Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/joc.5375 |
The stationarity of two statistical downscaling methods for precipitation under different choices of cross-validation periods | |
Wang, Yaoping1; Sivandran, Gajan1,2,4; Bielicki, Jeffrey M.1,2,3 | |
2018-04-01 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
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ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2018 |
卷号 | 38页码:E330-E348 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Statistical downscaling methods require the stationarity assumption, that is, the statistical relationship between the grid-scale input and the observed precipitation does not change between present-day and climate change conditions. We implemented a skill score to test the stationarity assumption in two simple and popular statistical downscaling methods, quantile-mapping and the generalized linear model method Rglimclim, in downscaling precipitation in the eastern United States, and examined the sensitivity of the results of the stationarity test to different ways to construct cross-validation periods that differ in climate conditions. The Rglimclim method passed the stationarity test at slightly more stations than quantile-mapping and was less impaired by increase in the resolution of input data. But neither method can be reliably applied to downscale the whole marginal distribution or time series of precipitation at the 54 stations in the study region, and only passed the stationarity test at a few stations on the annual extreme precipitation. We also found that the number of identified non-stationary stations was sensitive to which criterion (chronology, precipitation, temperature, large-scale circulation indices) was used to construct the cross-validation periods, and whether one or several criteria for cross-validation periods were used. These results raise caution against using the two statistical downscaling methods that we examined in climate change impact studies without testing their stationarity assumption, and also point to the need for more research into how to choose cross-validation periods and stationarity metrics in order to maximize their relevance to the reliability of statistical downscaling methods under future climate change. |
英文关键词 | statistical downscaling quantile mapping Rglimclim generalized linear model precipitation eastern United States |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000431999600023 |
WOS关键词 | CLIMATE-CHANGE IMPACTS ; NORTHEASTERN UNITED-STATES ; BIAS CORRECTION ; DAILY RAINFALL ; SIMULATED PRECIPITATION ; NONSTATIONARY CLIMATE ; NORTH-AMERICA ; MODEL ; REANALYSIS ; PERFORMANCE |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/36714 |
专题 | 气候变化 |
作者单位 | 1.Ohio State Univ, Environm Sci Grad Program, Columbus, OH 43210 USA; 2.Ohio State Univ, Dept Civil Environm & Geodet Engn, 483B Hitchcock Hall,2070 Neil Ave, Columbus, OH 43210 USA; 3.Ohio State Univ, John Glenn Coll Publ Affairs, Columbus, OH 43210 USA; 4.Loyola Univ, Dept Engn Sci, Chicago, IL 60611 USA |
推荐引用方式 GB/T 7714 | Wang, Yaoping,Sivandran, Gajan,Bielicki, Jeffrey M.. The stationarity of two statistical downscaling methods for precipitation under different choices of cross-validation periods[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2018,38:E330-E348. |
APA | Wang, Yaoping,Sivandran, Gajan,&Bielicki, Jeffrey M..(2018).The stationarity of two statistical downscaling methods for precipitation under different choices of cross-validation periods.INTERNATIONAL JOURNAL OF CLIMATOLOGY,38,E330-E348. |
MLA | Wang, Yaoping,et al."The stationarity of two statistical downscaling methods for precipitation under different choices of cross-validation periods".INTERNATIONAL JOURNAL OF CLIMATOLOGY 38(2018):E330-E348. |
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