Analysis of rainfall extremes based on grid data

The identification, monitoring, and characterization of extreme weather events, like extreme storms and drought, are of great importance, especially in Singapore. Increasing rainfall could overwhelm our local drainage system and this may lead to flooding. Gumbel Distribution is one of the probabilit...

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Main Author: Koh, Wei Theng
Other Authors: Qin Xiaosheng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/158971
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1589712022-06-08T03:30:25Z Analysis of rainfall extremes based on grid data Koh, Wei Theng Qin Xiaosheng School of Civil and Environmental Engineering XSQIN@ntu.edu.sg Engineering::Civil engineering::Water resources The identification, monitoring, and characterization of extreme weather events, like extreme storms and drought, are of great importance, especially in Singapore. Increasing rainfall could overwhelm our local drainage system and this may lead to flooding. Gumbel Distribution is one of the probability distribution methods that makes use of the precipitation data to hydrological peaks corresponding to specific return periods or probabilities. This report uses grid data daily gridded rainfall data obtained from APHRODITE’s Asian Precipitation dataset V1101 (1951-2007) and V1101EX_R1 (2007-2015) and MATrix LABoratory (MATLAB) matrix programming software and Quantum Geographic Information System (QGIS) to analyse and edit spatial information. Extreme Value Type I Gumbel Distribution, Frequency of Occurrences of Wet Days, Total Number of Wet Days, Longest Duration of Wet Days, and the Total Annual Precipitation of Wet Spells are computed as functions in MATLAB to study the region of 10 selected capital cities across Southeast Asia. Bachelor of Engineering (Civil) 2022-06-08T03:30:24Z 2022-06-08T03:30:24Z 2022 Final Year Project (FYP) Koh, W. T. (2022). Analysis of rainfall extremes based on grid data. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158971 https://hdl.handle.net/10356/158971 en WR-13 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::Civil engineering::Water resources
spellingShingle Engineering::Civil engineering::Water resources
Koh, Wei Theng
Analysis of rainfall extremes based on grid data
description The identification, monitoring, and characterization of extreme weather events, like extreme storms and drought, are of great importance, especially in Singapore. Increasing rainfall could overwhelm our local drainage system and this may lead to flooding. Gumbel Distribution is one of the probability distribution methods that makes use of the precipitation data to hydrological peaks corresponding to specific return periods or probabilities. This report uses grid data daily gridded rainfall data obtained from APHRODITE’s Asian Precipitation dataset V1101 (1951-2007) and V1101EX_R1 (2007-2015) and MATrix LABoratory (MATLAB) matrix programming software and Quantum Geographic Information System (QGIS) to analyse and edit spatial information. Extreme Value Type I Gumbel Distribution, Frequency of Occurrences of Wet Days, Total Number of Wet Days, Longest Duration of Wet Days, and the Total Annual Precipitation of Wet Spells are computed as functions in MATLAB to study the region of 10 selected capital cities across Southeast Asia.
author2 Qin Xiaosheng
author_facet Qin Xiaosheng
Koh, Wei Theng
format Final Year Project
author Koh, Wei Theng
author_sort Koh, Wei Theng
title Analysis of rainfall extremes based on grid data
title_short Analysis of rainfall extremes based on grid data
title_full Analysis of rainfall extremes based on grid data
title_fullStr Analysis of rainfall extremes based on grid data
title_full_unstemmed Analysis of rainfall extremes based on grid data
title_sort analysis of rainfall extremes based on grid data
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158971
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