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Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes / HAN WANG

Swansea University Author: HAN WANG

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DOI (Published version): 10.23889/SUthesis.58888

Abstract

In recent decades, climate change has caused a more volatile climate leading to more extreme events such as severe rainstorms, heatwaves and floods which are likely to become more frequent. Aiming to reveal climate change impact on the hydroclimatic extremes in a quantitative sense, this thesis pres...

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Published: Swansea 2021
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Xuan, Yunqing; Karunarathna, Harshinie
URI: https://cronfa.swan.ac.uk/Record/cronfa58888
first_indexed 2021-12-03T17:56:08Z
last_indexed 2021-12-04T04:18:05Z
id cronfa58888
recordtype RisThesis
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The first strand focuses on developing a quantitative modelling framework to quantify the spatiotemporal variation of hydroclimatic extremes for the areas of concern. A spatial random sampling toolbox (SRS-GDA) is designed for randomizing the regions of interest (ROIs) with different geographic locations, sizes, shapes and orientations where the hydroclimatic extremes are parameterised by a nonstationary distribution model whose parameters are assumed to be time-varying. The parameters whose variation with respect to different spatial features of ROIs and climate change are finally quantified by various statistical models such as the generalised linear model. The framework is applied to quantify the spatiotemporal variation of rainfall extremes in Great Britain (GB) and Australia and is further used in a comparison study to quantify the bias between observed and climate projected extremes. Then the framework is extended to a multivariate framework to estimate the time-varying joint probability of more than one hydroclimatic variable in the perspective of non-stationarity. A case study for evaluating compound floods in Ho Chi Minh City, Vietnam is applied for demonstrating the application of the framework. The second strand aims to recognise, classify and track the development of hydroclimatic extremes (e.g., severe rainstorms) by developing a stable computer algorithm (i.e., the SPER toolbox). The SPER toolbox can detect the boundary of the event area, extract the spatial and physical features of the event, which can be used not only for pattern recognition but also to support AI-based training for labelling/cataloguing the pattern from the large-sized, grid-based, multi-scaled environmental datasets. Three illustrative cases are provided; and as the front-end of AI study, an example for training a convolution neural network is given for classifying the rainfall extremes in the last century of GB. The third strand turns to support decision making by building both theory-driven and data-driven decision-making models to simulate the decisions in the context of flood forecasting and early warning, using the data collected via laboratory-style experiments based on various information of probabilistic flood forecasts and consequences. The research work demonstrated in this thesis has been able to bridge the knowledge gaps in the related field and it also provides a precritical insight in managing future risks arising from hydroclimatic extremes, which makes perfect sense given the urgent situation of climate change and the related challenges our societies are facing.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Hydroclimatic extremes; Quantitative modelling; Climate Change; Nonstationary univariate and multivariate probability analysis; Decision-making under uncertainty</keywords><publishedDay>3</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-12-03</publishedDate><doi>10.23889/SUthesis.58888</doi><url/><notes>ORCiD identifier: https://orcid.org/0000-0002-3018-4707</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Xuan, Yunqing; Karunarathna, Harshinie</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><degreesponsorsfunders>Chinese Scholarship Council; ZCCE scholarship</degreesponsorsfunders><apcterm/><lastEdited>2021-12-03T18:13:54.8937880</lastEdited><Created>2021-12-03T17:52:51.1663272</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>HAN</firstname><surname>WANG</surname><order>1</order></author></authors><documents><document><filename>58888__21786__5b5d9ff33f39417aa0ad589fdd487b71.pdf</filename><originalFilename>Wang_Han_PhD_Thesis_Final_Redacted_Signature.pdf</originalFilename><uploaded>2021-12-03T18:02:04.6982304</uploaded><type>Output</type><contentLength>12918243</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><documentNotes>Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes &#xA9; 2021 by Han Wang is licensed under a CC-BY-SA license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2021-12-03T18:13:54.8937880 v2 58888 2021-12-03 Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes a346e806bdf9650d3ff6eda184d7dd38 HAN WANG HAN WANG true false 2021-12-03 In recent decades, climate change has caused a more volatile climate leading to more extreme events such as severe rainstorms, heatwaves and floods which are likely to become more frequent. Aiming to reveal climate change impact on the hydroclimatic extremes in a quantitative sense, this thesis presents a comprehensive analysis from three main strands. The first strand focuses on developing a quantitative modelling framework to quantify the spatiotemporal variation of hydroclimatic extremes for the areas of concern. A spatial random sampling toolbox (SRS-GDA) is designed for randomizing the regions of interest (ROIs) with different geographic locations, sizes, shapes and orientations where the hydroclimatic extremes are parameterised by a nonstationary distribution model whose parameters are assumed to be time-varying. The parameters whose variation with respect to different spatial features of ROIs and climate change are finally quantified by various statistical models such as the generalised linear model. The framework is applied to quantify the spatiotemporal variation of rainfall extremes in Great Britain (GB) and Australia and is further used in a comparison study to quantify the bias between observed and climate projected extremes. Then the framework is extended to a multivariate framework to estimate the time-varying joint probability of more than one hydroclimatic variable in the perspective of non-stationarity. A case study for evaluating compound floods in Ho Chi Minh City, Vietnam is applied for demonstrating the application of the framework. The second strand aims to recognise, classify and track the development of hydroclimatic extremes (e.g., severe rainstorms) by developing a stable computer algorithm (i.e., the SPER toolbox). The SPER toolbox can detect the boundary of the event area, extract the spatial and physical features of the event, which can be used not only for pattern recognition but also to support AI-based training for labelling/cataloguing the pattern from the large-sized, grid-based, multi-scaled environmental datasets. Three illustrative cases are provided; and as the front-end of AI study, an example for training a convolution neural network is given for classifying the rainfall extremes in the last century of GB. The third strand turns to support decision making by building both theory-driven and data-driven decision-making models to simulate the decisions in the context of flood forecasting and early warning, using the data collected via laboratory-style experiments based on various information of probabilistic flood forecasts and consequences. The research work demonstrated in this thesis has been able to bridge the knowledge gaps in the related field and it also provides a precritical insight in managing future risks arising from hydroclimatic extremes, which makes perfect sense given the urgent situation of climate change and the related challenges our societies are facing. E-Thesis Swansea Hydroclimatic extremes; Quantitative modelling; Climate Change; Nonstationary univariate and multivariate probability analysis; Decision-making under uncertainty 3 12 2021 2021-12-03 10.23889/SUthesis.58888 ORCiD identifier: https://orcid.org/0000-0002-3018-4707 COLLEGE NANME COLLEGE CODE Swansea University Xuan, Yunqing; Karunarathna, Harshinie Doctoral Ph.D Chinese Scholarship Council; ZCCE scholarship 2021-12-03T18:13:54.8937880 2021-12-03T17:52:51.1663272 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised HAN WANG 1 58888__21786__5b5d9ff33f39417aa0ad589fdd487b71.pdf Wang_Han_PhD_Thesis_Final_Redacted_Signature.pdf 2021-12-03T18:02:04.6982304 Output 12918243 application/pdf E-Thesis – open access true Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes © 2021 by Han Wang is licensed under a CC-BY-SA license. true eng
title Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes
spellingShingle Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes
HAN WANG
title_short Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes
title_full Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes
title_fullStr Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes
title_full_unstemmed Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes
title_sort Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes
author_id_str_mv a346e806bdf9650d3ff6eda184d7dd38
author_id_fullname_str_mv a346e806bdf9650d3ff6eda184d7dd38_***_HAN WANG
author HAN WANG
author2 HAN WANG
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
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description In recent decades, climate change has caused a more volatile climate leading to more extreme events such as severe rainstorms, heatwaves and floods which are likely to become more frequent. Aiming to reveal climate change impact on the hydroclimatic extremes in a quantitative sense, this thesis presents a comprehensive analysis from three main strands. The first strand focuses on developing a quantitative modelling framework to quantify the spatiotemporal variation of hydroclimatic extremes for the areas of concern. A spatial random sampling toolbox (SRS-GDA) is designed for randomizing the regions of interest (ROIs) with different geographic locations, sizes, shapes and orientations where the hydroclimatic extremes are parameterised by a nonstationary distribution model whose parameters are assumed to be time-varying. The parameters whose variation with respect to different spatial features of ROIs and climate change are finally quantified by various statistical models such as the generalised linear model. The framework is applied to quantify the spatiotemporal variation of rainfall extremes in Great Britain (GB) and Australia and is further used in a comparison study to quantify the bias between observed and climate projected extremes. Then the framework is extended to a multivariate framework to estimate the time-varying joint probability of more than one hydroclimatic variable in the perspective of non-stationarity. A case study for evaluating compound floods in Ho Chi Minh City, Vietnam is applied for demonstrating the application of the framework. The second strand aims to recognise, classify and track the development of hydroclimatic extremes (e.g., severe rainstorms) by developing a stable computer algorithm (i.e., the SPER toolbox). The SPER toolbox can detect the boundary of the event area, extract the spatial and physical features of the event, which can be used not only for pattern recognition but also to support AI-based training for labelling/cataloguing the pattern from the large-sized, grid-based, multi-scaled environmental datasets. Three illustrative cases are provided; and as the front-end of AI study, an example for training a convolution neural network is given for classifying the rainfall extremes in the last century of GB. The third strand turns to support decision making by building both theory-driven and data-driven decision-making models to simulate the decisions in the context of flood forecasting and early warning, using the data collected via laboratory-style experiments based on various information of probabilistic flood forecasts and consequences. The research work demonstrated in this thesis has been able to bridge the knowledge gaps in the related field and it also provides a precritical insight in managing future risks arising from hydroclimatic extremes, which makes perfect sense given the urgent situation of climate change and the related challenges our societies are facing.
published_date 2021-12-03T06:28:30Z
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score 11.088929

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