{"id":{"repo_id":"auckland-ms","oai_identifier":"oai:researchspace.auckland.ac.nz:2292/73252"},"canonical_url":"https://search.dev.ndltd.org/etd/auckland-ms/oai:researchspace.auckland.ac.nz:2292/73252","repository":{"repo_id":"auckland-ms","name":"University of Auckland","base_url":"https://researchspace.auckland.ac.nz/server/oai/request"},"display":{"title":"Assessment of Future Impact of Climate Change on Structural Protections and Hydrological Extremes – Floods","abstract":"The certainty of flood protection in the face of climate change is linked to the likelihood that a system will function reliably under various future flood scenarios. It is increasingly crucial to comprehend and forecast the frequency and intensity of random hydrological factors for efficient flood management. This study centres on modelling the performance of the flood protection system in New Zealand’s Manawatu region. The modelling framework prioritizes rainfall as the primary driver of flood flow generation and enables the assessment of various climate change scenarios produced by Global Circulation Models (GCMs). The study created a statistical downscaling model called SDCRR, using the Volterra series realization, principal components, and ridge regression. The model was applied at four stations in the Manawatu catchment to downscale daily rainfall. The performance of the SDCRR model was compared with that of the widely used statistical downscaling model (SDSM). The performance of two was assessed by using the coefficient of determination (R2), which quantifies the proportion of variance in observed rainfall data explained by the models. The results showed that SDCRR has better performance than the SDSM. Furthermore, to align more closely with the true nature of rainfall the study improved the performance of SDCRR, by incorporating climate classification with C-means clustering into the SDC2R2 model to capture the non-linear relationships among climate variables. Consequently, the developed model integrated fuzzy clustering along with Volterra series realization, principal components and ridge regression. The model performance was assessed based on its capacity to more accurately simulate daily rainfall data compared to SDCRR model. The model utilizes large-scale climate variables from the National Centres for Environmental Predictions (NCEP) reanalysis data, focusing on wide range predictors. Additionally, the MIKE 11 NAM was employed to develop the rainfall runoff relationship for the catchment. Further, the generated stream flow was used in the MIKE 11 HD model. The MIKE 11 HD model was used to access the performance of structures in the heterogeneous catchment, Manawatu. The streamflow data obtained for future scenarios indicated that there will be no flood event in near future and in mid-future, no overtopping of bank lines protected by stopbanks. These findings will support decision-makers in evaluating the effectiveness of current flood control structures in the context of anticipated climate variability and change in heterogeneous catchment, aiding in planning efforts.","abstract_html":"The certainty of flood protection in the face of climate change is linked to the likelihood that a system will function reliably under various future flood scenarios. It is increasingly crucial to comprehend and forecast the frequency and intensity of random hydrological factors for efficient flood management. This study centres on modelling the performance of the flood protection system in New Zealand’s Manawatu region. The modelling framework prioritizes rainfall as the primary driver of flood flow generation and enables the assessment of various climate change scenarios produced by Global Circulation Models (GCMs). The study created a statistical downscaling model called SDCRR, using the Volterra series realization, principal components, and ridge regression. The model was applied at four stations in the Manawatu catchment to downscale daily rainfall. The performance of the SDCRR model was compared with that of the widely used statistical downscaling model (SDSM). The performance of two was assessed by using the coefficient of determination (R2), which quantifies the proportion of variance in observed rainfall data explained by the models. The results showed that SDCRR has better performance than the SDSM. Furthermore, to align more closely with the true nature of rainfall the study improved the performance of SDCRR, by incorporating climate classification with C-means clustering into the SDC2R2 model to capture the non-linear relationships among climate variables. Consequently, the developed model integrated fuzzy clustering along with Volterra series realization, principal components and ridge regression. The model performance was assessed based on its capacity to more accurately simulate daily rainfall data compared to SDCRR model. The model utilizes large-scale climate variables from the National Centres for Environmental Predictions (NCEP) reanalysis data, focusing on wide range predictors. Additionally, the MIKE 11 NAM was employed to develop the rainfall runoff relationship for the catchment. Further, the generated stream flow was used in the MIKE 11 HD model. The MIKE 11 HD model was used to access the performance of structures in the heterogeneous catchment, Manawatu. The streamflow data obtained for future scenarios indicated that there will be no flood event in near future and in mid-future, no overtopping of bank lines protected by stopbanks. These findings will support decision-makers in evaluating the effectiveness of current flood control structures in the context of anticipated climate variability and change in heterogeneous catchment, aiding in planning efforts.","abstract_has_math":false,"creators":["Singh, Pooja"],"institution":"ResearchSpace@Auckland","degree_name":"PhD","degree_level":"Doctoral","degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Shamseldin, Asaad Y."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T01:05:49Z","subjects":["Climate Change","Principal Component Analysis","Ridge Regression","Fuzzy Clustering","Volterra Series Realization"],"languages":[],"rights":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."],"rights_urls":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2292/73252","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Shamseldin, Asaad Y."]},{"key":"dc:creator","label":"Author","values":["Singh, Pooja"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-28T19:27:25Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-28T19:27:25Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["ResearchSpace@Auckland"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil and Environmental Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["PhD"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Auckland"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Climate Change","Principal Component Analysis","Ridge Regression","Fuzzy Clustering","Volterra Series Realization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2292/73252"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The certainty of flood protection in the face of climate change is linked to the likelihood that a system will function reliably under various future flood scenarios. It is increasingly crucial to comprehend and forecast the frequency and intensity of random hydrological factors for efficient flood management. This study centres on modelling the performance of the flood protection system in New Zealand’s Manawatu region. The modelling framework prioritizes rainfall as the primary driver of flood flow generation and enables the assessment of various climate change scenarios produced by Global Circulation Models (GCMs). The study created a statistical downscaling model called SDCRR, using the Volterra series realization, principal components, and ridge regression. The model was applied at four stations in the Manawatu catchment to downscale daily rainfall. The performance of the SDCRR model was compared with that of the widely used statistical downscaling model (SDSM). The performance of two was assessed by using the coefficient of determination (R2), which quantifies the proportion of variance in observed rainfall data explained by the models. The results showed that SDCRR has better performance than the SDSM. Furthermore, to align more closely with the true nature of rainfall the study improved the performance of SDCRR, by incorporating climate classification with C-means clustering into the SDC2R2 model to capture the non-linear relationships among climate variables. Consequently, the developed model integrated fuzzy clustering along with Volterra series realization, principal components and ridge regression. The model performance was assessed based on its capacity to more accurately simulate daily rainfall data compared to SDCRR model. The model utilizes large-scale climate variables from the National Centres for Environmental Predictions (NCEP) reanalysis data, focusing on wide range predictors. Additionally, the MIKE 11 NAM was employed to develop the rainfall runoff relationship for the catchment. Further, the generated stream flow was used in the MIKE 11 HD model. The MIKE 11 HD model was used to access the performance of structures in the heterogeneous catchment, Manawatu. The streamflow data obtained for future scenarios indicated that there will be no flood event in near future and in mid-future, no overtopping of bank lines protected by stopbanks. These findings will support decision-makers in evaluating the effectiveness of current flood control structures in the context of anticipated climate variability and change in heterogeneous catchment, aiding in planning efforts."]},{"key":"dc:title","label":"Title","values":["Assessment of Future Impact of Climate Change on Structural Protections and Hydrological Extremes – Floods"]}]}],"canonical_facts":{"dc:contributor.advisor":["Shamseldin, Asaad Y."],"dc:creator":["Singh, Pooja"],"dc:date.accessioned":["2025-08-28T19:27:25Z"],"dc:date.available":["2025-08-28T19:27:25Z"],"dc:date.issued":["2025"],"dc:description.abstract":["The certainty of flood protection in the face of climate change is linked to the likelihood that a system will function reliably under various future flood scenarios. It is increasingly crucial to comprehend and forecast the frequency and intensity of random hydrological factors for efficient flood management. This study centres on modelling the performance of the flood protection system in New Zealand’s Manawatu region. The modelling framework prioritizes rainfall as the primary driver of flood flow generation and enables the assessment of various climate change scenarios produced by Global Circulation Models (GCMs). The study created a statistical downscaling model called SDCRR, using the Volterra series realization, principal components, and ridge regression. The model was applied at four stations in the Manawatu catchment to downscale daily rainfall. The performance of the SDCRR model was compared with that of the widely used statistical downscaling model (SDSM). The performance of two was assessed by using the coefficient of determination (R2), which quantifies the proportion of variance in observed rainfall data explained by the models. The results showed that SDCRR has better performance than the SDSM. Furthermore, to align more closely with the true nature of rainfall the study improved the performance of SDCRR, by incorporating climate classification with C-means clustering into the SDC2R2 model to capture the non-linear relationships among climate variables. Consequently, the developed model integrated fuzzy clustering along with Volterra series realization, principal components and ridge regression. The model performance was assessed based on its capacity to more accurately simulate daily rainfall data compared to SDCRR model. The model utilizes large-scale climate variables from the National Centres for Environmental Predictions (NCEP) reanalysis data, focusing on wide range predictors. Additionally, the MIKE 11 NAM was employed to develop the rainfall runoff relationship for the catchment. Further, the generated stream flow was used in the MIKE 11 HD model. The MIKE 11 HD model was used to access the performance of structures in the heterogeneous catchment, Manawatu. The streamflow data obtained for future scenarios indicated that there will be no flood event in near future and in mid-future, no overtopping of bank lines protected by stopbanks. These findings will support decision-makers in evaluating the effectiveness of current flood control structures in the context of anticipated climate variability and change in heterogeneous catchment, aiding in planning efforts."],"dc:identifier.uri":["https://hdl.handle.net/2292/73252"],"dc:publisher":["ResearchSpace@Auckland"],"dc:rights":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."],"dc:rights.uri":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"],"dc:subject":["Climate Change","Principal Component Analysis","Ridge Regression","Fuzzy Clustering","Volterra Series Realization"],"dc:title":["Assessment of Future Impact of Climate Change on Structural Protections and Hydrological Extremes – Floods"],"dc:type":["Thesis"],"thesis:degree_discipline":["Civil and Environmental Engineering"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["PhD"],"thesis:institution_name":["The University of Auckland"]},"updated_at":"2026-07-24T01:05:49Z"}