جستجوی مقالات مرتبط با کلیدواژه « آنالیز حساسیت » در نشریات گروه « آب و خاک »

Simulation of runoff and hydrograph production is widely used in analyzing the behavior of basin against precipitation, calculating the volume and peak of floods, the amount of losses and the possibility of designing the dimensions of structures. HEC-HMS is one of the most common simulator models In this study, with the aim of analyzing the sensitivity of flood hydrograph routing parameters in the Merk River basin, first, the physiographic characteristics of the basin were constructed with the HEC-GeoHMS plugin in the Arc GIS environment and the DEM map of the area and they were called in the HEC-HMS model. then by entering the three flood event data and its corresponding precipitation, computational hydrograph is created.  For calibration, at first by trial and error, the model was placed in a suitable range which can be optimized using differential evolution algorithm. In optimizing the target function to make the average at least of the sum of squares of error, the K and X Muskingum parameters, which are particularly sensitive to hydrograph production, placed the model optimization and its performance in the excellent category so that for the event of November 2015 floods and a stop of 50 iteration (NSE=0.871, PBias=25.52, RMSE=0.4, NRMSE=2.63), for the event of March 2016 floods (NSE=0.731, PBias=28.82, RMSE=0.5, NRMSE=1.01) and in February 2020 (NSE=0.834, PBias=7.96, RMSE=0.4, NRMSE=0.95) which indicate an excellent performance of the model after optimization of the Muskingum coefficients by differential evolution algorithm (DE).

While having economic advantages, non-linear weirs have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM), gene expression programming (GEP), software (QNET) and artificial intelligence network (ANN) in predicting the discharge coefficient of non-linear Weirs of 318 data series for the first scenario And the second scenario includes the number of 363 data series and the third scenario includes data integration (the sum of the first and second scenario) which includes 681 data series. The difference between the first and second scenarios is in the shape of the quarter-circle and semi-circle weir crown. The geome tric and hydraulic lines used in this research include total water load ratio (H_T/p), magnification) L_C/W), cycle wall angle (α) and discharge coefficient (Cd). The results of artificial intelligence showed that the combinations (Cd, H_T/p, α, L_C/W) in QNET, ANN, GEP, SVM algorithms in the training stage related to the superior scenario are equal to the evaluation indicators respectively (R2=0.9960), (RMSE=0.0080), (DC=0.9961), (R2=0.9980), (RMSE=0.0057), (DC=0.9980), (R2=0.9837), (RMSE=0.0207), (DC=0.9838) and (R2=0.9902), (RMSE=0.0186), (DC=0.9830). Which has led to the most optimal output compared to other combinations, which indicates a very favorable accuracy in all four methods, namely ANN, QNET, SVM and GEP in predicting the weir discharge coefficient is non-linear. The results of the sensitivity analysis showed that the effective parameter in determining the nonlinear weir discharge coefficient in all methods is the total water load ratio parameter (H_T/p).

 In order to simulate the yield and water productivity of cucumber plant (Cucumis sativus L.), an experiment was conducted in the form of a completely randomized block design with three irrigation levels of 100, 85 and 75% of the water requirement in two growing seasons during 2017 and 2018 and using perceptron neural networks (MLP) and support vector machine (SVM) methods were used and finally, to select the appropriate and optimal model, the indices of explanatory coefficient, mean squared error and normalized mean squared error were used. The amount of irrigation water, number of leaves on the plant, temperature, evaporation rate and relative humidity were selected as input data and 60%, 20% and 20% of the total data were allocated for training, validation and testing of the model, respectively. The results showed that the MLP neural network with the inputs of irrigation water and number of leaves was more accurate in simulating fruit yield and water productivity in cucumber plants with an explanation coefficient of 0.92 and 0.86, respectively. The results of the sensitivity analysis indicated that the irrigation water input parameters are the most important effective parameters on the water consumption efficiency model and cucumber fruit yield with sensitivity coefficients of 0.9 and 0.86, respectively.

Class A pan evaporation method as one of the most common methods for reference evapotranspiration (ET0) estimation has been widely used in the world due to its simplicity, relatively low cost, and ability to estimate daily ET. In this study, the performance of 8 empirical methods consisting of Allen and Pruitt (1991), Cuenca (1989), Snyder (1992), modified Snyder, Pereira, et al. (1995), Orang (1998), Raghuwanshi and Wallender (1998), and FAO/56 were analyzed to estimate class A pan coefficient and ET0 at Fasa synoptic station located in Fars province. The calculated pan evaporation coefficients from the above equations were compared with measured pan evaporation coefficients which were obtained from the ratio of evapotranspiration calculated by the FAO-Penman-Monteith method to the rate of evaporation from the pan. The results showed that all empirical methods did not predict pan coefficient values well (R2 < 0.3 and NRMSE > 0.25). The comparison results between ET0 from empirical methods and ET0 obtained from FAO-Penman–Monteith indicated that the FAO/56 method had the best performance (R2 = 0.72 and NRMSE = 0.3). To increase the accuracy of empirical pan coefficient equations, these equations were modified with eight years (2007-2015) of meteorological data from the Fasa synoptic station and validated using two years of independent data (2015-2017). The results showed that the accuracy of all empirical models was improved and the Cuenca equation with NRMSE = 0.16 and R2= 0.63 was selected as the best equation for pan coefficient estimation and ET0 (R2 =0.85; NRMSE =0.18) in Fasa region. The sensitivity analysis revealed that the estimated pan coefficient is more sensitive to wind speed, followed by relative humidity, fetch distance, the slope of the saturation vapor pressure curve, sunshine hours, and air pressure. According to statistical results and sensitivity analysis, an equation was expanded for the Fasa region and other areas with the same climate.

The decrease in rainfall, the increase in drought and the development of agriculture in recent years have led to an increase in the consumption of underground water and an increasing decline in water levels. In this study, using version 10 of GMS software, the condition of Qazvin Plain aquifer was modeled in two stable states for October 2017 and unstable from October 2017 to September 2018. The sensitivity analysis of the model was performed with respect to the parameters of hydraulic conductivity and specific yield, and these parameters were determined after calibration and validation of the model. The sensitivity analysis of the model with respect to the two mentioned parameters shows that the model is more sensitive to changes in hydraulic conductivity compared to changes in specific yield. The root mean square error in stable and unstable state was 0.84 and 1.06 m, respectively, and the absolute mean error was 0.66 and 0.75 m, respectively, which shows the high accuracy of the model simulation. The average value of hydraulic conductivity in the aquifer area was estimated as 14.06 m/day and specific drainage was 0.064. Then the underground water level in 6 scenarios of changing the irrigation systems (maintaining the existing situation, increasing the irrigation level under pressure, changing the systems to surface, changing the systems to rain, changing the systems to drip and irrigation with 100% efficiency) was simulated. The results showed that assuming that the underground water extraction is constant, in the surface irrigation scenario, the underground water level will be at its highest level with 30% return water and in drip irrigation conditions at its lowest level with 3.6% return water.

In this study, a hybrid model was developed in order to approximate the discharge coefficient of side weirs located on converging channels for first time. In other words, the ANFIS network was optimized by means of the Firefly Algorithm (FA). After that, six ANFIS and ANFIS-FA models were defined by input parameters. In addition, in this study, the Monte Carlo simulation was employed to study the modeling accuracy. Furthermore, the k-fold cross validation approach was implemented for validating the modeling results. By analyzing the modeling results, it was concluded that hybrid models are more accurate than ANFIS models. The superior model simulated the discharge coefficient values with reasonable accuracy. For example, the values of R2, MAE and RMSE for the superior model were calculated 0.003, 0.011 and 0.015, respectively. Also, about 98% of the superior model results have an error less than 12%. According to the uncertainty analysis results, the superior model had an overestimated performance. A sensitivity analysis indicated that the flow Froude number at the side weir downstream is the most effective input parameter.

Due to the scarcity of water resources and increasing human demand, the optimal operation of reservoirs has become one of the most important issues in the world. In this regard, the correct and optimal operation of dams is one of the most efficient tools for water resources management. Due to the large number of decision variables, relationships, and constraints in the problems, reservoir management, and optimization have many complexities. Therefore, many researchers in their research have paid special attention to this issue. In this research, a new algorithm namely Atom Search Optimization (ASO), which is derived from the concepts of molecular dynamics, will be developed for multi-reservoir water resource systems. On the other hand, failure to implement appropriate policies to protect the soil has led to an important phenomenon of erosion in the lands above the reservoirs, one of the negative consequences of which will be sedimentation. The transfer and accumulation of suspended sediments, in turn, will reduce the useful volume of the reservoir, which is neglected in most reservoir optimization issues. However, in this study, the optimal operation of a single reservoir dam is investigated by the Atom Search Optimization algorithm in terms of monthly sediment yield.

First, the performance of the atom search algorithm on mathematical benchmark functions will be investigated. Then, by performing sensitivity analysis to logically determine the effective coefficients of the algorithm and selecting the appropriate number of particles and the number of iterations of each operation, the performance of the algorithm on conventional systems of four and ten reservoirs is analyzed. In order to supply water downstream of Dez Dam, considering the important issue of monthly sediment flow in the reservoir, the atom search algorithm and four other common algorithms are used. The results are modified by selecting the objective function value criteria, RMSE, MAE, NSE, and PBIAS values and prioritized using TOPSIS and Modified-TOPSIS ranking techniques.

The performance of the atom search algorithm on conventional systems of four and ten reservoirs is analyzed, which shows the results of 95.33% with an absolute optimal solution of four reservoirs, ie 308.29, and 89.67% with an absolute answer of ten reservoirs, 1194.44. Also, by comparing the atom search algorithm and four common Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) in a single reservoir system under deposition, the absolute superiority of the ASO search algorithm was demonstrated.

The use of an atom search algorithm in solving optimization problems in the field of water resources management is recommended, especially in terms of the effectiveness of soil protection and sedimentation of reservoirs.

The recent growth in population has led to an increase in food supply requirements which, in turn, has resulted in an increase in groundwater exploitation. In areas where fresh and saline aquifers are adjacent, the overexploitation of fresh groundwater leads to a decrease in fresh ground water level and, as a result hydraulic gradient of saline groundwater towards fresh groundwater increases. Also, a decrease in fresh groundwater level leads to a decrease in the plain discharge. This way, the saline water cannot exit the plain and pressure head rises leading to extra waterlogging and salinization. Interception drain is an effective solution to intercept saline flow and discharge it out of the plain. In effect, the drainage system reduces saline water table level and prevents waterlogging and salinization. Salt marshes of Qazvin plain, located in the south-east of Qazvin, is facing the same problem and saline soil areas in the region have accordingly increased 10000 ha in recent years (Saman Abrah and Kamab Pars engineers, 2010). An interception drain system was thus implemented in the study area in order to reduce the saline water table level and prevent waterlogging and salinization. Following the construction of the drain in the region, 99 observation wells were also drilled around the drain in 9 sections (A-I) such that each row has 11 observation wells so as to monitor the groundwater fluctuation and salinity. At each section, 7 wells were located on the upslope and the remaining 4 wells were on the downslope (Sotoodehnia et al, 2014). However, it takes a lot of money and time to measure water table level and salinity from observation wells continuously. Modelling is indeed a cost-effective way to predict the water table level and salinity in future. HYDRUS is a powerful tool that can be used to simulate and monitor the drain situation reliably, but it should be calibrated for the study area. Calibration of HYDRUS means estimating soil hydraulic and solute transport parameters that can be done with HYDRUS inverse solution ability. Using HYDRUS-2D, Abbasi and Tajik (2007) estimated both soil hydraulic and solute transport parameters in the field scale. They also compared the measured and simulated values of soil moisture and salinity. Though soil moisture was almost overestimated, and salinity was almost underestimated, the results showed that the errors were small and the model was accurately calibrated (Abbasi and Tajik, 2007). The main objective of this study was thus to assess HYDRUS-2D ability for simulating solute transport in a very saline aquifer in the study area, and also to consider saturated and unsaturated regions simultaneously. Moreover, this study employed HYDRUS-2D to simulate salinity transport in the areas where the amounts of salinity was extremely high. According to Latifi et al. (2020), evaporation is an effective factor in groundwater fluctuations.This study also examines the effect of evaporation and aquifer thickness in an inverse solution. Aquifer thickness and evaporation have an important effect on the inverse solution due to the statistical indexes involved. Thus, some statistical indexes were carried out to assess the accuracy of calibration, and the results showed that the model was calibrated accurately.

Sea water intrusion is a major environmental threat to groundwater resources in coastal areas, where groundwater is the main source of water supply in demotic agriculture sections, etc. Due to the over-exploitation of this source, we are witnessing increased pollution in coastal aquifers. In this study, the GALDIT method was used to assess the water vulnerability in the Urmia plain. Then, to improve the model accuracy, the two parameters i (hydraulic gradient) and P (pumping rate), were added to GALDIT. The results of the correlation coefficient between the GALDIT and GALDIT-iP indices with the parameter of EC showed that the correlation coefficient was increased from 0.42 to 0.66, which showed a significant improvement for the modified model. Therefore, the GALDIT map presents a more accurate vulnerability. The results also showed that the vulnerability index is high in the eastern and central parts of the plain and the areas near the coast of the plain, and also the index is low in the western parts and the areas distant from the coast. Also, the sensitivity analysis for removing the model parameters showed that the change of the vulnerability index by removing the status of Impact of seawater intrusion (I) in GALDIT and the hydraulic gradient (i) in GALDIT-iP is more than other parameters. Then, based on the single-parameter sensitivity analysis, the aquifer type (G) and thickness (T) are the most effective parameters in both methods, and in addition, the hydraulic gradient and pumping rate are effective parameters in evaluating the GALDIT-iP index. Overall, the results of the study showed that by using the modified GALDIT method, the percentage of areas swept by the high and very high vulnerability indices increased, while the areas with very low to medium vulnerability indices decreased.

Urbanization causes the increase of runoff to stormwater collection systems. In this research, using the SWMM model, the stormwater collection system of districts 10 and 11 of Mashhad has been evaluated. Furthermore, sensitivity analysis model has been used to determine the relationship between model variables as well as to determine priorities of the effect of parameters on output. The results of studies show that the existing collection system has about 80% of discharge capacity of surface runoff and in the modeling 38.2 and 29.1% of the canals with a 50 and 5 years of return period, respectively, face with waterlogging and back flow. In order to assess the effect of parameters on model performance, 8 parameters were selected and their effect on output discharge was evaluated. The results showed that the percentage of impervious surfaces, the equivalent width and the roughness coefficient in pervious and impervious surfaces, respectively, have the most effect on the output discharge. Also, the parameter of the percentage of impervious surfaces without the surface reserve has the least effect. With a 30% increase in impervious surfaces, the peak discharge and the surface runoff increase by 20.1% and 19.1%, respectively. The assessment indicated that the land use change, the increase of impervious surfaces, the decrease of roughness coefficient and unsuitable and old design of existing canals are the main reasons for decrease of performance of stormwater collection system in the study area.

Flood is one of the most common and dangerous natural disasters occurring worldwide (Samanta et al., 2018). Iran is ranked sixth in the world in terms of flooding (Khosroshahi and Saghafian, 2003) due to the disturbance of the hydrological balance of the country's watersheds, of which Abolabbas River Basin is no exception. In effect, due to the hydrological imbalance of the watershed, the river runoff cannot be transferred well (Tehrany et al., 2013). Given that each part of the basin has a certain potential in the production of runoffs and floods, once water overflows into the surrounding lands floods occur. It is thus necessary to prepare flood sensitivity maps and identify susceptible areas for flood prevention and management (Bout et al., 2018). This study was thus intended to prepare hazard maps for different parts of the watershed, and to prioritize the effective parameters in the occurrence of floods in the study area.

This study considered 11 important indicators including altitude, runoff curve number, slope, slope direction, rainfall, vegetation, drainage density, distance from waterway, land use, stream power index and topographic wetness index to determine the flood potential of Abolabbas River Basin with an area of 281.23 square kilometers. For the purpose of this study, the layers were scaled and the individual pixels were weighted and then prioritized using the TOPSIS technique (Rasooli et al., 2018).

In this study, the potential development of a pressurized irrigation system in the irrigation and drainage network of Qazvin plain investigated using the Analytic Hierarchy Process (AHP). The hierarchical analysis structure consisted of five technical, economic, socio-political, environmental, and managerial dimensions, each of which had several sub-criteria. Experts completed eighteen questionnaires. After completing the questionnaires, the results analyzed using Expert Choice software. The calculated incompatibility rate was 0.084, which was less than the maximum allowable (0.1). Examination of the final weighting of each of the sub-criteria showed that firstly, the technical dimension is the most critical factor influencing the development of pressurized irrigation systems. Secondly, the sub-criterion of the water supply of the plant with 0.129 weight, water delivery with 0.099 weight, and cultivation pattern with 0.088 weight, all of them from technical dimension; these most important factors have received special attention from experts and specialists. In other words, of the top six sub-criteria, five were the technical dimension, and only one was the economic dimension. Sensitivity analysis results also showed the overall weight of technical, economic, socio-political, managerial, and environmental criteria equal to 0.43, 0.2, 0.18, 0.11, and 0.08, respectively, indicating greater technical dimension effectiveness and consensus of different researchers. Also, the effect of different network water supply scenarios on cultivation pattern by ant colony optimization (ACO) algorithms confirmed the results of AHP.

Interrill erosion is one of the most important types of erosion, in which various factors such as soil, runoff, and rainfall influence its process and rate. Few studies have been conducted using artificial neural networks (ANNs) to determine the factors affecting interrill erosion in Iran. Furthermore, no research has been carried out in Jiroft on this matter. Therefore, this study was conducted to evaluate the factors influencing interrill erosion using ANNs in four different regions around Jiroft in Kerman province.

For this research, 100 soil samples were collected from two pastures and two forest land uses at depths of 0-10 cm using a random sampling method. Some physical and chemical properties were determined. The amount of interrill erosion was measured using Kamphorst rainfall simulator. Modelling was performed using feedforward multi-layer perceptrons (MLP) with the error backpropagation and Levenberg-Marquardt training algorithm along with 11 soil characteristics in two scenarios. Hill sensitivity analysis was used to investigate the significance of the input variables.

The results revealed that in the study areas, clay, silt, sand (0.05-2 mm), geometric standard deviation (σg), and geometric mean diameter (dg) of particles play a crucial role in interrill erosion while cementing agents such as organic matter (OM) and calcium carbonate equivalent (CCE) were less important. According to the results, the protected forest with high contents of sand as well as low amounts of silt, organic matter and calcium carbonate equivalent showed the lowest erosion rate (2.63 tons /ha). The R2 values for the test datasets in the scenario 1 (with input variables including soil acidity (pH), electrical conductivity (EC), bulk density (BD), organic matter, calcium carbonate equivalent, particulate organic matter (POM), sand, silt, and clay) were 0.81. Whereas the R2 values in the scenario 2 (with input variables such as pH, EC, BD, OM, CCE, POM, the dg and σg) were 0.72. In addition, root-mean-square error (RMSE) for the testing dataset in scenarios 1 and 2 were 0.77 and 1.14, respectively.

Both scenarios had almost the same accuracy in interrill erosion modeling. However, according to the values of R2 and RMSE of the data in scenario 1, this scenario showed better accuracy than scenario 2. In general, the results showed that the ANNs can estimate the amount of interrill erosion using appropriate input variables with high accuracy, and therefore it might be considered as a useful technique to estimate interrill erosion.

The application of hydrological models in watersheds has always been considered by water resources researchers. This subject is of special importance in arid and semi-arid regions due to the greater complexity of hydrological processes in these region. In this research, the efficiency of WetSpa distributed hydrological model in Menderjan semi-arid basin has been evaluated and the sensitivity analysis and uncertainty analysis of the model parameters have been performed using PEST software.  Moreover, the efficiency of the Extended Kalman filter in improving the results has been investigated. The Nash-Sutcliffe coefficient for the calibration and validation periods was 0.63 and 0.58, respectively, confirming that the WetSpa model has a good performance in runoff simulation in Menderjan basin. The results of sensitivity analysis also show the high sensitivity of Kg and K_ss parameters and the low sensitivity of G_max and P_max parameters in the study area. Moreover, the results of uncertainty analysis are generally consistent with the results of sensitivity analysis and indicate the high certainty of sensitive parameters and the low certainty of non-sensitive parameters. The results of applying Kalman filter also show the improvement of the results. So that the Nash-Sutcliffe coefficient increased from 0.63 to 0.71 in the calibration period and from 0.58 to 0.69 in the validation period.

Water infiltration into the soil is one of important hydrological parameters. This study was conducted for Phillip and Horton parameters and final water infiltration rate prediction using artificial neural network and support vector machine (SVM). The soil water infiltration was measured in 100 points of Abarkouh city landscape (Yazd province) with double ring method. The samples from 0-30 cm of soil surface were analyzed for bulk density, texture, organic matter, sodium adsorption ratio, porosity, geometric mean particle diameter and geometric standard deviation of soil particle. The multilayer perceptron neural network (MLP) with 4 different scenarios with 3, 5, 7 and 9 inputs and SVM with 9 inputs were analyzed for infiltration parameters and final water infiltration rate prediction. The results showed that the network with 9 inputs had the greatest R2 and the lowest error in Phillip and Horton parameters prediction. The study of prediction ability of ANN for Horton and Phillip parameters showed that the greatest capability was related to final infiltration rate to net design as 9-5-1 with R2 equal to 0.84. The sensitivity analysis showed that the designed nets had greater sensitivity to soil sodium adsorption ratio and the organic matter than other 7 parameters. The SVM model had good ability to water infiltration rate prediction based on basic soil properties. SVM model had better ability in soil water infiltration rate prediction compared to ANN model.

Aggregate stability of soils informs about their relative strengths against erosive forces and mechanical disruption. In this research, a hybrid Genetic Algorithm-Artificial Neural Network method was used to select the best subset of features affecting the mean weight diameter (MWD. In addition, the ability of ANNs and multiple linear regression (MLR) for quantifying the relationship between the MWD index and some soil properties was assessed. After the modeling process, the importance of the selected features in relation to spatial variability of aggregate stability was investigated. In order to prepare a suitable data set; MWD index and some soil features were measured in collected soils from 90 sampling points. Feature selection results showed that six soil features including clay, sand, organic matter, calcium carbonate, electrical conductivity, and sodium adsorption ratio had the greatest effect on the aggregates stability of the studied soils. According to the MWD modeling results, the obtained values of coefficient of determination (R2), mean absolute error percentage (MAEP), and root mean square error (RMSE) for the ANN model performance were 0.94, 21.39, and 0.07% respectively. These findings indicated that the developed ANN model was able to predict the complex and nonlinear relationships between the MWD index and the soil properties selected by the algorithm. Based on the sensitivity analysis results, calcium carbonate equivalent, sand particles, and organic matter were identified as key factors in estimating aggregate stability. Overall, this study provides a robust framework for the prediction of aggregate stability and identifying the most determinant parameters influencing it in arid and semi-arid soils that could be applied to other regions with similar challenges.

In recent years, hydrological models such as the SWAT model have been widely used by managers and hydrologists as a tool to identify natural and human activities affecting the basin hydrological system and their management and planning. As the eastern plains of Mazandaran is the only forbidden groundwater harvesting area in the north of the country, thus, with accurate prediction of inflow to the Gelevard dam for future periods, can provide accurate planning for downstream water supplies and manage demand for water and agricultural development. Therefore, the purpose of this study was to investigate the effect of climate change on the fluctuations of the flow discharge of Gelevard Dam in the east of Mazandaran province.

In this study, HadGEM2 and EC-Earth models were used to produce the minimum and maximum temperature and precipitation data for the period of 2021-4040 using a combination of Berma station data and global data at the Gelevard Dam. The LARS-WG6 model was used to fine-tune this data. Also, the Mann-Kendall and Sen Gradient tests were used to study the trend of climate parameters. Then, the data were entered to the SWAT model which was calibrated using local data and information (from 1984 to 2010 for calibration and 2011 to 2014 for validation) and the runoff changes were evaluated. The accuracy of the SWAT model in simulation of the output current was evaluated using evaluation indexes.

The results showed that the climate change had negative effects on the climate of the Gelevard dam area which increased the minimum and maximum temperatures by 1.40 and 2.40 °C, respectively. The SWAT model appropriately simulated the time of peak discharge and the peak discharge values so that it corresponds to the time of high rainfall event. Comparison of the effects of precipitation and the minimum and maximum temperatures on the outflow indicates that the effects of temperature are more than the precipitation, so that in the basic period, the minimum temperature is equal to 1.41 and the maximum temperature is 2.34. Also, the changes of monthly mean discharges in RCP4.5 (-0.01) and RCP8.5 (-0.11) scenarios are lower than the observed discharges (-0.16) and the output discharge is reduced in the hot months of the year. With investigation of the percentage of difference between maximum and minimum temperature and rainfall under RCP4.5 and RCP8.5 scenarios compared to the base period found that increasing of temperature caused more reduction in outflow rate in the future period. So that the effect of climate change on rainfall-runoff process reduced the water resources of Gelevard dam by 13%. The results of sensitivity analysis showed that soil evaporation coefficient, average length (slope), mean air temperature for snowfall (degree Celsius) and curve number in moisture conditions had the most effect on the output discharge.

The results, irrespective of the changes in each component, indicate the importance of the temporal pattern of changes throughout the year which has an important role in the water resources management of the basin. The results of the present study, emphasize the importance of potential effects of climate change at basin hydrology status, highlights the climate change and implications in the management of water resources in the Gelevard Dam.

The heterogeneous temporal and spatial distribution of fresh water and rapid population growth in recent decades have led to problems in supplying required water resources for various uses. Therefore, new mathematical models have been developed to study the complex hydrological processes. In this study, SWAT model in Samalqan catchment with area of 1148 km2 was used to simulate the flow. In the modeling process, the basin was divided into 21 sub-basins and 402 hydrological units. Modeling of watersheds has uncertainties due to limited access to enough hydrological data. For this purpose, model calibration and validation were performed using SUFI2 algorithm for 1995 to 2012 and 2012 to 2014, respectively. Based on the sensitivity analysis results, the parameters RCHRG_DP (value of penetration into the deep aquifer), GWQMN (amount of water in the shallow aquifer to produce the base stream), ALPHA_BF (groundwater reaction coefficient), SOL_AWC (soil available water capacity) and CN (SCS curve number) had the most effect. The results showed that the values of Nash-Sutcliffe index and R2 coefficient were 0.65 to 0.80 for calibration period and 0.40 to 0.65 for validation period. Out of the total inflow into the basin, 87% is spent on evapotranspiration, 3.2% for surface runoff, 3% for infiltration and the rest is related to changes and moisture storage in soil. Since no estimation of water balance components in this basin has been done so far, this research can provide useful information about the water balance of the basin and help to plan water resources more accurately.

Given the fact that the DRASTIC index is ineffective in addressing the saltwater uprising issue in coastal plains, in the present study, three factors including land use, distance to shoreline, and differences between groundwater and sea level were added to the DRASTIC index. The proposed modification to DRASTIC was validated using the measured electrical conductivity (EC) data gathered from groundwater monitoring wells throughout the Talesh Plain. The results showed that the coefficient of correlation between the map of EC over the region and the modified DRASTIC was 0.52, while for the original DRASTIC, the coefficient was 0.45, thereby implying a stronger relationship between EC and the modified DRASTIC in the Talesh Plain. Sensitivity analysis also showed that DRASTIC and the modified DRASTIC were the most sensitive to, respectively, depth to groundwater (D) and land use (Lu). According to the single-parameter sensitivity analysis results, depth to water table and net recharge were the most effective parameters in DRASTIC,  whereas the modified DRASTIC was the most sensitive to land use and depth to groundwater. It could be concluded that modifying the DRASTIC index would result in decreasing the area of very high and high vulnerable classes, and the area classified as low and moderate vulnerable could be increased.

One way to balance the environment is to study and manage aquifer vulnerabilities. In order to decide and manage the aquifer, the need for planning to identify vulnerable areas is very important. Several methods and indicators to assess the vulnerability of the aquifer are presented in each of these indicators is based on a set of factors used. In this study, 5 vulnerability indicators have been used in the coastal region to investigate and compare these methods. The characteristics of DRASTIC, SINTACS, SI, GALDIT and GODS were investigated to assess the sensitivity of the aquifer inherently. The results of these 5 indicators were different according to the nature of each of them and in order to increase the accuracy of the results and practical use of these results in coastal areas, weight and rankings were performed using hierarchical and entropy analysis method. For this purpose, the correlation between the vulnerability index concentrations of TDS, nitrate and chloride and weights of each indicator was calibrated. Sensitivity analysis of various parameters in vulnerability indicators showed that GALDIT index reflects this sensitivity in the results, considering the effect of sea salt water progress. Depth parameter to static level in other indicators and distance parameter from the sea is a sensitive and important criterion in GALDIT index and after model calibration, it was found that 30% of aquifers are exposed to the saline fronts of sea water infiltration.