DOI: 10.5194/hess-24-2017-2020
论文题名: A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context
作者: Berthet L. ; Bourgin F. ; Perrin C. ; Viatgé J. ; Marty R. ; Piotte O.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 4 起始页码: 2017
结束页码: 2041
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
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). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162717
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
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]. Hydrology and Earth System Sciences,2020-01-01,24(4)