The process of precipitation – runoff of each basin, is influenced by hydrologic, geomorphology conditions, geological formation and vegetation. There are different methods in drainage basins. One way to estimate the runoff height is Curve Number (CN) method. That reperesents the hydrological behavior of basin. data were collected for statistics of climate and then topographic map of 1: 25000 and geological map of 1: 100000 by GSI was used. Smada software for calculate the short – term rainfall at different return periods. As well as Arc GIS software for mapping Curve Number by combining maps of land use, soil hydrological groups and moisture of soil with using the table of America Soil Conservation Service (SCS). And then modeling related in the GIS mappings of runoff height of Hesarak catchment was prepared. The aim of this study, was to determine Curve Number and estimate runoff coefficient and maximum discharge runoff with SCS method in various units catchment is. The results showed, hydrologic condition and soil hydrological groups are the most important parameters to determine the CN and affect on runoff. The most potential for runoff is in downstream of basin that specified with urban land use. Also average weight of Curve Numbers those obtained for each sub catchments, Chapdareh sub catchment (S2) with 88 Cure Number and mean of runoff 28. 15 mm had the highest volume of runoff, Dochenaran sub catchment (S3) with 89. 3 Curve Number and mean of runoff 24. 54 mm and sub catchment of the twin branches (S1) with 90. 1 Curve Number and mean of runoff 17. 36 mm had The lowest amount of runoff probability and spill potential. But in general potential runoff in this basin is relatively high. The maximum amount of runoff Curve Number in condition of high humidity is 99 and 78 is the lowest.

Estimating the amount of surface runoff and studying the relationship between rainfall and runoff is the most important issue in surface water hydrology studies. Using simple methods to estimate runoff in hydrological applications, especially in ungauged watersheds, is of particular importance. Probably the simplest conceptual method for predicting runoff depth is the use of the curve number rainfall-runoff model, which was originally developed to model the depth of runoff caused by rainstorms in small agricultural and rangeland catchments, but so far it has been applied in applications other than those originally intended. Various studies show that this method is still growing and improving and it needs to improve. In the present study, firstly, a review of the studies that investigated the factors involved in the curve number method to estimate the runoff value was done, and one of the main results of these studies is to suggest changing the value of the standard initial loss ratio (that is, changing the value of λ from 0.2 to 0.05) based on extensive field measurements and also considering rainfall-runoff response classes when using the curve number method. Secondly, some remaining challenges and future perspectives regarding this method are mentioned.

Assessment of relations between rainfall and runoff is an important and complex issue in hydrology In. this respect, much research has been carried out and different empirical methods have been presented to estimate volume and peak discharge of runoff in watershed scale. Since, there is little or in some cases no hydro-climatic data available in Iran, using empirical procedures seems to be the best solution. Among these methods, the Soil Conservation Service (SCS) runoff Curve Number (CN) technique because of flexibility and simplicity is a practical one, which has acquired widespread application all over the world.In the current research, CN was computed in Lighvan watershed located in northwest of Iran using the related watershed factors such as land use, soil hydrologic group as well as vegetation cover and then compared against observed runoff data. The National Engineering Handbook-Section 4 (NEH-4) CN method was compared to show alternate methods for determining CN to investigate their applicability in estimating runoff depth. In S-probability technique frequency values at the 10% and 90% probabilities for maximum soil water retention (S) were then used to determine CN for antecedent moisture conditions (AMC) I and III respectively. Also the mean probability of S determined the CN for AMC-II. In asymptotic method also rainfall-runoff depths were sorted and for each rank-ordered pairs, S was calculated and the CN values computed. The relationship between the calculated CN and rainfall depth was used to determine CN for AMC-II. The comparison showed high accuracy of asymptotic technique in estimating runoff volume as well as peak discharge of the watershed. The NEH-4 CN method is of the lowest accuracy in estimating runoff volume. This should serve as a caution to managers and research workers utilizing CN for hydrologic modeling and application.

The runoff curve number method is widely used to predict runoff and exists in many popular software packs for modeling. The curve number is an empirical parameter important but depends largely on the characteristics of soil hydrologic groups. Therefore, efforts to reduce this effect and extract more accurate soil information are necessary. The present study was conducted to integrate fuzzy logic for extraction runoff curve numbers. A new distribution model called CNS2 has been developed. In the first part of this research, the formulation and programming of the CNS2 model were done using the Python programming language environment, then the model was implemented in the Beheshtabad watershed. This model simulates the amount of runoff production in a watershed in the monthly time step with the fuzzy curve number and takes into account the factor of rainy days, the coefficient of management of the RUSLE-3D equation, and the soils theta coefficient. The results indicated that the model with Nash-Sutcliff 0.6 and the R2 coefficient 0.63 in the calibration set and Nash index 0.53 and R2 coefficient 0.56 in the validation set had appropriate efficiency in runoff simulation. The advantage of the model is that distributive and allows for the identification of areas with higher runoff production.

The one of the natural hazards is Flood that affects almost all of the country and brings financial and human damages; therefore recognition of the areas with flooding potential is one of the most important measures to decreasing these damages. In this research Darrehshahr drainage basin has been analyzed in view of flooding potential and identifying efficient factors in flooding potential. The run-off has been estimated by using curve number method (CN) of American Soil Conservation Service (SCS).For this purpose, in the beginning the needed data and information including land use and hydrologic groups of basin soil maps were gathered and inputted to GIS system. The CN and the infiltration maps were prepared by using of SCS method and compilation the data and information, then were calculated the run-off volume (Q) for basin using of SCS formula and maximum 24 hours precipitation of basin. Finally, the basin study were divided into 4 classes in view of flooding potential according to first quarter, median and third quarter of values of the run-off volume that 9.1 km2 of it included the very high flood potential, 7.8 km2 of it high potential,13 km2 of it average potential and 6.6 km2 of it had also low potential.

The curve number (CN) method is one of the most common methods for estimating runoff in watersheds. The CN value depends on the soil infiltration, vegetation cover, and antecedent soil moisture content. According to the antecedent soil moisture content, three types of CN can be used: CN for dry soil moisture condition, CN for medium soil moisture condition, and CN for wet soil moisture condition. However, the determination of CN from the vegetation cover and soil infiltration needs high accuracy. In this study, 63 measured rainfall-runoff events in the Baghan watershed and 34 measured rainfall-runoff events in the Booshigan watershed were used for estimating runoff with seven conditions: 1) Calculating the CN value based on soil information and vegetation cover in different parts of the watershed. 2) Calibrating the surface storage coefficient of the runoff estimation equation by considering the medium condition for all events. 3) Calibrating the surface storage coefficient of the runoff estimation equation by considering the real soil moisture condition (dry or medium or wet) of each event. 4) Considering the average of observed CN in all measured rainfall-runoff events. 5 to 7) Calculating the relationship between the observed CN and measured rainfall as linear, power, and standard equation. For mentioned conditions, 48 measured rainfall-runoff events in the Baghan watershed and 26 measured rainfall-runoff events in the Booshigan watershed were used for calibrating the results, and the remained measured data in each watershed were used to evaluate the results. The results in two watersheds demonstrated that the linear, power, and standard conditions were better for runoff estimation. In the three mentioned conditions, the CN value depends on the measured rainfall data instead of using the soil infiltration information and vegetation cover of the watershed. Also, the results showed that it is not suitable to use the common method for determining the CN value (based on soil infiltration and vegetation cover of the watershed), and then estimating the runoff amount.

It is a matter of utmost importance to estimate accurately watersheds’ surface runoff in order to manage a region› s water resources. Due to the inadequacy and lack of surface runoff monitoring in some watersheds, as well as high error and relatively high data demand of the daily runoff estimation methods, it is necessary to rely on the monthly runoff estimation methods. Different scenarios of combining monthly surface runoff estimation methods (the monthly SCS method, SCS-based runoff coefficient, the integrated curve number (CN), and the runoff coefficient method) with three methods of calculating monthly curve number (based on the leaf area index, on the retention potential, and on the average CN-based reference tables) as input parameters for the surface runoff estimation methods. These estimations were applied and compared across the Araz-Kouseh Watershed, east of the Province of Golestan. Results indicated that the monthly SCS method in combination with all methods of monthly CN has appropriately simulated the surface runoff with the Nash-Sutcliffe (NS) coefficient and bias values of higher than 0. 6 and lower than 0. 3, respectively. While the SCS-based runoff coefficient method performed poorly (with negative NS values and bias values above 4), particularly in combination with the average CN estimation based on the reference table. The integrated method of curve number and runoff coefficient in combination with all methods of monthly CN estimation indicated relatively acceptable results (with the NS values of about 0. 6) during the calibration period; however, for the validation period, the results in combination with some monthly CN calculation methods were less reliable. Therefore, the monthly SCS method was selected as a suitable and robust method for the monthly surface runoff simulation of the Araz-Kouseh Watershed; thus it may be recommended for estimation of the water balance in similar watersheds in the region.

Introduction: There are many models for flood prediction that are based on different conceptual bases. The standard SCS-CN method was developed in 1954 and it is documented in Section 4 of the National Engineering Handbook (NEH-4) published by Soil Conservation Service (now called the Natural Resources Conservation Service), U. S. Department of Agriculture in 1956. The document has been revised several times. It is one of the most popular methods for computing the volume of surface runoff for a given rainfall event from small agricultural, forest, and urban watersheds. The method is simple, easy to understand, and useful for ungauged watersheds. The method accounts for major runoff producing watershed characteristics, viz., soil type, land use/treatment, surface condition, and antecedent moisture condition. Recent researches focus on the improvement of this model and improve its efficiency but it is necessary to evaluate the improved models for Iran's watersheds. The purpose of this study was the comparison of standard SCS-CN and developed three parameter Mishra-Singh models for flood hydrograph and peak estimation using data of five watersheds in Golestan Province. Methodology: Study Area and Used Data Five watersheds (including Galikesh, Tamer, Kechik, Vatana, and Nodeh) located in Golestan Province were considered to evaluate different models for flood hydrograph estimation. The characteristics of the selected watersheds are different. For Tamer, Galikesh, Kechik, Nodeh, and Vatana watersheds, the areas are equal to (1527, 401, 36, 790 and 11 km2), the parameters are (289, 139, 26, 208 and 20 km), the mean altitudes are (1131, 1358, 928, 1540 and 899 m), the mean slope of the watersheds are (19, 27, 19, 28 and 33%), the length of the main channels are (94, 58, 10, 66 and 8 km), and the number of rainfall-runoff events are (10, 13, 3, 9, and 4 cases). Descriptions of Models: The standard curve number (SCS-CN) model was based on the following basic equations: (1) (2) P is total rainfall, Q is excess rainfall, CN is curve number, Ia is initial abstraction, and S is maximum retention. Using the concept of the degree of saturation (C=Sr), where C is the runoff coefficient (= )), Mishra and Singh (2002) and Mishra et al. (2006) modified the original SCS-CN model after the introduction of antecedent moisture Mas: (3) The relationships developed by Mishra et al. (2006) for Mare: (4) (5) P5 is prior 5-day rainfall depth. Three model accuracy criteria including root mean square error (RMSE), Nash-Sutcliff efficiency (NSE) and percentage error in peak (PEP) were applied to compare the results of models (Adib et al., 2010-2011). Results: There were 39 rainfall-runoff events, of which 25 and 14 events were respectively selected for the calibration and validation steps. The parameters of investigated models for different events and watersheds and related model accuracy criteria were calculated. The root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) criteria can be used for the analysis of the flood hydrograph simulation while percentage error in peak (PEP) criteria is suitable for the analysis of the flood peak discharge simulation. In the Gallikesh watershed, for the developed three parameter Mishra-Singh and standard SCS-CN models, the RMSE criteria values were (16, 11. 05, 2. 8, and 10. 63) and (17. 94, 14, 6. 56 and 13. 56), the values of NSE values were (-0. 88,-84. 44,-0. 9 and-4. 77) and (-1. 37-,-1. 38,-9. 7, and-8. 4), and the PEP values were (0. 4,-1. 4, 0. 55,-0. 3) and (0. 24,-2. 11,-1. 39 and-0. 62). For the Nodeh watershed in different events, the RMSE criteria values were (13. 22, 23. 57, 79. 53 and 68. 15) and (11. 83, 22. 74, 88. 96 and 69. 92), the NSE values were (-6. 88,-2. 7,-0. 17 and-66) and (-5. 31,-2. 46,-0. 46 and-69. 5), and the values of PEP were (-1. 19,-1. 98, 0. 83,-2. 48) and (-1,-2. 4, 0. 99 and-2. 57) for the developed three parameter Mishra-Singh and standard SCS-CN models were calculated. In the Tamer watershed for two models of developed three parameter Mishra-Singh and standard SCS-CN, the values of different criteria estimated as the RMSE criteria values were (13. 04, 26. 85, 5. 9 and 19. 26) and (12. 04, 92. 62, 5. 26 and 48. 81), the values of NSE criteria were (-0. 92,-20. 3,-4. 9 and-0. 14) and (-0. 73,-252. 5,-3. 75 and-6. 37), and the PEP criteria values were (0. 52,-0. 2,-0. 8, and 0. 62) and (0. 62,-5. 14,-0. 74 and 1. 09). In Vatana and Kechik watersheds for the developed three parameter Mishra-Singh model different criteria were calculated as the RMSE values (2. 5) and (1. 5), the NSE criteria values (0. 51) and (-0. 07), the PEP criteria values (0. 45) and (-0. 3). However, in these two watersheds for the SCS-CN standard model, the RMSE criteria values were (4. 8) and (2. 91), the NSE criteria values were (-0. 82) and (-2. 93) and the PEP criteria values were (0. 95) and (0. 6). Discussion and Conclusion: The values of root mean square error (RMSE), Nash-Sutcliff efficiency (NSE) showed that the developed three parameter Mishra-Singh model improved the accuracy of the flood hydrograph estimation relative to the standard SCS-CN model for 71% of the studied events and the difference between two models for remaining 29% event was negligible. Also, the values of percentage error in peak (PEP) revealed that the three parameter Mishra-Singh model led to a decline equal to 78% in flood peak estimation in comparison with standard SCS-CN model application. In addition, the standard SCS-CN and the three parameter Mishra-Singh models were respectively 64% of and 57% of the studied cases. In this study, the accuracy of the standard SCS-CN andthedeveloped three parameter Mishra-Singh models compared the flood hydrograph and peak estimation considering data of five watersheds in Golestan Province. The investigation of the model accuracy criteria revealed that the developed model led to a considerable improvement of flood estimation in studied watersheds.