afshin ashrafzadeh

Urban wetlands are rapidly deteriorating due to urbanization and wastewater disposal, posing a threat to water quality and human livelihoods. Assessing the water resources carrying capacity (WRCC) of these wetlands is crucial to facilitate sustainable development and synergy between economic growth and water resource conservation. This study evaluated the WRCC of Anzali Wetland, located in the Fumanat region of Gilan Province, over a 10-year period (2011-2021). eight evaluation indicators were defined based on available data and information, considering three subsystems: water resources, economy, and environment. Each indicator was classified into four levels: I (loadable), II (weak), III (critical), and IV(Overload). The weight of each indicator was calculated using three methods AHP, Entropy, and CRITIC. The weights obtained from the three methods were combined using the geometric mean. Subsequently, the annual values of each indicator, along with their corresponding weights and four evaluation levels, were applied to a model combining Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, based on the annual WRCC results, the obstacle factors were identified. The WRCC assessment indicated that the wetland has been in a overloading condition (IV) since 2013. The water resources subsystem is in a critical or overloading condition, reducing the overall carrying capacity, while the economic subsystem contributes to this capacity. According to the obstacle degree model results, the four main factors affecting the WRCC include the quality of incoming water, the percentage of water supply required, the percentage of water supply for upstream areas, and the ratio of total available water to the population.

This study investigates the optimal management of water and energy resources in the Sefidroud irrigation and drainage network of Guilan province. Given the critical role of these resources in agriculture, along with water scarcity and increasing food demands, the need for an integrated approach is becoming more evident. A multi-objective optimization model under uncertainty was developed for this study, aiming to minimize agricultural water shortages and maximize hydropower generation from the Sefidroud reservoir dam. The developed model was solved using the NSGA-II algorithm, and the irrigation requirements for rice and tea were calculated based on the soil-water balance method. The results indicate that water shortages vary across different confidence levels, with the central irrigation zone experiencing the highest deficit. Additionally, rice cultivation, especially in June and July, faces more significant water shortages, whereas tea cultivation does not encounter major water scarcity issues. This research highlights the necessity of optimal resource management and precise planning to prevent water shortages during critical months. The findings have the potential to inform effective decision-making aimed at sustainable agricultural development.

Kiwi is one of the Iran's agricultural products, which is mainly exported to the world market. Data mining methods are well able to provide manufacturers with the necessary information in the field of product performance modeling. This research has investigated the efficiency of data mining methods of advanced Feedforward backdrop Neural Network, K-Nearest Neighbor, Genetic Programming and Multivariate Linear Regression in estimating kiwi yield in Gilan province using water and soil characteristics. 74 data series were obtained from the field measurement of water and soil information and yield of kiwi fruit in orchards equipped with pressure irrigation system in 2021-2022. Water and soil data including maximum daily evaporation and transpiration, soil electrical conductivity and soil reaction index, clay percentage, silt percentage, soil organic matter percentage, water electrical conductivity and water reaction index, and irrigation volume as model inputs and crop yield, model output selected. The results showed that the leading artificial neural network model has a better performance than the other three models due to the higher explanation coefficient statistics (0.96) and lower root mean square error (0.019). Also, Genetic Programming has correlation coefficient (0.89), root mean square error (0.033) and K-Nearest Neighbor method has correlation coefficient (0.88) root mean square error (0.059) and Multivariate Linear Regression method has coefficient The correlation was (0.58) and the root mean square error (0.093) which indicates the higher accuracy of the Genetic Programming method. Therefore, the advanced artificial neural network model can act as a powerful tool in the estimation of Kiwi performance.

In this study, the accuracy of estimating shallow aquifer recharge values in Astane-Kouchsefahan using two models: SWAT (a surface water hydrological model) and MODFLOW (a groundwater flow model) was evaluated. Then the need for using SWAT in the modeling of groundwater flow, which is typically done by MODFLOW, was evaluated. For this purpose, the simulation of the Astane-Kouchsefahan aquifer was done by MODFLOW using GMS graphical user interface. Then the SWAT model was built for the Astane-Kochsefahan watershed and calibrated by the SUFI2 algorithm in SWAT-CUP software. In the following, after determining the two models' common spatial and temporal range, the aquifer recharge amounts were compared according to the outputs of the two models. MODFLOW results showed that the total aquifer recharges including recharge from the river in 1391-1392 were equal to 102.71 and 23.71 million m3, respectively. The highest and the lowest amounts of aquifer recharge occurred in December and April, respectively. The results of SWAT showed that the amounts of aquifer recharges including recharge from the river are estimated to be 138.34 and 35.09 million m3, respectively. So, the highest and the lowest recharge amount occurred in December and September, respectively. Based on the regional circumstances, SWAT offers more dependable estimates of the groundwater recharge parameters by considering surface water parameters and factors like the influence of dams and water transfer channels, soil characteristics, land use, climatic and meteorological data, and information regarding agriculture and irrigation management. Consequently, integrating the precise recharge results from SWAT into groundwater models such as MODFLOW leads to enhance and more reliable evaluations of the aquifer's state.

Predicting crop yield using data has an important impact on socio-economic issues and political decision-making at the regional scale. Guilan province is considered as the hub of hazelnut production in the country. In the science of statistics, there are various methods for categorizing, recognizing patterns, forecasting and modeling data. To predict the performance of agricultural products with data mining, various methods such as K-nearest neighbor (KNN), Classification and Regression Tree (CART), neural network feedforward backprop (FFB) and multi linear regression (MLR) are leading. Since the prediction of hazelnut yield using data mining methods is very important for the decision makers of the agricultural sector in terms of knowing the positive and negative factors in the yield. This research aims to answer this need by using all these methods for hazelnut orchards equipped with drip irrigation system in Guilan province.

The information used in this study includes the amount of water consumption and yield of hazelnut and the parameters related to these two indicators in hazelnut orchards equipped with pressurized irrigation systems under the management of operators in the cities of Guilan province, including Roodsar, Amlash, Siyahkol and Roodbar, which in a research The measured quantities included the salinity of the irrigation water, the salinity of the saturated soil extract, the number of times of irrigation, the yield of corn, and the volume of water consumed per tree in each irrigation round. To determine the characteristics of the soil in each garden, the depth of activity The roots (0-30 cm) were sampled from the soil. At the same time, water samples were also obtained from the gardens of the region and the electrical conductivity of the water samples was measured. The volume of water needed in each round of irrigation for each tree was obtained by measuring the irrigation water. The percentage of sand, silt and clay was also obtained by determining the soil texture by hydrometric method. The amount of yield in one hectare was also obtained after the harvest at the end of the harvest in the year 2021-2022.

To model the input variables including daily maximum evaporation and transpiration data, electrical conductivity and soil acidity, percentage of clay and percentage of silt and percentage of sand, electrical conductivity and acidity and irrigation volume for each tree in each irrigation round and hazelnut yield as the output of the model. Work was done. The results obtained from each of the models were compared with the measured values using statistical indices of correlation coefficient (R), root mean square (RMSE), Nash Sutcliffe coefficient (NSE) and the best model was selected. The variables of volume of water required by the tree in the irrigation cycle, irrigation water reaction index, soil silt percentage and soil reaction index showed a positive correlation with crop yield.Also Irrigation hours, electrical conductivity of irrigation water, sand percentage, clay percentage, sand percentage, soil electrical conductivity and maximum daily evaporation and transpiration showed a negative correlation with crop yield. The results showed that the leading artificial neural network model performs better than the other three models due to higher correlation coefficient statistics (0.98) and higher Nash-Sancliffe coefficient (0.96). Also, CART decision tree has correlation coefficient (0.93), Nash Sutcliffe coefficient coefficient (0.93) and K-nearest neighbor method has correlation coefficient (0.9), Nash-Sancliffe coefficient (0.7) and multiple linear regression method. The variable has correlation coefficient (0.67), Nash-Sancliffe coefficient (0.47), which indicates higher accuracy of CART decision tree method

By evaluating the results, it was found that the forward neural network method with the error back propagation method provides more accurate results than the decision tree, K-nearest neighbor, and multivariate linear regression models. The CART decision tree method was more accurate than the K-nearest neighbor method, which is the reason for the higher efficiency of the decision tree model due to the uncertainty in the phenomena related to water and soil or the appropriateness of the measured values. The multivariate linear regression model also cannot provide accurate results due to the non-linearity of water and soil characteristics. Therefore, artificial intelligence techniques can be introduced as an efficient tool for developing correct management plans in the field of product performance.

In this study, a fuzzy multi-objective planning model was used for the optimal allocation of irrigation water and land use under multiple uncertainties. The effect of effective rainfall for determining the irrigation requirement of cultivated crops and also the limitation of surface water and groundwater resources were taken into account in the developed model in the Talash study area, which is located outside the Sefidroud irrigation and drainage network. The study area of Talesh was divided into three irrigation areas: Astara, Talesh, and Rezvanshahr. Then, the results of the optimal model were investigated at different levels of α-cut (0, 0.2, 0.4, 0.6, 0.8, and 1). Allocated amounts of surface water and groundwater showed that maximum shortages occurred in June and July in Talash area, So that the shortage of Talash area in the upper and lower bounds of a-cut=0.8 was 1.7 and 2.7 times more than Astara area as well as 1.2 and 1.8 times more than Rizvanshahr area, respectively. The optimal ratio of groundwater consumption to the total allocated water in Astara, Talesh, and Rezvanshahr areas were 13.4%, 58.1%, and 28.5% respectively. Also, 100% of the allowable groundwater is consumed in most of the dry months of the year. Due to the unavailability of surface water resources to many farmers in this area, proper approaches should be given to the farmers for more access to surface water. Therefore, the results of this study could be a warning for the regional manager and planners to consider this issue in future planning to select the best decision regarding the use of the type of irrigation water resource.

In many parts of the world today, water and soil resources are threatened with salinization. Extensive research has been done to desalinate water and soil resources. One of the solutions is soil leaching. To assess different methods of saline soil leaching, an experiment was performed in a randomized complete block design with three factors and three replications. Water type treatments include: magnetic water, water passing through merus ring and ordinary water as the main factor and two sub-treatments which are the salinity of water including saline water with electrical conductivity of 2.5 dS/m, fresh water with electrical conductivity of 1 dS/m, and saline-fresh water, in fact, the combination of saline and fresh water and leaching method (permanent and intermittent flooding) were in a total of 54 soil columns gathered from an olive orchard in Roudbar city. The results showed that the magnet treatment compared to the control was able to increase the yield of saline water to the level of leaching with saline-fresh water in the alternating method. The merus ring treatment performed better, as it was able to increase the yield of saline water in the leachate to the level of fresh water in the alternating method, by releasing more salt than the magnet.

Given the broad application of long-term meteorological data in various sciences and the need to predict their possible changes at local and global scales, it is very important to ensure the accuracy and homogeneity of such data. The penalized maximal T and F tests in the Rhtests software package were used to control the climatic parameters of the western half of the Caspian region on a daily and monthly scale. The data included precipitation, maximum and minimum temperature, wind speed, relative humidity, and sunshine hours, during 1979-2017 in Bandar Anzali, Rasht, and Ramsar stations. The missing data were estimated using the standards of the World Meteorological Organization and using the k-nearest neighbors algorithm. For the monthly time series, 23 change points were identified, which were homogenized in two stages, before and after reconstruction. The standard normal method was less sensitive than the penalized maximal F method by identifying 9 change points. Then, the daily data of the mentioned parameters were homogenized, which was homogenized by identifying a total of 32 points of change. However, it was not possible to thoroughly homogenize the sunshine hours due to the consecutive missing data. Data homogenization reversed the trend in 33% of cases. The studied method had acceptable results in homogenizing meteorological data in the study area based on the obtained results

Considering Sefidrud River discharge decrease in the last decade in Guilan province in the north of Iran, groundwater and surface water resources can meet the water demand of rice cultivation in this area. It is evident that irrigation water quality should be considered in rice cultivation. Electrical conductivity (EC) is one of the essential parameters of assessing the quality of groundwater. The purpose of this research is to identify areas at risk of groundwater EC decrease for rice cultivation. For this purpose, zoning and probabilistic maps were prepared by ArcGIS software. The models were evaluated using ME, RMSE, MSE, RMSSE, and ASE statistical indices. The accuracy of the models was very good; the RMSE values for ordinary kriging were between 0.2674 and 0.4172 dS/m, and for indicator kriging, they ranged from 0.2841 and 0.4087 dS/m. The zoning and probabilistic maps showed an increase in EC of more than 1 dS/m from 2002 to 2015. In addition, the highest EC in Guilan province was in the central and eastern parts of the province, including Rasht, Astaneh, and Lahijan cities. More than 30% of groundwater resources were exposed to excessive salinity exceeding rice's tolerance level. Therefore, to prevent the quality mitigation of groundwater resources in the province and prevent yield penalty related to irrigation water salinity, the regional water companies should take appropriate management measures such as a ban on digging new wells or reducing groundwater extraction in hazardous areas.

Unplanned operation and excessive extraction of groundwater in coastal areas increases the risk of seawater intrusion; so water quality monitoring and management in these areas are of great importance. In this study, an optimal monitoring network with minimum number of wells in Talesh coastal aquifer is designed according to the aquifer vulnerability map and evaluation of the accuracy of monitoring network. In this regard, the aquifer vulnerability map was prepared using the modified DRASTIC index; and genetic algorithm was used to optimize the monitoring network. Three objectives were used in the optimization model including (1) maximizing the correlation between vulnerability index and EC values, (2) minimizing the number of monitoring wells, and (3) maximizing the Nash-Sutcliffe (NS) efficiency. NS, represented the match between the calculated EC distribution obtained from existing monitoring wells and those wells from the newly generated network. Applying the weighting factor w for economic objective, the three above-mentioned objectives are integrated in a single objective function and different weights of w were evaluated. The results showed that the selection of an optimal solution greatly depends on the weighting coefficient w and the best weight was selected according to the vulnerability index and the accuracy of monitoring network. Due to long-term quantitative and qualitative changes in groundwater, it is better to periodically evaluate and redesign groundwater quality monitoring network so that the network could effectively use in planning and applying methods to improve groundwater quality.

In recent years, the pollution rate of rivers entering the Anzali Lagoon has increased sharply due to population growth and industrial development. The purpose of this study was to investigate the pollution status and water quality index (WQI) in the Pasikhan River as one of the most important rivers entering the Anzali Lagoon.

This research was conducted at three hydrometric stations in the Pasikhan basin namely Mubarak Abad, Alisara and Noukhale within three years, 2014, 2015 and 2016. Sampling was carried out on a monthly basis and various parameters including sulfate, chloride, calcium, and bicarbonate were measured. Also, the WQI was used to quantify the overall quality of water. During the study, all the moral standards were observed.

The results showed that 81235 tons of suspended sediments were passed the Noukhale station annually entering the Anzali Lagoon. The values of chlorine at Noukhale, Mubarak abad, and Alisara stations were equal to 2.37, 0.18 and 0.18 mg/l, respectively. Based on the Wilcox diagram, the water resources were grouped as C1S1 and C2S1. The results of WQI revealed that the water status at all three stations was not favorable, which can be attributed to the discharge of municipal, industrial, and agricultural wastewaters into Pasikhan river.

In general, the results show that accumulation of sediments and pollutants transferred from the Pasikhan River to the Anzali Wetland increases the degradation of its ecosystem and also endangers human and fish health in the area.

Three vital sources of Water-Energy-Food (WEF) are inextricably interrelated so that may also produce environmental impacts such as greenhouse gas emissions in production/delivery process. Therefore, green-house-gas emission control plays a key role in WEF nexus management. Effective planning and management of limited WEF resources to meet current and future socioeconomic demands for sustainable development is challenging. Nexus management for WEF security necessitates integrated tools that are useful for effective planning and management strategies and policies. Hence, the comprehensive tools should be used for identifying the trade-offs and interactions among various sectors of water, energy, and food. In this study, the nexus in the integrated management of the sources by optimisation method is investigated. Also, a hypothetical optimisation model of the water, energy, and food nexus is examined. This model is applied to quantitatively analyse the interrelationships and trade-offs among system components including energy supply, electricity generation, water supply-demand, food production, and mitigation of environmental impacts. The results demonstrated how these types of analyses could be helpful for decision makers and stakeholders to make cost-effective decisions for optimal WEF management.

In the present study, the effect of climate change on the flow of the Hablehroud River was assessed, using the HadCM3 general circulation model, the LRAS-WG downscaling model, and the SWAT hydrologic model. First, using the LARS-WG model, the output of HadCM3 model in the period of 2011-2040 was downscaled and the necessary data for the SWAT model were obtained. The downscaling results showed that there is no meaningful difference between the future precipitation and the precipitation observed in the base period of 1995-2010. It was also observed that in the A2, B1, and A1B emission scenarios, the mean of minimum temperature will increase by 0.59, 0.47, and 0.43 0C, respectively, and the mean of maximum temperature will increase by 0.51, 0.39, and 0.35 0C. In the next stage, the SWAT model was calibrated and validated using the monthly flow data recorded at Bonkouh gaging station located at the outlet of the basin. The values of the coefficient of determination and Nash-Sutcliffe in validation phase were 0.8 and 0.72, respectively. Introducing the downscaled rainfall and temperature data to the SWAT model, the variation of the basin outflow was simulated in the period of 2011-2040. Results showed that in the three scenarios under study, the annual flow will increase 10.66, 17.72, and 17.52% in comparison to the base period.

Understanding the variations of streamflow of rivers is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. An overview of the impact of climate change on the streamflow in the Hablehroud River, the main river of a semi-arid basin in north-central Iran, is provided. Using the LARS-WG statistical downscaling model, the outputs of HadCM3 general circulation model under the IPCC SRES A1B, A2, and B1 emission scenarios were downscaled to a finer spatial scale and the daily precipitation and temperature time series over the period of 2011-2030 for the study area were obtained. Results showed that the study area would experience a decline in precipitation (8.2% on average). The IHACRES rainfall-runoff model was then calibrated in the study area. Based on the fit statistics in calibration and validation phases, the overall performance of the developed model was judged to be satisfactory. The calibrated hydrological model was driven by the downscaled rainfall and air temperature data to project the effect of changing climate on the outflow of the basin under study. Results showed that, with some exceptions in June, July and August, all emission scenarios predict a decrease in the long-term monthly average outflow of the Hablehroud Basin. The outflow reduction in winter, spring, summer, and autumn had an average value of 25.7, 14.3, 1.9, and 48.8%, respectively. It was also observed that if climate change would occur in the basin, monthly flows associated to each return period would decrease.

Because of the significant impact of water on economic development, social stability and ecological balance, the allocation of water resources has become a worldwide issue. In this paper, a multi-objective planning model consist of two objective functions was developed for water allocation to maximize the productivity of economical benefit and the equity of water allocation in Sefidroud basin located in Iran. A Compromise Programming method was applied to trade off both the objective functions. The different weights of the objective functions and the definitions of TDS, DSA and DSB schemes were investigated in terms of equity, economical benefit productivity and equity establishment. The results showed that TDS is the best scheme for balancing the target functions. With the except of TDS, surface water allocation and economical benefit do not follow a particular pattern in other weights of target functions, so that decision makers are confused to choose the best weights of the objective functions. Shannon Entropy theory is a suitable solution for selecting the best weights of the objective functions. The results obtained by applying the Shannon Entropy theory showed that the best weights of the objective functions for the productivity of economical benefit and water allocation equity according to the decision maker’s priority were 0.35 and 0.65, respectively. Generally, the results of this study showed that if decision maker’s priorities were not clear, Shannon Entropy theory and Compromise Programming method could be used to determine the weights of each objective functions in order to make a balance among the objective functions.

Having information about qualitative and quantitative parameters distribution of groundwater supplies is one of most important parameters in integrated groundwater management. Thus, in this study it has been attempted to determine a proper model and input combination for estimation of quality parameters including electrical conductivity (EC), calcium (Ca) and sodium (Na) ions in aquifers of Guilans plain. In this study, the data from 132 observation wells during 2001 to 2013 were used and artificial neural network (ANN) and support vector model (SVM) were applied. In the first approach, estimations were conducted according to five different combinations, including water level, distance from see, total precipitation of six months and coordinates of observation wells. In the second approach, estimations were conducted based on combination of the selected qualitative parameters of gamma test with combinations of the best input in the first part. Comparison of the results from the first part indicated that SVM model outperformed the ANN mode in the estimation of Ca, Na and EC parameters. Support vector machine error values for estimating Ca, Na and EC variables at the test period were 1.218 (meq/l), 0.867(meq/l), and 175.742 (µmos/cm), while for artificial neural network these values were 1.268 (meq/l), 0.933 (meq/l), and 186/448 (µmos/cm) respectively. The results from this part showed that adding the distance from see input improves the estimation of models in all cases. In the second part, using gamma test for measuring the nine quality parameters, the best combination of quality parameters was determined to estimate the three parameters: Ca, Na and EC. The results from the second part show that both ANN and SVM models have an excellent performance in the estimation of the three qualitative parameters. ANN model error values in estimating Ca, Na and EC variables in validation period were 0.662 (meq/l), 0.305(meq/l), and 47.346 (µmos/cm), while these values were 0.671 (meq/l), 0.356 (meq/l), and 55.412 (µmos/cm) for SVM model respectively.  Obviously, the results from ANN model in this section were better than those from SVM model. Results showed that both ANN and SVM models have a great ability in predicting qualitative parameters in the aquifers. Also, in less inputs, the results of SVM model are better than those of ANN model and in more inputs it is vice versa. Results of the second section showed that gamma test is fully practical and accurate in determining the effective input combinations.

In this study, a combination of the SWAT and WEAP models were utilized for planning and integrated water resources allocation management in the Hablehroud Basin. The SWAT model was calibrated and validated using the monthly discharge at the basin outlet in the period of 1998-2012. The coefficient of determination, the Nash–Sutcliffe efficiency coefficient, root mean square error, and model efficiency, in validation phase were calculated 0.80, 0.71, 1.81 and 0.89, respectively. These values suggest that the model performance can be classified as “good”. To simulate the supply and demand in the basin and to assess different management scenarios, the estimated flows produced by SWAT for each subbasin were considered as the input for the WEAP model and the percentage of met demand in the present situation (Reference scenario) for each demand node was obtained. The results showed that the agricultural lands of Firoozkouh and Garmsar irrigation and drainage network are facing a shortage of water, especially in the warm months of the year. Therefore, scenarios such as changing cropping pattern, reducing crop area, reducing water per capita in urban and rural demand nodes, increasing irrigation efficiency and combined scenarios were considered to balance the supply and demand of water. The results showed that water consumption reduction and striking a balance between the supply and demand in the basin can be better achieved if simultaneous use of different water management strategies (increasing irrigation efficiency, per capita reduction, and crop pattern change) are to be considered in the basin.

The concept of eco-efficiency is used as a tool to analyze farm sustainability to relate economic value of an activity to its impact on the environment. SEAT and EVAT software are two modelling tools which combine both economic and environmental viewpoints into a single modelling framework and allow the user to evaluate the water consumption system. In this research, the eco-efficiency index with regard to the environmental impacts of diesel fuel was evaluated in the one of commands area in Sefidroud Irrigation and Drainage. The agricultural lands in this area including paddy rice, tea-garden and fish-culture. The environmental performance of the system was evaluated for climate change due to greenhouse gas emissions from diesel consumption as well the economic performance was measured by using the total value added to the system's final products due to water use and the adopted management practices. Then, the value of area vulnerability was assessed according to eco-efficiency index of climate change. The results showed that the eco-efficiency index in baseline for paddy rice, tea-garden and fish-culture were calculated 3.46, 0.76 and 0.02 Milion Rials/kg 〖CO〗_2 eq, respectively. the results showed that eco-efficiency indicator of scenarios 3 (the percent of irrigation efficiency and return flow are 63% and 5% in paddy rice, respectively) and 4 (replacing 50% of diesel engines with electric motors in fish farming) were increased 17 and 86% in the entire of F1, respectively, in comparison with base scenarios.

Environment protection and sustainable development have recently grabbed the attention of policy makers towards renewable energies. Agriculture has been a major energy consuming sector in Iran in recent years as pressurized irrigation systems have changed into a new policy priority. This paper explains how photovoltaic (PV) agricultural water pumps can be adopted under different conditions. A life cycle cost analysis has been done for solar, diesel and electric pumping systems for the case of grape cultivation in Kaashmar region. According to the results, under subsidized fuel supply for electricity generation, solar pumps can only compete with diesel pumps for demands of less than 4.5 kilowatts. Moreover, the feasibility of solar irrigations highly depend on the area under cultivation, the type of cultivated crop, access to other energies and their costs, solar system technologies, and the type of irrigation system. Thus, feasibility studies for solar irrigation should be done on a case by case basis.

Climate change could largely affect the surface water and groundwater sources. This impact should be simulated using appropriate models. In the present study, using LARS-WG and SDSM statistical models, the output of HADCM3 general circulation model under four emission scenarios was downscaled in the Hablehroud Basin in the period of 2018-2047. The SWAT model was calibrated in the basin and utilized to simulate the discharge, groundwater recharge, and soil water content in the mentioned period. Three different methods including Hargreaves, Penman-Monteith and Priestley-Taylor in the SWAT model were used to estimate reference evapotranspiration. Different combinations of the factors effecting the uncertainty, including downscaling model, emission scenario, and evapotranspiration estimation method were used. The results showed that the method used in downscaling the output of the general circulation model is the most effective factor affecting the uncertainty of the output of the SWAT model. It was also observed that the different combinations produce more outliers in simulating groundwater recharge, in comparison with simulating the discharge and soil water content. The median of the annual discharges simulated using all combinations was calculated to be 13.32 cms. The results showed that the combinations of downscaling model, emission scenario, and evapotranspiration estimation method that simulate values less than 13.32 cms have less uncertainty than other combinations. In the case of groundwater recharge (with a median of 2.07 mm/ year) and soil water content (with a median of 112.4), same results were observed.

Nowadays, evapotranspiration, which is an important component of water balance calculations, can be estimated using the technology of remote sensing. MOD16 actual evapotranspiration data are produced using the MODIS sensor imageries and have a spatial resolution of 1 Km. In the present study, MOD16 actual evapotranspiration data are compared with the actual evapotranspiration estimated using SWAP model. Using the measured soil moisture content data during the growing season in two 38.9 and 45.6 ha corn fields located in Qazvin Province, SWAP model was calibrated. The average RMSE values in the two fields under study were, respectively, 0.026 and 0.025. Considering the actual evapotranspiration estimated by the SWAP model as the evaluation basis, the MOD16 data were assessed. In this assessment, the RMSE values in the two fields under study were obtained, respectively, 1.46 and 1.94 mm/day. Also, the r2 values were calculated 0.86 and 0.87. The results of the present study suggest that MOD16 product can be effectively used when the measured data of evapotranspiration are not available.

Given that Qazvin Province located in the seventh drought place in the country and need to manage vast water resources. Water storage estimation methods have developed over the years and observation of changes in the gravitational field by satellite GRACE is one of these methods. This satellite produces a series of water storage changes data with spatial resolution of 1 ° on a regional scale. This study aimed to evaluate data from GRACE satellites in Qazvin province to find a comprehensive solution for quick and easy access to the water resources. To validate the GRACE satellite data, land surface model data (GLDAS) and wells data in the region were used. The results show that the GRACE satellites as a Gravity satellite just produce to estimate changes in Earth's water supply, give a good estimate of the changes in the ground water storage and groundwater level changes. Statistical analysis showed that RMSE =5.54, MAE = 4.16 and MBE = 1.5 the seasonal scale in millimetre between groundwater level data from the GRACE satellites and observation wells, and RMSE =2.92, MAE = 4.24 and MBE =0.184month scale in centimetre between the data changes in land water storage from GRACE satellite and GLDAS model. Also, the correlation between changes in water storage data from the GRACE satellite and GLDAS model and groundwater level changes from GRACE satellite and observational data is significant at the 99% probability level. The results showed that GRACE satellite shows terrestrial water storage changes better than GLDAS model.

Estimation of discharge using regional analysis is an effective method for water resources management in ungauged basins. In the present study, for the purpose of estimating annual discharge in the ungauged sub-basins of Hablehroud Basin, linear regression and three independent variables including area, precipitation and slope were used. The basin under study was divided into five major and 44 minor sub-basins and using the data from five active hydrometric stations in the basin, regression equations for estimating annual discharge and specific discharge (the ratio of discharge to basin area) were extracted. The extracted equations were assessed in both major and minor sub-basins. The results showed that discharge estimation using the three variables of area, precipitation and slope in the five major sub-basins has an average error of 1.3%. However, the average error in the 44 minor sub-basins was totally unacceptable. The results also showed that the average error of specific discharge estimation in the five major sub-basins is 2.6%. Despite the increase of error in the major sub-basins, the average error of specific discharge estimation in the 44 minor sub-basins was reduced to 5.33%. The results of the present study indicate that when the basin under study is divided into small sub-basins, specific discharge can be considered as an appropriate variable in regional analysis of runoff.

Labyrinth weirs are often a favorable design option to regulate water surface elevation in upstream canal and increase flow capacity. Due to their hydraulic performance and geometric versatility, labyrinth weirs have been used extensively in canals, rivers, ponds, and reservoirs as energy dissipaters, flow aerators, and spillways. Nevertheless, it can be difficult for engineer to optimally design, labyrinth weirs due to the complexities of nappe behavior and many geometric and hydraulic variables. A level of sedimentation in upstream and submergence in downstream of the weirs are effective parameters which affects the discharge coefficient, and the performance of these structures. In this investigation effects of 408 tests of different levels of tail water (increase of downstream canal bed level to one third and two third of weir height) and upstream sedimentation level (non-sedimentation and sedimentation level of 90 percent of weir height) on the discharge coefficient of different geometers of trapezoidal labyrinth weirs was investigated experimentally. The analysis of results indicated that increase of tail water level to one third of weir height didn’t affect on the discharge coefficient of weirs. By increasing tail water depth to two third of weir height, air cavity volume under nappe decreased. Therefore, the flow pattern of the nappe changed from leaping to clinging condition and upstream head decreased. As a result, the labyrinth weirs discharge coefficient in non-sedimentation and sedimentation level of 90 percent of weir height increased about 3.3 to 12.2 and 2.1 to 9.2 in comparison with non-submergence tail water for maximum discharge respectively. Finally, the statistical equation for estimation of the discharge coefficient of trapezoidal labyrinth weirs for upstream sedimentation and submerged conditions was developed.

The assessment of Guilan’s meteorological radar data (PPI and SRI) during autumn 2013 and winter 2014 shows the presence of a large number of pixels, mostly located in Guilan mountainous areas, involving incorrect reflectivities. For example, the daily average of incorrect reflectivity on the 26th of December 2014 calculated by PPI (plan position indicator) products of Guilan’s meteorological radar resulted in 47.014 dBZ in Lahijan synoptic station which would be equal to 31.6 mm/hr rain rate through Marshall-Palmer Z-R relationship. The importance of these results could be appreciated when we note that Lahijan synoptic station had a daily gauge rainfall height equal to zero on the same day. Thus, according to the position of incorrect reflectivities of Guilan’s meteorological radar, this study aims to evaluate the ground clutter influence on incorrect reflectivity appearance. The method used is to study the overlapping of the beam blocking map and the daily average of incorrect reflectivity map by changing the elevation angle of the radar beam at a clear air day. The results show that the ground clutter is the main factor responsible for incorrect reflectivities in %75.34 pixels of the image. Using comparison of the mean distance of the blocked pixels and the pixels containing the ground clutter to the radar antenna leads us to conclude that the real amount of the elevation angle is equal to -0.20°. The method adopted by assuming 5 km donut-shape regions around the radar antenna and using it to determine the radar elevation angle is one of the innovations of this study. Furthermore, to ensure the working of the actions undertaken for the clutter contamination mitigation, one requires a numerical threshold to determine the effectiveness of the actions. For this purpose, therefore, a method is developed based on the correlation between the radar’s 24-hr rainfall heights and their respective gauge data. The method provides a maximum threshold in dBZ unit for the clutter contamination while Z = 200 R1.6 is applied. The numerical thresholds resulted from the application of the method for the meteorological stations of Rasht (the Airport), Rasht (the Agriculture Faculty), Anzali, Talesh, Lahijan, Jirandeh, Masouleh, Deylaman and Manjil are equal to -3.748, -1.638, -6.074, 14.952, -3.611, -1.482, 12.466, 4.872 and 6.852 in dBZ, respectively. Also, a value of 9.536 dBZ is obtained for all the stations considered together. The latter value can be extended to all pixels of the radar image. As the conclusion, in spite of the fact that on the 26th of December 2014 as a non-rainy day, the average clutter was considerable, a close correlation between the radar’s rainfall heights and their respective gauge data is observed during the 15 days studied. The Pearson correlation coefficients (Pcc) in the meteorological stations of Rasht (the Airport), Rasht (the Agriculture Faculty), Anzali, Lahijan, Jirandeh, Deylaman and Manjil are significant at 1% statistical significance level, and also Pcc values in the meteorological stations of Talesh and Masouleh are larger than that can be excluded. Therefore, the clutter contamination of Guilan’s meteorological radar data cannot be considered an impediment to its application in practical purposes. However, for further progress, a higher degree of accuracy would be required.