scholarly journals Impact of Monthly Curve Number and Five-Day Antecedent Rainfall-Runoff Data Set on Performance of SCS-CN Method for Ozat Watershed in India – A Case Study

2014 ◽  
Vol 1 (09) ◽  
Keyword(s):  
Curve Number ◽  
Rainfall Runoff ◽  
Antecedent Rainfall ◽  
Data Set ◽  
Scs Cn

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 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 Determination of Watershed Characteristics and Curve Number (CN) for Ponnaniyaru Dam Catchment Area

2018 ◽  
Vol 7 (3.12) ◽  
pp. 558
Author(s):  
Selvakumar R ◽  
Nasir N ◽  
Suribabu C R
Keyword(s):  
Catchment Area ◽  
Curve Number ◽  
Drainage Density ◽  
Significant Parameter ◽  
Present Case Study ◽  
Scs Cn ◽  
Hydrologic Conditions ◽  
Selection Of

In SCS-CN method, curve number is significant parameter in estimating runoff from the catchment of the reservoir or inflow to the reservoir. As this curve number is function of several parameters like hydrological soil group, LULC, land treatment, hydrologic conditions and AMC, the selection of CN for prediction of inflow to the lake or reservoir is considered as a crucial in the hydrological studies. LULC, micro-watershed, drainage density, and catchment slope are obtained using spatial analysis and also SCS Curve Number value for Ponnaniyaru dam catchment area is derived from the LULC data. Further, CN value is evaluated from actual rainfall data and runoff volume collected at the reservoir. The study reveals the significant variation of CN value among each event. The present case study highlights the sensitiveness of CN value in the computation of runoff from the watershed. Keywords: Curve number, LULC, AMC, drainage density. 

An approach to accommodate and estimate antecedent moisture in runoff curve number methodology- An experimental study

2020 ◽  
Author(s):  
Surendra Kumar Mishra ◽  
Ishan Sharma ◽  
Ashish Pandey ◽  
Shailendra Kumar Kumre
Keyword(s):  
Soil Moisture ◽  
Water Conservation ◽  
Curve Number ◽  
Yield Potential ◽  
Water Yield ◽  
Absolute Maximum ◽  
Rainfall Runoff ◽  
Antecedent Moisture ◽  
Scs Cn

<p>Modelling of the event-based rainfall-runoff process has considerable importance in Hydrology, especially for assessment of water yield potential of a watershed, planning of soil and water conservation measures, reducing sedimentation, and flooding hazards downstream. Antecedent moisture (M) plays a significant role in governing the rainfall-runoff modelling process. It has been the focal point of research in the last decade for improving the Soil Conservation Service Curve Number (SCS-CN) method (also known as NRCS-CN method) for surface runoff computation. In this study, an innovative procedure is proposed to accommodate M in the basic structure of the SCS-CN methodology which otherwise was incorporated externally; to compute M using rainfall-runoff data and verify its applicability by comparing M with the in-situ soil moisture.</p><p>Natural rainfall, runoff, and soil moisture data from 6 small experimental farms with different land-use viz. Maize, Finger Millet, and Fallow land, located at Roorkee, India, are utilized. The M is computed by optimizing two parameters, i.e., absolute maximum potential retention (S<sub>abs</sub>) and initial abstraction ratio (λ), and the optimization is accomplished by minimizing the root mean square error (RMSE). Results show that there exists a good correlation between theoretical M and measured in-situ moisture. Also, the optimized value of λ has the less error in computing M than the other standard values of λ (λ = 0.2; λ= 0.03). This study not only improves the SCS-CN method but also widens its application horizon in soil moisture studies.</p>

scholarly journals Estimation of Surface Water Runoff for a Semi-Arid Area Using RS and GIS-Based SCS-CN Method

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1924 ◽  
Author(s):  
Hussein Al-Ghobari ◽  
Ahmed Dewidar ◽  
Abed Alataway
Keyword(s):  
Land Use ◽  
Saudi Arabia ◽  
Surface Runoff ◽  
Curve Number ◽  
Accurate Estimation ◽  
Water Runoff ◽  
Rainfall Runoff ◽  
Study Region ◽  
Rs And Gis ◽  
Scs Cn

The proper planning of storage structures, waterways, irrigation schemes, water harvesting, erosion control structures, and groundwater development strategies requires accurate estimation of surface runoff. However, hydrologists in Saudi Arabia face serious challenges, specifically due to the rare availability of surface runoff data. In this study, the soil conservation service-curve number (SCS-CN) method integrated with geographic information system (GIS) and remote sensing (RS) was utilized to estimate the surface runoff in Wadi-Uranah basin, in the western region of Saudi Arabia. Different thematic maps such as slope, hydrologic soil group (HSG), land use/land cover (LULC), and daily rainfall have been created in GIS environment and processed to generate the curve number (CN) and surface runoff maps. Based on the soil classification results, the study area was categorized into two HSGs (B and C). The dominant HSG was group C, representing about 98.8% of the total area. The LULC analysis showed four main land use types in the study region: urban, rocks, barren soil, and agricultural areas. Furthermore, the finding results showed that CN values for the normal conditions (CNII) ranged between 74 and 93 in agricultural and both urban and rock areas, respectively. The CNII values were further corrected using slope data to derive slope-adjusted CNII. Moreover, the rainfall-runoff results showed an increase in the daily runoff of the study region with a minimum of 15 mm to a maximum of 74 mm. Another interesting result was rainfall-runoff linear regression analysis that showed a good correlation of 0.98. Additionally, the peak runoff hydrograph flows for 10-, 50-, and 100-year return periods obtained from the SCS-based dimensionless unit hydrograph were 828, 1353, and 1603 m3/s, respectively. Therefore, this study highlights that the SCS-CN method integrated with RS and GIS deserves further attention for estimating runoff of ungauged basins for better basins management and conservation purposes.

scholarly journals Rainfall-runoff estimation of Bojiang lake watershed using SCS-CN model coupled with GIS for watershed management

2019 ◽  
Vol 4 (1) ◽  
pp. 16 ◽  
Author(s):  
Nametso Matomela ◽  
Li Tianxin ◽  
Lehlohonolo Morahanye ◽  
Obadia Kyetuza Bishoge ◽  
Harrison Odion Ikhumhen
Keyword(s):  
Remote Sensing ◽  
Watershed Management ◽  
Control Strategies ◽  
Management Strategies ◽  
Curve Number ◽  
Distribution Data ◽  
Rainfall Runoff ◽  
Gis And Remote Sensing ◽  
Water Abstraction ◽  
Scs Cn

A proper understanding of watershed spatio-temporal hydrological characteristics is critical to the management of a watershed and its natural resources such as water and vegetation. Rainfall runoff estimation plays an important role as an integral part of watershed management. Runoff volume and distribution data provides valuable information for water management strategies such as selection of artificial water abstraction sites, water storage facilities, and soil erosion control strategies. In the present study Bojiang lake watershed was used to indicate the application of Soil Conservation Service Curve Number method (SCS-CN) coupled with Geographic Information System (GIS) and Remote Sensing (RS) techniques. The watershed falls within Erdos Larus Relictus National Nature Reserve (ELRNNR) which was listed under the wetlands of international importance in 2002. Rainfall runoff is influenced by a variety of factors within a watershed such as soil and land use/cover types, soil moisture content, rainfall, drainage density, and shape and size of the watershed. The SCS Curve number is the most popular and widely applied method for runoff estimation. GIS and Remote Sensing play an important role in estimating surface runoff by SCS-CN method. ArcGIS 10.2 software was used to overlay different thematic layers and develop an attribute table and calculate a weighted curve number. The weighted curve number was applied to the SCS-CN equations to estimate daily, monthly, and yearly runoff. Correlation coefficient (r) was used to test for the relationship between rainfall and runoff, and verify the computation of the method. The results show an average runoff of 17.78 mm which is about 7.18% of the annual average rainfall for the years 2001-2016. The derived output maps can assist in identifying suitable areas for water recharge/abstraction. The study demonstrates that SCS-CN in conjunction with GIS and RS can be used to calculate runoff for ungagged watersheds and assist in watershed management strategies.

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