Investigation of rainfall disaggregation with flexible timescales based on point process models

Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Dis...

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Main Authors: Qin, Xiaosheng, Dai, Chao
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/175809
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1758092024-05-07T01:53:22Z Investigation of rainfall disaggregation with flexible timescales based on point process models Qin, Xiaosheng Dai, Chao School of Civil and Environmental Engineering Engineering Disaggregation Tropical rainfall Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Disaggregation (PPMRD) framework for rainfall disaggregation with flexible time scales. The framework was tested on two tropical sites in Singapore, using six point-process models and four disaggregation schemes (24 h to 5 min, 3 h to 5 min, 24 h to 1 h, and 6 h to 1 h). Most models excelled fundamental statistics and extreme value analysis. The Bartlett-Lewis Rectangular Pulse Seven parameter (BLRP7) model was found to be the best performer for simulating and disaggregating tropical rainfall. The study findings emphasized the utility of disaggregation models in generating fine-resolution time series for analyzing rainfall extremes. However, it is crucial to underscore the importance of employing ensembles to comprehensively address uncertainties arising from the stochastic nature of rainfall. The study also emphasized the importance of model selection in the proposed framework, as the disaggregation method depends on the generator's performance. The PPMRD surpasses traditional methods by handling subdaily upper-level series disaggregation to minute-level rainfall. This advancement offers greater capabilities and flexibility, promising benefits for generating high-temporal-resolution rainfall data in hydrological modeling and climate change impact studies. Ministry of Education (MOE) This research is supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (Grant No. RG72/22). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Ministry of Education, Singapore. 2024-05-07T01:53:22Z 2024-05-07T01:53:22Z 2024 Journal Article Qin, X. & Dai, C. (2024). Investigation of rainfall disaggregation with flexible timescales based on point process models. Journal of Hydrology, 634, 131101-. https://dx.doi.org/10.1016/j.jhydrol.2024.131101 0022-1694 https://hdl.handle.net/10356/175809 10.1016/j.jhydrol.2024.131101 2-s2.0-85188688515 634 131101 en RG72/22 Journal of Hydrology © 2024 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Disaggregation
Tropical rainfall
spellingShingle Engineering
Disaggregation
Tropical rainfall
Qin, Xiaosheng
Dai, Chao
Investigation of rainfall disaggregation with flexible timescales based on point process models
description Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Disaggregation (PPMRD) framework for rainfall disaggregation with flexible time scales. The framework was tested on two tropical sites in Singapore, using six point-process models and four disaggregation schemes (24 h to 5 min, 3 h to 5 min, 24 h to 1 h, and 6 h to 1 h). Most models excelled fundamental statistics and extreme value analysis. The Bartlett-Lewis Rectangular Pulse Seven parameter (BLRP7) model was found to be the best performer for simulating and disaggregating tropical rainfall. The study findings emphasized the utility of disaggregation models in generating fine-resolution time series for analyzing rainfall extremes. However, it is crucial to underscore the importance of employing ensembles to comprehensively address uncertainties arising from the stochastic nature of rainfall. The study also emphasized the importance of model selection in the proposed framework, as the disaggregation method depends on the generator's performance. The PPMRD surpasses traditional methods by handling subdaily upper-level series disaggregation to minute-level rainfall. This advancement offers greater capabilities and flexibility, promising benefits for generating high-temporal-resolution rainfall data in hydrological modeling and climate change impact studies.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Qin, Xiaosheng
Dai, Chao
format Article
author Qin, Xiaosheng
Dai, Chao
author_sort Qin, Xiaosheng
title Investigation of rainfall disaggregation with flexible timescales based on point process models
title_short Investigation of rainfall disaggregation with flexible timescales based on point process models
title_full Investigation of rainfall disaggregation with flexible timescales based on point process models
title_fullStr Investigation of rainfall disaggregation with flexible timescales based on point process models
title_full_unstemmed Investigation of rainfall disaggregation with flexible timescales based on point process models
title_sort investigation of rainfall disaggregation with flexible timescales based on point process models
publishDate 2024
url https://hdl.handle.net/10356/175809
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