Application of a K-Nearest Neighbour weather generator for simulation of historical and future climate variables in the Upper Thames River basin

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.

Name thesis:degree_name

M Eng Sci

Discipline thesis:degree_discipline

Civil and Environmental Engineering

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2012

Author dc:creator

• King, Leanna M

Advisors dc:contributor.advisor

• Slobodan P. Simonovic
• A. Ian McLeod

• Climate change
• Weather generator
• Hydrology
• Stochastic
• K-Nearest Neighbour
• Downscaling

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/29766

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/29766