Snowmelt-runoff simulation is a significant and common module in hydrological models. During the last decade, a number of hydrological distributed models, have been developed, using different snowmelt algorithms and many of the previous models have been updated with more improved snowmelt approaches. Also, an especial consideration has been developed to distributed modeling. In the present paper, snowmelt modules of hydrological models have been reviewed. Data problems to some extent have been resolved compared with the previous decade, but it is still the main obstacle. Application of Geographical Information System (GIS) has been one of the great successes and some of the models have been facilitated by GIS interface. In spite of these improvements, because of the rareness of snowmelt measurements in a few studies snowmelt components of hydrological models have been independently evaluated. It has been suggested that under the coordination of International Association of Hydrological Science (IAHS), in an international attempt, snowmelt data set of different research works that have been done so far to be collected and made available to snow hydrologist and modelers.

Hydrological models are one of the currently used techniques for simulating runoff produced from rainfall. These models by simulation of rainfall-runoff process, are able to estimate runoff values in ungauged cachments without spending high cost and long time. AWBM rainfall-runoff model developed by Boughton in 1993, can calculate runoff based on hourly and daily rainfall. Daily and hourly results obtained from the modelling are used in flood management and planning, respectively. This model includes set of surface storage parameters (C1, C2, C3), partial area parameters (A1, A2, A3), and using daily rainfalls and discharges, monthly runoffs and evaporations. In this study, in order to evaluate the model performance, six sub-catchments located in the south of Sistan and Balochestan province were chosen under a case study. The analysis was carried out by the available data from these sub-catchment including Bah in Sistan and Balochstan Province. Daily rainfall data by using TPSS method were converted to regional data and daily diacharge to specific discharge in mm. Finally, accurancy and efficiency of the AWBM model in simulating runoff evaluated by efficiency was and determination cofficients. The results show the model can simulate runoff reasonably in all sub-catchments under study and other ungauged catchments, and also can be used as a useful tool for research and modelling hydrological process of rainfall-runoff in catchments located in arid and semi-arid regions.

Darband and Golabdareh watersheds are located in the north of Tehran with a combined area of 33 km2. In July 1987, they exprienced a sever flood that caused immense damages to the northern part of Tehran and claimed number of lives. The objective of the present study is to investigate the said flood as well as flood frequency in the region. Unfortunately, the existing dischargegauging stations have short records and there are not enough measurements from past floods. Because of the above problem, rainfall-runoff simulation was identified as the appropriate way to analyze flood peaks and hydrograph. Among the different models for this kind of simulation, HEC1, HYMO, rational and matrix models were selected for the purposed simulations. The results show better performance of matrix model. For the flood of July 1987, this model estimated peak flood equal to 356 m3/s.

Run off simulation is one of the most important topics in hydrology And its study is based on rainfall-run off models. Several rain fall and run off models have been developed and the most appropriate model should be selected for each catchment. By applying the appropriate model the water consumption will be optimized. The model should be selected for each catchment based on the model abilities and limits. In this study, the performance of two rain fall and runoff models, GR2M and GR4J were compared in Darehtakht Basin in Lorestan Province during 1379 to 1392. The Nash coefficient was used as a decision criteria for comparing two model performances. Nash coefficient for GR4J and GR2M were 42. 7 and 65. 5, respectively. Results showed that both models can predict the performance of the catchment accurately, but, based on Nash coefficient the GR2M is more accurate than the GR4M.

Flood hydrograph simulation is affected by uncertainty in Rainfall - Runoff (RR) parameters. Uncertainty of RR parameters in Gharasoo catchment, part of the great Karkheh river basin, is evaluated by Monte-Carlo (MC) approach. A conceptual-distributed model, called Monte- Carlo, was used for basin simulation, in which the basin's hydrograph was determined using the superposition of runoff generated by individual cells dividing the catchment in a raster-based discretization. A narrow parameter range was obtained through application of the MC method. Parameter range depended on goodness -of-fit measures. The results of various goodness-of-fit measures are discussed in this paper. The selected goodness-of-fit measures gave high weight to peak discharge to reduce peak discharge error.

Introduction: Flooding behavior of rivers of Iran, lack of water and necessity of surface water controlling detect the importance of river’s behavior simulating and modeling. In this way we can have a long term plan for proper and reasonable operating from potential of rivers. Rain -Runoff water simulation is main step for managing of basins. This process is one of the complicated nonlinear phenomenons in water engineering. Most of calculation and designing in water engineering need a proper evaluation of quantity and quality of running water that comes from a determined rain. There are common and various methods for evaluating of basin’s runoff. Nowadays, using of Artificial Neural Networks (ANN) in various branch of hydrology engineering is acceptance because this method is capable with good accuracy simulate and predict the nonlinear functions. This research tries to predict runoff in southern sub-basin of Gharasoo river in Ardabil province by Artificial Neural Networks (ANNs). This research based on 5 climatic parameters (2007-2010) that affects runoff. These data is obtained from Hydrometric station that located in the end of this basin.Methodology :Artificial Neural Networks (ANNs) is a simple model from human’s brain that with a special mathematic structure in each system is able to clarify the process and nonlinear relation between inputs and outputs. These networks during teaching process are teached and are used for feature predicting. For best designing of ANN model in this research to predict runoff in basin under study, first correlation of humidity average, rain average, monthly temperature average and evaporation average with the flow of basin is obtained. Then effective parameter with more correlation for multi-layer perceptron network is selected. Data matrix with following input and output is made. Inputs: monthly rain average (mm), monthly rain average (million m3), monthly relative humidity average (percent) and monthly temperature average (c) Outputs: Predicting monthly runoff in next year’s. From existing 39 year statistical period, 90 percent of them is used for net teaching and other 10 percent for test step is used. After selecting input and output data of net and defining net structure (stimulator function, number of neurons ,hidden layers ,number of cycle, amount of educational parameters) Net teaching by program teaching algorithm, first with one hidden neuron is began and with increasing that up to all neuron number is continuing. After each teaching, net is tested via regression analyzing and correlation coefficient between input and output data (in teaching step) and error percent (in test step). Basis of neuron numbers and cycles was maximum correlation and errors less than 5 percent. By detected number of hidden optimum neurons and cycles, to reach an optimum network several times the value of teaching parameters and the number of hidden layers is changing. For this reason network is designed in a way that by entering last years information (rain, relative humidity, temperature, evaporation, runoff) is able to predict next year’s flow with error less than 5 percent. After designing of 12 various networks for predicting of basin flow, various structure of percepetron is selected to reach an optimum network. For evaluating of ANN function, amount of R2, RMSE, MAE and Rare used.Discussion :The result of this research show that in all months there is a high correlation (more than 93%) between runoff and average of rain, humidity, temperature, evaporation and monthly flow. Minimum correlation coefficient in teaching step belongs to April (93%) and Maximum belongs to Jun (98%). For this research, Marcoart-Levenberg algorithm is the best algorithm because of more correlation in Teaching step and lower error in test step. For defining proper number of hidden neurons, maximum number of neurons for all 12 networks is 10 neurons. In the selected network, most of neuron number belongs to January with10 neuron in the first- layer and 2neuron in the second hidden layer and minimum of them is related to October with 3neuron in the first hidden layer. For defining hidden layer number, some of the networks (except February, March, May, July, October and December) with one hidden layer have a good result and some other with 2 hidden layers have a good result. The primary number of teaching cycles of network for each month in Marcoart-Lonberg algorithm first with 10 cycle for each neuron in hidden layer and with initial error value (=0.005) starts and maximum up to 700 continuing and in the end network with minimum cycle (10 cycle) in July and maximum cycle (700 cycle) in December reached to its goal. 4- Result The result of this research show that one model with 5 parameter including(monthly rain average, monthly runoff average, monthly relative humidity average, monthly evaporation average and monthly temperature average) is the best ANN for predicting the flow of river because with error less than 5 percent and high correlation can predict the runoff level. Number examination of various neuron in hidden layers show that one model with 4 neuron in the first hidden layer and 3neuron in second hidden layer sigmoid tangent stimulator function in the first hidden layer and 10 cycle, has best accuracy. The best ANN model in this research is one perceptron model with 3 layers and 4neuron in the first hidden layer and 4 neuron in the second hidden layer a 2 hidden layer stimulator function an one output and Marcoart-Lonberg teaching algorithm. The result of this research show that ANN model with low error and proper capability for predicting of basin rivers flow is a good model for evaluating of this parameter in future.

Although many kinds of rainfall-runoff models have been developed by hydrologists, the unit hydrograph methods are still gainful tool for flood estimation in many basins where recorded hydrological data are not sufficient to support distributed rainfall- runoff models. The aim of this paper is to derive the analytical unit pulse response functions of quick and slow runoff of stream flow using a conceptual model containing three serial tanks as well as a parallel hybrid tank. The interrelation of the tanks can be shown using exponentially structured models which their parameters reflect the physiographical characteristics of the basin. Parameters of the model were estimated using Nelder–Mead optimization method. Efficiency and validity of the developed conceptual model were evaluated for various observed events using statistical criteria such as Nash-Sutcliffe, mean relative absolute error for discharge, peak flow and runoff depth. Results showed that the developed model had good capability in predicting rainfall- runoff process considering soil moisture conditions before rainfall occurrence without the need for definition of excess rainfall.

In this article, the amount of pollutants and water discharge in Firouzabadi drainage channel were estimated to be able to simulate the existing and future quality and quantity of the channel, using EPA Storm Water Management Modeling (SWMM). In addition, several best management practices (BMPs) such as; swales and porous pavements were utilized. The goal of using such techniques were to reduce runoff peak flow and non-point source pollution through evaporation, filtration, and chemical or biological treatments. It is obvious that non of above techniques alone, could not improve the runoff quality effectively. It sounds wise to combine two or more techniques together to reach the optimum efficiency in controlling the amount of runoff. In this research, two main models were used: rainfall-runoff model and optimization model. SWMM was applied to simulate quality and quantity of runoff. Nevertheless, Multi Objective Particle Swarm Optimization (MOPSO) was used to optimize the BMPs layouts so that the peak flow will be minimized effectively. The results show that in additon to improve the quality of runoff, BMPs can reduce the peak discharge flow between 8 to 15 percent for 5-year return period.

Since it is not possible to fully measure the data required for the catchments for runoff rainfall models, so choosing a model that can accurately represent the output hydrograph while simplifying the structure usin

Introduction: SWAT is a continuous-time model that operates on a daily time step at the basin scale. The objective of such a model is to predict the long-term impacts of management and the timing of agricultural practices within a year (i. e., crop rotations, planting and harvest dates, irrigation, fertilizer, and pesticide application rates and timing) on large basins. It could, at the basin scale, be used to simulate the water and nutrients cycle of landscapes whose dominant land use is agriculture. It could also help assess the environmental efficiency of best management practices and alternative management policies. The SWAT model uses a twolevel disaggregation scheme: a preliminary sub-basin identification is carried out based on topographic criteria followed by further discretization, using land use and soil type considerations. Areas with the same soil type and land use form a Hydrologic Response Unit (HRU), a basic computational unit assumed to be homogeneous in hydrologic response to land cover changes. The development of the digital computer has added a new dimension to hydrology. Previously, finding solutions for different problems took hours with a pen and pencil method, but now it takes seconds with modern computers. Moreover, much more complex methods of analysis are now feasible because of the speed of the solution-finding provided by the computer. The impact of the computer has been particularly great in the area of rainfall-runoff modeling. As flood routing and unit hydrograph analysis are mathematical modeling’ s, surfacewater hydrology is, historically, concerned with modeling. Due to the climate type and the spatial and temporal inconsistency of rainfall in Iran, large floods cause many damages in different parts of the country annually, as the Mediterranean climate and different weather conditions throughout a year provide the ground for the majority of short-term atmospheric rainfall. Materials and methods: Karkheh Basin is one of the main watersheds of Iran which has a Mediterranean climate whose level increases during the spring due to simultaneous rains and snowmelt. As one of the most important hydrological processes of the watershed for better understanding the hydrological issues of flood control structures for long-term planning, applying best management practices and making better use of their potentials, Runoff simulation plays an important role in water resources studies. Thus, to calibrate the model, select sensitive parameters were used in the sensitivity analysis step. Having imported the sensitive parameters into SWAT-CUP software, they were repeated 500 times with the SUFI2 algorithm, and finally, the optimal value for each parameter was determined. Result: At Hamidiyeh station, the Nash Sutcliffe coefficient was-0. 19 and-0. 04 in both calibration and validation periods, respectively, and was 0. 76 and 0. 77 in Chamangir Station, respectively. The coefficients of determination for the Hamidiyeh station in the calibration and validation periods were 0. 02 and 0. 22, respectively, and for the Chamangir station, they were 0. 88 and 0. 75, respectively. . This study investigated simulated runoff, using the SWAT model based on the meteorological data regarding the Karkheh watershed. A comparison of simulated runoff results with observational runoff at the hydrometric stations was performed automatically by the SWAT_CUP software package SUFI2 algorithm. Correlation between observed and simulated data was calculated based on the Nash Sutcliffe coefficient and the determination coefficient at different stations of the basin. Nash coefficient-Sutcliffe and coefficient of determination at all hydrometric stations except for the five stations which differed in their calibration and validation periods, were found to be close to their optimum values. Discussion and Conclusion: The coefficient-Sutcliffe of the other 6 stations was more than 0. 5, indicating that the model was capable of simulating runoff. In the mirage stations of Sarab Seyed Ali, Pulchehr, and Noorabad, the SWAT model failed to simulate runoff well, which could be due to the location of these stations in the elevated areas of the basin and its branches that were snowy. The lack of proper distribution of meteorological stations in these areas makes the model unable to simulate well the snow runoff. In Hamidiyeh and Pai-Paul stations, the SWAT model was could not establish a reliable relationship between the observed and simulated runoff due to the impact of the construction of the Shahid Abbaspour Dam on the river flow hydraulics.