JWSS - Journal of Water and Soil Science

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Estimation of flood flow rate represents a method of preventing damages associated with this natural phenomenon. This estimation is one basis in the design of various hydraulic structures, dam spillways, watershed management and flood control. The maximum flow rate of floods is determined by methods such as Creager, Jarvis-Meyer, Cypress-Creek, and rational method. Rational-probability method is an alternative to estimate peak flood rates, and is expressed as:

 Q(y) = F. C(y). I(tc.y).A

 where Q is maximum flood flow rate (m³/sec) y is the return period (year) C(y) is runoff coefficient with a return period of y A is watershed area (km²) I is rainfall intensity (mm/hr) for a specified return period equal to time of concentration of the watershed and F is the conversion factor equal to 0.278 when the above units are used. The basic concept of this method is the same as that in the rational method except that the return period is also included in the equation. Usually, runoff coefficient, C(y), is determined empirically from tables cited in the literature (e.g., Chow et al., 1988). In the present research, data from 18 hydrometry and 6 rainfall-recording stations (located in Caspian - Sea watershed) were analysed using TR software. The Caspian - Sea watershed (which covers eastern and centeral parts of Iran's No. 1 main watershed) has the sub-basins of Atrak, Tadjan, Chalus, Sardabrood, Siahrood, Gorganrood, Safarood, Kesilian, Babolrood and Neka. Runoff coefficients with return periods of 2, 5, 10, 25, 50 and 100 years were determined for these sub-basins and iso-coefficient curves were plotted. The results showed that computed runoff coefficients were less than the values given in the literature because they are determined from observed flow rate and rainfall intensity in each catchment. It was also shown that runoff coefficient increased with increasing return periods. Application of the computed runoff coefficients in three sub-basins of the area resulted in more accurate estimations of maximum flood rate than when the values for these coefficients cited in the literature were applied.

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Prediction of watershed responses and simulation of runoff rate and volume are required for design purposes in most water resources projects. For this purpose, different hydrologic methods and events based on continuous hydrologic mathematical models are applied. In this research, a continuous hydrologic model, Stanford Watershed Model-IV (SWM-IV) is used for simulation of annual and monthly volumes and mean daily runoff flow produced in Roodzard representative basin with an area of 896 km2 located in southwest of Iran. The accuracy of the simulation outputs were checked using the sensitivity analysis over reasonable ranges of input sata related to Roodzard watershed. Calibration and verification of the Stanford model were performed using the data of 1976-1977 and those of the four consecutive years (1978- 1981). The output of the SWM-IV model showed that the values of annual and monthly runoff, groundwater, and monthly interflow can be simulated in close agreement and acceptable precision corresponding to the observed data. The model is also capable of combining the hydrologic components of the basin to determine the dominant flow of the study watershed. Actual evapotranspiration and annual runoff coefficients, are two other parameters that have been estimated successfully by the model. However, the coefficient of determination (R²) for the observed and predicted daily flow values ranged from 0.44 to 0.81 for the available data. Therefore, application of the model is recommended for predicting the hydrologic responses of various sizes of watershed in Iran.

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Cultivating rangeland to be shifted to crop land farms commonly causes soil degradation and runoff generation. This study was conducted to evaluate the cultivation effects on runoff generation and soil quality. The experiment was performed in a rangeland and a 40-year cultivated land located at two slope positions (back slope and shoulder) of a hillside in Dorahan, Chaharmahal & Bakhtiari Province. A 60±5 mm.hr^-1 rainfall intensity was simulated by a rainulator. Organic matter, mean weighted diameter, saturated hydraulic conductivity, collected runoff and sediments were measured. The differences between the means were tested using T-test. Results showed 35, 53 and 8% increases in the organic matter, mean weighted diameter, and saturated hydraulic conductivity in back slope, respectively. The increases in these parameters in shoulder position were 39, 60 and 33%. The values for runoff and sediments in back slope were 3 and 8 times greater than in other similar positions while the values in the shoulder position were 11 and 55 times greater than the same values in other positions.

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Soil erodibility in arid regions, particularly in less developed soils, greatly depends on parent material. The objectives of this study included comparison of the potential of runoff and sediment production in soils with different parent materials and identification of the highly sensitive parent materials in Golabad watershed, 60 km northeast of Isfahan, with about 160 mm of annual precipitation and various geological formations, as one of the highly erodible watersheds in Iran. Soils formed on twelve different parent materials were selected. Rainfall simulator was run for 80 minutes on three replicates of each soil. To have an idea about the rate of runoff and sediment generation with time, runoff loaded with sediment was collected every 10 minutes using plastic containers. After measuring the volume of each runoff sample, it was dried and the amount of sediment was measured. The mechanical parameters of the applied rain were: intensity about 40 mm/hr, rain drop average diameter: 6.56 mm plot size: 1 m² and kinetic energy of 13.7-17.2 J/m².mm. Based on the rainfall simulation experiments, soils formed on green andesite and slightly dissected alluvium derived from both sedimentary and igneous rocks created the highest amount of runoff. They also created runoff much more rapidly as compared to other soils. In contrast, soils developed on granodiorite and moderately undulating alluvium produced the least volume of runoff. Furthermore, maximum quantity of sediment was produced from the soils occurring on green andesite and shale. The least sediment yield was observed in soils developed on granodirite and moderately undulating alluvium. Soils formed on shale created the highest sediment concentration and no significant differences were observed among other soils. Based on the results obtained, soils were ranked according to sensitivity to erosion. It is concluded that soil parent materials have a high influence on the production of runoff and sediment yield in Golabad watershed.

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The model in this research was created based on the Artificial Neural Network (ANN) and calibrated in the Sefid-rood dam basin (excluding Khazar zone). This research was done by gathering and selecting peak flows of hydrographs from 12 sub basins, the concentration time of which was equal to or less than 24 hours and was caused only by rainfall. From all the selected sub basins, totally 661 hydrographs were prepared and their peak flows data wes used to make prediction model. The input variables of the model consisted of the depth of daily flooding rainfalls, and so the five days before rainfall of every peak flow, the area of sub basins, the main stream length, the slope of 10-85 percent of main stream, the median height of sub basins, the area of geological formations and rock units, classified at three hydrological groups of I, II, III, the base flow, and output variable was only peak flow. By using Feed Forward Artificial Neural Network with training method of back propagation error the function approximation of inputs to output was created by passing the three processes of training (learning), testing and validation. So based on that data and variables, the Multivariable Linear Regression model was created. The comparison of observed peak flows, based on validation data package, showed that the statistical parameters of (R²) coefficient and Fisher’s test parameter coefficient (F) for ANN model and MLR respectively were 0.84, 33.66 and 0.33, 3.60, indicating the superiority of ANN to traditional methods.

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  Soil erosion is one of the most threatening issues for crop production and environmental qualities, especially for soil and water resources. Appropriate knowledge about total soil loss and runoff is valuable in order to perform soil and water conservation practices in watersheds. EUROSEM, "a single event, dynamic and distributed model," was developed to simulate soil loss, sediment transportation and deposition by rill and interrill processes. This study was conducted to evaluate EUROSEM model in order to simulate soil loss and runoff in Sulijan sub-basin, which covered 20 ha, from Charmah-Bakhtari province. The sub-basin was divided in to 19 homogeneous elements using topographic, land use, plant cover, slope and channel properties throughout it. Soil, plant cover, land surface and climate characteristics were measured and evaluated by field observations and laboratory measurements. Actual soil loss and runoff for studied events were determined by direct measurement in the field. After sensitivity analysis, calibration and validation steps were carried out to simulate runoff and soil loss. The results of sensitivity analysis showed that the EUROSEM model for predicting runoff was more sensitive to hydraulic conductivity, capillary drive and initial soil moisture. On the other hand the model for predicting soil loss was more sensitive to Manning's coefficient and soil cohesion. The results showed that the EUROSEM model was able to simulate well the total runoff, peak of runoff discharge, total soil loss and time for the peak of soil loss discharge. But that could not simulate well the peak of soil loss discharge and time for the peak of runoff. Although it seems that EUROSEM is able to predict soil loss and runoff partially well in individual events, it is necessary to evaluate the efficiency of the models for different basins with varieties of soil, plant cover and climatic properties.

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In this research, using geographic information system (GIS) and different geostatistical methods including the kriging and co-kriging (ordinary, simple and universal) as well as the radial basis functions, the spatial distributions of runoff coefficient were evaluated in Hamedan province. To this end, the annual runoff were calculated in 18 existing hydrometery stations and another 11 auxiliary points, using digital elevation model (DEM) and 11 years available data of the stations. The performance criteria for evaluating the methods were mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), and general standard deviation (GSD) along with the cross validation examination. A high regression between the runoff coefficient and watershed average slope was selected as auxiliary variable. The results showed that the runoff coefficient of the region changes between 3.5 and 85%. The findings also indicated that the universal co-krigings with spherical semi-variogram model had better performance with the values of MBE (-0.0014), MAE (0.036), RMSE (0.054) and GSD (20.152). The universal and simple kriging with spherical model were equal in runoff estimation of the region and were ranked as the second methods to this propose.

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Soil erosion is an important economical, social and environmental problem requiring intensive watershed management for its control. In recent years, modeling has become a useful approach for assessing the impact of various erosion-reduction approaches. ِDue to limited hydrologic data in mountainous watersheds, watershed modeling is, however, subject to large uncertainties. In this study, SWAT2000 was applied to simulate runoff and sediment discharge in Beheshtabad watershed, a sub-basin of Northern Karun catchment in central Iran, with an area of 3860 km2. Model calibration and uncertainty analysis were performed with SUFI-2. Four indices were used to assess the goodness of calibration, viz., P-factor, d-factor, R2 and Nash-Sutcliffe (NS). Runoff data (1996-2004) of six hydrometery stations were used for calibration and validation of this watershed. The results of monthly calibration p-factor, d-factor, R2 and NS values for runoff at the watershed outlet were 0.61, 0.48, 0.85 and 0.75, respectively, and for the validation, these statistics were 0.53, 0.38, 0.85 and 0.57, respectively. The values for calibration of sediment concentration at the watershed outlet were 0.55, 0.41, 0.55 and 0.52, respectively, and for the validation, these statistics were 0.69, 0.29, 0.60 and 0.27, respectively. In general, SWAT simulated runoff much better than sediment. Weak simulation of runoff at some months of the year might be due to under-prediction of snowmelt in this mountainous watershed, model’s assumptions in frozen and saturated soil layers, and lack of sufficient data. Improper simulation of sediment load could be attributed to weak simulation of runoff, insufficient data and periodicity of sediment data.

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Erosion plots are basically used for studying erosion processes and many related problems. However, the possibility to extend the results of experimental plots to surrounding watersheds is rarely taken into account. In the present study, an attempt was made to study on the accuracy of soil erosion plots in estimation of runoff and sediment yield from small watersheds. Towards this attempt, 12 experimental plots with length of 2, 5, 10, 15, 20 and 25 meter were installed on two north and south facing slopes in Sanganeh watershed, northeastern Razavi Khorasan Province with an area of ca. 1 ha. The performance of the plots in estimation of runoff and sediment was controlled by data collected at the main outlet associated with 12 storm events occurred during November 2006 to June 2007. The results showed that the accuracy of plot estimates on sediment and runoff improved while the plot length increased. The optimal length for estimation of sediment and runoff parameters was found to be equal to average slope length and more than 20m.

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Accurate estimation of runoff for a watershed is a very important issue in water resources management. In this study, the monthly runoff was estimated using the rainfall information and conditional probability distribution model based on the principle of maximum entropy. The information of monthly rainfall and runoff data of Kasilian River basin from 1960 to 2006 were used for the development of model. The model parameters were estimated using the prior information of the watershed such as mean of rainfall, runoff and their covariance. Using the developed model, monthly runoff was estimated for different values of runoff coefficient, , return period, , at different probability levels of rainfall for the basin under study. Results showed that the developed model estimates runoff for all return periods satisfactorily if the runoff coefficient value is taken 0.6. Also, it is observed that at a particular probability level and runoff coefficient, the estimated runoff decreases as return period increases. However, the rate of change of runoff decreases slightly as return period increases.

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The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff from rainfall events. The ratio of initial abstraction (λ=Ia/S) to maximum potential retention (S) was assumed in its original development to be equal to 0.2 (λ=Ia/S=0.2) in SCS-CN method. Application of the initial abstraction ratio equal to 0.2 out of the area where it has been developed may lead to a non logical estimation of runoff. Thus, the study was conducted to determine the initial abstraction ratio (λ=Ia/S) by analyzing measured rainfall-runoff events. The dataset consisted of 58 rainfall-runoff events during 15 years (1987-2001) of rainfall and runoff measurements from Taham-Chay watershed, northwest of Zanjan, Iran. Based on the results, the estimated runoff value on the basis of Ia= 0.2S was 26.7 times higher than the measured value, on average. There was a very low relationship between the measured and estimated runoff values (R2=0.09) and mean model error was 0.13. The Ia/S values varied from 0.004 to 0.008 with an average of 0.006. When Ia/S value was modified to 0.08, ratio of the measured to estimate runoff value was 1.4 and the determination coefficient (R2) of the relationship between the two was 0.41. When seven rainfall events that had the low rainfall intensity values (lower than 0.14 mm/h) and two events that had the high rainfall depth (bigger than 10.47 mm) during the past five days were removed from the data analysis process, ratio of the measured to estimated runoff value decreased to 1.3 and the determination coefficient (R2) of the relationship between the two enhanced to 0.90. The mean model error for the modified Ia/S value also decreased to 0.007. It also improved model efficiency coefficient (EF) to -0.089 compared with 0.91 for traditional Ia/S value (0.2).

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Models are helpful tools to predict runoff, sediment and soil erosion in watershed conservation practices. The objectives of this research were to investigate sensitivity analysis, calibration and validation of EUROSEM model in estimation of runoff in Tangh-e-Ravagh sub-basin of Karoun watershed. The model was tested in a one hectare experimental test site. The area was divided into nine elements according to EUROSEM user's manual. A triangular weir was installed at the outlet of the area to collect runoff in specified time periods for six rainfall events. Sensitivity analysis of the model was performed by a ±10% change in the dynamic parameters of the model and examining the outputs for a rainstorm. Sensitivity analysis showed that total runoff was sensitive to saturated hydraulic conductivity and insensitive to soil cohesion. Sensitivity analysis indicated that the model sensitivity depends on evaluation conditions and it is site-specific in nature. Calibration and validation of the model was performed on input parameters. Calibration of hydrographs was performed by decreasing saturated hydraulic conductivity and capillary drive and increasing initial soil moisture. Validation results showed that EUROSEM model simulated well the total runoff and peak of runoff discharge, but it could not simulate well the time of runoff, time to peak discharge

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The present study aimed to compare the effect of land cover on runoff and sediment with different coverage levels in Mehrian watershed. The study was carried out in a plot with the dimensions 3 × 2 meters during one year, in three different treatments (including without coverage, grass treatment and integrated treatment having brush and grass coverage) and in three replications. At the end of each plot, runoff and sediment collection tanks were installed. Sampling was performed during a year. The monthly rainfall, and runoff and sediment after harvest were determined. Then, runoff and sediment samples were transferred to the laboratory and calculated through decantation method. Also, the amount of plant cover with the plots of 60 × 25 was determined. Statistical analysis using SPSS was performed. Results indicated that the minimum and maximum runoffs in covers without plots and shrub cover and integrated cover were equal to 38 and 162, , 15 and 74, 15 and 96 liters, respectively. The minimum and maximum sediments were equal to 8.3 and 21, 8.1 and 11, 9.1 and 13 gr.l. Statistical analysis in the Spilt plot design showed significant differences between treatments in runoff and sediment (P <0.01). Also, the results showed that the amount of runoff in a bush cover is 2.1 times more than the cover without treatment, 8.1 times more than the integrated treatment, and in the integrated treatment 1.1 times more than bush cover. In contrast, the rates of sedimentation in the above treatments were 4.2, 6.1 and 5.1, respectively.

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In this study, the influence of spatial heterogeneity of rainfall on flood hydrograph prediction in three mountainous catchments in south west of Iran was studied. Two interpolation techniques including Thiessen polygons method and Inverse Distance Weighting method were applied to compare the rainfall patterns of surrounding rain-gages in hydrograph simulation with rainfall patterns of nearest rain-gage from the catchment outlet. It was found that the best simulated hydrograph is obtained from rainfall pattern of the nearest rain gage. Moreover, the results did not show any relationship between spatial variation of rainfall and outlet hydrograph. Formation of different local rainfall patterns due to non-stationary rainfall field provoked by irregular topography and their effect on interpolation procedure caused important biases in interpolated rainfall hyetographs obtained by Thiessen and IDW methods. It seems that the observed biases in the response of the catchments are the result of inaccurate representation of spatially averaged rainfall rather than its spatial variability. Hence, in mountainous catchments with irregular topography, the lack of sufficient records caused by poor rain gage arrangement can be highlighted as the dominant source of uncertainty in modeling the spatial variations of rainfall.

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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.

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Rainfall-runoff modeling and prediction of river discharge is one important parameter in flood control and management, hydraulic structure design, and drought management. The goal of this study is simulating the daily discharge in Kasilian watershed by using WetSpa model and adaptive neuro-fuzzy inference system (ANFIS). The WetSpa model is a distributed hydrological and physically based model, which is able to predict flood on the watershed scale with various time intervals. The ANFIS is a black box model which has attracted the attention of many researchers. The digital maps of topography, land use, and soil type are 3 base maps used in the model for the prediction of daily discharge while intelligent models use available hydrometric and meteorological stations' data. The results of WetSpa model showed that this model can simulate the river base flow with Nash- Sutcliff criteria of 64 percent in the validation period, but shows less accuracy with flooding discharges. The reason for this result can be the small and short Travel time noted. This model can simulate the water balance in Kasilian watershed as well. The sensitivity analysis showed that groundwater flow recession and rainfall degree-day parameters have the highest and lowest effect on the results, respectively. Also, ANFIS with the inputs of rainfall 1-day lag and evaporation 1-day lag, with Nash-Sutcliff criteria of 80, was superior to WetSpa model with Nash-Sutcliff criteria of 24 percent in the validation period.

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The rainfall-runoff process and flooding are hydrological phenomena that are difficult to study due to the influence of different parameters. So far, different methods and models have been provided to analyze these phenomena. The purpose of this study is evaluation of adaptive neuro-fuzzy inference system (ANFIS) for storm runoff coefficient forecasting. To that end, Barariyeh watershed was chosen in Neishabour and the data of 33 events were collected from 1952 to 2006. Factor analysis (FA) was used for determination of independent variables in storm runoff coefficient forecasting. Four variables were selected as independent variables, including average rainfall, third, first and fourth quartiles of rainfall intensity and also five other variables included &phi index and first to fourth quartiles of rainfall intensity. Other variables combined based on their hydrological role were considered as ANFIS inputs. The results revealed that the ANFIS inputs including first to fourth quartiles of rainfall intensity, &phi  index, and total rainfall of five days before can predict storm runoff coefficient with R²=0.91, RMSE=0.02506, MAE=0.0666 and CE=0.87.

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Knowledge of variation in soil properties from each event to another is very important for the determination of critical periods during which soil is susceptible to erosion processes. This study was carried out to investigate soil loss in sequential rainfall events in Zanjan Province. Toward this, ten soil textures samples were taken and were transported to small plots (60 cm×80cm) with 20-cm depth) on a 8% slope land at three replications. The plots were exposed to ten simulated rainfalls with an intensity of 55 mm h^-1 for 30-min and 5-day intervals. A total of 300 simulated rainfall trials were carried out at the plots.  Results indicated that soil moisture, runoff production and soil loss were significantly affected by rainfall events (P< 0.001). Increasing soil moisture and consequently decreasing soil infiltration capacity were the most crucial element in increasing runoff production and soil loss in the sequential rainfall events, in a way that about 84% of soil loss variation in the rainfall events could be explained based on antecedent soil moisture. After the fifths rainfall event, no significant differences  was found in soil infiltration capacity as well as runoff production because of soil moisture reaching to the water-holding capacity. Nevertheless, an increasing trend was observed in soil loss after fifth event which could associate with presence of more erodible soil particles on the surface and consequently increasing the concentration of surface flows.

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With regard to the confirmation of climate change in most regions of the world and its effects on different parts of the water cycle, knowledge of the status of water resources is necessary for proper management of resources and planning for the future. Hence many studies have been done in different areas with the aim of analyzing the impact of climate change on hydrological processes in the upcoming periods. In present research, the effect of climate change on surface runoff in Bazoft watershed has been studied. Bazoft watershed, located in North-West of Chahar Mahal & Bakhtiari province, has significant contribution in the production of water resources of the region due to its special topographical and geographical status. In this study, climatic model – HadCM3- and A2 and B2 emission scenarios have been used to assess uncertainty in forecasting climate change. For this purpose, a statistical model –SDSM- has been applied to downscale large- scale precipitation and temperature data and hydrological model –WetSpa- has been used to simulate runoff. After calibration of the hydrological model, downscaled precipitation and temperature data in near future (2020-2050) and far future (2070-2100) periods were introduced to WetSpa model and runoff was simulated for mentioned periods. Results of this study represent suitable performance of SDSM model in downscaling climatic data, especially minimum and maximum temperature. Also, performance evaluation of Wetspa model shows proper performance of this model for runoff simulation in Bazoft watershed, so that Nash- Sutcliffe efficiency during calibration and validation was 0.63 and 0.65, respectively. Moreover, assessing the amount of predicted runoff for future periods indicates an increase in annual runoff in the Bazoft watershed under both A2 and B2 scenarios.

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This research is aimed to determine the contribution of sub-basins flow to total watershed flood in Khersan river basin located in Kohkilooyeh and Boyer Ahmad province. To do this, the rainfall-runoff model HEC-HMS was used to simulate peak runoff values for 11 sub-basins. HEC-HMS input was constructed using GIS. The results suggest that the change in different return periods is accompanied by small change in prioritization of flood-potential of the sub-basins; so that for return periods of 2, 50 and 100 years, the most contributions came from sub-basins 1 through 11, respectively. With respect to area and flow rate, contribution of sub-basins to watershed total flow was different. The effect of area was between 0.31 to 1.03 percent; namely, sub-basin 6 showed the highest rank and basin 7 showed the lowest one. With respect to peak flow rate, the effect of individual exclusion of sub-basins, resulted in contribution between 51.2 to 1004.2 m³/s, that is, sub-basin 6 showed the lowest effect and the sub basin 11 showed the highest contribution.