Projection of future extreme precipitation: a robust assessment of downscaled daily precipitation

Hoa X. Pham (), Asaad Y. Shamseldin and Bruce W. Melville
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Hoa X. Pham: The University of Auckland
Asaad Y. Shamseldin: The University of Auckland
Bruce W. Melville: The University of Auckland

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 107, issue 1, No 14, 329 pages

Abstract: Abstract Statistical and dynamic downscaling approaches are commonly used to downscale large-scale climatic variables from global circulation (GCM) and regional circulation (RCM) model outputs to local precipitation. The performance of these two approaches may differ from each other for daily precipitation projections when applied in the same region. This is examined in this study based on the estimation of extreme precipitation. Daily precipitation series are generated from GCM HadCM3, CGCM3/T47 and RCM HadCM3 models for both historical hindcasts and future projections in accordance with the period from 1971 to 2070. The Waikato catchment of New Zealand is selected as a case study. Deterministic and probabilistic performances of the GCM and RCM simulations are evaluated using root-mean-square-error (RMSE) coefficient, percent bias (PBIAS) coefficient and equitable threat score (ETS). The value of RMSE, PBIAS and ETS is 2.89, − 2.16, 0.171 and 8.72, − 4.01, 0.442 for mean areal and at-site daily precipitation estimations, respectively. The study results reveal that the use of frequency analysis of partial duration series (FA/PDS) is very effective in evaluating the accuracy of downscaled daily precipitation series. Both the statistical and the dynamic downscaling perform well for simulating daily precipitation at station level for a return period equal to or less than 100 years. However, the latter outperforms the former for daily precipitation simulation at catchment level.

Keywords: Daily precipitation; Extreme precipitation; Statistical downscaling; Dynamic downscaling; CGCM3; HadCM3; Regional frequency analysis; GP/PDS (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04584-1

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