Impact of stream network structure on the transition break of peak flows

Lee, Kwan Tun; Chen, Nai-Chin; Гарцман, Б. И.

doi:10.1016/j.jhydrol.2009.01.021

Cited by 17 publications

(12 citation statements)

References 18 publications

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“…where R is the rainfall characteristics. Considering the interrelation between the geomorphologic features (Gray 1961;Mesa and Gupta 1987;Lee et al 2009), equation (1) can be simplified as:…”

Section: Runoff Nonlinearity Analysismentioning

confidence: 99%

“…where α 1 is the coefficient, θ 1 is the scaling exponent reflecting the degree of nonlinearity between Q p andR tc . In general, the relationship between peak discharge (Q p ) and drainage area (A) at a stream network position is expressed as (Huang and Willgoose 1993;Lee et al 2009):…”

Section: Runoff Nonlinearity Analysismentioning

confidence: 99%

“…The Horton's equation (Horton 1939) was applied to calculate infiltration loss and effective rainfall. The runoff concentration time, which was needed to determineR tc , was defined as (Lee et al 2009)…”

Section: Empirical Analysis Based On Hydrological Recordsmentioning

confidence: 99%

“…In figure 8(a), the data points are found to distribute in several groups. By following the method in Goodrich et al (1997) and Lee et al (2009), the two-segment Power-law approach was applied. As shown in figure 8(b), because a large tributary with contributing area A = 30.77 km 2 joins the mainstream at Point b, the total drainage area increases from 31.98 km 2 at Point a to 62.75 km 2 at Point b. Consequently, the peak discharges increase evidently from 11.82 m 3 /s (at Point a) to 25.04 m 3 /s (at Point b) for the test rainstorm and result in a manifest transition break.…”

Section: Watershed Size On Scaling Nonlinearitymentioning

confidence: 99%

“…Based on numerical flow modeling, the variation of Power-law exponent for a self-similar network has also been analyzed (Gupta et al 1996;Menabde et al 2001;Morrison and Smith 2001). Lee et al (2009) reported that the transition break is due to a significant inflow from a large tributary to result in the Power-law relation transferring from linearity to nonlinearity. In the most recent, Gupta et al (2010) observed excellent fit of Power-law scaling relationship for the flood of June 2008 in the eastern Iowa River basin.…”

Section: Introductionmentioning

confidence: 99%

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Influence of storm magnitude and watershed size on runoff nonlinearity

Lee

Huang

2016

J Earth Syst Sci

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The inherent nonlinear characteristics of the watershed runoff process related to storm magnitude and watershed size are discussed in detail in this study. The first type of nonlinearity is referred to rainfallrunoff dynamic process and the second type is with respect to a Power-law relation between peak discharge and upstream drainage area. The dynamic nonlinearity induced by storm magnitude was first demonstrated by inspecting rainfall-runoff records at three watersheds in Taiwan. Then the derivation of the watershed unit hydrograph (UH) using two linear hydrological models shows that the peak discharge and time to peak discharge that characterize the shape of UH vary event-to-event. Hence, the intention of deriving a unique and universal UH for all rainfall-runoff simulation cases is questionable. In contrast, the UHs by the other two adopted nonlinear hydrological models were responsive to rainfall intensity without relying on linear proportion principle, and are excellent in presenting dynamic nonlinearity. Based on the two-segment regression, the scaling nonlinearity between peak discharge and drainage area was investigated by analyzing the variation of Power-law exponent. The results demonstrate that the scaling nonlinearity is particularly significant for a watershed having larger area and subjecting to a small-size of storm. For three study watersheds, a large tributary that contributes relatively great drainage area or inflow is found to cause a transition break in scaling relationship and convert the scaling relationship from linearity to nonlinearity.Keywords. Rainfall-runoff process; scaling effect; runoff nonlinearity; kinematic-wave-based geomorphologic instantaneous unit hydrograph; storm magnitude; watershed size.

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Section: Runoff Nonlinearity Analysismentioning

confidence: 99%

Section: Runoff Nonlinearity Analysismentioning

confidence: 99%

Section: Empirical Analysis Based On Hydrological Recordsmentioning

confidence: 99%

Section: Watershed Size On Scaling Nonlinearitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of storm magnitude and watershed size on runoff nonlinearity

Lee

Huang

2016

J Earth Syst Sci

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Improvement of Two-Dimensional Flow-Depth Prediction Based on Neural Network Models By Preprocessing Hydrological and Geomorphological Data

Huang

Hsü

Lee

2021

Water Resour Manage

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Analysis of runoff in ungauged mountain watersheds in Sichuan, China using kinematic-wave-based GIUH model

Cao¹,

Lee

et al. 2010

J. Mt. Sci.

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Floods are one of the most common natural hazards occurring all around the world. However, the knowledge of the origins of a food and its possible magnitude in a given region remains unclear yet. This lack of understanding is particularly acute in mountainous regions with large degrees in Sichuan Province, China, where runoff is seldom measured. The nature of streamflow in a region is related to the time and spatial distribution of rainfall quantity and watershed geomorphology. The geomorphologic characteristics are the channel network and surrounding landscape which transform the rainfall input into an output hydrograph at the outlet of the watershed. With the given geomorphologic properties of the watershed, theoretically the hydrological response function can be determined hydraulically without using any recorded data of past rainfall or runoff events. In this study, a kinematic-wave-based geomorphologic instantaneous unit hydrograph (KW-GIUH) model was adopted and verified to estimate runoff in ungauged areas. Two mountain watersheds, the Yingjing River watershed and Tianquan River watershed in Sichuan were selected as study sites. The geomorphologic factors of the two watersheds were obtained by using a digital elevation model (DEM) based on the topographic database obtained from the Shuttle Radar Topography Mission of US's NASA. The tests of the model on the two watersheds were performed both at gauged and ungauged sites. Comparison between the simulated and observed hydrographs for a number of rainstorms at the gauged sites indicated the potential of the KW-GIUH model as a useful tool for runoff analysis in these regions. Moreover, to simulate possible concentrated rainstorms that could result in serious flooding in these areas, synthetic rainfall hyetographs were adopted as input to the KW-GIUH model to obtain the flow hydrographs at two ungauged sites for different return period conditions. Hydroeconomic analysis can be performed in the future to select the optimum design return period for determining the flood control work.

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Impact of stream network structure on the transition break of peak flows

Cited by 17 publications

References 18 publications

Influence of storm magnitude and watershed size on runoff nonlinearity

Influence of storm magnitude and watershed size on runoff nonlinearity

Improvement of Two-Dimensional Flow-Depth Prediction Based on Neural Network Models By Preprocessing Hydrological and Geomorphological Data

Analysis of runoff in ungauged mountain watersheds in Sichuan, China using kinematic-wave-based GIUH model

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