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

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• 作者：Donal Mullan ; Jie Chen ; Xunchang John Zhang
• 关键词：Climate impacts ; Statistical downscaling ; Precipitation ; Non ; stationary series ; Validation
• 刊名：Climate Dynamics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：46
• 期：3-4
• 页码：967-986
• 全文大小：1,038 KB
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• 作者单位：Donal Mullan (1)
  Jie Chen (2)
  Xunchang John Zhang (3)
  
  1. School of Geography, Archaeology and Palaeoecology, Queen’s University Belfast, Elmwood Avenue, Belfast, County Antrim, BT7 1NN, Northern Ireland, UK
  2. Department of Construction Engineering, École de Technologie Supérieure, Université du Québec, Montreal, Canada
  3. Grazinglands Research Laboratory, USDA-ARS, El Reno, OK, USA
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Geophysics and Geodesy
  Meteorology and Climatology
  Oceanography
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-0894

文摘

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