Generation of multi-site stochastic daily rainfall with four weather generators: a case study of Gloucester catchment in Australia

Fu, G., Chiew, F. H.S. and Shi, X. (2018) Generation of multi-site stochastic daily rainfall with four weather generators: a case study of Gloucester catchment in Australia. Theoretical and Applied Climatology, 134(3-4), pp. 1027-1046. (doi: 10.1007/s00704-017-2306-3)

174222.pdf - Accepted Version



Four weather generators, namely, R-package version of the Generalised Linear Model for daily Climate time series (RGLIMCLIM), Stochastic Climate Library (SCL), R-package multi-site precipitation generator (RGENERATRPREC) and R-package Multi-site Auto-regressive Weather GENerator (RMAWGEN), were used to generate multi-sites stochastic daily rainfall for a small catchment in Australia. The results show the following: (1) All four models produced reasonable results in terms of annual, monthly and daily rainfall occurrence and amount, as well as daily extreme, multi-day extremes and dry/wet spell length. However, they also simulated a large range of variability, which not only demonstrates the advantages of multiple weather generators rather than a single model but also is more suitable for climate change and variability impact studies. (2) Every model has its own advantages and disadvantages due to their different theories and principles. This enhances the benefits of using multiple models. (3) The models can be further calibrated/improved to have a “better” performance in comparison with observations. However, it was chosen not to do so in this case study for two reasons: to obtain a full range of climate variability and to acknowledge the uncertainties associated with observation data. The latter are interpolated from limited stations and therefore have high pairwise correlations—ranging from 0.69 to 0.99 with a median and mean value of 0.87 and 0.88, respectively, for daily rainfall. These conclusions were drawn from a case study in Australia, but could be extended to general guidelines of using weather generators for climate change and variability studies.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Shi, Dr John Xiaogang
Authors: Fu, G., Chiew, F. H.S., and Shi, X.
College/School:College of Social Sciences > School of Social & Environmental Sustainability
Journal Name:Theoretical and Applied Climatology
ISSN (Online):1434-4483
Published Online:20 November 2017
Copyright Holders:Copyright © 2017 Springer-Verlag GmbH Austria
First Published:First published in Theoretical and Applied Climatology 134(3-4):1027-1046
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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