Multivariate disaggregation of daily to hourly rainfall series
This study focuses on a method to disaggregate daily rainfall into hourly precipitation. This method uses the multivariate technique in generating a small scale data from a larger scale data. In this method, the lower-level synthetic series must be consistent with the higher-level series. Hence, thi...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/44666/25/LimTouHinMFS2013.pdf |
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| Summary: | This study focuses on a method to disaggregate daily rainfall into hourly precipitation. This method uses the multivariate technique in generating a small scale data from a larger scale data. In this method, the lower-level synthetic series must be consistent with the higher-level series. Hence, this method would involve a spatialtemporal rainfall modeling that combines several univariate and multivariate rainfall models operating at different timescales, in a disaggregation framework that can appropriately modify outputs of finer timescale models so as to become consistent with given coarser timescale series. The methodology can be applied to derive spatially consistent hourly rainfall series in rain gauge where only daily data are available. The simulation framework provides a way to take simulations of multivariate daily rainfall and generate multivariate fields at fine temporal resolution. In this study, rainfall stations with daily and hourly scale data in Johor are used. The multivariate method would emphasis on using several daily data from the nearby stations to be disaggregated to hourly scale data. According to literature this method has shown promising results in other countries. This method has the ability to preserve important properties of the hourly rainfall process such as marginal moments, temporal and spatial correlations, and proportions and lengths of dry intervals. Multivariate rainfall disaggregation models have greater potential in hydrological applications including enhancement of historical data series and generation of simulated data series. |
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