2018
DOI: 10.1080/02626667.2018.1450985
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Timing the time of concentration: shedding light on a paradox

Abstract: From the origins of hydrology, the time of concentration, t c , has conventionally been tackled as a constant quantity. However, theoretical proof and empirical evidence imply that t c exhibits significant variability against rainfall, making its definition and estimation a hydrological paradox. Adopting the assumptions of the Rational method and the kinematic approach, an effective procedure in a GIS environment for estimating the travel time across a catchment's longest flow path is provided. By application … Show more

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Cited by 56 publications
(53 citation statements)
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References 24 publications
(41 reference statements)
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“…In the case of complex river networks, i.e., with confluences, we considered the longest flow path across each corresponding area. Theoretical proof and empirical evidence imply that t c is definitely not a constant property of the basin, but it varies significantly with the flow [13,48]. In fact, the variability of t c is explained by the dependence of the kinematic wave celerity on the flow rate.…”
Section: Hydrological Model Assumptions and Representation Of Uncertamentioning
confidence: 97%
“…In the case of complex river networks, i.e., with confluences, we considered the longest flow path across each corresponding area. Theoretical proof and empirical evidence imply that t c is definitely not a constant property of the basin, but it varies significantly with the flow [13,48]. In fact, the variability of t c is explained by the dependence of the kinematic wave celerity on the flow rate.…”
Section: Hydrological Model Assumptions and Representation Of Uncertamentioning
confidence: 97%
“…(2) Copula-based rainfall analysis considers the joint distribution of rainfall amounts with different critical durations, which enables the generation of different types of rainfalls rather than a deterministic type of rainfall for a certain return period. In addition, since copula-based design rainfall uses joint probability and IDF-curve-based design rainfall uses only marginal probability, according to the copula's Frechet-Hoeffding bounds [30], given a return period, copula-based design rainfall will always be larger than IDF-curve-based design rainfall, as illustrated in Figure 12. With regard to this study, this phenomenon can also be interpreted such that flooding from multiple sources will always be more dangerous than flooding from only a single source.…”
Section: Discussionmentioning
confidence: 99%
“…The relationship between the response characteristics of flooding and inundation from different sources can therefore be treated as the correlation between rainfall amounts and different critical rainfall durations. Many empirical formulas have been proposed to estimate the time of concentration based on the natural characteristics of a basin (e.g., path length, basin slope, land use), including the Kraven's formula, Izzard's formula, Kodoya and Fukushima's formula, Loukas and Quick's formula, and Michailidi's formula [21,30]. Predetermining the time of concentration from different flood sources and using them as critical rainfall durations to analyze rainfall data and generate rainfall events is likely to be an efficient way to assess spatial flood risk considering multiple flood sources.…”
Section: Introductionmentioning
confidence: 99%
“…Haan et al (1984), Viessman and Lewis, (1995) and Musy and Higy (2004), in contrast, still refer to time of concentration in the glossary definition. This has also continued in the recent papers of, for example, Li et al (2018) and Michailidi et al (2018). More embarrassingly, Shaw et al (2011) following earlier editions, treat time of concentration as the longest travel time based on velocities on both a catchment and in a pipe network while more correctly discussing celerities elsewhere.…”
Section: Mathematical Celerities and Natural Hillslopesmentioning
confidence: 96%