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Showing 3 results for Curve Number

R. Mostafazadeh, Sh. Mirzaei, P. Nadiri,
Volume 21, Issue 4 (2-2018)
Abstract

The SCS-CN developed by the USDA Soil Conservation Service is a widely used technique for estimation of direct runoff from rainfall events. The watershed CN represents the hydrological response of watershed as an indicator of watershed potential runoff generation. The aim of this research is determining the CN from recorded rainfall-runoff events in different seasons and analyzing its relationship with rainfall components in the Jafarabad Watershed, Golestan Province. The CN values of 43 simultaneous storm events were determined using SCS-CN model and the available storm events of each season have been separated and the significant differences of CN values were analyzed using ANOVA method. The Triple Diagram Models provided by Surfer software were used to analyze the relationships of CNs and rainfall components. Results showed that the mean values of CN were 60 for summer and winter seasons and the CN values in the spring and autumn seasons were 50 and 65, respectively. The inter-relationships of CN amounts and rainfall characteristic showed that the high values of CNs were related to high rainfall intensities (>10 mm/hr) and rain-storms with total rainfall more than 40 mm. Also the CN values were about >70 for the storm events with 40-80% runoff coefficient values.

A. Talebi, E. Abyari, S. Parvizi,
Volume 23, Issue 4 (12-2019)
Abstract

Flood is a natural disaster making the heavy humanistic and economic damages each year in most parts of Iran. In this research, the SWAT model performance in flood prediction and sub-basin priority was investigated in terms of flooding in Araz-Kose watershed in Golestan province. To calibrate the model, SUFI2 was applied. The calibration and validation were done for the 1991-1998 period based on the data of 2001-2009. After validation, the indices (R2, bR2, and NS) were estimated. They were equal to 0.81, 0.81 and 0.73 for calibration and 081, 0.78 and 0.64 for validation, respectively. The sensitivity analysis results showed 13 effective parameters. The curve number (CN2) was determined as the most effective parameter. For studying the flooding in a watershed, the Araz-Kose watershed was divided into six parts. Based on the obtained results from the SWAT model with different CN and F indexes (with/without considering the sub-watershed), the sixth sub-basin with 22.4% decrease in discharge was chosen as the most effective region in flooding. Meanwhile, the other sub-basins including 4, 1, 3, 5 and 2 had more flood potential, respectively.

T. Tahmasbi, Kh. Abdollahi, M. Pajouhesh,
Volume 26, Issue 2 (9-2022)
Abstract

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.


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