Runoff Probability, Storm Depth, and Curve Numbers

Hawkins, Richard H.; Hjelmfelt, Allen T.; Zevenbergen, Adrian W.

doi:10.1061/(asce)0733-9437(1985)111:4(330)

Cited by 189 publications

(117 citation statements)

References 4 publications

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“…These values, that were valid for a 5% slope steepness, were adjusted to the actual values using a relationship developed by Huang et al (2006) in a similar environment to that considered by Gao et al (2012). The adjusted values were converted to dry and wet conditions, depending on the magnitude of P5, according to Hawkins et al (1985). The relationships by Hawkins et al (2002) were finally applied to convert the CN based on l=0.20 to the CN based on l=0.05.…”

Section: Reviewmentioning

confidence: 99%

Predicting plot soil loss by empirical and process-oriented approaches. A review

Bagarello

Ferro²,

Flanagan

2018

J Agricult Engineer

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Soil erosion directly affects the quality of the soil, its agricultural productivity and its biological diversity. Many mathematical models have been developed to estimate plot soil erosion at different temporal scales. At present, empirical soil loss equations and process-oriented models are considered as constituting a complementary suite of models to be chosen to meet the specific user need. In this paper, the Universal Soil Loss Equation and its revised versions are first reviewed. Selected methodologies developed to estimate the factors of the model with the aim to improve the soil loss estimate are described. Then the Water Erosion Prediction Project which represents a process-oriented technology for soil erosion prediction at different spatial scales, is presented. The available criteria to discriminate between acceptable and unacceptable soil loss estimates are subsequently introduced. Finally, some research needs, concerning tests of both empirical and process-oriented models, estimates of the soil loss of given return periods, reliability of soil loss measurements, measurements of rill and gully erosion, and physical models are delineated.

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Section: Reviewmentioning

confidence: 99%

Predicting plot soil loss by empirical and process-oriented approaches. A review

Bagarello

Ferro²,

Flanagan

2018

J Agricult Engineer

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Section: Surface Runoff Simulationmentioning

confidence: 99%

“…The composite CN value in Equation (7) is calculated under the AMC II condition. The CN values for the AMC I and AMC III conditions are adjusted using the following conversion formulas, respectively [5,54] (9) where CN II is the composite curve number calculated with Equation (3), and CN I and CN III are the adjusted curve numbers for the AMC I and AMC III condition, respectively. Furthermore, CN is affected by the basin slope, especially for a slope value greater than 5%.…”

Section: Surface Runoff Simulationmentioning

confidence: 99%

Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China

Zhao

Zhong

et al. 2016

Water

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Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA) method is modified to extract high-precision impervious surface, vegetation, and soil fractions. In the modified LSMA method, the representative endmembers are first selected by combining a high-resolution image from Google Earth; the unmixing results of the LSMA are then post-processed to reduce errors of misclassification with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The modified LSMA is applied to the Landsat 8 Operational Land Imager (OLI) image from 18 October 2015 of the main urban area of Guangzhou city. The experimental result indicates that the modified LSMA shows improved extraction performance compared with the conventional LSMA, as it can significantly reduce the bias and root-mean-square error (RMSE). The improved impervious surface, vegetation, and soil fractions are used to calculate the composite curve number (CN) for each pixel according to the Soil Conservation Service curve number (SCS-CN) model. The composite CN is then adjusted with regional data of the terrain slope and total 5-day antecedent precipitation. Finally, the surface runoff is simulated with the SCS-CN model by combining the adjusted CN and real precipitation data at 1 p.m., 4 May 2015.

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“…Runoff R was also estimated using AquaCrop following USDA curve number approach (Hawkins et al, 1985). The change in soil water storage ∆S is measured using specialized instruments such as neutron probe and time-domain reflectrometer.…”

Section: Soil Water Balance Methodsmentioning

confidence: 99%