{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/29766"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/29766","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Application of a K-Nearest Neighbour weather generator for simulation of historical and future climate variables in the Upper Thames River basin","abstract":"Climate change has the potential to significantly alter the hydrologic cycle, changing the frequency and intensity of precipitation events in an area. It is necessary to quantify these effects to effectively manage water resources in the future. Atmosphere-Ocean coupled Global Circulation Models (AOGCMs), often used in climate change research, have spatial resolutions that are too large to capture the local climate characteristics of a watershed. As a result, several downscaling tools have been developed, including stochastic weather generators. A methodology for the simulation of historical and future climate data using a nonparametric K-Nearest Neighbour block resampling weather generator with perturbation is presented (KnnCAD Version 4). The proposed approach is illustrated using a case study of the Upper Thames River basin in Ontario, Canada. KnnCAD V4 is shown to effectively reproduce the historical climate and can produce future climate change scenarios based on AOGCM data.","abstract_html":"Climate change has the potential to significantly alter the hydrologic cycle, changing the frequency and intensity of precipitation events in an area. It is necessary to quantify these effects to effectively manage water resources in the future. Atmosphere-Ocean coupled Global Circulation Models (AOGCMs), often used in climate change research, have spatial resolutions that are too large to capture the local climate characteristics of a watershed. As a result, several downscaling tools have been developed, including stochastic weather generators. A methodology for the simulation of historical and future climate data using a nonparametric K-Nearest Neighbour block resampling weather generator with perturbation is presented (KnnCAD Version 4). The proposed approach is illustrated using a case study of the Upper Thames River basin in Ontario, Canada. KnnCAD V4 is shown to effectively reproduce the historical climate and can produce future climate change scenarios based on AOGCM data.","abstract_has_math":false,"creators":["King, Leanna M"],"institution":"The University of Western Ontario","degree_name":"M Eng Sci","degree_level":null,"degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Slobodan P. Simonovic","A. Ian McLeod"],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-01-01","date_published":"2012-01-01","updated_at":"2026-07-27T21:56:07Z","subjects":["Climate change","Weather generator","Hydrology","Stochastic","K-Nearest Neighbour","Downscaling"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/29766","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Slobodan P. Simonovic","A. 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In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["Climate change has the potential to significantly alter the hydrologic cycle, changing the frequency and intensity of precipitation events in an area. It is necessary to quantify these effects to effectively manage water resources in the future. Atmosphere-Ocean coupled Global Circulation Models (AOGCMs), often used in climate change research, have spatial resolutions that are too large to capture the local climate characteristics of a watershed. As a result, several downscaling tools have been developed, including stochastic weather generators. A methodology for the simulation of historical and future climate data using a nonparametric K-Nearest Neighbour block resampling weather generator with perturbation is presented (KnnCAD Version 4). The proposed approach is illustrated using a case study of the Upper Thames River basin in Ontario, Canada. KnnCAD V4 is shown to effectively reproduce the historical climate and can produce future climate change scenarios based on AOGCM data."]},{"key":"dc:title","label":"Title","values":["Application of a K-Nearest Neighbour weather generator for simulation of historical and future climate variables in the Upper Thames River basin"]}]}],"canonical_facts":{"dc:contributor.advisor":["Slobodan P. Simonovic","A. Ian McLeod"],"dc:creator":["King, Leanna M"],"dc:date.accessioned":["2025-07-10T18:33:30Z"],"dc:date.available":["2025-07-10T18:33:30Z"],"dc:date.issued":["2012-01-01"],"dc:description":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["Climate change has the potential to significantly alter the hydrologic cycle, changing the frequency and intensity of precipitation events in an area. It is necessary to quantify these effects to effectively manage water resources in the future. Atmosphere-Ocean coupled Global Circulation Models (AOGCMs), often used in climate change research, have spatial resolutions that are too large to capture the local climate characteristics of a watershed. As a result, several downscaling tools have been developed, including stochastic weather generators. A methodology for the simulation of historical and future climate data using a nonparametric K-Nearest Neighbour block resampling weather generator with perturbation is presented (KnnCAD Version 4). The proposed approach is illustrated using a case study of the Upper Thames River basin in Ontario, Canada. 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