IRAN-WATER RESOURCES RESEARCH
Year:2019 | Volume:14 | Issue:5 |Papers Count:34

Considering the limitation of water resources and their value in agriculture, the use of any alternative for efficient consumption and optimal use of existing water is essential. One of these solutions is Deficit irrigation. The purpose of this study was to evaluate how different percentages of reduction in water consumption in different growing stages as deficit irrigation scenarios affect the Optimization of Water Use and dominant cropping pattern (Wheat, Barley, Sugar beet, Forage Corn, Maize corn and Alfalfa) in Qazvin plain irrigation network. In order to maximize farm income, the Positive Mathematical Programming (PMP) method was used along with the method of Maximum Entropy (ME) based on the reaction functions of the products yield to water in the space of other constraints. The required information for this research was obtained by using two-stage cluster sampling method through completing 144 questionnaires in farming years of 2015-2016. Results showed that by applying deficit irrigation at best condition (Reduction of 5% deficit irrigation at the Ripening stage for Wheat, Barley, Corn forage, Maize and sugar beet and 5% deficit irrigation at vegetative growth stage for Alfalfa), despite the 5. 2% saving in water consumption, it is possible to increase farm income by 0. 4%. On the other hand, depending on the severity of the crisis and the amount of the water scarcity, different decisions to determine the deficit irrigation strategy and the region's cultivation pattern would be made. This mean that if more water conservation is desired, the results of the second and third scenarios are best options for the optimal use of land resources and the minimum water consumption. The result also showed that the total area under cultivation of the selected products has decreased in comparison to the base year and lands have changed to uncultivated and fallow lands which in turn reduces the pressure on land resources and increases the possibility of tillage and soil conservation.

The proper prediction of water level variation in dam reservoirs is considered as one of the important issues for design and operation of dams and water supply management. In this study, based on five soft models such as support vector regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), artificial neural network (ANN), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN) and the combined use of their results as input to one of these five models, a new structure called supervised intelligent committee machine (SICM) was proposed to predict the monthly reservoir water level of Karaj Amirkabir dam. The data used in this paper are water level, precipitation, evaporation, and inflow to and outflow from the dam. The evaluation of these models was done by nine error indexes and also the best model among all was selected using Vikor decision maker method. Evaluations showed that among the used soft models, the ANN was the best model with Nash– Sutcliffe efficiency (NS) and mean square error (MSE) equal to 0. 89 and 23. 37 square meters, respectively. The results of the proposed approach showed that the supervised (hybrid) neural network (SICM-ANN) has been able to provide high performance in predicting the monthly reservoir water level in Karaj dam with increasing the NS coefficient to 0. 94 and decreasing the MSE index to 12. 85 square meters (more than 45 percent decrease). Accordingly, hybrid use of soft models can effectively be applied for a significant reduction in the predicted error of water level compared to single models.

In the management of water resources systems, there are various inherent uncertainties which are exacerbated in longer planning horizons. Among these uncertainties, those related to climate change and its consequences are among the most challenging and attributed as deep uncertainty since there is no clear probabilistic distribution for the outcomes. For such cases, it is crucial to take more robust decisions and maintain their performances during different climatic scenarios. This issue is addressed as the objective of current research in which the concept of “ robustness” is applied for evaluation of climate change adaptation strategies. For this purpose, this study used Robust Decision Making (RDM) technique, which was linked to Soil and Water assessment Tool (SWAT) model for the required water and agricultural simulations. The case study of this research was Zarrineh Rud River basin which includes plenty of cultivated lands. The basin is also the main feeding river of Lake Urmia in northwestern Iran. At this stage, the paper evaluated the capacity of the current management of the basin as a climate change adaptation strategy. For this, different indicators including; inflows to the lake, change in groundwater storage, actual evapo-transpiration, and crop yield were assessed. The results showed that the current management is not robust and fails in most of the projected climate scenarios. The situation also worsened as the rainfall decreases and temperature increases more than 0. 75 Celsius. This suggests necessity of a new vision for the basin management and evolving strategies, which can be properly evaluated through the developed modeling framework.

Iran's electricity industry is looking for better more efficient ways in generation, transmission and distribution of electrical power in recent years. The industry rules have changed to become more competitive. With uncertainty of market price, water inflow, the beneficiary's behavior, access to information and corresponding increased risk, it is increasingly challenging for hydroelectric energy producers to maximize the profit in electricity market. In this situation, pursue a decision-making procedure for optimum operating in power plants is one of the main concerns. For this purpose, time series of average monthly data during 2005– 2015 was used and profit is forecasted for twenty-four months. For this study, energy data from Karun1 HPP on Karun River was used. Firstly, Gó mes-Maravall model was used to predict the electricity price and river inflow. Then, using an ARIMA model, operating and maintenance cost in the hydropower plant was estimated. In this research the hydropower plant profitability was estimated using system dynamics approach and multiple scenarios like climate change and penalty costs scenarios.

Protection and water quality management of rivers, as one of the valuable natural resources, is of high importance, especially where water scarcity is as well an issue. In the present study, in addition to identifying the contamination resources, different scenarios were surveyed for controlling the major pollutants in Zarrineh-rud River in order to meet the water quality standards for a healthy aquatic life. To achieve these objectives water quality sampling and monitoring of the contamination resources and plant coverage were conducted along the river during three seasons in 2016. By using these data in QUAL2Kw, water quality and hydraulic modeling was conducted, calibrated and confirmed. Evaluation of the hydraulic behavior and the river water quality indicated that during dry seasons both reaction kinetics and advection process affect the variations in water quality parameters, whereas during the wet periods advection dominants. Subsequently, analysis of the river water quality pollutants revealed that the distributed sources like pile of trashes and animal wastes at river banks have the largest share in contamination (Nutrients) of the Zarrineh-rud River. Among the point sources of pollution and agricultural pollutants, the point source had the larger share in contamination during summer and fall and agricultural pollutants had the main contribution during the spring. Evaluation of water quality management scenarios during the fall season in the present condition showed that restricting Sugar Factory Sewage BOD5 by 210 mg/l resulted in keeping the amount of BOD within standard values for the aquatic life.

Joint occurrence of coastal and inland urban flooding is considered as a major problem in coastal developed cities. An appropriate perception of flood hazard is imperative to face new challenges in flood management in these areas. To address this issue, flood frequency and intensity are determined based on historical storm data of the case study utilizing statistical analysis. Five copula functions have been fitted to hydrological data to find joint return periods of coastal and inland flooding and the best one has been detected based on the Kolmogorov– Smirnov and the root-mean-square error test. Afterward, flood delineation map is used as an effective tool in the improvement of decision making process in urban development plans. For this end, a geographic information systems based software named WMS has been utilized to delineate flood inundation and depth in Bronx brought of New York city in United States. Best management practices as flood management/mitigation strategies are categorized in five classes, namely resist, delay, store, discharge, and retreat. A performance index has then been derived based on five factors of degree of hazard mitigation, aesthetic, capital cost, maintenance and operation cost, and adaptability utilizing Multi Criteria Decision Making methods. Finally, one of the strategies is selected as a viable strategy to reduce flood hazards.

Conceptual modeling of rainfall-runoff procedure involves large number of parameters and climate data. Uncertainty in these input parameters are very likely which leads to output errors which can affect long-term predictions and management policies. In this study Soil and Water Assessment Tool (SWAT) is implemented to simulate rainfall-runoff process in Chelgerd sub-basin. To develop an appropriate model with acceptable and reliable performance, Ensemble Kalman filter (EnKF) is used as data assimilation technique to assimilate the variables of model which are known as sources of error; i. e. model parameters and input data. The research concluded that as a data assimilation technique, EnKF is capable of reducing the computational error inherited in the simulation model. Results of the proposed model is evaluated by Nash-Sutcliff (NS) factor with value of 0. 86 which have better performance compared to modeling without EnKF technique. Also the performance of the developed model is improved with a NS value of 0. 82 for the validation period.

Given the complexity of urban environments, flood risk has increased in urban areas in recent years. In order to establish a correct urban management and the control and optimal use of surface water, the complex rainfall-runoff process in urban environments should be thoroughly understood. When dealing with urban basins, the shortcomings can list as the lack of precise input parameters, the lack of knowledge on the runoff producing processes, the lack of flow measurement system at the outlet of sub-basins needed for model calibration, and the uncertainty in input parameters and results of mathematical and numerical models such as SWMM. This research investigated and analyzed the uncertainty of SWMM urban flood model in District 2 of the Metropolitan Municipality of Tabriz, using the GLUE method. In order to quantify the uncertainty, the initial range of input parameters including CN, fraction of impervious surface, N (roughness) for pervious and impervious area were determined. Using the GLUE algorithm, initial sampling operations were performed using parametric space by lattice square sampling. According to the results of simulations and the magnitudes of observational events, 20% of the total outputs and the series of generated parameters were extracted as acceptable simulations. According to the results of the evaluation of the distribution diagrams, the input parameters of Imperv % and N Imperv were identified as sensitive parameters affecting the model simulation and the optimal range of parameters was obtained.

In this paper, by using variance based methods, the sensitivity analysis of the two well recognized water quality indices, namely NSFWQI and IRWQISC, is presented in a comparative approach. The research was conducted by employing monthly sampling in thirteen stations on Pasikhan River during 2015. Sensitivity analysis for the parametres of the two models on one hand led to recognizing the most important parameters for a better measurement and on the other hand helped evaluation of the correctness of the parametr weights used in the model developed in Iran. In seasonal analysis, BOD was determined as the most sensitive parameters for both indices. In spatial analysis, NSFWQI classified the river water quality as “ Good” and “ Bad” in upstream and downstream of Pasikhan River, respectively. Using Factor prioritization approach, it was found that DO was the most effective parameter in NSFWQI, for which applying the approach minimized the uncertainty of the model output. In IRWQISC, DO at U/S stations and BOD at D/S stations were the most influencing parameters on the model output variance, which emphasized the importance of the frequency and precision of sampling for these two parameters against the others. Another important achievement of the present research was revealing the inconsistency of the weights used in IRWQISC, with respect to the sensitivity of the parameters and their influence on the model output in Pasikhan River.

The various semi-distributed and distributed hydrological models have great potential for water catchment comprehensive simulations, but there still is a need for development in order to give out a more realistic simulation representing the actual conditions of the catchment area. Changes in model codes are required in order to simulate the karstic basins using conventional hydrologic models. The SWAT model, as a free open-source package, offers this possibility to set up and configure various conditions in the catchment area. In this research, a new model called SWATML has been developed in which different hydrological characteristics were assigned to karst regions. The studied area is the karstic basin of Maharlu Lake and the statistical simulation period was selected from 1980 to 2013 for calibration and validation of surface runoff, base flow, agricultural production, and actual evapotranspiration. The model was calibrated with an average of determination factor and Nash Sutcliff of 0. 65, and it was concluded that the SWAT model has a great potential for estimating the yields of agricultural products. The developed model showed better performance in karstic watershed simulation compared to the original SWAT model. The calculated balance values by SWAT and SWAT-ML model showed significant differences compared to hydro climatology budget.

The provision of appropriate legal instruments for precise oversight of trade process and resolution of possible issues is one of the requirements for establishment of an efficient water market. The dominant approach of Iran’ s water legal system is the incentive of water market formation. However, there are some legal documents such as Articles 27 and 28 of the Fair Water Distribution Law that limit the water trade process. Although the guidelines for the implementation of these clauses have been amended by the Ministry of Energy but there are still the ambiguities in the water market conditions. In this paper along with the identification of the incentive and restrictive laws of the water market, some of the legal challenges of water trade are presented through the analysis of laws related to the Iran’ s water legal system. Then, by examining the status of trades within the Borkhar pilot plain, the challenges and considerations for the success of this water market are identified. Based on field-level visits in the study area inter-sectional trades are of general interest. Accordingly, the agriculture-based industries and building industries have a potential and actual willingness to purchase water from agricultural wells. The results showed that the establishment of an efficient water market needs such considerations as issuance of a separate document and independence of the withdrawal permits from the lands, modification and adjustment of exploitation licenses, equipping wells with smart meters and establishments of groundwater users’ associations by strengthening incentive laws and amending barrier laws.

Rising water demands and over-extraction of groundwater have caused critical status in more than 50% of the aquifers in Iran. Simulating complex condition of such aquifers and selecting an appropriate management approach are required to compensate drawdown. Therefore, this study aimed to provide a management approach for the Ravansar-Sanjabi aquifer in Kermanshah, an aquifer under a severe stress during last decades, using the MODFLOW model. After calibrating and evaluating the model, as well as providing a spatial distribution map for the actual drawdown during the period of 2007-2015, the aquifer is divided into four drawdown zones, including 0 to 5, 5 to 10, 10 to 20, and 20 to 45 meters. Several options of combining scenarios were then applied to compensate the drawdown in each of the zones. The first scenario included the continuation of the last eight-year condition of sources, sinks and recharge of the aquifer, which resulted in a 7. 2 m decline during the next eight years. In the second and third scenarios, with the effects of drought and wet conditions, the results showed that groundwater level would decline by 8. 4 and 5. 2 m, respectively. The results of the first three scenarios showed that the effect of climate fluctuations is not the main factor affecting the groundwater level decline. In the fourth scenario different combinations of 10% to 70% reduction in pumping rates were applied and the results showed that the combination of 10% reduction in zone1 and 20% reduction in other zones provides the best results in improving aquifer conditions.

Efforts to achieve suitable estimation of parametric or the structural uncertainty of mathematical or conceptual frameworks have led to development of various probabilistic, possibilistic, and innovative methods. In this paper, uncertain parametric behavior in a monthly water balance model has been studied using the structure of the UNEEC-P method. In the approach proposed by this paper and for the first time, instead of using a variety of regression methods to estimate the upper and lower uncertain bounds, the original conceptual model (monthly water balance model) has been used. The applied conceptual model is a three-parameter monthly water balance model and the case study of the paper is a small basin with an area of 82 square kilometers in southern France. Also, in order to evaluate the performance of the proposed method, Generalized Regression Neural Network (GRNN) has been used to evaluate the results of the conceptual models. The estimation of parametric uncertainty has been used to simulate the results of GLUE method in a Confidence Level (CL) equal to 90%. In order to measure the accuracy and validity of the new mechanism proposed in this study, in addition to the usual evaluation indicators of similarity and dissimilarity, the AIC index is also used, and different statistical indicators such as Mean Square Error (MSE), Normal Mean Square Error (NMSE), Nash-Sutcliff (NS), Correlation Coefficient (CC), and AIC demonstrate the better performance of proposed method comparing to the GRNN.

In the past decade, machine learning is considered to be a promising approach for empirical rainfall– runoff modeling as a useful complement to hydrologic models, particularly in basins where data to support process-based models are limited. In this paper, we used black-box models (i. e. neuro-fuzzy and support vector machine) and gray-box models (i. e. TOPMODEL and HBV) for simulating the transformation of daily rainfall-runoff process in the Nodeh Khormaloo watershed located in Gorganrood River Basin and compare their performance in terms of predictive accuracy. For the black-box models, the three input vectors including discharge, temperature and rainfall were selected in nine different scenarios based on the sequential time series data. Our result showed that the neuro-fuzzy model which consisted of three antecedent values of flow and one antecedent values of temperature outperformed other models when the root mean square error and coefficient of determination were used as quality indicators. In general, the black-box models outperformed the HBV and TOPMODEL simulations for the calibration and validation data sets. A detailed comparison of the overall performance indicated that the neuro-fuzzy and SVM models predicted runoff in warm months were consistently lower than that in the cold months.

Assessment of groundwater vulnerability to pollution is a proper tool for groundwater protection. In this study, the modified version of DRASTIC model versus the original DRASTIC model was used to investigate the vulnerability potential of Urmia groundwater which has a potential for contamination due to various industrial and agricultural activities. Precision to this model was achieved based on the revising of the ratings and weights of various factors using correlation methods in GIS environment and taking into account the correlation with the concentration of groundwater nitrate. The results showed that aquifer environment (A) did not have a significant correlation with nitrate concentration and was accordingly eliminated from the initial equation. The highest and lowest significant correlation was respectively found between soil factors (s) and depth to groundwater (D) and weighed 5 and 2. 7, respectively. The correction of ratings and weights led to improved zoning map with an increased correlation of DRASTIC index with nitrate concentration versus the original DRASTIC by 11%. Based on the modified index, 4. 3, 30. 8 and 64. 9 percent of the Urmia Plain were ranked as low, very low and without risk of pollution, respectively.

Increasing water demands and insufficient water resources has became the main reasons of raising water conflicts among stakeholders in Sefidroud basin, Iran. Sustainable allocation of water resources with viewpoint of economic efficiency and social equity is necessary to reduce these conflicts which is assessed in this study. Setting the sustainability of water allocation as the model constraint guarantees the minimum environmental requirements in water resource planning. In this study, a multi-objective programming model was developed to supply the requirements of the environmental, industrial and agricultural sectors in Sefidroud basin. The objectives of the model were to maximize the economic benefit efficiency for the development of improved water allocation strategies as well to maximize the water allocation equity (measured by using of the Gini coefficient). NSGA-II algorithm was used to solve the developed multi-objective programming model. The best solution among pareto front was selected according to equilibrium viewpoint due to considering economic efficiency and social equity among stakeholders simultaneously. Additionally, two scenarios were defined in terms of the rate of water loss and available water to assess the developed multiobjective programming model. The results showed that river basin authorities should decrease the rate of water loss and increase the available water to improve water allocation efficiency and equity. Additionally, the decrease in available water had highest impact on water allocation equity, but it had least impact on economic benefit efficiency. Furthermore, it should be considered for implementing water-saving policies according to critical conditions of water resources.

A comprehensive model based on AHP and GIS is developed to locate areas susceptible to artificial groundwater recharge through flood spreading. Khoy plain which faced an increased exploitation from the groundwater in recent years is selected as the case study. Sixteen criteria considered which included slope, land-use, geomorphology, geology, distance from fault zones, roads, waterways, wells, springs and aqueducts, rate of rainfall, temperature, evapotranspiration, electrical conductivity of available groundwater and the hydraulic gradient, thickness of non-saturated alluvial section, ability of aquifer transfer, and surface permeability of soil. Abovementioned attributes were classified into four groups, i. e. physical, climatic, hydrological and hydro-geological clusters. Gathered data were prepared in GIS environment, and based on pair wise comparisons, the preference of each cluster and its sub-criteria was determined. By combination of the layers using Weighted Sum method in ArcGIS, unacceptable zones were ignored and the remaining zones were classified into five categories. Finally six scenarios were proposed in very suitable zones. The proposed scenarios were then prioritized based on developed model and the 3rd scenario was selected. Results indicated that in non-wide areas the final decision will have a small effect on these indicators due to the similarity of the climatic and hydrological characteristics of selected sites.

Climate change effects on the hydrological system of the basin by changing amount of precipitation and temperature and, because of the interaction among systems, it would influence the environmental, economic, and social systems. The intensity of these unfavorable effects could be different given that vulnerability of the systems. Accordingly, presenting appropriate strategies is of importance for reduction of system vulnerability. Therefore, use of water security indicators considerably facilitates evaluating effective adaptation strategies. In this study, Tashk-Bakhtegan, with high interaction among its systems, was studied. Firstly, To simulate the effects of climate change, precipitation and temperature variable were extracted from the Atmosphere-Ocean General Circulation Model (CanESM2 and MPI-ESMLR, GFDL-ESM2G) by the new scenario of greenhouse gases (RCP2. 6). To minimize uncertainty caused by the downscaling method, three methods of multisite downscaling (XDS, Sequential k-nearest neighbor imputation method (SKNN) and Statistical Downscaling Model– Decision Centric (SDSM– DC)) were used. The integrated dynamic model for the catchment scale was then prepared. The effects of the climate change scenario and its adaptation strategies were simulated in the basin for a future period. Finally, measures of water security indicators were identified based on social, economic and environmental dimensions during the base and future period. Moreover, the influences of appropriate strategies on the reduction of vulnerability have been evaluated on the basis of change in indicators. The results showed that to decrease the vulnerability of system in the future period it is necessary to increase the economic water productivity in agricultural sector by increasing the irrigation efficiency and to reduce the agricultural activity. It is also essential to increase industrial activity for the economic and social indemnity.

The monitoring and analyzing of drought conditions is one of the main requirements for water resources management. In present paper, Standard Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were compared in order to assess the drought conditions in the Esfarayen-Sabzevar region. As in the aforementioned region sufficient long-term data is not available to assess the drought, reanalysis data of ERA-Interim were used to be combined with the observed data. For this purpose, climatic data of precipitation and temperature were extracted for each station in the statistical period of 1979-2016 using the web interfaces, Python script, ECMWF WebAPI and ArcGIS software. After correcting the bias of the data based on observational data, combined data of precipitation and temperature were obtained for the aforementioned period and used as the basis for calculating the drought. Finally, drought assessment and estimating the correlation of SPI and SPEI were conducted for three stations of Sarcheshme, Ghasemabad, and Jaghtay in the time scales of 3, 6, 12, 18, and 24 months. After generating data combination, the Root Mean Square Error (RMSE) and Bias were decreased from 0. 39 and 6. 69 to 0. 32 and 0. 24 respectively. Thus similar approach to data can be used for drought assessment in areas with lack of observed data. Results showed that in the short-term scales the frequency of dry and wet periods is high. By increasing the time scale, the frequency of the dry and wet period decreases but their duration increases. In most cases in the stations both indices showed coordination in the dry and wet periods. Based on the provided results regarding the correlation between SPI and SPEI indices, there is a positive and significant correlation between the above indices and the correlation is higher in the humid regions. As a result, the SPI index can be used in the regions with no temperature data with a precession similar to the SPEI index.

The primary objective of this study is to develop a hydroeconomic framework in Zayandehrud River Basin to optimize allocation of water released from Zayandehrud dam to downstream irrigation districts in order to maximize the economic benefits from agricultural activities. To achieve this goal, the existing agricultural activities in each irrigation district were simulated by applying the Positive Mathematical Programming (PMP) economic model. Coupling the developed water allocation planning model of the basin (MODSIM) with this economic model, the hydro-economic framework was then generated. Calibration and convergence of the models were performed by using the data from the baseyear (2014-2015). Results of employing the hydro-economic framework in the management of water distribution revealed significant changes in water-allocation priorities among irrigation districts. For instance, allocation priorities of Neko-Abad and Rudasht Irrigation districts which were respectively first and fifth under the current conditions, have significantly changed to fourth and second after employing the developed hydro-economic framework. As a case study, the potential of existing operating system in North Rudasht Irrigation District to make an infrastructure for optimum water allocation by using the hydro-economic framework, was investigated as a representative of other irrigation districts in the basin. Results of modeling the existing operating system of North Rudasht Irrigation District showed an average and inadequate performance in applying optimum water allocation with an economic approach, especially in the middle and lower sections.

One of the most widely used methods for estimation of erosion over the catchment and the sediment yield is the revised universal soil loss equation (RUSLE). Previous research showed the importance of the topographic factor (LS), which includes slope steepness and slope length factors, in applying this equation. Over the past decades, different methods have been developed for the calculation of topographic factor and hence, choosing the best one is a confusing and challenging issue. Moreover, by increased use of geographic information system (GIS) application in hydrological modeling, mathematical calculations on grid-based datasets can easily be done in GIS environ. Due to depending on grid-based datasets, RUSLE model is influenced by several factors which among them, the effect of DEM resolution and the method of LS estimation are the most important ones. This research addresses the effects of all important factors on sediment yield load at the outlet of the Barajin catchment by coupling the RUSLE model and GIS environ. Also, in order to evaluate the efficiency of this model, mean annual sediment delivery load is calculated by analyzing discharge-sediment curve and observed data at Barajin hydrometric station between 1987 and 2015. Findings showed that using different methods for estimation of LS factor without considering their limitations leads to a significant relative error (RE) in the calculation of sediment delivery load. Furthermore, it is shown that McCool et al (1989) and Moore-Burch (1986a, b) equations are the best methods for calculation of LS factor, due to an RE of lower than 10% in estimating the sediment yield. Also, investigating the effect of DEM resolution indicated that the relative error in DEM resolution results in 1. 5% to 58% RE in sediment load estimation.

Optimal allocation of water resources for supply and demand management with respect to effects of the climate change on water systems is essential. The purpose of this study is to simulate and optimize various components of Aharchay water system under the climate change conditions and to evaluate different climate and management scenarios. The effect of climate change on weather variables is investigated using the outputs of GCMs under A1B, A2 and B1 scenarios. For the period of 2011-2030 in the study area results showed a decrease in rainfall and an increase in minimum and maximum temperatures. For estimation of the inflow to Sattarkhan dam reservoir during the priod of 2015-2025, the IHACRES model is used. Results for R2 and RMSE criteria were obtained respectively as 0. 65 and 1. 21 for the calibration stage and 0. 56 and 1. 4 for the validation stage. Moreover, to estimate the water requirement under the climate change condition, evapotranspiration (ET) are calculated by CropWat model. For demand management in agricultural sector, optimization of soil and water allocation is carried out through linear and nonlinear programming models (LP, NLP) considering plants’ growth stage and its sensitivity to water stress. Water balance in the reservoir was simulated by WEAP under climate change scenarios aiming at allocating water to demand nodes. Considering the climate and management scenarios, the best option is then identified by AHP and the associated weights are calculated. Resalts demonstrated the use of optimal cultivating levels, new irrigation method and reducing per capita of the drinking water was selected as the best management scenario with the highest values of temporal and spatial reliability.

Fractal geometry is used as a suitable tool for analysis of the sub-basin and the river networks geomorphology characteristics and simulation of complex natural phenomenon. The main objective of this article is the analysis and evaluation of Walnut Gulch Watershed based on multi-fractal analysis of the river network images and geomorphology characteristics. The basin is located at the south eastern Arizona State in the united states with total area of over 150 km2 that surrounds the historical western town of Tombstone (31o42' N, 110o03' W). To achieve this goal we inserted the required data in GIS template in the Arc Map software including Dem 10 topographic maps of the drainage network and sub-basins classification. Accordingly geo-morphologic characteristics including stream length gradient index (SL), river sinuosity (S), transverse topographic symmetry factor (T), drainage density (μ ), elongation ratio (Re) and form factor (Bs) are calculated. We also developed MATLAB codes for multi-fractal indices including fractal generalized dimension D (q), singularity spectrum f (α ) and the scaling exponent T(q). Besides, the fractal dimension is calculated using Box Counting method. Results depicted that the non-linear nature of the river network is mostly based on the multi-fractal characteristics rather than the fractal dimension. Results also showed that significant relations are recorded between the geomorphologic indices such as: μ , SL, S, T, BS and Re and the multi-fractal characteristics D (q), T (q) and f (α ). Results reported that sub-basins with most S, SL and μ values including sub-basins 3, 6, 9 and 10, also hold the greatest T (q) index and singularity angle, α . Meanwhile, the absolute difference between the fractal dimension and D (q), which is called Δ , was minimum and generalized dimension graph showed the most monotonic behavior. On the other hand for sub-basins 5, 7, 15, 2, and 1 for which the S, SL and μ indices were the smallest, T (q), D (q) and α also delivered their minimum values, Δ was maximum and generalized dimension diagram showed sharp variations. Results also showed that T has significant relation with the symmetry of the singularity spectrum. Sub-basins 7, 9, 5 and 11 with the smallest T have non-symmetric singularity spectrums and right asymmetric while sub-basins 3, 6, 8, 1 and 15 with the highest T values are more symmetric and sub-basin 10 with the maximum T is fully symmetric.

Total Precipitable Water Vapor is one of the important parameters in meteorology and hydrology. The aim of this study is the evaluation of remote sensing algorithms to estimate TPW over land using microwave and optical wavelengths in the western part of Iran. In this regard, the algorithm of TPW estimation in the microwave wavelength range by using Advanced Microwave Scanning Radiometer 2 (AMSR2) data on cloudy and clear sky days as well as the product of near infrared TPW of Moderate Resolution Imaging Spectroradiometer (MODIS) MOD05 in clear sky days were evaluated (MOD05 product does not work on cloudy days). The evaluation results based on radiosonde data on clear sky days showed that MOD05 product had higher precision than AMSR2 algorithm, so that the coefficient of determination (R2) derived from AMSR2 and MOD05 were 0. 516 and 0. 650, respectively. Moreover, the Root Mean Square Error (RMSE) of AMSR2 and MOD05 were acquired as 5. 129 and 4. 542 mm, respectively. On cloudy days R2 and RMSE of the obtained TPW from AMSR2 algorithm were 0. 284 and 7. 367 mm, respectively. Overall, on cloudy and clear sky days, the value of R2 and RMSE of estimated TPW from AMSR2 was 0. 420 and 5. 976 mm, respectively.

In the last few decades nitrate contamination of groundwater has been concerned as one of the major environmental problems. Recent studies have established that the nitrogen (N) and oxygen (O) isotopes of nitrate (NO3) can be used to trace nitrogen dynamics including identifying nitrate sources and nitrogen transformation processes. Stable isotope ratios (δ 15N– NO3, δ 18O– NO3 and δ D-H2O) of groundwater samples were determined and used to identify contamination sources and transformation processes occurring in Varamin aquifer, Southeast Tehran, Iran with intense human activities. The approach is based on the fact that NO3 originating from different sources would exhibit different isotopic compositions. In addition, δ 18O– NO3 is more useful to identify nitrate from atmospheric sources and nitrogen transformation processes. In the case study aquifer, the nitrate concentration of groundwater was in the range of 0. 4 to 79. 59 mg/l with mean value of 25. 14 mg/l. The nitrogen and oxygen isotopic compositions of nitrate in pore water extracts from the groundwater samples indicated at least two potential sources of nitrate in Varamin Aquifer. These sources included human wastewater and animal wastes as well as soil N. The most common source of nitrate contamination in groundwater is identified as wastewater. The significant process in the aquifer is the denitrification process occurred specially in the West and South West of Varamin aquifer.

Nowadays, such interactions between human and hydrologic cycle are occurring that previously has never been observed. Feedbacks and interactions between human and hydrological systems show that anthropogenic activities have led to a strong coupling between them and both positive and negative impacts of social developments can affect on hydrological system via management decisions. Although the fact that man and water are linked through a “ system of mutual interaction” has been recognized for many years, the quantitative understanding of feedbacks, mutual interactions and co-evolution concepts of coupling systems are not fully established yet. These conditions have led to the emergence of the new science of "socio-hydrology" in 2012 with the aim of understanding "the dynamics and co-evolution of coupled human-water systems". Socio-hydrology is the study of coupled human-water systems based on co-evolution of social and hydrological systems, which analyzes how two-way feedbacks and interactions occur in these systems. Socio-hydrology has led the boundaries of hydrology to accept human as an integrated part of the hydrological cycle and represents new advancement in hydrological science, which requires further rethinking. In this Paper, the history of emergence, concepts, characteristics, applications and goals of socio-hydrology was introduced and it was compared to integrated water resource management.

In this paper temporal and spatial upstream to downstream trend of water quality were examined in Greater Karun basin. To assess the trend of change 16 stations with a statistical period of 37 years (1353-1390) and with an overlaying spatial scatter within the basin were selected. In order to analyze the trend, the MK test was performed which showed that the discharge has decreased throughout the basin, except for the southeastern part. Changes in z statistics for the quality parameters were higher and at a significant level of 1% at Ahwaz and Shushtar stations located downstream of the basin. After analysis of the concentrations of anions and cations along with the type and facies of water it was determined that Ahwaz, Gotvand and Shushtar stations have chlorine, Sodic facies while in other stations the water exposed Bicarbonate and Calcic type. The quality of drinking water in the stations has decreased from the upstream to the outlet of the Karun basin and the worst quality are recorded in stations in Ahwaz, Gotvand and Shushtar, which are still within acceptable limits. In the Investigation of agricultural water quality, it was also determined that the water quality in Ahwaz, Gotvand and Shushtar stations lays in the C3S1 class. For other stations the class of water is C2S1. The results show that the water quality of the Karun basin has deteriorated from the upstream to downstream.

Groundwater is a vital source of water supply in Iran and sustainable utilization of this source is an inevitable necessity. In many watersheds, overexploitation and neglecting the reasonable criteria for sustainable use of groundwater led to destruction of groundwater-dependent ecosystems as well as many other inevitable losses. This study aimed the importance of this issue and analyzed the Sustainable Groundwater Management Act (SGMA) relying on the similarities of the climate conditions in Iran and the California State. SGMA introduced six criteria for sustainable use of groundwater. To achieve the sustainable groundwater resources, all watersheds must develop their groundwater sustainable plan based on these six criteria. The primary results of the studies by California Department of Water Resources (DWR) have shown that in most of the watersheds, the groundwater withdrawal exceeded the minimum threshold. Therefore, a long-term (25 year) program for achieving sustainable groundwater management goal must be developed. In Iran, it is also urgent to set a planned sustainability goal for all aquifers in the watersheds.

Due to the need to simulate rainfall time series at different time scales for engineering purposes on one hand and lack of recordings for these parameters in small scales caused by the administrative and financial problems, on the other hand, disaggregation of rainfall time series to the desired scale is an essential topic in water resources engineering. In this study, to disaggregate Tabriz and Sahand rain gauges time series, the wavelet-artificial neural network (WANN) hybrid model is proposed according to nonlinear characteristics of the time scales. For this purpose, ten years of daily data from four rain gauges and monthly data from six rain gauges in Urmia Lake Basin were decomposed with wavelet transform. Then using mutual information and correlation coefficient criteria, the subseries were ranked and dominant subseries were used as input to ANN model for disaggregating the monthly rainfall time series into daily time series. Results obtained by the WANN disaggregation model were also compared with the results of ANN and conventional multiple linear regression models. The efficiency of WANN model at validation stage for Tabriz rain gauge showed an increase of up to 8. 5% and 33% with regards to ANN and multiple linear regression models. For Sahand rain gauge a respectively increase of up to 13. 7% and 26% were remarked. It was concluded that WANN hybrid model can be considered as an accurate model for disaggregation of the hydro-climatological time series.

In this study, the groundwater level of the Kabodarahang aquifer located in Hamadan Province, Iran, is simulated using MODFLOW, Extreme Learning Machine (ELM), and Wavelet-Extreme Learning Machine (WA-ELM) Models. The correlation coefficient and scatter index values for the MODFLOW model are calculated 0. 917 and 0. 0004, respectively. Then, by different input combination and using the stepwise selection, 10 different models are introduced for the ELM and WA-ELM models with different lags. By evaluating all activation functions of the ELM model, the sigmoid activation function predicts groundwater level values with more accuracy. Also, Daubechies2 is selected as the mother wavelet of the WA-ELM models. According to different numerical models results, the WA-ELM model is selected as the superior model in prediction of groundwater level. For the superior model, the correlation coefficient and Nash-Sutcliffe efficiency coefficient are calculated 0. 959 and 0. 915, respectively. These values for ELM model was respectively computed as 0. 828 and 0. 672.