فهرست مطالب بنفشه زهرایی

Expert manpower is the most important capacity needed to face crises. Qualitative and quantitative unsustainability of water resources and growing water scarcity has become one of the country’s biggest environmental challenges and has endangered the livability of parts of the country. Some experts consider the technocratic approach by the graduates of water engineering related fields as one of the factors influencing the formation and continuation of the water crisis in the country. In this article, the abilities that a water engineer must have to face today’s challenges are examined and the weaknesses of the existing educational system in the country in strengthening these abilities are examined. Based on the surveys, water engineering education programs in many leading universities in other countries have been modified to train T-shaped specialists. This type of specialists have deep knowledge in the specific field of water engineering and in other related technical fields, they have a limited level of information to work in teams with different specializations, as well as to understand the interrelationships between water, food, energy, environment and climate systems. Furthermore, T-shaped professionals have better understanding of nexus relationships between water, food, energy, environment, and climate systems and have been empowered for management and leadership. In this research, while explaining the knowledge components of the T-shaped professional personality, the way to design educational programs for the training of such engineers are explained. The findings of this research can be used for improvement of water engineering education programs in universities in Iran.

Greenhouse gas emissions and air pollutants are major challenges facing humanity today, mainly due to the consumption of energy sources. Industry makes an important contribution to the development of a country, but also contributes to increased energy consumption, which is unavoidable. To address this issue, the use of energy and emissions modeling can be very helpful in understanding the processes involved in reducing energy consumption, identifying emissions patterns and assessing the social costs associated with these emissions.

In this study, a systemic dynamics model was developed to calculate greenhouse gas emissions and air pollutants in the industrial sector of Isfahan province. The model considers industrial energy consumption for the years 2011-2020 and estimates the social costs associated with these emissions. Various development scenarios were applied to the model and the reduction in social costs was calculated.

The social costs associated with greenhouse gas emissions and air pollutants were between 2.58 and 3.79 million dollars (based on 2002 USD). The highest social costs occurred in 2012and the lowest in 2014. Carbon dioxide was found to be the largest contributor to these social costs.

Mazut fuel and the resulting greenhouse gas emissions are the main contributors to social costs in the study area, and industry and power plants are known to be the main consumers of this fuel. Therefore, the scenario in which mazut fuel is banned from industry has the greatest impact on reducing social costs. Furthermore, despite the higher efficiency of combined cycle power plants compared to gas-fired power plants, the scenario of converting gas-fired power plants into combined cycle power plants is recognized as the last proposed scenario to reduce social costs.

This study compared three different snowmelt scenarios using a monthly water balance model in the Taleghan-Alamut Basin in north of Iran. Three scenarios were tested in this study: a temperature-based, a net radiation-based, and an energy balance-based. Remote sensing data were utilized to mitigate the challenges of modeling snowmelt in a basin with limited ground information. The calibration and validation processes were carried out in a two-stage method. First, snow modeling was conducted grid-based throughout the basin, and the model parameters were validated. Using snow cover observed by the MODIS sensor, the model dispcipancy between computed and observed snow accumulation was calculated by comparing the percentage of to the calculated snow storage in each cell of the basin. In the second stage, the other model parameters were calibrated as a lumpt hydrologic model across the basin. Ultimately, the net radiation-based and energy balance-based models showed superior performance compared to the temperature-based model. During the validation period, the Kling-Gupta efficiency metric for the temperature-based snowmelt model was 0.72, while for the net radiation-based and energy balance-based models were 0.78 and 0.86, respectively. Additionally, the correlation coefficient between MODIS snow cover data and snow storage calculated in the three models ranged from 0.62 for the energy balance-based model to 0.72 for the temperature-based model. According to the results, the proposed methodology is suitable for assessing snow budget and the snow hydrology in mountainous areas with limited data availability.

The formulation of strategies for sustainable groundwater resources needs an integrated view of land-use, climate changes, social system, and the response of the hydrological system. Negligence in creating a balance between these variables in charging and discharging groundwater resources has led to problems such as land subsidence, saline water advancement, reduced water quality, increased pumping costs, etc. in most of the country aquifers. In this study, the Lenjanat region was selected as a case study and one of the important regions of the Zayandehrud Watershed due to its interaction with the river, the existence of land-use changes, and increasing harvest from the regional aquifer. The results of temperature and precipitation prediction in an optimistic and pessimistic scenario until 2035 by the LARS Model and the results of examining past and future land-use changes and calculating the impact factor (adjustment coefficient) using IDRISI SELVA, ENVI, and CA-Markov were included in the study model (a combination of WEAP and ANFIS) to investigate future runoff and its contribution to aquifer recharge. The results showed that both climate and land-use changes have some impacts on water resources. In the case study, the amount of runoff decreases due to the decrease in rainfall and increase in temperature, and runoff infiltration, and subsequent feeding and groundwater level decrease due to land-use changes and increase in groundwater abstraction. The average rainfall volume was estimated at 194.17 million cubic meters in the observed years, of which 99 million cubic meters rechrged the aquifer. The average volume of groundwater supply after the effect of an adjustment coefficient of 1.051 was predicted in the optimistic scenario of 10.09 and the pessimistic scenario of 93.72 million cubic meters, respectively.

Assessment of water security can be considered as a comprehensive approach towards analysis of status of water resources and water resource management practices. This approach can be especially helpful for Iran, which faces complex problems in the management of water and natural resources. In this paper, an index is developed for assessing the state of water security in Iranian provinces using the geometric mean as the aggregation method. Finally, the Iran provinces were ranked by this index and a number between 0 (for the worst condition) and 1 (for the best condition) is estimated as water surety index for each of them. The results indicated that the security index value can not be more than 0.43 for none of the provinces, which shows weak water security for all of the provinces. The provinces located on the eastern border and the eastern part of the central Iranian plateau, Sistan and Baluchestan, Khurasan Razavi, Yazd, and Kerman are provinces that face the worst conditions and had the least values for water security index (all below 0.22). Mazandaran province with water security index of 0.416 was ranked first mostly due to good condition in resource dimension. East Azerbaijan province due to balance and relatively good status in all dimensions was ranked second. Hormozgan province with water security index of 0.386, because of the best condition in economic dimension was ranked 3rd.

The matter of excessive withdrawal of groundwater resources as a common problem in arid and semi-arid regions of the world has caused depletion in the quantity and quality of groundwater in many plains of Iran, such as Qanavat district in Qom, and the stakeholders of these resources have confronted serious challenges. This research aims to evaluate farmers' adaptive capacity to the quantitative and qualitative decline of groundwater resources. In this regard, first, the content, structure and relative importance of the dimensions of adaptation capacity have been measured using the self-assessment method and psychometric approach. Then, in order to study and compare the adaptation of different villages, the adaptive capacity index has presented and calculated by measuring the relationship between the dimensions of adaptive capacity and individual and technical characteristics of farmers. findings of this research indicate that the most important dimensions influencing adaptive capacity are innovation and risk behavior with highest factor loadings (0.91 and 0.85, respectively), whereas governance and trust in government with lowest factor loadings (0.42 and 0.38, respectively) has not found to be significant dimensions. This finding illustrates the low confidence of farmers in government executive policies on agriculture and groundwater management in this region. Calculating the adaptive capacity index for different villages, it was found that Dolatabad, Momenabad, Seraje and Abdullahabad vllages with more than 0.58 have the highest adaptation in this region. contrarily, due to the multiplicity of smallholders and unwillingness to plant new crops, Valijard and Morad Abad have the lowest adaptation.

Estimation of discharge in ungauged basins is of prominent importance in hydrologic and water resource management studies; however, it is not possible to determine the runoff coefficient in different watersheds without streamflow data. In many study areas of the country (unit of hydrological basis and balance of water resources in the studies of the Ministry of Energy), there is no hydrometric station to measure the surface flow out of the area. Several methods have been introduced to estimate the discharge of ungauged basins, which can be classified into three main categories. The first category contains the methods that make a relation between precipitation and the produced runoff (such as the Inglis and De’Souza and the Indian Department of Irrigation (IDOI) methods). The second category includes the methods that estimate annual runoff deficit and predict the yearly runoff accordingly (such as Turc, Langbein, Coutagine, and Khosla methods). The third category covers the methods that take into account the physiographic characteristics of basins to estimate runoff (such as the Indian Council of Agricultural Research (ICAR), Justin, and Lacey methods). The SCS Curve Number (CN) method is also among the most common methods of estimating runoff produced by rainfall and considers various conditions in its formulations; nonetheless, determining the CN and its initial absorption coefficient is still challenging. The aim of the present study is to evaluate the efficiency of different empirical methods in the estimation of runoff in watersheds with different hydrologic and physiographic characteristics and climatic conditions in addition to giving some insights on the selection of the proper runoff estimation methods in ungauged basins.In this study, the application of empirical methods in the calculation of the outgoing discharge from various areas in the Sefidroud watershed was investigated. The Sefidroud watershed has a total number of 11 areas, 10 of which have hydrometric stations in their outlets. For these ten sub-basins, the observed annual runoff was compared with the results yielded by the aforementioned empirical methods, and the efficiency of each method was assessed accordingly for each sub-basin. The Root Mean Squared Error (RMSE), Standard Deviation (SD), Correlation Coefficient, and the Centered Root Mean Squared Deviation (CRMSD) were used to analyze the data. The runoff estimation methods investigated in this study included Khosla, Lacey, Inglis De’Souza, Coutagine, Turc, ICAR, IDOI, Justin, and the SCS-CN methods. Moreover, the authors of the present study tried to find the optimized value of the initial absorption coefficient in the SCS-CN method in order to obtain a reasonably accurate estimation of runoff for each sub-basin. 

The results of the present study indicated that the Khosla and the SCS-CN methods with an initial absorption coefficient of 0.05 and 0.2 showed the poorest performance in all sub-basins. Moreover, the Inglis De’Souza method was not applicable in Iran’s sub-basins due to its different approach in dealing with plains and highlands. Because the study areas in the catchments of Iran are all a combination of plains and elevations and sometimes include a combination of several plains and several elevations with different characteristics. The optimized values of the initial absorption coefficients varied between 0.0006 and 0.25, which implies that a specific value of initial absorption cannot be used in all of the sub-basins to achieve the best accuracy in the estimation of runoff. Comparison between the results yielded by other methods (i.e. Turc, Coutagine, IDOI, ICAR, and Justin) with the observed streamflows indicated that the choice of the best method depends on the error index used for comparison. In other words, the Justin method had the best performance in terms of correlation with the observed runoff in the Sefidroud watershed. But, in terms of the RMSE error index, the IDOI method generally performs better. Finally, the Coutagine method had a good performance in terms of both correlation and RMSE in the main study areas. 

According to the results of the present study, the Justin method is recommended for areas that have a high altitude and temperature gradient and at the same time have a high flow coefficient. The IDOI method performs best for sub-basins that have a high runoff to rainfall ratio. As this ratio decreases below 0.2, the IDOI method is likely to produce poorer results. The Coutagine method showed a moderate performance in most of the studied areas, which suggests that it can be employed to produce conservative results in many areas under study. 

Nowadays, changes in environmentaldynamics including changes in land cover, land use, water supplies and climate are considered to be challenging issues of human communities. Thus, it is especially important to investigate different aspects of land use change and their effects on the past and future trends ofdifferent plains. Identification of previous changes and prediction of future trends help planners and managers to compensate for losses and avoid similar mistakes in future.Therefore, the present study is divided into two parts. In the first part, land usechanges of Lenjanat Plain in the 1990-2015 period are analyzed. In the second part, future land use changes (2015-2035) of the area are investigated.Adjustment coefficient is calculated to show the effect of land use changes on runoff coefficient.  

2.1 Study area and its characteristics Lenjanat Plain is a sub-basin of Gavkhuni Wetland located in the central part of the Iranian plateau with the longitude of 51˚ 8' to 51˚45' E and latitude of 32˚2' to 32˚24' N.   2.2  Land use change In the first step, preprocessing of satellite data and preparation of the information were carried out through geometric and atmospheric correction of the digital imageswith the purpose of correcting errors, removing defects in the images and omitting system errors. Landsat-4 and 5, TM sensor, Landsat-7, ETM+ sensor and Landsat-8 OLI sensor were used to evaluate and predict land use changes in the study area. Image selection was performed based on the availability criteria, and Landsat satellite images were thus obtainedfor 1990, 2005, and 2015. In the next step, unsupervised classification was used to create a general understanding of land use classes in the study area as a useful tool for determining training samples. ENVI software was used to identify suitable training samples for classification. To realize the second goal of the study, Marco integration model and a cellular automaton model were used and future land use changes in Lenjanstudy area were predicted for 2035 based on the base map and the assumption that the current trend in land use changes will continue. For this purpose, the Marco and CA-MARKOV modules were utilizedin IDRISI SELVA. CA-Markov model was used to predict land use changes with spatial contiguity and spatial transitions over time. 

3.1 Measuringland usechanges Finall and use maps represent the percentage and spatial distribution of each landuse type in the study area in the past, and at present. These maps area also used to evaluate the effects of management on the intensity of land use changes in the study area. Man-made surfaces have almost doubled in the region and reached from 3922 to 7202 hectares. In the past, 3922, 22516, 81613 and 367 hectaresof man-made areas (such as residential and industrial), agricultural lands, barren lands, and riverbeds were located in the study area which have reached 7202, 17943, 82793 and 229 hectares, respectively.   3.2 Prediction of future land usechanges Land use types in 2035 were predicted using CA-Markov chain model. Results indicate that manmade surfaces will exhibit a rising trend and increase from 7,202 to 9,122 hectaresduring 2015-2035 period.To determine the compatibility or incompatibility of actual maps and modelingresults, model validation was performed. In this regard, land usesof the study area was predicted for 2015 through the aforementionedmodel and the predicted map was compared with the actual land use map in 2015. In this method, the Kappa index of 0-1 was used to interpret the results.   3.3 Adjustment Factor Before anything else, the present study have determinedland usechangespercentage. Then, runoff coefficient of the forecast period was divided by runoff coefficient of land use changes in the pastto calculate the adjustment factor.Based on the findings of this study and the land use changesforecasted forLenjanat plain in 2035, the adjustment coefficient for the region equals 1.051.

The present study aimed to evaluate various criteria affecting the quantity of water resources. Moreover, it has evaluated and determinedadjustment factor. For this purpose, Lenjan plain was used as a representative of the plainsin the country. Five land use types, including man-made areas (such as residential and industrial), agricultural lands, Barren lands, riverbeds and rock beds were identified for 1990, 2005 and 2015. CA-Markov was applied to predict land use changes for 2035. Adjustment coefficient is also calculated to show the effect of land use changes on runoff coefficient

Understanding the frequency of dust storms in each area and being mindful of temporal-spatial variation of this event can help to monitor and reduce the damages induced by dust events. Due to the increasing development of metamodels and their combination with optimization algorithms used to model and predict hydrological variables, machine learning models due to high accuracy in forecasting, in the form of a black box, have received a lot of attention. Therefore, in the present study, a hybrid approach is proposed to predict the Frequency of Dust Storm Days (FDSD) on a seasonal scale, which uses a combination of Lang Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. In this study, the performance of the proposed hybrid model was compared with a neural network based on Radial Basis Functions (RBF) and Support Vector Machine (SVM). For this purpose, hourly dust data and codes of the World Meteorological Organization were used on a seasonal scale with a statistical period of 30 years (1990-2019) for seven synoptic stations in Khuzestan province. The results of the evaluation criteria in the training and testing Stages showed that the GRU-LSTM hybrid model offered better performance than other models used to predict the frequency of days with dust storms; The proposed hybrid model with correlation coefficient (R) of 0.905-0.988, Root Mean Square Error (RMSE) of 0.313-0.402 day, Mean Absolute Error (MAE) of 0.144-0.236 day, and Nash-Sutcliffe (NS) of 0.819-0.903 had better performance compared to the other models used in predicting the FDSD index. In general, comparing the models used in this study, the GRU-LSTM hybrid method and the SVM model, respectively, provided the best prediction skills. As a result, application of the proposed hybrid model can be used as a suitable tool to predict the FDSD index and adopting management decisions to reduce the dust storms damages in the study area.

Iran is located in an arid and semi-arid region and has experienced a reduction of average rainfall in recent years. This has turned the attention to the use of new methods such as cloud seeding to achieve more water resources. In this regard, cloud seeding operations have been carried out in the country since 1998. The purpose of this study was to evaluate cloud seeding projects in the 2015 water year (January, February, and March 2015) in the central region of Iran, including the provinces of Yazd, Kerman, Fars, Isfahan, and some adjacent provinces. The evaluation was performed statistically using stepwise multiple regression. Two different approaches have been used for evaluation. In the first approach, precipitation at stations located in the target area of cloud seeding operations is estimated based on the precipitation at stations in the control area using stepwise multiple regression and then taking into account a 90% confidence interval for this estimate, the effectiveness or ineffectiveness of the cloud seeding operation at each station is determined. In the second approach, the volume of precipitation in each province in the target area is estimated based on the precipitation in stations outside in the control area using stepwise multiple regression and then by considering a 90% confidence interval for this estimate, the effectiveness of cloud seeding operations on the rainfall volume of each province has been investigated. The target area in different months was selected based on the HYSPLIT model results. Due to the inconsistent spatial distribution of rain gauges in the target areas, parts of the target areas lacking enough rain gauges were excluded from further analysis. To define the boundaries of the exclusion areas, Inverse Distance Weighted (IDW) method was used to find the influence of the radius around each rain gauge. The influence radius values were selected as 93940, 89569, and 149015 m for the months of January, February, and March, respectively. Finally, the minimum value of 89569 m was selected as the influence radius. The results of both methods indicate the impact of cloud seeding operations this year in these areas. In particular, the volume of precipitation in February in all provinces located in the target area of cloud seeding operations has increased from 15 to 80 percent. Surface runoff generated from the increased precipitation due to cloud seeding were estimated by the two methods of Soil Conservation Service (SCS) and Rational method. The estimated surface runoffs generated by SCS and rational methods were 1318.5 and 1329.5 million m3, respectively. The groundwater recharge in the three months of January, February, and March is estimated as 105.3, 425.6, and 156.3 million m3, respectively. It is important to note that runoff and groundwater recharge estimations by the method used in this study are subject to high uncertainties, and the estimations can only represent the order of magnitude of impacts of cloud seeding operations, and therefore, exact numbers should not be used for water resources planning and management purposes. Further investigation in areas with more rain gauges can assist in a more accurate assessment of could seeding operations.

Accurate spatial estimation of precipitation plays a key role in many hydrological modelling, water resources planning and agricultural management studies. In order to estimate high-resolution precipitation in the current research, an estimation model based on PRISM framework considering Digital Elevation Model (DEM) and other spatial attributes is developed and applied to Sefidroud basin during the years 2000-2015. In the first step, the monthly results were compared with modified Inverse Distance Weighted (IDW) as a benchmark method. Considering both models’ degrees of freedom, the parameters are calibrated with Genetic Algorithm in annual time scale and the obtained parameters are utilized to estimate model monthly cross validation error. According to the monthly results, both models had roughly same statistical performance in estimating precipitation at the selected rain gauges. In spite of similar statistical performance of the models, the spatial distribution of precipitation provided by the PRISM based method is more consistent with topographic pattern and existing evidences. In the next step the proposed model’s ability to estimate mean monthly precipitation is compared with ordinary kriging method and according to the results, in wet months the developed model and in dry months kriging method represents more optimal results. According to small amount of summer precipitation, PRISM based approach is considered to be a better tool for spatial interpolation of precipitation than kriging.

Due to the growing development of meta-models and their combination with optimization algorithms for modeling and predicting meteorological variables, in this research four metaheuristic optimization algorithms of Particle Swarm Optimization (PSO), Genetics Algorithms (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolutionary (DE) were combined with the adaptive neural-fuzzy inference system (ANFIS) model. The performance of four combined models developed with ANFIS model to predict the Frequency variables of Dust Stormy Days (FDSD) on a seasonal scale in Khuzestan province in the southwest of Iran was evaluated. For this purpose, hourly dust data and codes of the Word Meteorological Organization were used on a seasonal scale with a statistical period of 40 years (1980-2019) in seven synoptic stations of Khuzestan province. The results of good fit indices in the training and testing phase showed that there is no significant difference between the ANFIS method and other combined models used. R and RMSE values of the best combined model (ANFIS-PSO) from 0.88 to 0.97 and 0.10 to 0.19, respectively, and in the ANFIS model from 0.83 to 0.94 and 0.11 to 21, respectively, were variable. The results also showed that the combination of optimization algorithms used with the ANFIS model does not significantly improve the results of the model compared to the individual ANFIS model.

Monthly water balance models (rainfall-runoff models on a monthly basis) are major tools in long-term planning of water resources. The basic structure of these models includes continuity equations for soil moisture storage, subsurface flow and groundwater. Due to the complexity of the formation process and the different sources of flow, Rainfall-runoff models have different structures and need to be improved, simplified and revised according to the studied catchment conditions. If there is a karst origin in the study area, because of its importance in providing drinking water, it can not be simply ignored in the modeling. In this study, the structure of daily reservoir Saturation Area Model (SAM) is modified to improve the prediction of base-flow and monthly runoff in Kazeroon and Barm plain karst basins. The results of the modified model (SAM-KARST( and original model (SAM) have been compared and then the performance of the proposed model has been evaluated. The obtained results showed a relative improvement in the performance parameters of SAM-KARST in comparison to SAM for the study basins.By considering the conceptual reservoir for karst origin in the model, the contribution of base flow obviously increased, which indicate the important role of karst origin in supplying base flow.

This study focused on proposing a new operational perspective within main and lateral irrigation canals based on the economic value of water. To achieve this objective, the operation-economic framework offered in this study consisted of two main components of the PMP model and Operation model. The estimated economic values of water in different regions of the network were employed as the starting point for connecting the economic model with the operation model. It is worth mentioning that the technical perspective targeting adequacy of water distribution within the canals was modified in this study to be applied for the operation-economic framework since the original forms of the indicator were based on physical inherent of the water. Roodasht Irrigation District, located at Zayandeh-Rud basin, was selected as the case study, and the proposed framework was tested on the district. The obtained results revealed that in response to implementing the proposed framework in water distribution within the canal under the water shortage condition, alfalfa and safflower were the two crops those cultivation was decreased drastically in comparison with the other crops. The primary reason for the decrease was the lower values of the economic value of water for these two crops. Also ,the results of the canal operation appraisal from the adequacy of water delivery revealed that for the traditional operating system (without considering the economic perspective), the maximum values of the adequacy indicator were obtained for the upstream four canal reaches. On the other hand, the off-takes numbers of 1, 6, 5, and 12 got the maximum values of the adequacy indicator when the proposed operation-economic framework was applied for the canal system.

Proper operation of gated spillways, which plays an important role in safety of dams during flood event, remains as a prominent challenge. Although telecommunication networks used in flood warning systems facilitate flood management by dams to some certain level but accurate prediction of both the magnitude and timing of peak flood flows with few days of lead time is not still possible. Therefore, operation of gated spillways may mostly benefit from the observed reservoir water level and observed flood discharges in the upstream gagging stations. In Iran, many of river-reservoir systems face severe floods every year. Most of these reservoirs are operated based on engineering judgments of dam personnel and static policies such as rule curves, which are not proper formulated for flood events with various return periods. In this study, we proposed a rule curve for operation of gated spillways using sequential approach and its performances was compared with previously developed operation policies. For this purpose, nine stages as critical control levels were assigned and spillway gate openings associated with each critical level were determined for Mahabad Dam, located in northwest of Iran, as the case study of this research. Each control level was related to given percentage of the design flood volume. When using the proposed rule curve, as a critical control level is reached, the discharge through the spillway and related opening of its gate are accordingly increased. The gate openings associated with each critical water level were optimized using a real coded Genetic Algorithm (GA). This attitude or policy of nine -stage operation includes an appropriate algorithm for determining critical levels, development of operational models spillway gates, calculation of gate opening in each stage, and applying the model to the case study of Mahabad dam. This study adopts the nine stages approach for which nine control levels and gate openings are determined by GA. As it mentioned before, the nine critical levels should be placed in flood retention reservoir between initial water surface elevation and maximum water surface elevation. So, it will have crucial role to determinate gate opening spillway during flood events, and increase of flood storage in the flood retention reservoir. This model was employed to route a flood with the same return period of design spillway flood through Mahabad reservoir. The performance of the proposed rule curve was then assessed by determining the amount peak flow discharge reduction. The results of the case study shows superiority of the proposed rule curve over previously developed operation policies for flood control in Mahabad Dam. One of the main advantages of the proposed methodology is that for using the proposed rule curve not flood forecast data is needed and therefore, it can be considered as a reliable tool for operation of gated spillways when there is no flood warning system in the upstream basin. The future studies can be focused on testing the proposed algorithm on other reservoirs. In the cases of dams with large flood control volumes, critical control levels can also be considered as decision variables of the GA optimization model.

Drought prediction is an important item in realm of hydrometeorology and hydrology, and selection of suitable meteorological variables for drought prediction is a goal in recent studies. In this paper, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’s time series and the well-known statistical machine learning methods, Support Vector Machine (SVM), is proposed to predict drought class based on Standardized Precipitation Index (SPI) in some seasonal scale scenario in the main watersheds of Tehran. In current study, ground weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) was applied in prediction models based on data from 1975 to 2005 in the main watershed of Tehran. Regarding to the amount of predictors, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’s time series and target time series and the well-known statistical machine learning methods, support vector machine (SVM), is applied to predict SPI class. One of the important issue in this research is use of different variables, for example regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in classification and prediction of SPI and it is suitable and applicable. The predicted SPI in winter and autumn are more accurate than the other scenarios.

Sustainable management of water resources in various basins depends on accurate estimation of water balance. Reviewing literature of water balance models reveals that most published works are focused on hydro-climatologic water balance models, which consider aquifers like tanks with one or two soil layers. These models use empirical equations to calculate the effects of groundwater on hydro-climatological water balance. In this study, we have tried to advance previous studies by linking previously developed hydro-climatological and groundwater balance models. In the developed model in this study, the aquifers were modeled more accurately by using the tank-model approach. Optimizaing the parameters of the joint water balance model is an important step for achieving proper water balance model performance. In order to assess the performance of the recommended model, hydro-climatological and groundwater water balance in Neyshabur and Rokh plains were modeled in a nine-year period. The results proved that the proposed approach can be used for modeling water balance of other basins in the country. Although it was suggested that it can be better to use distributed modeling which allows considering interactions between surface water and groundwater resources.

In the last decades, various methods have been proposed for stochastic operation optimization of reservoirs out pf which one is Stochastic Dual Dynamic Programming (SDDP). Reviewing the literature shows in most previous studies, inflow uncertainties have been mostly modeled by auto-regressive AR(1) or seasonal aut-regressive PAR(2) models when using SDDP. Another approach toward inflow uncertainty modeling by SDDP uses Markov Chain which has received less attention. In this study, MSDDP model has been developed which uses Markov chain concept. Furthermore, applicability of SDDP in developing long-term operation policies has not been tested before. In this study, after presenting an algorithm for using SDDP and MSDDP for formulating long-term operation policies, their performances have been compared in weekly operation optimizaition of Karoun multi-reservoir hydropower system. The benefits estimated for operation of the system based on MSDDP policies has shown 35 percent improvement compared with SDDP results. The results have shown absolute superiority of MSDDP in formulating long-term operation policies. Furthermore, operation policies obtained from SDDP have shown highest compatibility with the best possible operation policy when there is no uncertainty (deterministic conditions).

Construction projects, due to their size and complexity and their unique and dynamic nature, makes them impossible to model accurately. These attributes reveal the enormous need in these projects for a proper method of risk assessment. Furthermore, the main uncertainties involved in these projects are resulted because of vagueness (e.g. in cost estimation, time estimation, etc.), so, traditional methods used in the context of risk assessment, which are mainly based on probability theory, are not satisfactory for these projects. The aim of this paper is to propose a methodology based on hierarchical fuzzy rule based expert systems to assess the total risk of construction projects and also rank the risks based on their magnitude. For this purpose, a suitable hierarchical risk breakdown structure (HRBS) for construction projects is used to develop a formal frame for qualitative risk assessment. The highest layer of HRBS is the total risk of the project and the lowest layer consists of the factors in uencing each risk. Then, for relating the dierent layers of the HRBS and developing the expert fuzzy system, a method is used to extract the fuzzy rules based on expert opinion. Finally, this fuzzy system is programmed. The eciency of the proposed model has been analyzed for assessing risks of a construction project in Tehran. Risk assessment in this case study has been undertaken from the client, contractor and consultant points of view, separately, and also after aggregation of their opinions. The results show that the presented methodology can be used to assess risks related to cost, time, quality and safety in construction projects.

Improving the accuracy of the missing precipitation data, particularly in large watershed with low-density precipitation network, is one of the challenges of the hydrologists. This study investigated six different types of artificial neural networks, namely: the MLP, the TLFN, the RBF, the RNN, the TDRNN, the CFNN along with different optimization methods, and geostatistical methods namely the Kriging and the Cokriging models for infilling the missing daily precipitation. Daily precipitation records from 15 rain gaging stations located within the Karkheh Watershed in the southwest Iran, were used to evaluate the accuracy of different models for infilling data gaps of daily precipitation. The results suggest that the MLP, the TLFN the CFNN and the Cokriging can provide more accurate estimates of the missing precipitation values than the other ones. However, the MLP overall appears to be the most effective method for infilling the missing daily precipitation values. Moreover, the results show that the dynamically driven networks (RNN and TDRNN) are less suitable for infilling the daily precipitation records whereas the RBF are appeared to be fairly suitable. Also, the kriging model is less effective than the MLP, the Cokriging, the TLFN and the CFNN models, but shows better results than the RNN, TDRNN and RBF networks.

Monthly water balance models (monthly Rainfall-Runoff models) are considered as the main tools in the evaluation and long-term planning of water resources. The main structure of these models included continuity equations for soil moisture storage, subsurface water and sometimes groundwater. Variables considered in the modelling of water catchments consist of surface runoff, precipitation, evaporation, and in some of cases, monthly average temperatures. Since there are too many unknown parameters in the monthly water balance equation used in the modelling process, using auxiliary relations is inevitable. These equations are usually empirical and recommended as default in the models. In this paper, the used relations in the structure of the Budyko model for estimating the contribution of groundwater in base flow, soil moisture and evaporation were optimized. The uncertainty of the result in both traditional and optimized models was evaluated using GLUE method. The results indicated an improvement in the optimized model performance and uncertainty compared to the common traditional model in three different watersheds.

In the literature, optimization of reservoir operation has been mostly carried out to achieve certain objectives including water and/or energy supply, flood control, and environmental preservation. These objectives can be less important for the reservoirs with high sedimentation. Sefid-Rud Reservoir in Iran has been facing extreme sedimentation rates in the past. A major challenge in operation of this reservoir has been to create a tradeoff between sediment removal and water supply to downstream users. The lack of modeling tools for balancing sediment removal with water supply objectives has been the main driving force behind this study. In this paper, a new approach is proposed for monthly reservoir operation optimization of Sefid-Rud Reservoir considering water supply and sediment removal objectives concurrently. The model minimizes the total deficit in supplying demands and maximizes the total volume of sediments removed from the reservoir. The sediment outflow is determined debased on Tsinghua university flushing equation. A new evolutionary algorithm “Imperialist Competitive Algorithm (ICA)” is used to solve the resulted nonlinear optimization model. Five scenarios were assessed for depending on the relative weights assigned to each of objectives of sediment removal and water supply. Based on the results obtained, the feasibility of increasing efficiency of flushing operation while supplying downstream water demands was illustrated. Moreover, the significance of the improved system’s operation with higher long-term reliability of supplying downstream water demands and sediment removal, compared to historical operation, was investigated.