Application of a K-Nearest Neighbour weather generator for simulation of historical and future climate variables in the Upper Thames River basin
type: CreativeWork
description: 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: 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.
author: King, Leanna M
language: en_ca
keywords: Climate change, Weather generator, Hydrology, Stochastic, K-Nearest Neighbour, Downscaling
date published: 2012-01-01
date modified: 2026-07-27T21:56:07Z
provider: Western University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Western Ontario

PropertyValue
property ID: Handle
value: https://hdl.handle.net/20.500.14721/29766