Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

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Müller, H.; Haberlandt, U.: Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology. In: Molnar, P.; Peleg, N. (Eds.): Proceedings of the 10th International Workshop on Precipitation in Urban Areas (UrbanRain15), 2015. DOI: https://doi.org/10.3929/ethz-a-010549004

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Sum total of downloads: 330




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Abstract: 
For urban hydrology rainfall time series with a high temporal resolution are crucial. Observed time series of this kind are very short in most cases, so they cannot be used. On the contrary, time series with lower temporal resolution (daily measurements) exists for much longer periods. The objective is to derive time series with a long duration and a high resolution by disaggregating time series of the non-recording stations with information of time series of the recording stations. The multiplicative random cascade model is a well-known disaggregation model for daily time series. For urban hydrology it can be assumed, that a day consists of only 1280 minutes in total as starting point for the disaggregation process (e.g. Molnar & Burlando, 2005). Three new variants for the cascade model have been analyzed, which are functional without this assumption. These methods are extensions of the uniform splitting approach with a branching number b=3 in the first disaggregation step of the cascade model, introduced by Müller and Haberlandt (2015). For all further disaggregation steps b=2 is applied, so that temporal resolutions of e.g. 15, 7.5 or 3.75 minutes are achieved.
License of this version: In Copyright - Non-Commercial Use Permitted
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2015
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 234 70.91%
2 image of flag of United States United States 22 6.67%
3 image of flag of Russian Federation Russian Federation 12 3.64%
4 image of flag of France France 7 2.12%
5 image of flag of Netherlands Netherlands 5 1.52%
6 image of flag of Austria Austria 5 1.52%
7 image of flag of Czech Republic Czech Republic 4 1.21%
8 image of flag of China China 4 1.21%
9 image of flag of Hungary Hungary 3 0.91%
10 image of flag of United Kingdom United Kingdom 3 0.91%
    other countries 31 9.39%

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