DOI: 10.5194/hess-20-3601-2016
Scopus记录号: 2-s2.0-84986277821
论文题名: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts
作者: Crochemore L ; , Ramos M ; -H ; , Pappenberger F
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 9 起始页码: 3601
结束页码: 3618
语种: 英语
Scopus关键词: Catchments
; Mapping
; Reliability
; Reservoir management
; Reservoirs (water)
; Risk assessment
; Risk management
; Runoff
; Stream flow
; Water supply
; Weather forecasting
; Bias-correction methods
; Corrected precipitation
; Empirical distributions
; Hydrological modeling
; Hydropower reservoirs
; Precipitation forecast
; Seasonal precipitations
; Streamflow forecasting
; Forecasting
; accuracy assessment
; benchmarking
; catchment
; error correction
; forecasting method
; hydrological modeling
; precipitation assessment
; streamflow
; France
英文摘要: Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability. © Author(s) 2016. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78744
Appears in Collections: 气候变化事实与影响
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作者单位: Irstea, Hydrosystems and Bioprocesses Research Unit, 1 rue Pierre Gilles de Gennes, Antony, France; ECMWF, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, United Kingdom; School of Geographical Sciences, University of Bristol, University Road, Bristol, United Kingdom; Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
Recommended Citation:
Crochemore L,, Ramos M,-H,et al. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts[J]. Hydrology and Earth System Sciences,2016-01-01,20(9)