scholarly journals The Effects of Rainfall Intensities and Duration on SCS-CN Model Parameters under Simulated Rainfall

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1595
Author(s):  
Xiaoxian Wang ◽  
Huaxing Bi
Keyword(s):  
Rainfall Intensity ◽  
Model Fitting ◽  
Curve Number ◽  
Model Parameters ◽  
Event Analysis ◽  
Simulated Rainfall ◽  
Rainfall Runoff ◽  
Analysis Method ◽  
Initial Abstraction ◽  
Scs Cn

The Soil Conservation Service curve number (SCS-CN) method has been widely used in rainfall-runoff modelling; however, the direct effects of rainfall intensities and duration, which are major factors in hydrological engineering design, on its parameters (initial abstraction ratio (λ) and potential maximum storage (S), the latter is transformed into curve number (CN)) have seldom been studied. In this study, we chose simulated rainfall experiments on runoff plots (30 mm/h, 180 min; 60 mm/h, 90 min; 90 mm/h, 60 min) to obtain synchronized rainfall-runoff data and calculated the parameters using general model fitting and event analysis methods every ten minutes throughout the duration. The results indicate that the parameters changed with rainfall intensities and duration. S decreased as the rainfall intensities increased. Under the same rainfall intensity, the S increased with the duration overall. The corresponding λ changed with rainfall intensities and duration, and has no obvious relationship under different rainfall intensities. Initial abstraction in the event analysis method is the same as the actual situation and we chose these parameters obtained with the event analysis method as our selected parameters. These selected parameters and the parameters obtained using Fu et al.’s method which was based on the standardized procedure in NEH-630 were evaluated by the Nash–Sutcliffe efficiency (NSE), the percentage deviation coefficient (PBIAS), and the ratio of the root mean square error to the standard deviation of measured data (RSR). The results show that the statistics for the selected parameters satisfied the evaluating standard, and have a better value, while the statistics for the parameters obtained by Fu et al.’s method declined as the rainfall intensity increased, and PBAIS was out of the standard range under 90 mm/h rainfall intensity condition. This indicates that the rainfall intensities and duration have important effects on the parameters of the model, and the parameters vary dynamically with the rainfall intensity and duration. These results could be useful for hydrological design in which engineers consider the return period (i.e., rainfall intensities and duration).

scholarly journals Improvement of SCS-CN Initial Abstraction Coefficient in the Czech Republic: A Study of Five Catchments

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1964 ◽  
Author(s):  
Martin Caletka ◽  
Monika Šulc Michalková ◽  
Petr Karásek ◽  
Petr Fučík
Keyword(s):  
Czech Republic ◽  
Model Fitting ◽  
Principal Component ◽  
Event Analysis ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
Direct Runoff ◽  
Initial Abstraction ◽  
Universal Applicability ◽  
Scs Cn

The SCS-CN method is a globally known procedure used primarily for direct-runoff estimates. It also is integrated in many modelling applications. However, the method was developed in specific geographical conditions, often making its universal applicability problematic. This study aims to determine appropriate values of initial abstraction coefficients λ and curve numbers (CNs), based on measured data in five experimental catchments in the Czech Republic, well representing the physiographic conditions in Central Europe, to improve direct-runoff estimates. Captured rainfall-runoff events were split into calibration and validation datasets. The calibration dataset was analysed by applying three approaches: (1) Modifying λ, both discrete and interpolated, using the tabulated CN values; (2) event analysis based on accumulated rainfall depth at the moment runoff starts to form; and (3) model fitting, an iterative procedure, to search for a pair of λ, S (CN, respectively). To assess individual rainfall characteristics’ possible influence, a principal component analysis and cluster analysis were conducted. The results indicate that the CN method in its traditional arrangement is not very applicable in the five experimental catchments and demands corresponding modifications to determine λ and CN (or S, respectively). Both λ and CN should be viewed as flexible, catchment-dependent (regional) parameters, rather than fixed values. The acquired findings show the need for a systematic yet site-specific revision of the traditional CN method, which may help to improve the accuracy of CN-based rainfall-runoff modelling.

scholarly journals Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China

2012 ◽  
Vol 9 (3) ◽  
pp. 4193-4233 ◽  
Author(s):  
G. Y. Gao ◽  
B. J. Fu ◽  
Y. H. Lü ◽  
Y. Liu ◽  
S. Wang ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Soil Loss ◽  
Curve Number ◽  
Rainfall Runoff ◽  
The Loess Plateau ◽  
Rusle Model ◽  
Initial Abstraction ◽  
Runoff And Soil Loss ◽  
Scs Cn ◽  
Runoff Production

Abstract. Predicting event runoff and soil loss under different land covers is essential to quantitatively evaluate the hydrological responses of vegetation restoration in the Loess Plateau of China. The Soil Conservation Service Curve Number (SCS-CN) and Revised Universal Soil Loss Equation (RUSLE) models are widely used in this region to this end. This study incorporated antecedent moisture condition (AMC) in runoff production and initial abstraction of the SCS-CN model, and considered the direct effect of runoff on event soil loss by adopting a rainfall-runoff erosivity factor in the RUSLE model. The modified SCS-CN and RUSLE models were coupled to link rainfall-runoff-erosion modeling. The effects of AMC, slope gradient and initial abstraction ratio on curve number of SCS-CN, as well as those of vegetation cover on cover-management factor of RUSLE were also considered. Three runoff plot groups covered by sparse young trees, native shrubs and dense tussock, respectively, were established in the Yangjuangou catchment of Loess Plateau. Rainfall, runoff and soil loss were monitored during the rainy season in 2008–2011 to test the applicability of the proposed approach. The original SCS-CN model significantly underestimated the event runoff, especially for the rainfall events that have large 5-day antecedent precipitation, whereas the modified SCS-CN model could predict event runoff well with Nash-Sutcliffe model efficiency (EF) over 0.85. The original RUSLE model overestimated low values of measured soil loss and under-predicted the high values with EF only about 0.30. In contrast to it, the prediction accuracy of the modified RUSLE model improved satisfactorily with EF over 0.70. Our results indicated that the AMC should be explicitly incorporated in runoff production, and direct consideration of runoff should be included in predicting event soil loss. Coupling the modified SCS-CN and RUSLE models appeared to be appropriate for runoff and soil loss simulation at plot scale in the Loess Plateau. The limitations and future study scopes of the proposed models were also indicated.

scholarly journals Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

2019 ◽  
Vol 5 (12) ◽  
pp. 2738-2746
Author(s):  
Abdul Ghani Soomro ◽  
Muhammad Munir Babar ◽  
Anila Hameem Memon ◽  
Arjumand Zehra Zaidi ◽  
Arshad Ashraf ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Availability ◽  
Curve Number ◽  
Resource Planning ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Direct Runoff ◽  
Wide Range ◽  
The Impact ◽  
Scs Cn

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

scholarly journals Research on the SCS-CN initial abstraction ratio using rainfall-runoff event analysis in the Three Gorges Area, China

CATENA ◽  
2009 ◽  
Vol 77 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Zhi-Hua Shi ◽  
Li-Ding Chen ◽  
Nu-Fang Fang ◽  
De-Fu Qin ◽  
Chong-Fa Cai
Keyword(s):  
Event Analysis ◽  
Three Gorges ◽  
Rainfall Runoff ◽  
Three Gorges Area ◽  
Runoff Event ◽  
Initial Abstraction ◽  
The Three Gorges ◽  
Scs Cn

scholarly journals Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Luiz Claudio Galvão do Valle Junior ◽  
Dulce Buchala Bicca Rodrigues ◽  
Paulo Tarso Sanches de Oliveira
Keyword(s):  
Conversion Factor ◽  
Poor Performance ◽  
Curve Number ◽  
Storm Events ◽  
Rainfall Runoff ◽  
Initial Abstraction ◽  
Rainfall Depth ◽  
Standard Value ◽  
Tropical Watershed ◽  
Runoff Events

ABSTRACT The Curve Number (CN) method is extensively used for predict surface runoff from storm events. However, remain some uncertainties in the method, such as in the use of an initial abstraction (λ) standard value of 0.2 and on the choice of the most suitable CN values. Here, we compute λ and CN values using rainfall and runoff data to a rural basin located in Midwestern Brazil. We used 30 observed rainfall-runoff events with rainfall depth greater than 25 mm to derive associated CN values using five statistical methods. We noted λ values ranging from 0.005 to 0.455, with a median of 0.045, suggesting the use of λ = 0.05 instead of 0.2. We found a S0.2 to S0.05 conversion factor of 2.865. We also found negative values of Nash-Sutcliffe Efficiency (to the estimated and observed runoff). Therefore, our findings indicated that the CN method was not suitable to estimate runoff in the studied basin. This poor performance suggests that the runoff mechanisms in the studied area are dominated by subsurface stormflow.

Rainfall runoff in soil erosion with simulated rainfall, overland flow and cover

Soil Research ◽  
1983 ◽  
Vol 21 (2) ◽  
pp. 109 ◽  
Author(s):  
MJ Singer ◽  
PH Walker
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Overland Flow ◽  
Podzolic Soil ◽  
Simulated Rainfall ◽  
Rainfall Runoff ◽  
Runoff Water ◽  
Soil Transport ◽  
Raindrop Impact

The 20-100 mm portion of a yellow podzolic soil (Albaqualf) from the Ginninderra Experiment Station (A.C.T.) was used in a rainfall simulator and flume facility to elucidate the interactions between raindrop impact, overland water flow and straw cover as they affect soil erosion. A replicated factorial design compared soil loss in splash and runoff from 50 and 100 mm h-1 rainfall, the equivalent of 100 mm h-1 overland flow, and 50 and 100 mm h-1 rainfall plus the equivalent of 100 mm h-' overland flow, all at 0, 40 and 80% straw cover on a 9% slope. As rainfall intensity increased, soil loss in splash and runoff increased. Within cover levels, the effect of added overland flow was to decrease splash but to increase total soil loss. This is due to an interaction between raindrops and runoff which produces a powerful detaching and transporting mechanism within the flow known as rain-flow transportation. Airsplash is reduced, in part, because of the changes in splash characteristics which accompany changes in depths of runoff water. Rain-flow transportation accounted for at least 64% of soil transport in the experiment and airsplash accounted for no more than 25% of soil transport The effects of rainfall, overland flow and cover treatments, rather than being additive, were found to correlate with a natural log transform of the soil loss data.

scholarly journals Improved Curve Number Estimation in SWAT by Reflecting the Effect of Rainfall Intensity on Runoff Generation

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 163 ◽  
Author(s):  
Dejian Zhang ◽  
Qiaoyin Lin ◽  
Xingwei Chen ◽  
Tian Chai
Keyword(s):  
Rainfall Intensity ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Daily Rainfall ◽  
Curve Number ◽  
Base Flow ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
The Relationship

Determining the amount of rainfall that will eventually become runoff and its pathway is a crucial process in hydrological modelling. We proposed a method to better estimate curve number by adding an additional component (AC) to better account for the effects of daily rainfall intensity on rainfall-runoff generation. This AC is determined by a regression equation developed from the relationship between the AC series derived from fine-tuned calibration processes and observed rainfall series. When incorporated into the Soil and Water Assessment Tool and tested in the Anxi Watershed, it is found, overall, the modified SWAT (SWAT-ICN) outperformed the original SWAT (SWAT-CN) in terms of stream flow, base flow, and annual extreme flow simulation. These models were further evaluated with the data sets of two adjacent watersheds. Similar results were achieved, indicating the ability of the proposed method to better estimate curve number.

scholarly journals Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

2021 ◽  
Author(s):  
Harry R. Manson
Keyword(s):  
Sensitivity Analysis ◽  
Curve Number ◽  
Monte Carlo Analysis ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Daily Average ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

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