نوید جلال کمالی

Given the fact that hydropower energy is the third largest source of electricity generation and also the most important renewable energy producer in the world, the optimal use of the huge and expensive source of water is essential. In this study, water cycle metaheuristic algorithm was used for optimization of the hydropower operation of Jiroft reservoir located in Halilrood basin (south of Iran) for a period of 223 months (from October 2000 to April 2019). The decision variables in the hydropower energy optimization model of the reservoir are the optimal release values of the monthly hydropower output from the reservoirs. After verifying the reliability of the WCA algorithm using several standard benchmark functions, a model was developed for optimal hydropower operation of the Jiroft reservoir. Also, the results of the algorithm were compared with the results of known metaheuristic algorithms of the Harmony search Algorithm (HS) and Imperialist Competitive Algorithm (ICA). The WCA, ICA and HS algorithms were capable to generate energy 7203, 273.66, and 7115.78 MW during the statistical period, respectively. Also, the objective function value for WCA, ICA and HS algorithms, was obtained 4.78, 5.82, and 8.63, respectively. The results revealed that the MSA algorithm was the superior algorithm in optimal hydropower operation.

In recent decades, metaheuristic algorithms have been successfully applied in various aspects of water resource engineering and management issues, especially in reservoirs' optimal operation. In this paper, a new method based on Symbiotic Organisms Search using the Fitness-Distance-Balance (FDB-SOS) algorithm has been developed for modeling optimal operation of complex multi reservoir system Baft, Safarood, and Jiroft reservoirs of Halilrood basin situated at latitude 56◦51ʹ-61◦30ʹ N and longitude 26◦18ʹ-29◦30ʹ E. The system consists of 3 different agricultural irrigation demands allocation with environmental prioritize in the long-term (from 2000 to 2019) approach. The developed model was used to solve the complex multi-reservoir system operating Halilrood basin. The new FDB-SOS algorithm results were compared with other developed evolutionary algorithms, including Harmony Search Algorithm (HS) and Imperialist competitive algorithm (ICA). Results showed the new FDB-SOS algorithm’s sustainability index was obtained 87.23, 74.34, and 96.03 percent of downstream demands for Baft, Safarood, and Jiroft reservoirs. The finding revealed that the new FDB-SOS algorithm was the superior algorithm in optimal operation of the multi-reservoir system.

Dam measurement and behavior assessment is a new issue that can be due to changes in available parameters to develop a model examining the behavior of individual parameters on the dam as well as on each other and analyze the changes and create the necessary policies. This study aims to propose a hybrid method involving logistic regression with particle swarm optimization algorithm with real value to predict the behavior of dam equipment.

In this study, from 365 days data, from 04/20/2018 to 04/20/2019, of which 600 sets of dam equipment data including parameters of water temperature, water level, valve pressure, sedimentation rate, pore pressure, air temperature, inlet water volume, specific dam characteristics, concrete conditions, reservoir water level, horizontal and vertical displacement, transmission connection components and ground acceleration, strength, pressure, tensile and high stress were used for modeling. Real value-logistic regression and 120 datasets were used for modeling the should be added of particle group optimization algorithm. To evaluate the performance of the proposed method, four statistics including coefficient of determination (R2), root mean square error (RMSE), scattering coefficient (SI), and means bias error (MBE) were used.

The results showed that the model has an acceptable performance in predicting piezometric pressure in the dam body. Also, the results of the artificial neural network model show acceptable convergence with R2 = 0.930 and SSI = 8.587. The results related to the training data of the model also indicate that the mean (µ) and standard deviation (σ) of the proposed model are equal to 1.341 and 1.526 for the training data and these values for the validation data are equal to 1.576 and 2.247, respectively indicating the good performance of the proposed model. In the cumulative probability criterion, the proposed model with P50 = 0.940 and P90 = 1.742 indicates that the results are acceptable.

The results indicate that the real value-logistic regression particle swarm optimization implements the principle of structural risk reduction instead of minimizing the experimental risk that provides excellent generalization for small sample sizes. The ratio of predicted piezometric values ​​to read values ​​for about 72% of the data in this model is about one, indicating the appropriate training and predictive power of this model. Finally, according to the evaluation criteria, the hybrid model performs better than the presented methods.

This study describes optimization of Support Vector Machine (SVM) using meta-optimization algorithms including the Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Ant Colony optimization for flood susceptibility mapping at Zahedan Basin. To determine the best factors among the 19 factors used in previous studies and the relative importance of input variables, two experiments of Information Acquisition Ratio (IGR) and multilinearity of parameters were used soil moisture content factor which is identified as a highly effective factor (IGR=0.767),  is used directly in this study for the first time. The flood inventory map was prepared from Sentinel-1 satellite data processing and validated. The generated data set was used to map the flood prone areas using SVM model optimized with the mentioned algorithms. For the first time, in this research, ACO algorithm was used to optimize SVM model. The prediction accuracy of the models was evaluated using statistical criterion. All three algorithms used in this study improved SVM performance by 4% in average, while the PSO-SVM model had the best performance among the others with AUC=0.953, MSE=0.025, RMSE=0.158 and StD=0.405.

Climate Climate change is one of the important factors that will affect different parts of human life on the planet and will have detrimental effects on the environment, socio-economic, and especially water resources. Knowledge of climate change can provide comprehensive plans in various areas of management regarding the monitoring of droughts and their potential risks. Drought can occur in any area, even wetlands. This phenomenon depends on various factors and parameters and one of the most important symbols of this phenomenon is the occurrence of drought is a decrease in rainfall and therefore the analysis of precipitation data is of special importance to study drought. The purpose of this study is to analyze drought variables using SPI and SPImod indices and detailed functions.

 In this study, to model the multivariate analysis of drought in Lake Urmia basin using RCP8.5 and RCP4.5 representative concentration pathway scenarios, data and models of atmospheric circulation of historical data (1991-2010) for three near horizons (2030- 2011), medium (2065-2046) and round (2099-2080) were simulated and produced. Then, using SPImod index and copula functions, drought multivariate analysis was performed in MATLAB software environment. In general, first, using the mentioned indicators (two indicators, SPI and SPImod), the characteristics of drought intensity and duration were extracted, then, using coding in MATLAB software environment, eight families of Archimedean detailed functions were used.

The results of multivariate analysis showed that the Joe copula function is the best copula function for drought multivariate analysis (For analysis of both severity and duration of drought for the study area). Also, the results of probability and the joint return period showed that in the coming periods, at least droughts of the same level as historical droughts and even more severe will occur. Thus, by studying the period of combined and conditional returns and Kendall, the results showed that at a certain critical probability level, the amount of Kendall return period is much more than the standard return period, so that this difference increases with increasing that certain amount.

The results obtained with the climate change approach on the meteorological drought of Lake Urmia showed that in the coming periods we will see an increase in temperature, which will affect the rate of trade in the region and water resources, on the other hand, because the data Meteorology and hydrology are used to calculate the types of droughts, so droughts affected by climate change will be so that in future periods 46% to 48% of the months will be dry in different horizons. Finally, the results of the time series of indicators showed that during the statistical period at least 40% of the months were dry and this intensity of droughts in the Urmia station is much higher than others. The modified SPI largely eliminates the disadvantages of conventional SPIs and takes into account seasonal variations in precipitation in the calculation of the SPI index.

The presence of positive surges can cause bank destruction, bed scouring, and damages to structures such as bridge piers installed on the path. On the other hand, the remains and bridge pier destruction waste on the path affect velocity variations and flow surface fluctuations resulting from these surges and require careful investigations.

This study comprises an experimental analysis of the effect of present damaged and undamaged bridge piers on flow pattern variations during the generation of positive surges in sloped and horizontal channels. To this aim, ADV was utilized to help collect three-dimensional flow velocity data. Moreover, PIV was used to help make a comparison with ADV data results. Four distinct probes were incorporated for bathymetry in the horizontal and the sloped channels.

The obtained results were indicative of non-breaking undular surges generated in the horizontal channel and non-breaking and breaking undular surges formed in the sloped channel. In the sloped channel, the maximum velocity increase occurred with installation of two piers, which showed a 34.39% increase compared to that under the same conditions in the horizontal channel. Furthermore, the average water level variations due to the slope of the channel in cases without an element, with a damaged element, with an undamaged element, and with a combination of damaged and undamaged elements were respectively equal to 31.66, 32.70, 29.88, and 27.40%. In addition, the Reynolds shear stress values were also calculated in the sloped channel to be 7.6, 3.57, 1.38, and 1.68 times those in the horizontal channel.

One of the useful techniques in flood management planning is identifying flood areas and calculating the flood potential of the basins. However, due to the environmental complexities, this identification has many challenges. Flood prioritization of a basin is the classification of sub-basins of a basin according to the role of each of them in producing the peak discharge of flood produced at the outlet of the basin. There is a wide range of methods for estimating runoff from the basin, such as the use of observational data, experimental and statistical techniques for estimating river discharge, more commonly known as rainfall runoff models. Deriving flood prone areas and identifying flood potential of basins is one of the crucial issues in flood control planning projects. In this way, it will be cleared that which of sub-basins in a whole basin are in priority of implementing flood control scenarios. Prioritization of basin area in term of flood generation is the procedure of dividing and classifying sub-basins based upon their contribution in generating the peak discharge of flood hydrograph. In the other words, the sub-basin with the most contribution in generating the peak discharge, will be categorized in the first flood prone priority.

In recent years, for the flooding status of watersheds, the division of the basin into a number of sub-basins and flood tracking in each sub-basin and then in the main waterway network has been used. With this method, flood-prone and critical sub-basins are identified according to their share in flood production of the entire basin. In the individual sub-basin removal method, first the total discharge flow of the basin is calculated and then in each step, each sub-basin is removed from the simulation process and the peak discharge is calculated again; Then, the flooding index of the sub-basin (F) is calculated and the priority of the said sub-basin in the production of total peak discharge is ranked. Single successive sub-basin elimination method (SSSEM) is one of widely accepted methods in this field. Considering the wide variation of temporal pattern and amount of precipitation, by combining GIS technique and HEC-HMS model, the efficiency of SSSEM method in Lar basin of Zahedan was assessed in this research. After deriving the optimum number of sub-basins, calibration and validation of the simulator model was carried out and flood prone priority method performed using different duration, return period and temporal pattern of precipitation. It should be noted that in previous studies, the sensitivity of the individual removal method in extracting flooding of sub-basins to the height and temporal pattern of precipitation has not been investigated and in this study we have tried to address the sensitivity of this method to the mentioned factors. In this study, different return periods were considered as a criterion for precipitation height and SCS standard time patterns and the dominant regional precipitation pattern were considered as criteria for time pattern change. Meteorological data used in this study include rainfall and hourly runoff; Collected from climatology, rain gauge and hydrometric stations over a period of 22 years (from 1997 to 2020).

According to the obtained results for determining the flooding potential and ranking of sub-basins by repeated method of individual sub-basin removal, it is observed that the individual removal method does not show sensitivity to rainfall time pattern, its continuation rate and return period. At a continuum of 1.5 times the concentration time, the sensitivity of this method to the time pattern of precipitation is observed and with increasing the return period, the anomaly in the ranking according to different patterns increases. There is a sensitivity to changes in the precipitation time pattern and return period at twice the concentration time, but the amount of anomaly is less than before. It should be noted that regardless of the rank of each sub-basin in terms of flood production and its effect on the peak hydrograph discharge, it should be examined what effect the application of flood control scenarios in that basin will have on the damage caused downstream of the city. In other words, in addition to the peak discharge that is the result of this study, other components of the flood, such as its volume, time corresponding to the peak discharge and also the response of the urban drainage system will be effective in the occurrence and severity of destructive flood effects; It may not be the first sub-basin in flood production, but the dynamics of its flood production in interaction with the city's drainage system will impose far more destructive effects, both economically and human losses in the city. Therefore, in order to complete the study and to evaluate the effectiveness of different flood risk scenarios, there is a need for a hydraulic study of rising water levels in different parts of the city, taking into account the sensitivity of each point in terms of economic and population density.

In recent decades, the technique of systems analysis has attracted serious attention in planning and management of water resources. This research was based on the idea of modeling surface water resources and connecting them to the aquifer in the form of separate subsystems, taking into account the side factors. In fact, this study considered the interaction of surface and groundwater, using system simulation and integrated dynamic connection between surface and groundwater resources in the area. In other words, one of the innovative aspects of this research was the integrated management of surface and groundwater through the dynamic system method. System dynamics (SD) is among the available tools for simulating complex water resources systems, in which the links between the system elements are expressed as causal relationships. The SD-based software packages can serve as flexible and integrated tools for a wide range of modeling applications, from participatory modeling to economic analysis. First, the conceptual model and causal relationships between the effective parameters in the groundwater equations of the plain were prepared, and then the model was made in the Vensim software. Monthly data for water use and resources were imported to the model, in Excel format. Afterwards, the scenarios were designed and defined for the model. Finally, the performance of the catchment was examined in terms of meeting the desired needs and maintaining groundwater balance. Since the climate is continuously changing due to man-made activities, the output of GCM models was used in this research to assess the different management scenarios. So, the GFDL-ESM2M model was employed to predict the future rainfall, under the fifth average future climate scenarios (RCP4.5). The reason for choosing GFDL_ESM2M model was that it has been recently developed by NOAA to gain better understand of the Earth's biochemical cycle, including human actions and interaction with the climate system. Then, river runoff was simulated using derived rainfall in the time period of 2015 to 2030, through ARMAX statistical model. This runoff time series then was fed into the system dynamic model to assess the water balance of the study area under different management scenarios. This study was to evaluate the condition of the aquifer, apply recharge scenarios, and improve the groundwater storage system of the Nesa plain in Bam County, Iran. A comprehensive model was thus developed based on the SD approach. Within this system, the relationships and the feedback of the changes in the sub-systems were expressed using cause-and-effect diagrams. In order to implement the model, several scenarios were further considered alone or in combination with each other. Due to the outputs of the prepared model and regarding the priorities for allocation of the volume of water stored in the reservoir of the Nesa plain (supply of the drinking water for Bam city, and consumption of industry and services, respectively), water budget in the plain was negative. The water budget of the plain could reach - 65.4844 mcm, in the following 20 years, indicting the critical situation of the region. Given that about 4395 ha of the lands in the region were under the predominant cultivation of alfalfa (approximately 60%) and wheat (about 30%) with a total of 60 mcm of water consumption. modifying the cultivation pattern (eliminating the cultivation of these crops) alone had no significant impact on water balance. Installation of volume meters and implementation of well regulation policy to about 75% of current permits would thus boost the water balance of the region to -44.84 mcm. Via artificial recharging of the plain by flood spreading method, the budget of water resources was equal to -35.17 mcm, which implies the success of the combined policy. Therefore, the application of regulatory policies and the implementation of modern irrigation systems, especially for date orchards (as the main crop in the region) with an annual consumption of about 2300 mcm/ha, and preventing the drilling of more wells could be steps towards balancing the water resources in the region. This could not be possible except through the cooperation of stakeholders and beneficiaries along with the implementation of support packages. In general, assessing the process of flood spreading, recharging the groundwater aquifers, raising the water level of wells, increasing the discharge of springs and canals, improving the quality of the groundwater, and meeting the satisfaction of local communities will bring about a decrease in urbanization and rural prosperity.

Water scarcity is one of the limiting factors for agricultural production in arid and semi-arid regions of the world. Being located on a major global dry belt, many parts of Iran tend to experience frequent fluctuations in precipitation levels. Under these conditions, it is necessary to make the best use unit volume of water. To achieve the maximum possible water use efficiency in agriculture, the plants should be given just enough water to reach an acceptable crop yield. This study investigated the effect of deficit irrigation on wheat yield in the Bardsir plain of Kerman, Iran, in the crop year 2019-2020. The experiment was performed in Randomized Complete Block Design (RCBD), using 4 treatments and 3 replications, 100, 85, 70 and 60 percent of water irrigation requirements were considered as 4 different treatments. The highest crop yield was observed in the treatment with 100% irrigation and the highest water use efficiency was achieved in the treatment with 15%deficit irrigation. Therefore, to maximize the water efficiency of wheat production in the Bardsir region, wheat farms in this area should receive water in amounts equivalent to 85% of their irrigation water requirement.

Apart from the understanding of the impact of land use and climate changes on the water cycle and hydrology regime, hydrological models are effective tools for designing and managing water resources. Currently, many hydrological models have been developed to simulate the basin, though choosing the right model is a challenge. To this end, a correct understanding of the model, its advantages, and limitations is necessary. In this regard, several studies have been conducted to evaluate the hydrological models performance in different regions and conditions. In the present study, the performance of two integrated hydrological and conceptual rainfall-runoff models of AWBM and MIKE NAM in the simulation of the average daily runoff in Gonbad Hamedan basin was investigated. Although both models are lumped models for rainfall-runoff process, the MIKE NAM model has a more complex structure compared to the AWBM. In addition to considering the initial conditions, MIKE NAM model is also capable of simulating snowmelt. The results of the runoff simulation during the calibration and validation periods were evaluated using two statistical indicators of the Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS). The NSE and PBIAS during the calibration and validation periods for the MIKE NAM model were 0.8, 6.3 and 0.71, -4.2; and 0.6, 14.33 and 0.55, -9.2 for AWBM model, respectively. The results showed that MIKE NAM model has a better performance in simulating daily runoff in Gonbad Moarref basin compared to the AWBM model.

Kerman province is currently suffering problems in water resources management and its freshwater storages are restricting continuously. A large number of wells are now salinized and lost their suitable quality in this province. Thus, a particular management is needed to use brackish water resources so as to achieve an acceptable level of agricultural production. In this study, the effect of irrigation by sodium and brackish water on soil and pistachio trees in Kerman county was investigated using I1 to I6 treatments. Six brackish water treatments in complete randomized blocks in three repetition were studied. Irrigated water was distributed uniformly in all treatments. The results showed that the use of gypsum in the treatments I3 and I6 leaded on better removal of harmful sodium ions than in the treatments I1, I2, I4 and I5 where soil was solely leached by irrigation water. Adding gypsum reduced the average of electrical conductivity, SAR and ESP of the soil by 39.35%, 47.4% and 41.3% respectively in comparison to before test conditions. Besides, using gypsum was resulted in improvement of yield performance by 11.9% and 15.6% for treatments I3 and I6 respectively and improvement of quality indicators such as increment of 1000 kernel weight, reduction in both blank pistachios percentage and non-split shells.

Climate change has relatively great effects on water demand of plants by changing temperature and precipitation pattern. this investigation aims was to predict temperature and precipitation in The new future and to evaluate their effects on evapo-transpiration and water demand of alfalfa plant in Bardsir region in kerman province. The best equation for estimating real evaporation-transpiration in the region was derived by using measured data obtained by a lysimetric device in during 7 months. For this purpose, 6 common equations were studied in addition to artificial neural network techniques under different scenarios. The best equation among the presented ones was Blaney-Criddle with R2= 0.884, RMSE=0.11 and the best artificial neural network scenario was mean daily temperature scenario with R2=0.802, RMSE=1.516. Therefore, the Blaney-Criddle equation was selected as a reference for prediction. Then, the GFDI-ESM2M model was used to simulate precipitation and temperature for the near future 2020-2050. Observed Temperature and precipitation data. which were obtained from a weather station in the region, were used for the reference period of 1987-2016. Temperature and precipitation changes trends were by extracted three scenarios; under optimistic RCP2.6, medium RCP6 and pessimistic RCP8.5 scenarios, during 2020-2050. The results showed that the mean temperature for mentioned three scenarios increased by 2.5, 2.8 and 3.1 Celsius degree in the 2050 respectively, and precipitation remained virtually unchanged. In this regard, it is expected that agricultural water demand will increase 3.64, 4.69 and 5.25 mm per year, respectively compared with the reference period.

Close relationship between water and food, a Comprehensive assessment of water resources is indispensable in large scale for strategic decision-making on food security. The most effective factors on the water resources of a region can be referred to the climate, climate change and hydrological parameters. GCM models are the main instruments for climate change review. In this study, we used the GFDL-ESM2M model, four RCP scenarios and climate change toolkit (CCT) for climate change analysis and used the SWAT model for hydrological simulation of Kerman province. In this research, the base period of climate change and hydrological simulation of Kerman province was considered during 1990 to 2012 and the simulate climate change during 2020 to 2050. The simulation results were evaluated using NS and R2. The NS coefficient for precipitation and temperature at all stations obtained 0.8 to 0.95 and 0.88 to 0.99, respectively. The climate change results showed that in all scenarios precipitation probably increase in most regions. By studying the SWAT model results, it was determined that the middle and western regions of the province have green water storage. Also, the effects of climate change on blue water, green water flow and green water storage have reduced their amount.

Iran is considered as one of the world’s lesser sources of water resource and in order to optimize the use of available water resources, recognizing these resources is inevitable. To simulate water resources at a widespread level, it is essential to use a comprehensive model that is capable of simulating large-scale models, one of which is the SWAT model. Also, the availability of water resources should be investigated in light of climate change which in this research the climate change bundle is used to study climate change. The base period for this study is from 1992-2016 and the era from 2020-2050. The results indicates that an increase in precipitation in the southwest and a 5% decrease in rainfall in the eastern part of the city. As well there is the possibility of an average temperature rise of up to 3 degree Celsius. Accessible water resources in the city of Bardsir include blue and green water that the amount of blue water in the middle regions is higher than that of other parts and the western parts will have higher evapotranspiration. In addition, the soil moisture content in the southern part will be higher than in the other areas. Therefore, it is unavoidable to heed these areas in order to devise grandiose plans for the dry farming.

The main scope of this research is evaluation of Soil Conservation Service Procedure in derivation of initial abstraction of precipitation in watershed scale. For this purpose Dalaki watershed which is located in south east of Iran was selected then by using hec-hms and GIS models and a number of observed rainfall runoff events some parameters like CN of watershed, K and X of Muskingam method and initial abstraction of precipitation were calibrated through two different search algorithm of univariate and Nelder & Mead methods. The early results of this research indicated the superiority of Univariate search algorithm over the Nelder&Mead method both in calibration and also validation processes. Then using calibrated CN and Initial abstraction parameters which were derived through Univariate search algorithm, the factor between initial abstraction and potential retention of surface runoff (S) in each of sub basins were estimated. 0.13, 0.43 and 0.19 were derived as the above mentioned factor respectively for Minimum, Maximum and mean of the above mentioned factor in this step of the research which showed an acceptable compatibility to the offered factor of 0.2 by SCS. Then in rainfall runoff modeling process of this watershed SCS offers a reliable method of initial abstraction estimation.

One method of decreasing loss resulted from flooding is to determine flood productive regions and applying appropriate methods to control flooding. In this study، by using HEC-HMS model accompanied by GIS technology the contribution rate of different sub-basins located upstream of Chamchit hydrometry station in Dalaki basin based on 2،50 and 100 years return period is determined. In this research upper sub-basins of Chamchit hydrometry station ranked due to flooding using Single Successive Subwatershed Elimination method in HEC-HMS environment. Results of this study showed that sub-basin contribution rate in whole output not only affected by area and peak discharge of sub-basin but also factors like location of sub-basin، distance from output، CN، and Flood Routing role in main river have significant impact on the portion of sub-basin in production of the whole basin flood (sub-basin flooding). Except above mentioned، it was found that there is no significant change in sub-basin flooding priority in different return periods.

Climate change is one of the most important challenges of mankind in the 21st century. Numerous climatic models have been developed to simulate and project the probabilities in changing climatic variables. Because of the uncertainty involved in using different models, simulations results were validated and the best of them for each variable was determined. In this study, simulation results of temperature parameters from 22 General Circulation Models, which have been approved by the Intergovernmental Panel on Climatic Change (IPCC), were evaluated for validity using the statistical criteria. It was observed that BCM2.0,CM3.0, INM, and AOM were appropriate for the minimum temperature prediction; HadGEM,ECHO, and G-Mk3.0 were useful for the mean temperature prediction; and PCM,Mk3.0, and AOM were appropriate for the maximum temperature prediction.

The optimal exploitation of water from a reservoir requires a comprehensive knowledge of future availability of water resources. In this case the amount of water that will be available in the future is important. Also, we need to examine the flows at the dam from a short-term perspective. The main aim of the shortterm estimation of the runoff is to evaluate the risk of floods. This is necessary to avoid overflowing and to minimize damage. In order to facilitate forecasting of the water resources, many different techniques have been developed through the years.
In this paper, using monthly data (since 1957 to 2003) obtained from Hydrometric station at Shaloo bridge (upstream of Shahid Abaspour reservoir), the Auto Regressive Integrated Moving Average (ARIMA) model and artificial neural networks (ANNs), prediction of monthly mean flow to shahid Abaspour reservoir was accomplished. On the basis of comparison of the results of the models with measured data and computation of standard deviation, variation coefficient, sum of square error, root mean square error and correlation coefficient of the results, the performance of ARIMA(0,1,1)´(1,1,1) model shows an improvement in comparison with the ANNs model.

There were many hydrological researches focused on dynamic and linear modeling of rainfall-runoff process. Conversion of rainfall data to runoff consists of nonlinear complex relationships which are resulted from interactions of different sets of hydrological process. The stochastic modeling is therefore seems to be more sensible in this estimate than deterministic ones. In this research observed runoff is modeled against total rainfall. This will mainly avoid misleading theories in breaking the rainfall and runoff time series into the excess rainfall and the direct runoff time series,. A Transfer Function (TF) model with single input and single output variable (SISO) is used in this research. This function is transferred to the state space equations. The stochastic Data-Based Mechanistic modeling (DBM) method relying upon recursive Kalman filtering algorithm is then used to identify the non-linear relationship between rainfall and runoff. This approach is applied to the Khersan sub basin in the Great Karun catchment south western Iran. The relation between the calibrated parameters and the routine characteristics of the basin flow showed a probable parallel structure of flow routine in this sub basin. Finally the sensitivity analysis is performed using the Monte Carlo Simulation (MCS) in order to quantify the reliability of the model.

A major part of hydrological researches focused on complex and non-linear rainfall-runoff process. Mathematical models were presented to describe this process including a wide range from simple black-box representation to complex physically-based models. Considering inherent uncertainty associated with the process as a result of uncertain input variables and uncertain calibrated parameters, stochastic modeling seemed preferable to deterministic approaches. In this study, data-based mechanistic modeling (DBM) was selected to identify non-linearities of the process. The method is categorized as a stochastic approach relying upon recursive parameter estimation using Kalman filtering algorithm in state space system of equations. In addition, it is capable to reflect a physical interpretation of rainfall-runoff conversion to describe the behavior of the system. The later capability differs it from other black-box modeling approaches. In this research, a parallel structure of flow routes was identified in upper-Karoun subbasin of the great Karoun catchment. Sensitivity analysis was also carried out based on Monte Carlo simulation (MCS) method and the reliability of the presented model were quantified.