مجله تحقیقات منابع آب ایران
سال شانزدهم شماره 3 (پیاپی 54، پاییز 1399)

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 non-separable 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. Flexible means that the results of this study allow the decision maker to select a limited number of high-priority wells, taking into account the budget level allocated to the project. It is also possible to use this method easily for wells under study or construction. Also, in this 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) 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 net recharge (the actual infiltration of water into the aquifer) had the highest correlation, which was confirmed by comparing net recharge and EC maps. With the help of WhAEM2000, 10-year capture zone of existing wells was calculated. The final priority of the wells was calculated by linking of these models.

One of the advantages of designing water distribution networks (WDNs) as a district metered areas (DMAs) is to identify the leakage in each area by controlling the input and output flow, which of course requires the separating areas and installation of flowmeters between the interconnect pipes of areas. Considering that the 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, a 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 costs for the flowmeters, also increases the speed of detection of leakage areas. In addition, there is no need to specify the number of leakage nodes before the leak operation begins. The proposed method has been applied for the Balerma WDN in Spain with 443 nodes and 454 pipes for two, three and four simultaneous leaks. The results of this paper show that the proposed theory in this method is able to detect leakage in each area, and this method can determine the number of optimal virtual DMA 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 can affect the severity and extent of these effects by affecting various components of system. Also, the application of these policies to social systems, which are composed of adaptable factors with complex behaviors (indirect effects) and interact with each other on micro-scale, causes emerging phenomena at system-level (macro-scale). In addition to the complicates of inner nature of socio-ecological systems and urban water supply-demand cycle, the complicates of urban infrastructure behaviors also present another challenge in this area. Therefore, 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 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 of 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 basic resources, including water resources, so it is necessary to assess this phenomenon, agriculture and water resources. In this study, due to this importance, the simulation of climatic parameters with the general ocean circulation model GFDL-CM3 under two commonly used scenarios RCP4.5 and RCP8.5 in two time periods of 2021-2040 and 2041-2060 in six different climates of Iran has been investigated and Using its results, the prediction calculations of the water footprint of agricultural crops were performed in two components, blue water and green water footprints. For this purpose, 31 stations from all over Iran were selected and classified by UNESCO classification into six climate classes and then their 30-year meteorological statistics data are entered into the LARS-WG model. 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 2.3 ° C and rainfall changes will increase from -16 mm to 100.2 mm. This causes 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, respectively. Among the climates studied, the two climates SA-K-W and PH-C-W 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 such as the 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 to solve water deficit problems in agricultural sector of Kerman province used. Implementation of these policies on the water supply side have many effects on the cropping pattern and farmers’ gross profit of in south of Kerman province and so it requires proper planning to water resources management and agricultural production in the region. For this purpose, in this study to estimate the economic value of agricultural land and water inputs and then to evaluate the potential impact of water supply policies on the agricultural sector in the south of Kerman province using of the statistical data related to crop years of 1996-1997 and the PMP advanced economic planning model. The results showed that by applying the water supply policies, the cropping pattern leads to the development acreage of low water and grain products and onions, cotton and vegetables experience 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 decrease the total gross margin of cropping pattern 0/92 to 3/46 percentage compared to the base year conditions,

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.

According to the heavy damage caused to humans by the occurrence of drought, it is important to predict the drought as accurately as possible.The wavelet-neural network integration method is one of the most accurate methods for this important.There are several categories of drought, including meteorological,hydrological,and agricultural drought.In this study,authors was tried to determine the optimal wavelet for predicting different types of drought.daily precipitation data,daily discharge,and satellite imageryrelated to Tehran from 1969 to 2016 were use as 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 bior1.1waves,and the prediction was make by neural network.It was found that the regression coefficient and error concentration for meteorological drought using haar wave are 0.68and 0.033and regression coefficient and error concentration for hydrological drought using haar wave are 0.76and0.066and regression coefficient and error concentration for agricultural drought using haar wave are 0.9269and0.1515.Then, the prediction of any kind of drought was done with bior1.1wave and it was found that the regression coefficient and error concentration for meteorological drought using bior1.1wave are 0.7116and 0.992.and the regression and concentration coefficientThe error for hydrological drought using bior1.1wave is0.14147and 0.0329and the regression and error concentration coefficient for agricultural drought using bior1.1 wave are0.82049and0.0016.The results showed that for meteorological drought, the bior1.1wave appeared better and gave us better results.But in two types of hydrological and agricultural droughts,it was found that the haar wave gives us better results.

Excessive harvesting of groundwater has caused a decrease in the water table, and as a consequence increasing in 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 costs and being 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 between 2003 to 2017 are employed. Water table variation of Qazvin’s aquifer was studied using 180 data points of the pizometric wells. Annually averaged land-subsidence in this years was obtained as 39.9 mm/year for aquifer zone and this value was 33 mm/year for Qazvin province. According to the land-subsidence zone in Qazvin province it was revealed that most of the land-subsidence occur in the region of the aquifer whose fine-grained layer thickness would be larger than other areas. The maximum 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.

Water quality in drip irrigation is crucial to prevent scaling formation and corrosion of system components. Groundwater resources are mostly used in drip irrigation. Qazvin province is one of the provinces facing water shortage. 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 sedimentation and corrosion properties. 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 zoning maps of the indices. The temporal trend of changes in these indices is then examined. According to the results of LSI index using Ordinary kriging, most of the area of Qazvin plain is in the range of 0 to 2 indicating low, moderate, and in some year high groundwater deposition. Also, according to the results of RSI index, most of the province is in the range of 6 to 7, indicating low corrosion of 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 using CMIP5 group models under RCP scenarios on groundwater resources in Varamin plain. The output of these models was downscaled by LARS-WG .For this purpose, the period(1989-2005)was used as a base period to select the regional model and the period 2050-2021 to study the NISTOR index in the future. The results showed that the EC-EARTH model has a high performance in simulating temperature and precipitation in the study area of CMIP5 series under three scenarios RCP2.6, RCP4.5 and RCP8.5 for the next period. On average, the average temperature in the study area, under the scenarios RCP2.6 and RCP4.5, respectively, predicted an increase of 1 to 1.2 ° C compared to the base period, while the same temperature increase based on the most pessimistic scenario That is, RCP8.5 in the period 2050-2020, an average increase of about 1.5 degrees 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, the RCP4.5 scenario showed slightly more annual precipitation increase over the next 30 years than the base period over the other two scenarios. Also, the results of the NISTOR index, considering the effective rainfall, and De-martone index showed in all three scenarios, the intensity of effects of climate change on the groundwater resources of Varamin plain in the future 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 show 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.

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 used 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. According to the results, all three scenarios predicted an increase in precipitation for August and December and a decrease for April and November, but the average annual precipitation in the Aras watershed during the period 2017-2036 will decrease. All three scenarios also predict an increase in the minimum and maximum temperatures. Although 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 have a large number of parameters in their structure. Such models produce different results using different sets of parameters’ value resulting in 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; and the generalized likelihood uncertainty estimation (GLUE) technique was used for uncertainty analysis. GLUE performs the uncertainty analysis and calibration of the model through inverse modeling. Thus, observed streamflow data of 2000-2006 and 2008-2012 were respectively used at 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 68 % and 93 % of the observations during calibration and validation steps, respectively. 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 its river basin planning and management. Then, 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.

Due to staggering growth of information in all scientific branches and the impossibility of pursuing them by researchers and thereof the urgent need to write review articles on one hand and strategic globally importance of water resources management on the other hand, and also with regard to emergence of a new scientific discipline called Socio-hydrology in most important scientific circles which addresses the inability of current hydrological methods and emphasizes on consideration of social and natural factors in water resources management, and even the relative ignorance of Iranian scholars to growing socio-hydrological concepts, it was motivated to write a comprehensive review on the issue from the time Socio-hydrology established up to the present time.To ensure the validity of information, scientific databases were applied and in almost information and resources reviewed including articles, books, or scientific documents. Using the Narrative Review method, information classified in four categories; 1. Conceptualization 2. Applications in agriculture 3. In flood and drought studies and 4. Modeling. As it is shown, despite rapid growth of discipline in scientific circles, there are still significant challenges in development of this branch that requires further studies on basic concepts, methodology and ontology of Socio-hydrology.

To optimize the management and optimal use of groundwater resources, it is necessary to be aware of the temporal-spatial changes of the stagnant level . For modeling and predicting hydrogeological variables, the question remains:To what extent these hybrid models can be effective compared to the individual model?, 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 four combined models performance developed with the ANFIS model and the time series model (SARIMA) as the reference model to estimate the average monthly groundwater level of the Sahneh plain aquifer in Kermanshah province was evaluated over 19 years.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 test and test phase 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, groundwater leveling can be given more attention in 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 does not improve the model's results compared to the individual ANFIS model in terms of significance.

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. 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 a with few cloud country compared to the global average cloudiness (50%) of Iran. In the long-term study, the maximum cloudiness was estimated on the southern and western coasts of the Caspian Sea and then in the highlands of Azerbaijan, Zagros and Khorasan. Among the seasons, the highest cloud fraction was estimated in winter and the lowest in summer. 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. Another result is the decreasing trend of cloud fraction the study period, which 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 achieve this aim, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modeling, 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, 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 is more accurate for the forecast horizon of the next month than two and three months. So that the forecast horizon of the next month has the highest Nash-Sutcliffe coefficient, in calibration 0.77 and in validation 0.48. The highest correlation coefficient in calibration 0.87 and validation 0.69, the lowest root mean square error in calibration 6.8 and validation 6.3 million cubic meters and also has the best relative bias value for calibration 0.96 and validation 1.1. Also the results, based on the POD and FAR probabilistic indices, showed that the developed predictive model has a high ability to detect different states of stream flow events, especially for extreme flows event.

Extensive exploitation of water resources for agricultural purposes has led to a lack of environmental water supply, 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 includes biophysical, socio-economic, scenario development, and integration sections.In the biophysical and socio-economic sectors, a comprehensive evaluation of all physicochemical, ecological, biological, economic, and socio-economic indicators was performed, and finally, water withdrawal from the wetland for agricultural purposes was selected as a socio-economic index and Sheng animal species as an ecological index. In the scenario development section, scenarios were determined to maintain the habitat suitability for the selected index in terms of minimum, optimum conditions in comparison with 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 much as 5.36 million cubic meters.

One of the most important consequences of Climate Change is the changing of climatic variables and the effects on hydrological variables and water resources. Karaj Dam is one of the important sources of water and electrical energy for Tehran and Alborz province. Thus, diagnosis of affective factors on the water resources of upstream of this catchment and entered runoff to Karaj dam could be vital for planners and policy makers 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 Optimal Fingerprint approach. In this research have been tried to detect and attribute the ALL, GHG and NAT signals by simulations from CanESM-2.0 model. Therefor every signal has been downscaled with MRQNBC method, and then simulated with SWAT calibrated model for selected signals for (1985-2011). At last those entered to optimal fingerprint method. By attention the results, just GHG signal (which shows the globe under the effects of Greenhouse gases without the other forcing has been detected and attributed and its scaling factor (β) got 0.76. While two other signals (ALL, NAT) haven’t been detected and attributed. In other words the changing of entered runoff to Karaj dam was very likely consonant with simulated runoff which affected from 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 of surface water. In this study, we have analyzed colored dissolved organic matter (CDOM) in lakes using Landsat 8 images in the period of 2013 to 2016 in the watershed area of North Siberia. The support vector regression model was used for selecting the most desirable band in determining the CDOM absorption coefficient, and 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 (R2=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 colored dissolved organic matter at a wavelength of 440 nm in watershed resources of North Russia. The scatter plot indicates an increase in colored dissolved organic matter in the lakes in the northeast of the area in 2015 compared to 2014.

The purpose of this study is to determine the concentration and distribution of cyclic aromatic hydrocarbons and the origin of these compounds in the protected river Sardabrood in Mazandaran province. Sampling of surface sediments of the riverbed was done from different stations (Karimabad, Gil Kola, Abbas Kola and the estuary of Sardabroud). To extract and analyze PAH compounds, by Soxhlet method, column chromatography and gas chromatography-mass spectrometry was performed in two stages. The highest total amount compounds was observed in the estuary 109.89 17 173.40 ng.g-1 and the lowest concentration in Karimabad station ng.g-1 of 141.39 ± 42.22 ng. The trend of accumulation of PAH in sediments showed that from upstream to downstream PAH concentration increases. Determining the origin of compounds in sediments using isomeric diagnostic ratios showed that PAH are mainly a mixture of petrogenic and pyrogenic sources. Surface sediments are in the average level of pollution in terms of polyaromatic hydrocarbon concentration 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 was 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 2016-1966 (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 map's pixels 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, the Caspian Sea have led to different patterns of atmospheric precipitation. The largest share of precipitation from large scale systems in the southwest of the Caspian Sea and the eastern region and the highest share of local precipitation in the Alborz highlands is seen. 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 indicate 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.

One way to deal with this phenomenon is to impose a fine policy by the competent authorities. Since farmers are over-exploitation under specific socio-economic and agricultural conditions, evaluation of the effects of penalty patterns based on a socio-economic 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 agricultural sub-factors in order to maximize individual profit under physical and behavioral constraints by using fuzzy inference system and mathematical programming, and other is the group-agricultural agents in order to maximize agricultural profitability under the constraints of individual preferences by combining non-dominated sorting genetic algorithm (NSGA-II) and social chose method. The proposed framework was applied to the Najafabad hydrological unit in Isfahan. The results showed that the level of unit hydrograph’s aquifer was drawdown 21.82, 17.18 and 10.5, respectively, while the fines 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 construct of different hydrologic structures such as reservoirs and dams as effective techniques for flood control, the appropriate estimation of the dimension and resilience of these structures needs correct estimation of the magnitude and return periods of flood for which the design flood in different return periods or probability level is applied. Design flood estimation is done through frequency analysis with the key stationary assumption. Nowadays the factors such as land use change, inappropriate management and climate change has influenced stationary conditions of flood peaks. Therefore, in the existence of non-stationary, the estimation based on stationary assumption is not confident and may have large errors. In this regard, this study for non-stationary flood frequency analysis the GAMLESS model for location, scale and shape parameter estimation as well as visual inspection of non-stationary are introduced and developed for quantile estimation in non-stationary conditions. Six hydrometery stations from different provinces in the North of Iran were selected. Frequency analysis with and without stationary assumptions was done for each station. Results indicated that location and scale parameters have trend with the linear and quadratic manner. In addition, results indicated that design flood estimated by non-stationary procedure has increased around 3 times higher than stationary estimation in Nodekhormalo station. Results also demonstrated that in a station with increasing non-stationary trend, return period of big floods is decreasing and for the same return periods, flood quantiles has increased.

In recent years, several studies have been carried out to estimate evapotranspiration of crops in different ways at Urmia Lake basin or locally. 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 a practical result in order to achieve the aims of restoring Lake Urmia. Estimated evapotranspiration values using SEBAL algorithm were compared with rainfall values in different zones to locate irrigated areas. From the results of this study, areas with high water consumption “Hot spots” were extracted at the basin. These 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 basin. The results showed that actual evapotranspiration values in the western (Urmia plain), south-eastern lakes (Miandoab and Mahabad plains), and in parts of the northeast and northwest (Sarab and Salmas) were much higher than the amount of rainfall. Which indicates the high concentration of irrigated lands in these areas. Land use maps also showed high density of irrigated land in these areas. In result, identifying irrigated areas with high water saving potential can be to implement water saving pilot schemes and to provide the lost water of the lake.