Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

Wang, Xixi; Liu, Tingxi; Yang, Wanhong

doi:10.1080/02626667.2012.701305

Cited by 36 publications

(19 citation statements)

References 32 publications

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“…Equations A11 and A12) documented by Wang et al . (,). Subsequently, the calibrated hydrologic simulation module was validated by comparing its predicted monthly runoff values to the corresponding observed monthly runoff values during the validation period.…”

Section: Methodsmentioning

confidence: 99%

“…The fluvial process module uses a parameter of soil erodibility to reflect threshold water shear stress and the other three parameters, namely runoff‐to‐precipitation ratio (Wang et al ., ,), slope factor (Nearing, ) and relative land coverage (Wang et al ., ,), to represent effective water shear stress. This module estimates fluvial erosion modulus/rate using a semi‐empirical equation with these four parameters.…”

Section: Formulation Of the Iafpmentioning

confidence: 99%

“…Although a number of methods or models are available for precipitation–runoff analysis, this study proposes that

\frac{R}{P}

be computed using the modified rational equation or MoRE, a newly developed model by Wang et al . (,), because the MoRE is robust and does not include initial abstraction as a variable.…”

Section: Formulation Of the Iafpmentioning

confidence: 99%

“…S is computed as (Wang et al ., ,)

S = S_{normalI} - a \cdot {((), S_{normale})}^{b}

a = S_{normalI}

b = \frac{ln (1 - \frac{S_{III}}{S_{I}})}{ln (\frac{θ_{fc} - ψ}{θ_{sat} - ψ})}

where S I and S III , respectively, are the potential maximum retentions for antecedent moisture conditions (AMCs) I and III, which are defined by Hjelmfelt () and USDA‐NRCS (), ψ is the water content at permanent wilting point, θ fc is the water content at field capacity, θ sat is the saturation soil moisture and

S_{normale} = \frac{θ - ψ}{{italicθ}_{sat} - ψ}

is the relative saturation.…”

Section: Formulation Of the Iafpmentioning

confidence: 99%

“…θ is computed as (Wang et al ., ,)

θ = {\begin{cases} ψ {for P}_{5} < 12.5 mm (dormant season) or P_{5} < 35.5 mm (growing season) \\ ψ + \frac{θ_{fc} - ψ}{15.5} \cdot (P_{5} - 12.5) for 12 .5 mm \leq P_{5} < 28.0 mm (dormant season) \\ ψ + \frac{θ_{fc} - ψ}{18.0} \cdot (P_{5} - 35.5) for 35 .5 mm \leq P_{5} < 53.5 mm (growing season) \\ θ_{fc} for P_{5} \geq 28.0 mm (dormant season) or P_{5} \geq 53.5 mm (growing season) \end{cases}

where P 5 is the antecedent 5‐day precipitation (mm).…”

Section: Formulation Of the Iafpmentioning

confidence: 99%

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Simulated soil erosion from a semiarid typical steppe watershed using an integrated aeolian and fluvial prediction model

Wang

Liu

et al. 2012

Hydrol. Process.

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Total soil erosion is a result of both aeolian and fluvial processes, which is particularly true in semiarid regions. However, although physically interrelated, these two processes have conventionally been studied and modelled independently. Recently, a few researchers highlighted the importance and need of considering both processes in concert as well as their interactions, but they did not give specific modelling approaches or algorithms. The objectives of this study were to (1) formulate an integrated aeolian and fluvial prediction (IAFP) model, (2) parameterize the IAFP model for a semiarid steppe watershed located in northeastern China by using literature and historical data and (3) use the model to predict soil erosion in the watershed and assess the sensitivity of predicted erosion to environmental factors such as soil moisture and vegetation coverage. The results indicated that the IAFP model can capture the dynamic interactions between aeolian and fluvial erosion processes. For the study watershed, the model predicted a higher occurrence frequency of fluvial erosion than that of aeolian erosion and showed that these two processes almost equivalently contributed to the average total erosion of 0.07 mm year−1 across the simulation period. The ‘existing’ vegetation cover can provide an overall good protection of the soils, although the vegetation cover was predicted to play a larger role in a drier than a wetter year as well as in controlling aeolian than fluvial erosion. In addition, soil erosion was predicted to be more sensitive to soil moisture than land coverage. A soil moisture level of 0.23–0.25 was determined to be the probable switch point from aeolian‐to fluvial‐dominant process or vice versa. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Section: Formulation Of the Iafpmentioning

confidence: 99%

“…Although a number of methods or models are available for precipitation–runoff analysis, this study proposes that

\frac{R}{P}

be computed using the modified rational equation or MoRE, a newly developed model by Wang et al . (,), because the MoRE is robust and does not include initial abstraction as a variable.…”

Section: Formulation Of the Iafpmentioning

confidence: 99%

“…S is computed as (Wang et al ., ,)

S = S_{normalI} - a \cdot {((), S_{normale})}^{b}

a = S_{normalI}

b = \frac{ln (1 - \frac{S_{III}}{S_{I}})}{ln (\frac{θ_{fc} - ψ}{θ_{sat} - ψ})}

S_{normale} = \frac{θ - ψ}{{italicθ}_{sat} - ψ}

is the relative saturation.…”

Section: Formulation Of the Iafpmentioning

confidence: 99%

“…θ is computed as (Wang et al ., ,)

θ = {\begin{cases} ψ {for P}_{5} < 12.5 mm (dormant season) or P_{5} < 35.5 mm (growing season) \\ ψ + \frac{θ_{fc} - ψ}{15.5} \cdot (P_{5} - 12.5) for 12 .5 mm \leq P_{5} < 28.0 mm (dormant season) \\ ψ + \frac{θ_{fc} - ψ}{18.0} \cdot (P_{5} - 35.5) for 35 .5 mm \leq P_{5} < 53.5 mm (growing season) \\ θ_{fc} for P_{5} \geq 28.0 mm (dormant season) or P_{5} \geq 53.5 mm (growing season) \end{cases}

where P 5 is the antecedent 5‐day precipitation (mm).…”

Section: Formulation Of the Iafpmentioning

confidence: 99%

See 3 more Smart Citations

Simulated soil erosion from a semiarid typical steppe watershed using an integrated aeolian and fluvial prediction model

Wang

Liu

et al. 2012

Hydrol. Process.

Self Cite

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Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach

et al. 2021

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Land-use changes often have significant impact on the water cycle, including changing groundwater/surface-water interactions, modifying groundwater recharge zones, and increasing risk of contamination. Surface runoff in particular is significantly impacted by land cover. As surface runoff can act as a carrier for contaminants found at the surface, it is important to characterize runoff dynamics in anthropogenic environments. In this study, the relationship between surface runoff and groundwater recharge in urban areas is explored using a top-down water balance approach. Two empirical models were used to estimate runoff: (1) an updated, advanced method based on curve number, followed by (2) bivariate hydrograph separation. Modifications were added to each method in an attempt to better capture continuous soil-moisture processes and explicitly account for runoff from impervious surfaces. Differences between the resulting runoff estimates shed light on the complexity of the rainfall–runoff relationship, and highlight the importance of understanding soil-moisture dynamics and their control on hydro(geo)logical responses. These results were then used as input in a water balance to calculate groundwater recharge. Two approaches were used to assess the accuracy of these groundwater balance estimates: (1) comparison to calculations of groundwater recharge using the calibrated conceptual HBV Light model, and (2) comparison to groundwater recharge estimates from physically similar catchments in Switzerland that are found in the literature. In all cases, recharge is estimated at approximately 40–45% of annual precipitation. These conditions were found to closely echo those results from Swiss catchments of similar characteristics.

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The reconstruction of the great 2020 torrential flood in Western Serbia

et al. 2023

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Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

Cited by 36 publications

References 32 publications

Simulated soil erosion from a semiarid typical steppe watershed using an integrated aeolian and fluvial prediction model

Simulated soil erosion from a semiarid typical steppe watershed using an integrated aeolian and fluvial prediction model

Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach

The reconstruction of the great 2020 torrential flood in Western Serbia

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