Climate Dynamics

, Volume 46, Issue 3–4, pp 967–986 | Cite as

Validation of non-stationary precipitation series for site-specific impact assessment: comparison of two statistical downscaling techniques

Article

Abstract

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

Keywords

Climate impacts Statistical downscaling Precipitation Non-stationary series Validation 

Notes

Acknowledgments

The authors wish to thank Dr Bofu Yu from Griffith University, Australia, for providing daily precipitation data for Cataract Dam and Port Macquarie stations and to Dr Alfredo Borges de Campos from Universidade Federal de Goias, Brazil, for precipitation data for Campinas station. Thanks must also go to Prof. Rob Wilby and Dr Christian Dawson for use of their SDSM model and to NOAA for the availability of predictor variables.

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Geography, Archaeology and PalaeoecologyQueen’s University BelfastBelfast, County AntrimNorthern Ireland, UK
  2. 2.Department of Construction Engineering, École de Technologie SupérieureUniversité du QuébecMontrealCanada
  3. 3.Grazinglands Research LaboratoryUSDA-ARSEl RenoUSA

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