气候变化领域集成服务门户(CCPortal): A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

CCPortal

DOI	10.5194/hess-24-2017-2020
	A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context
	Berthet L.; Bourgin F.; Perrin C.; Viatgé J.; Marty R.; Piotte O.
发表日期	2020
ISSN	1027-5606
起始页码	2017
结束页码	2041
卷号	24 期号:4
英文摘要	An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment set-up is based on (i) a large set of catchments in France, (ii) the GRP rainfall-runoff model designed for flood forecasting and used by the French operational services and (iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they use (log, Box-Cox and log-sinh) to account for heteroscedasticity and the evolution of the other properties of the predictive distribution with the discharge magnitude. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality were evaluated. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box-Cox transformation can be a reasonable choice for flood forecasting. © 2020 Author(s).
语种	英语
scopus关键词	Extrapolation; Flood control; Floods; Metadata; Quality control; Runoff; Uncertainty analysis; Box Cox transformation; Hydrologic uncertainty; Predictive distributions; Predictive uncertainty; Probabilistic performance; Rainfall-runoff modeling; Statistical treatment; Uncertainty quantifications; Weather forecasting; catchment; flood; flood forecasting; hydrological modeling; river discharge; uncertainty analysis; France
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159427
作者单位	Berthet, L., DREAL Centre-Val de Loire, Loire Cher and Indre Flood Forecasting Service, Orléans, France; Bourgin, F., GERS-LEE, Univ Gustave Eiffel, IFSTTAR, Bouguenais, 44344, France, Université Paris-Saclay, INRAE, UR HYCAR, Antony, 92160, France; Perrin, C., Université Paris-Saclay, INRAE, UR HYCAR, Antony, 92160, France; Viatgé, J., Université Paris-Saclay, INRAE, UR HYCAR, Antony, 92160, France; Marty, R., DREAL Centre-Val de Loire, Loire Cher and Indre Flood Forecasting Service, Orléans, France; Piotte, O., Ministry for the Ecological and Inclusive Transition, SCHAPI, Toulouse, France
推荐引用方式 GB/T 7714	Berthet L.,Bourgin F.,Perrin C.,et al. A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context[J],2020,24(4).
APA	Berthet L.,Bourgin F.,Perrin C.,Viatgé J.,Marty R.,&Piotte O..(2020).A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context.Hydrology and Earth System Sciences,24(4).
MLA	Berthet L.,et al."A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context".Hydrology and Earth System Sciences 24.4(2020).