison of the fuzzy possibilistic regression and fuzzy least squares regression models in estimating the groundwater table of Neyshabour plain show that the highest accuracy of water table prediction in the studied aquifer occurred in June and the lowest accuracy occured in January for both fuzzy possibilistic regression and fuzzy least squares regression models. Likewise, in both models, the estimation accuracy of the winter was lower than other seasons. The results of validation of the fuzzy regression model in summer showed that despite a sharp decrease in rainfall in this season, applying inputs with a delay of one to two months had good results in predicting the water level. In agreement with the findings of the present study, previous investigations reported that the ANFIS model with inputs of discharge, total rainfall of the current month, the total rainfall of the previous month, the total rainfall in the past two months, and the groundwater level had the best performance in the prediction of groundwater level of Neyshabour plain (Khashei-Siuki et al., 2013). While in most research done the data used is on a daily or annual basis, it is the monthly changes in the water level which is important: the ascending and descending trends were observed in winter and summer months, respectively. Using monthly forecasts helps managers to make more effective decisions regarding how much water is taken from groundwater resources. Therefore, the aquifer's response to monthly water changes is more effective than annual or daily. Also, the findings of the current study indicated that the fuzzy possibilistic regression model estimated values are more consistent with observed values and provide better estimation of groundwater subsidence and drainage.

 Comparison of the performances of the studied fuzzy regression models showed that the output of both models varies in different months due to fluctuations in the water level. Both fuzzy regression models have a good ability to estimate groundwater table of Neyshabour plain. The results showed that the estimation accuracy of both employed models in winter were lower than other seasons. The best accuracy was observed in May with a RMSE= 6.05, MAE= 6.01 and R2= 0.93 for the fuzzy possibilistic regression model. It is worth noting that according to the model validation indices, the accuracy of the fuzzy possibilistic regression model is more than the fuzzy least squares regression; therefore, the mentioned model can be introduced as an acceptable approach in estimating the groundwater level. It is also suggested that other fuzzy regression models and approaches, including fuzzation of independent variables, should be used in future research.

Monitoring network optimization is a decision making process for the best combination of available stations. Due to economic considerations and reduction of monitoring costs ، the optimization approach in this study is to reduce monitoring stations without reducing the amount and accuracy of the information obtained. In this study, an optimal design of groundwater quality monitoring network was carried out with the help of an optimization model in the Neishabour plain aquifer. The optimization of the wells network was accomplished by a Multi Objective Particle Swarm Optimization (MOPSO) algorithm. Two objectives containing of minimizing the root mean square error (RMSE) and the number of wells was applied in current research. Kriging interpolation was used for calculating groundwater chlorine concentration values and compared with observation values. As a result of this research was presented a Pareto front exctracted from MOPSO showing the number of wells against their corresponding RMSE, which could be a guide for the design of a groundwater quality monitoring network. The outcome showed that the sampling wells can be reduced to 58 percent with a minimum error increase (all 50 wells in base network with zero error may be reduced to 21 with chlorine concentration error of 13.57 mg/l) in the Neishabour aquifer. Also, the position of these wells was considered as the optimal position.

Simulation of rainfall-runoff process for planning and management of water resources and watersheds requires the use of a conceptual optimized hydrological model. In this study, the hydroPSO package was employed to optimize KINEROS2 (K2) parameters applied in the Tamar watershed, Iran. Four storm events were utilized in hydrograph simulation. Results indicated better efficiency of K2 based on the event #2 with the coefficient of determination and Nash-Sutcliffe Efficiency (NSE) of 0.9084 and 0.92, respectively. The events #3 and #4 with NSE of 0.89 and 0.86 showed the excellent and very good fitness of simulated flow compared to observed flow, respectively. Sensitivity analysis established that the parameters Ks_p, Ks_c, n_p, n_c, CV_p and Sat were the most effective parameters in K2 calibration, respectively. The posterior distributions of some parameters such as Ks_p and n_c appeared to be more sharply peaked than other parameters which established less uncertainty in hydrological modeling. Visual inspection of boxplots showed that for 6 out of 16 parameters (Ks_c, n_c, G_c, Rock, Dist_c and Smax) the optimum value found during the optimization coincided with the median of all the sampled values confirming that most of the particles converged into a small region of the solution space. Dotty plots showed that the optimum values found for Ks_p, Ks_c, and n_c define a narrow range of the parameter space with high model performance. On the other hand, the model performance was more impacted by the interaction of Ks and n parameters. The parameters CV_p and n_p showed a wider range of the optimized levels. Correlation analysis revealed that the highest linear correlation between the NSE and K2 parameters was obtained for the Ks _p, Ks_c and n_p, followed by CV_p, G_c, Por_p, Dist_p and Smax. Conclusively, this work demonstrated the capability of hydroPSO in optimization of the K2 as an event-based hydrological model.

One of the ways to predict and estimate the amount of runoff from rainfall is the use of hydrological models. Calibration of effective parameters in hydrological models is one of the basic steps in using these models. However, this process is a critical step which should be carried out carefully to optimize the model parameters. Multi-objective optimization algorithms as one of the most important and practical topics in various fields of study could be employed to achieve a reasonable calibration. The purpose of these algorithms is to determine the values of model parameters to find the best possible solution and achieve different goals. In this study، Multi-objective optimization algorithm (AMALGAM) used to calibrate conceptual daily hydrologic model (MILC). AMALGAM method combines two new concepts and takes place in two modes of evolution. In the first case, the evolutionary capabilities of four multi-objective algorithms NSGA-II, PSO, DE and AMS are used simultaneously to evolve the population, and in the latter case, the AMALGAM algorithm itself is used to evolve the population. This algorithm is called a multi-objective hybrid algorithm due to the simultaneous use of multiple multithreading algorithms. The reason for choosing the AMALGAM algorithm is the superiority of this algorithm in achieving a fast and accurate access to the sum total of Pareto's optimal responses to other multi-objective algorithms such as MOPSO, SPEA2 and NSGA-II. MILC model employs the Soil Conservation Service—Curve Number method for abstraction (SCS-CN) for estimation of losses، the geomorphological Instantaneous Unit Hydrograph (GIUH) for routing of rainfall excess of catchment. This paper applies a four-objective calibration strategy focusing on peak flows (NSE) ، low flows (TRMSE) ، water balance (ROCE) ، and flashiness (SFDCE) to parameter estimation of MILC model. After calibration process، a trade-off point extracted from Pareto- front was selected to include the appropriate values of all four objectives simultaneously. This point is applied to verify the validation period. The obtained values during the validation period (0. 71 ≤ NSE ≤ 0. 78) indicate that the MILC model has Good performance to simulate the amount of peak flows but according to ROCE and SFDCE values it has weak performances to simulate the balance water and median flow respectively. It’s recommended to use multi-objective optimization algorithm rather than one objective optimization for calibration of hydrological models because this optimization covers the all hydrograph flows. Selecting objective functions for calibrate the rainfall-runoff model is the key to recognizing the model as much as possible.

Studies of climate change impacts on water resources need to conversion of projection of climate variable pattern from coarser scales to a suitable scale. Downscaling processes improves projection of General Circulation Models (GCMs) significantly. In this study is assessed performance of Artificial Neural Network and Support Vector Regression. Observation rainfall was collected for 1961-1990 from Birjand synoptic station. Also value of 26 predictors from CanESM2 output in Assessment Report Five (AR5) was extracted. Predictor selection was performed by Stepwise regression. Model skill was evaluated using indices R2, RMSE and NSE. Also for better analysis using of various tests such as uncertainty assessment, reproduction of descriptive statistics, dry and wet spells and monthly rainfall. Results of assessment indices showed that estimation performances of both methods in daily rainfall are relatively suitable. Value of R2, RMSE and NSE are achieved 0.48, 1.5 mm and 0.47 for SVR with Polynomial kernel function in best case respectively. Results of skill model in estimation of descriptive statistics indicated that SVR with Polynomial kernel function outperforming others. Comparison of observed and downscaled monthly rainfall illustrates that SVR performs better than ANN in winter season. Also both methods have overestimation in summer months. Results of identification of dry days sowed that performance of both models is well and same. In this case ANN identified 96% dry days correctly. Results of identification of wet days showed that SVR outperforms ANN. Assessment of skill method for estimation of dry spells length indicated that both methods have more efficiently in long spells than short

Optimizing parameters of nonlinear Muskingum model done by the method of try and error and numerical. These methods are difficult and time-consuming. But Meta-Heuristic Algorithms can indicate the good estimation of these parameters with high-speed and more precision. In this study investigated performance of continuous ant colony algorithm (ACOr) for estimation of optimal parameters of the nonlinear Muskingum routing model and we used sum of squared error criteria to evaluate that. The results indicated that continuous ant colony algorithm has good efficiency on Wilson flood and Wye River flood with SSE=36.7679 and 37944.14. After ensuring the efficiency of ACOr algorithm, we used nonlinear Muskingum routing model investigated for Karoon River and the value of SSE was equal to 144691.735 that indicated higher performance in comparing to bee colony algorithm (SEE = 177161.4). Also, in this study the three flood routing have investigated by the convex method. This method didn’t has good performance on Wilson flood and Wye River flood but in Karoon River flood routing indicated better performance than nonlinear Muskingum methods. This model provided good estimation of peak of flow discharge, that this issue is very important for Implementation of flood warning systems.