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| Titel |
Scaling of Peak Flows with Constant Flow Velocity in Random Self-Similar Networks |
| VerfasserIn |
Ricardo Mantilla, Vijay K. Gupta, Brent M. Troutman |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250045137
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| Zusammenfassung |
| We present a methodology to understand the role of the statistical self-similar topology of
real river networks on flow hydrographs for rainfall-runoff events. Monte Carlo generated
ensembles of 1000 Random Self-similar Networks (RSNs) with geometrically distributed
interior and exterior generators are created. These networks emulate the topology of real
networks. Hydrographs for every link in each of these networks are obtained by numerically
solving the link-based mass and momentum conservation equation under the assumption of
constant flow velocity. From these simulated RSNs and hydrographs, the scaling parameters
for the peak of the width function β and the hydrograph peak flow Ï are estimated. It was
found that Ï > β, which supports a similar finding first reported for the Walnut
Gulch basin, Arizona, and that is in qualitatively different from previous results on
idealized river networks (e.g. Peano Network, Mandelbrot- Viscek Network). The
use of numerical simulations is necessary as theoretical estimation of β and Ï in
RSNs is a complex mathematical open problem. However, other scaling features of
the average width function can the average hydrograph are calculated analytically
and compared with the estimation from the RSN-ensemble. Scaling of peak flows
during individual rainfall runoff events is a new area of research that offers a path
to understand regional scaling of flood quantiles, an important open problem in
river network hydrology. For example, our results show an interesting connection
between unit-hydrograph theory and flow dynamics. In addition, our methodology
provides a reference framework to study scaling exponents under more complex
scenarios of flow dynamics and runoff generation processes using ensembles of RSNs. |
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