DOI: 10.5194/hess-19-3181-2015
Scopus记录号: 2-s2.0-84937775719
论文题名: Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments
作者: Dogulu N ; , López López P ; , Solomatine D ; P ; , Weerts A ; H ; , Shrestha D ; L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 7 起始页码: 3181
结束页码: 3201
语种: 英语
Scopus关键词: Catchments
; Decision making
; Floods
; Forecasting
; Hydrology
; Risk assessment
; Risk management
; Risk perception
; Runoff
; Emergency management
; Hydrologic uncertainty
; Hydrological characteristics
; Hydrological condition
; Operational hydrologies
; Predictive uncertainty
; Risk based decision making
; Uncertainty estimation
; Uncertainty analysis
; catchment
; climate conditions
; decision making
; early warning system
; flood control
; flood forecasting
; flood frequency
; hydrological modeling
; performance assessment
; regression analysis
; risk perception
; uncertainty analysis
英文摘要: In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78468
Appears in Collections: 气候变化事实与影响
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作者单位: UNESCO-IHE Institute for Water Education, Delft, Netherlands; Deltares, Delft, Netherlands; Delft University of Technology, Delft, Netherlands; Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, Netherlands; CSIRO Land and Water, Highett, VIC, Australia; Department of Civil Engineering, Middle East Technical University, Ankara, Turkey; Utrecht University, Utrecht, Netherlands
Recommended Citation:
Dogulu N,, López López P,, Solomatine D,et al. Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments[J]. Hydrology and Earth System Sciences,2015-01-01,19(7)