气候变化领域集成服务门户(CCPortal): Robustness of hydrometeorological extremes in surrogated seasonal forecasts

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DOI	10.1002/joc.8407
	Robustness of hydrometeorological extremes in surrogated seasonal forecasts
	Katharina, Klehmet; Berg, Peter; Bozhinova, Denica; Crochemore, Louise; Du, Yiheng; Pechlivanidis, Ilias; Photiadou, Christiana; Yang, Wei
发表日期	2024
ISSN	0899-8418
EISSN	1097-0088
起始页码	44
结束页码	5
卷号	44 期号:5
英文摘要	Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re-forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re-forecasts into 1000-year-long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub-samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re-forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re-forecasts will diverge into independent samples. The meteorological seasonal re-forecasts are taken from the SEAS5 system, and hydrological re-forecasts are generated with the E-HYPE process-based model for the pan-European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100-year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes. A method to generate 1000-of-years long surrogate timeseries for assessing extreme streamflow using seasonal forecast data is presented. Illustrative cases compare conventional bootstrap method on single shorter timeseries to the surrogate data, and show a rather good sampling of the uncertainty, but a lack of precision for the expected value. image
英文关键词	extremes; precipitation; robustness analysis; seasonal forecasts; statistical uncertainty; streamflow
语种	英语
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001179970700001
来源期刊	INTERNATIONAL JOURNAL OF CLIMATOLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304430
作者单位	Swedish Meteorological & Hydrological Institute; Institut de Recherche pour le Developpement (IRD); INRAE; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Centre National de la Recherche Scientifique (CNRS); Universite Grenoble Alpes (UGA); Swedish Meteorological & Hydrological Institute
推荐引用方式 GB/T 7714	Katharina, Klehmet,Berg, Peter,Bozhinova, Denica,et al. Robustness of hydrometeorological extremes in surrogated seasonal forecasts[J],2024,44(5).
APA	Katharina, Klehmet.,Berg, Peter.,Bozhinova, Denica.,Crochemore, Louise.,Du, Yiheng.,...&Yang, Wei.(2024).Robustness of hydrometeorological extremes in surrogated seasonal forecasts.INTERNATIONAL JOURNAL OF CLIMATOLOGY,44(5).
MLA	Katharina, Klehmet,et al."Robustness of hydrometeorological extremes in surrogated seasonal forecasts".INTERNATIONAL JOURNAL OF CLIMATOLOGY 44.5(2024).