An integrated framework for hourly rainfall generation at multiple sites in the tropical region

This study aims to introduce an integrated framework to disaggregate daily rainfall data into hourly intervals at multiple sites. Traditional methods that evenly distribute daily rainfall over 24 hours often fail to either accurately reflect observed hourly rainfall due to their inability to capture...

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Main Author: Zhou, Bingjie
Other Authors: Qin Xiaosheng
Format: Thesis-Master by Research
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/181026
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1810262024-12-03T05:20:50Z An integrated framework for hourly rainfall generation at multiple sites in the tropical region Zhou, Bingjie Qin Xiaosheng School of Civil and Environmental Engineering XSQIN@ntu.edu.sg Engineering Rainfall disaggregation Spatial correlations HyetosMinute MuDRain This study aims to introduce an integrated framework to disaggregate daily rainfall data into hourly intervals at multiple sites. Traditional methods that evenly distribute daily rainfall over 24 hours often fail to either accurately reflect observed hourly rainfall due to their inability to capture finer scale variability and intensity or capture the spatial correlation among various sites. Over the past decades, the Bartlett-Lewis Rectangular Pulse Model (BLRPM) can effectively capture temporal patterns at a single site, offering better representation of hourly rainfall data than simple disaggregation methods. Meanwhile, the MuDRain model, by accounting for spatial correlations among multiple sites, more accurately reproduces rainfall events across different locations, which is crucial for regions with complex rainfall patterns influenced by geographical and meteorological factors. This spatial consideration at a fine resolution (e.g. hourly) in synthesizing rainfall series is particularly valuable for urban rainfall-runoff modeling and flash flood simulations. However, such studies are very limited in the literature, especially for tropical cities like Singapore. This study specifically addressed this issue, discussing the pros and cons of simple disaggregation methods and investigating the advantages of using an integrated modeling scheme by sequentially coupling two stochastic models into a general framework. The results demonstrated that the integrated method could more comprehensively depict rainfall patterns in multi-site scenarios, enhancing the analysis of urban hydrological processes. Master's degree 2024-11-12T00:41:28Z 2024-11-12T00:41:28Z 2024 Thesis-Master by Research Zhou, B. (2024). An integrated framework for hourly rainfall generation at multiple sites in the tropical region. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181026 https://hdl.handle.net/10356/181026 10.32657/10356/181026 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Rainfall disaggregation
Spatial correlations
HyetosMinute
MuDRain
spellingShingle Engineering
Rainfall disaggregation
Spatial correlations
HyetosMinute
MuDRain
Zhou, Bingjie
An integrated framework for hourly rainfall generation at multiple sites in the tropical region
description This study aims to introduce an integrated framework to disaggregate daily rainfall data into hourly intervals at multiple sites. Traditional methods that evenly distribute daily rainfall over 24 hours often fail to either accurately reflect observed hourly rainfall due to their inability to capture finer scale variability and intensity or capture the spatial correlation among various sites. Over the past decades, the Bartlett-Lewis Rectangular Pulse Model (BLRPM) can effectively capture temporal patterns at a single site, offering better representation of hourly rainfall data than simple disaggregation methods. Meanwhile, the MuDRain model, by accounting for spatial correlations among multiple sites, more accurately reproduces rainfall events across different locations, which is crucial for regions with complex rainfall patterns influenced by geographical and meteorological factors. This spatial consideration at a fine resolution (e.g. hourly) in synthesizing rainfall series is particularly valuable for urban rainfall-runoff modeling and flash flood simulations. However, such studies are very limited in the literature, especially for tropical cities like Singapore. This study specifically addressed this issue, discussing the pros and cons of simple disaggregation methods and investigating the advantages of using an integrated modeling scheme by sequentially coupling two stochastic models into a general framework. The results demonstrated that the integrated method could more comprehensively depict rainfall patterns in multi-site scenarios, enhancing the analysis of urban hydrological processes.
author2 Qin Xiaosheng
author_facet Qin Xiaosheng
Zhou, Bingjie
format Thesis-Master by Research
author Zhou, Bingjie
author_sort Zhou, Bingjie
title An integrated framework for hourly rainfall generation at multiple sites in the tropical region
title_short An integrated framework for hourly rainfall generation at multiple sites in the tropical region
title_full An integrated framework for hourly rainfall generation at multiple sites in the tropical region
title_fullStr An integrated framework for hourly rainfall generation at multiple sites in the tropical region
title_full_unstemmed An integrated framework for hourly rainfall generation at multiple sites in the tropical region
title_sort integrated framework for hourly rainfall generation at multiple sites in the tropical region
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181026
_version_ 1819113024092372992