IRAN-WATER RESOURCES RESEARCH
Year:2020 | Volume:16 | Issue:3|Papers Count:27

The water crisis caused by rapid population growth, unbalanced economic development, climate change, and inefficient water resources management is recognized as the most significant threat in recent century. Groundwater resources, one of the most important available water resources, have faced many challenges in recent years as 404 out of 609 plains have been declared. The agricultural sector, as the main consumer of groundwater, has played a major role in creating this crisis. In the research, the impacts of different non-strategic crops on groundwater level and farmers’ income have been investigated using long term system dynamics. The Qazvin Aquifer, which has been declared a prohibited plain since 1962, was picked as a case study. After collecting time series data and preparing it, the 15-year causality model was built up using VENSIMPLE software, which calibrated using a genetic algorithm. The results showed that the transfer of water from Taleghan dam to Qazvin Plain helped to reduce ten meters of drawdown during a 15-year period. The impact of 17 different crops on groundwater level and farmers’ net income were researched for Qazvin Plain. The results showed that farmers’ income is most impacted by the grape crop with a high economic water footprint, while groundwater level is most affected by the wheat crop. Next, the influence of the removal of non-strategic crops with high water requirement was evaluated in the plain. The results showed that removing non-strategic crops is selected as optimal scenario by applying four conflict resolution methods in symmetric conflicts.

It is important to predict debris flood for reducing its damages. The aim of this study is the prediction of sediment concentration of debris floods and ordinary floods using bayesian network (BN) and artificial neural network (ANN) models in Ammameh, Navrood and Casilian basins which were located in Tehran, Gilan and Mazandaran provinces, respectively. Accordingly, average basin elevation (EL), average basin slope (S), watershed area (A), current day rainfall (R), antecedent rainfall (AR) of three-days ago and discharge of one-day ago were selected as input variables. Then, 32 scenarios were tested to determine the most effective factors on the sediment concentration of flood. For the scenario derived from all selected factors, indices R2 and MAPE in the test stage were obtained 0. 97 and 8. 55%, respectively. Assessment of the effect of different factors shows that the most effective factors on the BN model’ s prediction accuracy are EL, R, PQ, A and AR one-day ago. Indices R2 and MAPE for this scenario were obtained 0. 916 and 11. 01%, respectively. It was selected as the best scenario because the least number of predictors and the highest accuracy. The most effective factors identified in this study can be used to predict debris flood in similar basins.

Monitoring the quantity and quality of groundwater is a nonseparable part of the environmental information system. There are several ways to design a groundwater monitoring network. In the present study, we attempted to develop a new method for flexible design. By flexible we mean that the results of this study allow the decision maker to select a limited number of high-priority wells, taking into account the budget allocated to the project. It is also possible to easily use this method for wells under study or construction. Also, in the proposed design method, monitoring is not limited to just one parameter (in the present study, EC concentration) and one or more parameters can be easily replaced. The DRASTIC model was used to calculate the aquifer vulnerability, which consists of seven layers of aquifer, including groundwater depth, net recharge, aquifer media, soil type, topography, impact of vadose zone, and the hydraulic conductivity of aquifers. The layers were optimized using differential evolutionary algorithm (DE) in order to find the highest correlation between the vulnerable points and the points with the highest concentration of EC. As a result of this optimization, the amount of the net recharge (the actual infiltration of water into the aquifer) had the highest correlation, which was confirmed by comparing net recharge and EC maps. The aquifer was divided into 60×60m cells. The first scenario identified the cells in which at least 60 percent of simulations have vulnerabilities above average. The second scenario identified the cells which in at least 25 percent of simulations have vulnerabilities above average plus standard deviation. With the help of WhAEM2000, 10-year capture zone of existing wells were calculated. The final priority of the wells was calculated by monitoring the number of vulnerable cells enclosed in the capture zones.

One of the advantages of designing water distribution networks (WDNs) as district metered areas (DMAs) is that the leakage in each area can be identified by controlling the input and output flow, which of course requires that the areas are separated and flowmeters are installed between the interconnecting pipes of each area. Since most existing WDNs have been expanded traditionally and not as DMA, turning them into DMAs would require huge costs and might not be even practical in some networks. In this paper, the theoretical idea of virtual DMA is presented to identify the leakage in each areas. The innovation of this paper is the ability to transform networks into DMAs using a combination of the graph theory and artificial neural network to find leaks without using a flowmeter. The proposed method, in addition to reducing the costs for the flowmeters, increases the speed of detection of leakage areas. Furthermore, there is no need to specify the number of leakage nodes before the leak detection begins. The proposed method has been applied to the Balerma WDN in Spain with 443 nodes and 454 pipes for two, three and four simultaneous leaks. The results of this paper showed that the proposed theory is able to detect leakage in each area, and this method can determine the number of optimal virtual DMAs for each network. In all examples, the leakage area was correctly predicted and the maximum leakage error was about 6. 5%.

Water demand management policies have widespread positive and negative effects on various aspects and by affecting various components of the system can influence the severity and extent of such effects. Also, the application of these policies to social systems, which are composed of adaptable factors with complex behaviors (indirect effects) and interactions in micro scale, causes emerging phenomena at the system level (macro scale). In addition to the complications of the inner nature of socio-ecological systems and the urban water supply-demand cycle, the complications of urban infrastructure behaviors also present another challenge in this area. Therefore, the urban water system is known as a complex adaptive system that requires integrated evaluation and modeling of system complexities in different dimensions. So, this article reviews the two main concepts regarding the evaluation and modeling of environmental policies, including urban water management policies, namely (1) modeling of complex adaptive systems and (2) integrated evaluation and modeling and the types of common approaches for them. By reviewing these concepts and previous researches in the field of urban water management, the interactive cycle and modeling process in this field is extracted. This paper begins with an introduction to the importance of urban water management, especially demand management, and continues with an overview of the basic requirements and concepts in the structure of assessment and modeling of environmental policies. The proposed framework is then presented based on past researches. At the end, agent-based modeling, as a powerful tool in evaluation and modeling of urban water management, has been investigated using the two approaches above.

Climate change is one of the most important environmental challenges that has a significant impact on fundamental resources, including water resources and so it is necessary to assess this phenomenon and predict its impact on the environment, environmental resources, agriculture and water resources. In this study, climatic parameters were simulated for six different climates of Iran using the general ocean circulation model GFDL-CM3 under two commonly used scenarios RCP4. 5 and RCP8. 5 and in two time periods of 2021-2040 and 2041-2060. Then results were used to predict the water footprint of agricultural crops for its two components; blue water and green water footprints. For this aim, 31 stations from all over Iran were selected and classified into six climate classes using the UNESCO classification and their 30-year meteorological statistics data were given as the input to the LARS-WG model. With the production of 200 series of data, meteorological data were generated over the desired time periods. The results showed that during future periods the temperature in the study areas will increase from 0. 5 ° C to 2. 03 ° C and rainfall changes will increase from-16 mm to 100. 2 mm. This will cause changes in green and blue water footprints in different climates of Iran from-13. 44% to 37. 53% in green water footprint and-18. 77% to 38. 20% in blue water footprint. Among the studied climates, the SA-K-W and PH-C-W climates will have the largest reduction in green water footprint and the highest increase in blue water footprint, and will be most affected by climate change and its consequences.

In recent years, numerous policy programs are applied in the agricultural sector in south of Kerman Ptovince to overcome the water deficit. These include construction of dams on surface water resources in upstream areas, rationing of water input in the agricultural sector, installation of meters on groundwater wells and seal of the unauthorized exploitation wells. Implementation of these policies on the water supply side had impacted the cropping pattern and farmers’ gross profit of in the area and so it demands proper planning for water resources management and agricultural production in the region. To this end, this study firstly estimated the economic value of agricultural land and water inputs in south of Kerman Province using the statistical data related to crop years of 19961997 and the PMP advanced economic planning model. Then the potential impact of water supply policies on the agricultural sector in the area was evaluated. The results showed that by applying the water supply policies, the cropping pattern led to the development acreage of low water and grain products. It is also shown that onion, cotton and vegetables experienced the greatest reduction in acreage. Under these conditions, Kerman farmers pay about one-third of the economic value of agricultural water in the form of costs of extraction and water transfer. The farmers also receive a price elasticity of water demand of 0. 175. The water resources supply polices, despite encouraging farmers to saving the irrigation water, decreased the total gross margin of cropping pattern 0. 92 to 3. 46 percentage compared to the base year conditions. Finally, for managing the water resources under these policies on the supply side, it is recommended that these policies be applied together with moderation plans in water demand side (balanced pricing), removing the products with low “ benefit ratio in water” (onions, cotton and vegetables) from the cropping pattern and allocating the production of strategic products (such as potatoes) to regions with appropriate and profitability conditions in agricultural sector of Kerman province.

Evapotranspiration is one of the key components of water balance in a basin. In recent decades, several methods and models have been developed for estimating evapotranspiration using remotely sensed data. In this context, utilizing the surface energy balance equation has led to the development of Surface Energy Balance (SEB) models, such as SEBAL and METRIC. Despite the extensive usage of these models, their application is still challenging due to the underlying algorithm complexity and the time consuming process of selecting cold and hot pixels by an experienced user. The goal of this study is to develop and introduce a new toolbox within the MATLAB environment for estimating actual evapotranspiration using the METRIC algorithm and Landsat 8 imagery. Unlike the previously developed models based on the METRIC algorithm, the selection of hot and cold pixels has been made automatic in this new toolbox. The toolbox was also used to estimate actual monthly evapotranspiration of the Urmia plain in 2016, and the results were compared with those obtained from the MODIS images to highlight the applicability of high spatial resolution imagery in water and agricultural management studies.

Prediction of drought is a main challenge even for countries which apply dynamic monthly prediction modeling systems. Due to the severe damage caused to humans by drought, it is important to predict drought as accurately as possible. Wavelet-neural network integration method is one of the most accurate methods to predict drought. An important and influential factor in the results of wavelet transforms is the use of the appropriate wavelet. The aim of this study is to determine the optimal wavelet for more accurate prediction of drought types. To this end, daily precipitation data, daily discharge, and satellite imagery related to Tehran were used for the period of 1969 to 2016 as the raw data to calculate the indicators. The wave transformations (WT) conversion method and PNN neural network have been used to predict droughts. From each time series of drought, wavelet transformations were performed using haar and bior1. 1 waves, and the prediction was made by neural network. It was found that the regression coefficient and error concentration for meteorological drought using haar wave are 0. 68039 and 0. 03368. The parameters for hydrological drought are respectively 0. 76271 and 0. 0666 and for agricultural drought are respectively 0. 92697 and 0. 1515. Then, the prediction of the three kinds of drought was also done with bior1. 1 wave and the regression coefficient and error concentration for meteorological drought (0. 71169 and 0. 9923), hydrological drought (0. 14147 and 0. 0329), and agricultural drought (0. 82049 and 0. 0016) were respectively found. The results showed that for the meteorological drought, the bior1. 1 wave and for hydrological and agricultural droughts, the haar wave gave better results.

Excessive exploitation of groundwater resources has caused a decrease in the water table which as a consequence increased the phenomenon of the land-subsidence. Although, field inspection is the most accurate way to measure land subsidence and water table variation, it is less attractive due to the fact that they are costly and time consuming. Instead, the low cost and accurate remote sensing techniques are implemented for the large scale land subsidence measurement. The purpose of this study is to quantify the land subsidence in Qazvin province by using synthetic aperture radar interferometry and evaluating the effect of the groundwater depletion and soil profile on this phenomenon. For a long time land-subsidence evaluation, the combination of the outcomes of Envisat, Alos palsar-1, and Sentinel-1 satellite data from 2003 to 2017 are employed. Water table variation of Qazvin’ s aquifer was studied using data from 180 pizometric wells. Annually averaged landsubsidence in this year was obtained as 39. 9 mm/year for aquifer zone and 33 mm/year for Qazvin province. According to the land-subsidence zone in Qazvin province it was revealed that most of the land-subsidences occur in the aquifer area, the fine-grained layer thickness of which would be larger than other areas. The maximum extent of Land subsidence was obtained at the northern parts of Buin-Zahra and near the Takestan borderline. This area has the highest cultivated area and groundwater depletion. The results of this study showed a strong correlation between the groundwater water table variations and land subsidence values in Qazvin province.

In drip irrigation water quality control is crucial to prevent scale formation and corrosion of system components. Drip irrigation systems in the country mostly use groundwater resources. Qazvin province is one of the provinces facing water shortage and therefore, the use of drip irrigation has attracted the attention of farmers. The purpose of the present study is to investigate the quality of groundwater with regard to both sedimentation and corrosion indices. For this purpose, Langelier (LSI), Ryznar (RSI), and Puckorius (PSI) indices were used to determine the scaling formation and corrosion properties of groundwater in the study area. Then, the geostatistical methods were used to prepare the zoning maps of the indices. The temporal trend of changes in these indices is then examined. According to the results of the LSI index using Ordinary kriging, most of the area of the Qazvin plain is in the range of 0 to 2 indicating low, moderate, and in some of the years, high groundwater deposition. Also, according to the results of the RSI index, most of the plain is in the range of 6 to 7, indicating low corrosiveness of the groundwater. Indicator kriging results according to the LSI index, showed that the area of 80% to 100% of Qazvin groundwater resources is higher than the threshold. Also the results of the RSI index, the highest percentage of study area, are 80% to 100% above the threshold. Therefore, in areas where there is a problem with scaling formation, measures such as acidification of irrigation water or periodic acid flashing are recommended to prevent emitter clogging.

The aim of this study was to investigate the Intensity of the effect of climate change on groundwater resources in Varamin plain using CMIP5 group models under RCP release scenarios. The output of these models was downscaled by LARS-WG software. For this purpose, the period 1989-2005 was used as a base period to select the regional model and the period 20212050 to study the NISTOR index in the future. The results showed that the EC-EARTH model of CMIP5 series under three scenarios RCP2. 6, RCP4. 5 and RCP8. 5 for the next period has a high performance in simulating temperature and precipitation in the study area. On average, for the average temperature in the study area, under the optimistic and intermediate scenarios of RCP2. 6 and RCP4. 5, respectively, an increase of 1 to 1. 2° C was predicted compared to the base period. For the most pessimistic scenario, RCP8. 5, in the period 2050-2020, an average increase of about 1. 5 degrees Celsius was predicted compared to the base period. Also in future periods, an increase in precipitation was observed in the early months of the year to late spring and a decrease in precipitation in autumn. Overall and over the next 30 years, the RCP4. 5 scenario showed slightly more annual precipitation increase over the base period compared to the other two scenarios. Also, considering the effective rainfall, the results of the NISTOR inferential matrix showed that in all three scenarios, the intensity of climate change effects on the groundwater resources of Varamin plain in the next three decades is significant.

In this study, in order to investigate the resilience of the area in the catchment of Lake Urmia, in contrast to the destruction of the density of the stream network, a performance function matrix was developed in the probabilistic First Order Reliability Method (FORM). The basis for calculations in the Python programming environment was the changes in the flow accumulation layer, as an important factor in the surface recharge of Lake Urmia. Preparation of flow accumulation layer, based on the digital elevation model of 30 meter’ s altitude of Aster Landsat satellite in the geographic information system environment, and the criterion for its changes, according to the different layers of the stream network density and with certain thresholds of sequential power, has been the natural flow path in the automatic extraction method. The performance function in the FORM method was prepared by estimating the standard deviation, mean and linear correlation between the two categories of current stream network density, with thresholds of 100, 300 and 1000, as well as 1000, 3000 and 10000, and by deducting two sets of coordinate points. The results showed that the failure probability in the catchment area, without taking into account the lake environment, varies between 28% and 72%. For the boundary strip, the lake's vulnerability display is the least resilient in the western, northwestern and southern parts. Vulnerability occurs with the destruction of the stream network in accordance with the FORM method in its maximum value with 86% in the western part of Lake Area.

Climate change and its effects on temperature, precipitation and river flow have been the subject of many studies around the world. The purpose of this study was to evaluate the effects of climate change on Aras River catchment discharge at different time scales. In this study, RCP2. 6, RCP4. 5 and RCP8. 5 scenarios for the near future (2017-2036) were applied in HadGEM model and LARS-WG micro scale model to analyze the future and predict the minimum and maximum future changes of Aras discharge. Climate scenario outputs were entered into SWAT model for calibration and validation period and their results were analyzed. Monthly rainfall data of Pars Abad Synoptic Station during the period of 1985-2005 were used as control period. To evaluate and compare the accuracy of finely scaled data, comparison of baseline data and historical data produced by the model was performed using the calibration parameters. For example, the NSE index values for precipitation, minimum temperature and maximum temperature were 0. 695, 0. 624 and 2. 054, respectively. According to the results, all three scenarios predicted an increase in precipitation for August and December and a decrease for April and November, but it is shown overall that the average annual precipitation in the Aras watershed during the period 2017-2036 would decrease. All three scenarios also predicted an increase in the minimum and maximum temperatures. Also according to SWAT model results, the inlet discharge of Aras River should decrease by 30. 7, 30. 2, and 22. 2% under scenarios RCP8. 5, RCP4. 5 and RCP2. 6, respectively. In general, and based on the results of the models used, the temperature in the study area will increase and precipitation will decrease, which will have a negative impact on the Aras River discharge and will require proper and principled management of water resources utilization.

Distributed hydrological models use a large number of parameters in simulating a watershed and accordingly are subjected to model uncertainty. This study, therefore, analyzes the model uncertainty induced by its parameters instability. The soil and water assessment tool (SWAT)— a powerful semi-distributed hydrological model— is employed here to simulate the Kardeh river flow, in Iran. The model parameter sensitivity was assessed using the global sensitivity analysis method. For the uncertainty analysis, the generalized likelihood uncertainty estimation (GLUE) technique was used which performs the uncertainty analysis and calibration of the model through inverse modeling. Observed streamflow data of 2000-2006 and 2008-2012 were respectively used for uncertainty-analysis/calibration and validation periods. Consulting the Nash-Sutcliffe efficiency coefficient values obtained in calibration (0. 64) and validation (0. 68) steps, the developed SWAT model showed good performance for simulating Kardeh river flow. The produced uncertainty band was also able to encompass respectively 68 % and 93 % of the observations during calibration and validation steps. Results, while confirming the model goodness of fit, showed that the generated uncertainty interval by GLUE covers a large spectrum of the probable streamflow scenarios in the study area. Therefore, the model applicability in the study area is confirmed under different uncertainty scenarios and it can be applied for the river basin planning and management. The calibrated model can be reliably forced by climate change scenario driven data to assess the hydrological impacts of climate change in the study area.

The staggering growth of information in all scientific branches and the impossibility of pursuing them by researchers, urges the need for review articles. On the other hand, the global strategic importance of water resources management and the emergence of the new scientific discipline of Socio-hydrology which addresses the inability of current hydrological methods and emphasizes on concurrent consideration of social and natural factors in water resources management, coupled with the ignorance and demand of the Iranian scholars to the growing socio-hydrological concepts, motivated this article as a comprehensive review on the Socio-hydrology initiation and evolution. To ensure the validity of information, scientific databases were applied and most of the information and resources, including articles, books, and scientific documents, were reviewed. Using the narrative review method, information was classified in four categories; 1. conceptualization, 2. applications in agriculture, 3. flood and drought studies, and 4. modeling and watersheds. The results showed that despite rapid growth of the socio-hydrologic discipline in scientific circles, there are still significant challenges in development of the branch which demands for further studies on basic concepts, methodology and ontology of Socio-hydrology.

To optimize the management and use of groundwater resources, the temporal-spatial variation of the stagnant level and its prediction and modellig is essential to better understand the behavior of aquifers in response to the natural and human stimuli. Given the increasing development of metamodels and their combination with optimization algorithms for modeling and predicting hydrogeological variables, the question remains that to what extent these hybrid models can be effective compared to the individual model. To answer this question, in this study, four algorithms of particle overvoltage optimization (PSO), genetics (GA), ant colony (ACOR), and demand evolution (DE) were combined with the model of adaptive fuzzy-neural inference system (ANFIS). The performance of the combined models developed with the ANFIS model was evaluated to estimate the average monthly groundwater level of the Sahneh plain aquifer in Kermanshah province over 19 years. The time series model (SARIMA) was used as the reference model. To better compare the results of the models, the same input variables of the groundwater level in different time steps (maximum four months based on the self-correlation function of aquifer level) were considered for them. The results of fitness indicators in the training and test phases showed that there was no significant difference between the SARIMA time series model compared to other combined models used. However, given that SARIMA applies average moving processes, authorization, seasonal changes, and delays in modeling, it can be given more attention in groundwater leveling modeling. The RMSE values of the best hybrid model (ANFIS-GA) and SARIMA were 0. 950 and 0. 1012, respectively. The results also showed that the combination of optimization algorithms considered with the ANFIS model did not improve the model's results compared to the individual ANFIS model in terms of significance. The results of this research can help researchers in consciously choosing the appropriate model in predicting the time of the stagnant aquifer level according to the criteria of efficiency, time and cost of calculations and data preparation to enter the models.

Cloudiness is of particular importance among other climatic elements and is one of the important issues in predicting climate change on a global and regional scale. The purpose of this study is to investigate the spatial distribution and estimate the long-term average of cloudiness on a seasonal and monthly time scale in the geographical area of Iran's atmosphere. Therefore, Multi-angle Imaging SpectroRadiometer (MISR) products were used during the years 2001-2019. The cloud products used were extracted with monthly temporal resolution and spatial resolution of 0. 5° x 0. 5° and after quality control and preprocessing, were used to build network layers. Cloud cover data from 44 synoptic meteorological stations were used to verify the accuracy of the cloud data of the MISR sensor. Based on the results, the average percentage of cloudiness in Iran's atmosphere is about 25%, which is low compared to the global average cloudiness (50%). In the long-term study, the maximum cloudiness was estimated on the southern and western coasts of the Caspian Sea and the highlands of Azerbaijan, Zagros and Khorasan ranked next. On the other hand, the lowest amount of cloudiness was observed in a wide area of central, eastern and southeastern Iran. Among the seasons, the highest and the lowest cloud fraction was estimated in winter and in summer, respectively. On a monthly time scale, it was found that the highest/lowest amount of cloud fraction is related to February/September. These differences indicate changes in the weather during different months of the year. Also the decreasing trend of cloud fraction during the study period is important in terms of global warming and climate change.

Stream flow forecasting on a monthly time scale is essential for optimal water resources management and planning. In this paper using the predictions obtained from the ECMWF climate model, monthly stream flow forecast was made in Shahroud river Subbasin, part of Sefidrood basin northwest of Iran. To this end, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modelling, as a rainfall-runoff model, the stream flow was predicted based on the predicted precipitations. First, the results of precipitation forecast for the desired historical period and up to a 3-month forecast horizon for the study area were obtained from the Climate Data Store. Then, by using the SVR driven model, a linked Climate-Data-driven model was developed to predict the flow up to a 3-month forecast horizon. The results showed that flow forecasting based on climate forecasting models for the forecast horizon of the next month is more accurate than that of two and three months. The forecast horizon of the next month had the highest Nash-Sutcliffe coefficients of 0. 77 and 0. 48 in calibration and validation, respectively. It alo had the highest correlation coefficients in calibration (0. 87) and validation (0. 69), the lowest root mean square error in calibration (6. 8 million cubic meters) and validation (6. 3 million cubic meters) and moreover had the best relative bias value for calibration (0. 96) and validation (1. 1). Furthermore, based on the POD and FAR probabilistic indices, the results showed that the developed predictive model has a high ability to detect different states of stream flow events, especially for extreme flows event.

Balancing the needs of aquatic ecosystems with other uses in a catchment area, especially with agriculture uses, is one of the main concerns in macro-management of water resources nationally and internationally. Extensive exploitation of water resources for agricultural purposes has led to a lack of environmental water supply and weakening, destruction and disappearance of aquatic ecosystems, especially wetlands. In this study, Amirkalayeh International Wetland was selected as a case study and a comprehensive scenario-based method was used to determine its environmental water needs. The holistic model developed in this research included biophysical and socio-economic assessment, scenario development, and integration. In the biophysical and socio-economic assesment, a comprehensive evaluation of all physicochemical, ecological, biological, economic, and socioeconomic indicators was performed and finally water withdrawal from the wetland for agricultural purposes was selected as the socio-economic index and the Sheng animal species as the ecological index. In the scenario development section, scenarios were determined to maintain the habitat suitability for the selected index in terms of minimum and optimum conditions in comparison to the current condition for wetland stakeholders. Finally, in the integration section, the environmental water requirement of Amirkalayeh Wetland to supply the required water for the selected indicators was determined based on the targeted scenarios in the first and second half of the water year. The results showed that the amount of environmental water volume in Amirkalayeh wetland in the first and second half of the water year in optimum condition was equal to 7. 25 and 6. 74 million cubic meters, respectively. Furthermore, the minimum environmental water demand of Amirkalayeh wetland for the whole water year was obtained as high as 5. 36 million cubic meters. The water balance of Amirkalayeh Wetland showed that the calculated volumes for the minimum and optimum ecological conditions are achievable in the current conditions through a proper management policy and provide a good basis for the comprehensive management of water resources in the basin and wetland.

One of the most notable consequences of Climate Change is the change in climatic variables and its effects on hydrological variables and water resources. Karaj Dam is one of the profound sources of water and electrical energy for Tehran and Alborz province. Thus, the diagnosis of factors affecting the water resources upstream of the catchment and the runoff entering Karaj dam could be vital for plans and policies for the future periods. One of the best methods for diagnosing the effects of Climate Change on climatic and hydrological variables is Detection and Attribution with an Optimal Fingerprint approach. This research has tried to detect and attribute the ALL, GHG, and NAT signals using simulations from the CanESM-2. 0 model. Every signal has been downscaled with the MRQNBC method and then simulated with the SWAT calibrated model for selected signals for the period 1985-2011. Finally, detection and attribution of each signal were performed using the optimal fingerprint method. Results showed that just the GHG signal, showing the globe under the effects of Greenhouse gases (without the other factors) has been detected and attributed and its scaling factor (β ) was obtained as 0. 76). Two other signals (ALL, NAT) could not been detected and attributed. In other words, the variation in the inflow to the Karaj dam was very likely consonant with the simulated runoff under the GHG signal only.

Colored dissolved organic matter (CDOM), one of the main constituents of DOM in surface water, is an important indicator of water quality, the biochemical status, and the nutritious material content and plays an important role in the carbon cycle in surface water resources. In this study, we have analyzed colored dissolved organic matter (CDOM) in lakes at the watershed area of North Siberia for the period of 2013 to 2016 using Landsat 8 images. The support vector regression model was used for selecting the most desirable band in determining the CDOM absorption coefficient. Using the support vector machine model to classify and compare the changes in the amount of α CDOM(440), the CDOM scatter plots for the years of 2014 and 2015 were obtained. Based on the results, regarding the correlation coefficient (R 2 =0/71) and the amount of errors (MSE=1/60 m-1, RMSE=1/0775 m-1, and MAE=0/9464 m-1), it was concluded that the application of green/red band ratio in Landsat 8 satellite was the most desirable choice for measuring CDOM at a wavelength of 440 nm in watershed resources of North Russia. The scatter plot indicated an increase in CDOM in the lakes in the northeast of the area in 2015 compared to 2014.

This study aimed at determining the concentration and distribution of cyclic aromatic hydrocarbons and their origin in the protected river Sardabrood in Mazandaran province. Sampling of surface sediments from the riverbed was performed in four stations (Karimabad, Gil Kola, Abbas Kola and the estuary of Sardabroud). To extract and analyze PAH compounds by Soxhlet method, two stages were performed using column chromatography and gas chromatography-mass spectrometry. The highest total amount of compounds was observed in the river estuary as 1059. 89 ± 173. 40 ng. g-1 and the lowest concentration was obtained in Karimabad station as 141. 42 ± 39. 22 ng. g-1. The trend of accumulation of PAH in sediments showed that PAH concentration increases from upstream to downstream. Determining the origin of compounds in sediments using isomeric diagnostic ratios showed that PAH are mainly from a mixture of petrogenic and pyrogenic sources. In terms of polyaromatic hydrocarbon concentration, surface sediments have an average level of pollution according to NOAA sediment quality guidelines. The presence of a sand factory along the river, the entry of agricultural effluents into the surrounding fields, increased urbanization, car traffic and the relatively high amount of rainfall and urban runoff have caused the pollution of aromatic hydrocarbon compounds in the river sediments.

In current research, attempts were made to analyze the hidden aspects of precipitation in the Caspian region. To this end, the daily data of 385 stations, under Meteorological Organization and the Ministry of Energy's supervision was used for the period of 1966-2016 (51 years). These stations were used in order to create maps with a spatial resolution of 3 × 3 km. Sinusoidal behaviors of monthly Precipitation in each pixel of the map were investigated. Accordingly, first to sixth harmonics were extracted. Finally, to identify the spatial patterns of precipitation based on the contribution of different periodic and its zoning, the cluster analysis method was used based on the Euclidean distance and the "Ward" method of linkage. The results showed that large-scale systems and local factors such as the Alborz Mountains and the Caspian Sea have led to different patterns of atmospheric precipitation. The largest share of precipitation from large scale systems was seen in the southwest of the Caspian Sea and the eastern region and the highest share of local precipitation was met encountered in the Alborz highlands. This suggests that precipitation is concentrated in the coastal and southwest parts of the Caspian Sea and east of the region, and is less concentrated in the Alborz highlands. The results of cluster analysis indicated that in the Caspian region three classes of distinct precipitation diffraction can be identified. The geographical dispersion of these classes shows that the difference between these classes is affected by latitude and distance from the Caspian Sea.

Illegal and over exploiting the aquifer is among factors which threaten the country's groundwater stability. One way to deal with this phenomenon is to impose a fine policy by the competent authorities. Since farmers' over-exploitation is under specific socio-economic and agricultural conditions, evaluation of the effects of penalty patterns based on a socioeconomic simulation is required. This research has provided an agent based simulation framework to study the status of three agricultural, environmental and regulator agent in agricultural environment. In this research, agricultural sector behavior has been modeled in two layers, one is the agricultural sub-factors in order to maximize individual profit under physical and behavioral constraints by using fuzzy inference system and mathematical programming, and the other is the groupagricultural agents in order to maximize agricultural profitability under the constraints of individual preferences by combining non-dominated sorting genetic algorithm (NSGAII) and social chose method. The proposed framework was applied to the Najafabad hydrological unit in Isfahan. The results showed that the level of aquifer unit hydrograph was respectively 21. 82, 17. 18 and 10. 5 lower than no-penalty condition while penalties of 2, 3, and 4 thousand Rials per cubic meter were imposed by regulator agent.

Flood control and management is a fundamental issue for hydrology researchers and managers. Regarding the design and construction of different hydraulic structures such as reservoirs and dams, as effective techniques for flood control, accurate estimation of the magnitude and return periods of flood is required for appropriate estimation of the dimension and resilience of structures. Design flood estimation is done through frequency analysis with the key stationary assumption. Nowadays, factors such as land use change, inappropriate management and climate change has influenced stationary conditions of flood peaks. Therefore, in the presence of nonstationary conditions, the estimation based on stationary assumption is not confident and may lead to large errors. In this study for non-stationary flood frequency analysis, the GAMLESS model for location, scale and shape parameter estimation are introduced while visual inspection of nonstationary are as well presented and developed for quantile estimation. Six hydrometery stations in different provinces in the north of Iran were selected. Frequency analysis in stationary and non-stationary conditions was performed for each station. Results indicated that location and scale parameters have linear and quadratic trend. In addition, in Nodekhormalo station the design flood estimated by nonstationary assumption was around 3 times higher than of that obtained for the stationary conditions. Results also demonstrated that in stations with increasing non-stationary trend, return period of large floods was decreasing and for the same return periods, flood quantiles has increased.

In recent years, several studies have been carried out in Urmia Lake basin or in local scale to estimate evapotranspiration of crops using different methods. In many of these studies, the feasibility of estimating the actual water-use has been investigated with different algorithms. The purpose of this study was to extract an applied result aiming at achieving the Lake Urmia restoration goals. Estimated evapotranspiration values using SEBAL algorithm were compared with rainfall values in different zones to locate the irrigated areas. From the results of this study, areas with high water consumption, “ Hot spots” , were extracted for the basin. Such areas can represent areas with maximum water saving potential. Estimated actual evapotranspiration (ETa) values using satellite images compared with rainfall values (P-ETa = (+) rainfed or = (-) net irrigation requirement) for the years 2013-14 and 2014-15 were extracted in different zones at the basin. The results showed that actual evapotranspiration values in the western (Urmia plain), south-eastern (Miandoab and Mahabad plains), and in parts of the northeast and northwest (Sarab and Salmas) zones were much higher than the amount of rainfall. This indicated the high concentration of irrigated lands in these areas. High density of irrigated land in these areas was also confirmed by land use maps. In result, identifying irrigated areas with high water saving potential can be to implement water saving pilot schemes and to revive the lost water right to the lake.