scholarly journals A Calibrated, Watershed-Specific SCS-CN Method: Application to Wangjiaqiao Watershed in the Three Gorges Area, China

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 60 ◽  
Author(s):  
Lloyd Ling ◽  
Zulkifli Yusop ◽  
Wun-She Yap ◽  
Wei Lun Tan ◽  
Ming Fai Chow ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Curve Number ◽  
Calibration Method ◽  
Sum Of Squares ◽  
Prediction Errors ◽  
Rainfall Runoff ◽  
Fitting Method ◽  
Index Value ◽  
Service Curve ◽  
Scs Cn

The Soil Conservation Service curve number ( S C S-C N) method is one of the most popular methods used to compute runoff amount due to its few input parameters. However, recent studies challenged the inconsistent runoff results obtained by the method which set the initial abstraction ratio λ as 0.20. This paper developed a watershed-specific S C S-C N calibration method using non-parametric inferential statistics with rainfall–runoff data pairs. The proposed method first analyzed the data and generated confidence intervals to determine the optimum values for S C S- C N model calibration. Subsequently, the runoff depth and curve number were calculated. The proposed method outperformed the runoff prediction accuracy of the asymptotic curve number fitting method, linear regression model and the conventional S C S-C N model with the highest Nash–Sutcliffe index value of 0.825, the lowest residual sum of squares value of 133.04 and the lowest prediction error. It reduced the residual sum of squares by 66% and the model prediction errors by 96% when compared to the conventional S C S-C N model. The estimated curve number was 72.28, with the confidence interval ranging from 62.06 to 78.00 at a 0.01 confidence interval level for the Wangjiaqiao watershed in China.

scholarly journals Comparison of antecedent precipitation based rainfall-runoff models

2020 ◽  
Author(s):  
Pankaj Upreti ◽  
C. S. P. Ojha
Keyword(s):  
Curve Number ◽  
Rainfall Runoff ◽  
Antecedent Rainfall ◽  
Antecedent Precipitation ◽  
Error Metrics ◽  
Linear Behaviour ◽  
The Usa ◽  
Land Condition ◽  
Service Curve ◽  
Scs Cn

Abstract The Soil Conservation Service Curve Number (SCS-CN) method is one of the popular methods for calculating storm depth from a rainfall event. The previous research identified antecedent rainfall as a key element that controls the non-linear behaviour of the model. The original version indirectly uses five days antecedent rainfall to identify the land condition as dry, normal or wet. This leads to a sudden jump once the land condition changes. To obviate this, the present work intends to improve the performance of antecedent rainfall-based SCS-CN models. Two forms of SCS-CN model (M1 and M2), two recently developed P-P5 based models (M3 and M4), and an alternate approach of considering P5 in the SCS-CN model (M5 and M6), as proposed here, were investigated. Based on the evaluation of several error metrics, the new proposed model M6 has performed better than other models. The performance of this model is evaluated using rainfall-runoff events of 114 watersheds located in the USA. The median value of Nash Sutcliffe Efficiency was found as 0.78 for the M6 model followed by M5 (0.75), M3 (0.73), M4 (0.72), M2 (0.63) and M1 (0.61) model.

scholarly journals Exploratory Research of New Curve Number System

2018 ◽  
Vol 65 ◽  
pp. 07005
Author(s):  
Wen Jia Tan ◽  
Jen Feng Khor ◽  
Lloyd Ling ◽  
Yuk Feng Huang
Keyword(s):  
Optimization Technique ◽  
Curve Number ◽  
Number System ◽  
Exploratory Research ◽  
Decimal Point ◽  
Rainfall Runoff ◽  
Fitting Method ◽  
Runoff Prediction ◽  
Scs Cn ◽  
Selection Of

In the past, the CN was determined through SCS handbook. In order to determine runoff prediction using SCS-CN model, selection of CN is important. However, the conventional CN methodology with inappropriate CN selection often produces inconsistent runoff estimation. Thus, the new direct curve number derivation technique based on rainfall-runoff datasets with supervised numerical optimization technique under the guide of inferential statistics was developed to improve the accuracy of surface runoff prediction. Furthermore, the two decimal point CN system was proposed in this study. The optimum CN of Melana site is 90.45 at alpha 0.01 with BCa 99 % confidence interval range from 90.45 to 95.12. The regional specific calibrated SCS-CN model with two decimal point CN derivation technique is out-performed the runoff prediction of conventional SCS-CN model and the asymptotic curve number fitting method.

scholarly journals Improved SMA-based SCS-CN method incorporating storm duration for runoff prediction on the Loess Plateau, China

2020 ◽  
Vol 51 (3) ◽  
pp. 443-455
Author(s):  
Wenhai Shi ◽  
Ni Wang
Keyword(s):  
Loess Plateau ◽  
Curve Number ◽  
The Loess Plateau ◽  
Moisture Condition ◽  
Soil Moisture Accounting ◽  
Antecedent Moisture ◽  
Storm Duration ◽  
Service Curve ◽  
Scs Cn ◽  
Runoff Events

Abstract In the Soil Conservation Service Curve Number (SCS-CN) method for estimating runoff, three antecedent moisture condition (AMC) levels produce a discrete relation between the curve number (CN) and soil water content, which results in corresponding sudden jumps in estimated runoff. An improved soil moisture accounting (SMA)-based SCS-CN method that incorporates a continuous function for the AMC was developed to obviate sudden jumps in estimated runoff. However, this method ignores the effect of storm duration on surface runoff, yet this is an important component of rainfall-runoff processes. In this study, the SMA-based method for runoff estimation was modified by incorporating storm duration and a revised SMA procedure. Then, the performance of the proposed method was compared to both the original SCS-CN and SMA-based methods by applying them in three experimental watersheds located on the Loess Plateau, China. The results indicate that the SCS-CN method underestimates large runoff events and overestimates small runoff events, yielding an efficiency of 0.626 in calibration and 0.051 in validation; the SMA-based method has improved runoff estimation in both calibration (efficiency = 0.702) and validation (efficiency = 0.481). However, the proposed method performed significantly better than both, yielding model efficiencies of 0.810 and 0.779 in calibration and validation, respectively.

scholarly journals Pemodelan Tutupan Lahan Untuk Menjamin Keberlanjutan Debit Sungai (Suatu Studi Di Sub DAS Cikapundung-Maribaya)

2021 ◽  
Vol 6 (2) ◽  
pp. 121-139
Author(s):  
Aditya Dwifebri Christian Wibowo ◽  
Mahawan Karuniasa ◽  
Dwita Sutjiningsih
Keyword(s):  
Land Use ◽  
River Water ◽  
Water Scarcity ◽  
Soil Conservation ◽  
Curve Number ◽  
Soil Conservation Service ◽  
Land Conversion ◽  
Forest Land ◽  
Service Curve ◽  
Scs Cn

Changes in land use in the Cikapundung watershed, ie changes in forest land to built-up land, have an impact on the quantity of river water. Changes in land use in the Cikapundung River catchment are not ideal conditions for absorbing water. If land conversion is not controlled, it can have a large impact on reducing the availability of water resources for subordinate areas or what is called water scarcity. Analysis that takes into account land use and discharge can be done with several hydrological analysis methods, one of them is the Soil Conservation Service Curve Number (SCS-CN) method. Based on the calculation, the CN value was changed in 2014 from 57.275 to 62.591 where land cover changes began to occur.   Keywords: land use, river water, water scarcity, hydrology, CN value

scholarly journals ASSESSING THE IMPACT OF URBANIZATION ON DIRECT RUNOFF IN DISTRICT 8, HO CHI MINH CITY

2021 ◽  
Vol 11 (3) ◽  
pp. 145-156
Author(s):  
Van Minh NGUYEN ◽  
Elena Yurievna ZAYKOVA
Keyword(s):  
Remote Sensing ◽  
Surface Runoff ◽  
Soil Conservation ◽  
Curve Number ◽  
Ho Chi Minh City ◽  
Soil Conservation Service ◽  
Impact Of Urbanization ◽  
Service Curve ◽  
The Impact ◽  
Scs Cn

Ho Chi Minh City (HCMC) is among the cities that are most at risk of fl ooding worldwide. Urbanization processes have led to a change in land use, which in turn has resulted in an increase in impervious surfaces and runoff , thus again leading to the risk of fl ooding in the city. The aim of the article is to study the impact of urban development (on the example of District 8 of HCMC) on surface runoff using a combination of the interpretation of remote sensing images of the earth (ERS) Google Earth and the SCS-CN model (the Soil Conservation Service curve number). Theoretical models are used to analyze the relationship between the typology of buildings and areas of open and impervious surfaces. The interpretation of remote sensing images was carried out in the ArcGIS program. The method used to calculate surface runoff is the Soil Conservation Service Curve Number (SCS-CN) method developed by the US Soil Conservation Service and is suitable for assessing the eff ects of land-use/land cover change due to urbanization. The results of the study show the volume of surface runoff in areas with diff erent levels of urbanization in district 8 of HCMC, assessing the impact of urbanization processes on surface runoff and revealing new opportunities for managing this process. The combination of remote sensing interpretation and SCS-CN model makes it possible to assess the impact of urban development on surface runoff . Urbanization and an increase in built-up area strongly aff ect fl ooding, reducing the soil retention.

On the Ia–S relation of the SCS-CN method

2006 ◽  
Vol 37 (3) ◽  
pp. 261-275 ◽  
Author(s):  
M.K. Jain ◽  
S.K. Mishra ◽  
P. Suresh Babu ◽  
K. Venugopal
Keyword(s):  
Curve Number ◽  
United States Department ◽  
Large Set ◽  
Department Of Agriculture ◽  
Validation Data ◽  
Proposed Model ◽  
Storm Rainfall ◽  
Service Curve ◽  
Scs Cn ◽  
Better Than

The initial abstraction (Ia) versus maximum potential retention (S) relation in the Soil Conservation Service Curve Number (SCS-CN) methodology was revisited, and a new non-linear relation incorporating storm rainfall (P) and S was proposed and tested on a large set of storm rainfall-runoff events derived from the water database of United States Department of Agriculture-Agriculture Research Service (USDA-ARS). Employing root mean square error (RMSE), the performance of both the existing and proposed models was evaluated using the complete database, and for model calibration and validation, data were split into two groups: based on ordered rainfall (P-based) and runoff (Q-based). A specific formulation of the proposed model Ia=λS(P/(P+S))α with λ=0.3 and α=1.5 was found to generally perform better than the existing Ia=0.2S, and therefore was recommended for field applications. When evaluated using the observed Ia data, the proposed version performed significantly better than the existing one.

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