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Permanent link (DOI): https://doi.org/10.7939/R3RS91

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Iterative interpolation of daily precipitation data over Alberta Open Access

Descriptions

Other title
Subject/Keyword
Hybrid 1.0
IDW
Nearest-station method
SPI
Precipitation
ANUSPLIN
Hybrid 2.0
Rule N test
Cross-validation
Alberta
EOF
Mann-Kendall test
North’s Rule of Thumb
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Dai, Qingfang
Supervisor and department
Hooper, Peter (Mathematical and Statistical Sciences)
Shen, Samuel (Mathematical and Statistical Sciences)
Examining committee member and department
James Ramsay (Psychology, McGill University)
Lin, Yanping (Mathematical and Statistical Sciences)
Reuter Gerhard (Earth and Atmosphere Sciences)
Bowman, John (Mathematical and Statistical Sciences)
Department
Department of Mathematical and Statistical Sciences
Specialization

Date accepted
2010-02-02T22:41:22Z
Graduation date
2010-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
This thesis develops an optimal interpolation method that takes daily precipitation values collected from weather stations and produces precipitation estimates on a grid. The method, called Hybrid 2.0, combines EOF-based linear interpolation with the nearest-station method. Gridded monthly precipitation is first obtained via EOF, then distributed among days via nearest station. Hybrid 2.0 builds on an earlier method, called Hybrid 1.0, that applies an inverse-distance weighting method to obtain gridded monthly values. Hybrid 2.0 uses these monthly Hybrid 1.0 values as inputs when constructing EOF functions. The data used in this thesis were obtained from the Meteorological Service of Canada. Few weather stations were located in the northern and mountain regions of Alberta prior to 1950. As a result, the Hybrid 1.0 gridded results underestimate precipitation in these regions for that period. The main contribution of Hybrid 2.0 is a substantial reduction in this bias, obtained by implicitly taking topographic elevation into account. Bias reduction is achieved by extracting EOFs from Hybrid 1.0 output for 1951-2002, when many more stations were present in the northern and mountain regions. Hybrid 2.0 is shown to be more accurate in interpolating both monthly and daily precipitation in Alberta, when compared with Hybrid 1.0 and other methods. The thesis also provides detailed analyses of precipitation trends and droughts using the gridded Hybrid 2.0 daily values. Optimality of the selected EOF modes and sensitivity to data error in the EOF-based linear reconstruction are also discussed in this thesis. Agricultural uses of historical climate data have become extremely important. Applications include: enabling prompt, optimal decisions on market prices and disaster aid, designing future agricultural practices such as adaptation to climate and technology changes, and managing risks for agricultural producers and governments in areas such as drought monitoring. Many applications require a reliable interpolation technique to accurately reconstruct daily climate estimates onto grids of various resolutions. The gridded Hybrid 2.0 daily precipitation values produced by this thesis satisfy this requirement and can be used as inputs for many agricultural applications.
Language
English
DOI
doi:10.7939/R3RS91
Rights
License granted by QINGFANG DAI (qdai@math.ualberta.ca) on 2010-02-02T18:59:11Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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