DOI: 10.5194/hess-19-275-2015
Scopus记录号: 2-s2.0-84921346683
论文题名: The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models
作者: Demirel M ; C ; , Booij M ; J ; , Hoekstra A ; Y
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 1 起始页码: 275
结束页码: 291
语种: 英语
Scopus关键词: Atmospheric movements
; Calibration
; Climate models
; Forecasting
; Hydrology
; Neural networks
; Runoff
; Calibration and validations
; Data-driven model
; Hydrological models
; Meteorological forcing
; Meteorological input
; Model outputs
; Potential evapotranspiration
; Precipitation data
; Weather forecasting
; artificial neural network
; ensemble forecasting
; hydrological modeling
; performance assessment
; uncertainty analysis
; Moselle River
; Hepatitis B virus
英文摘要: This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78638
Appears in Collections: 气候变化事实与影响
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作者单位: Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, AE Enschede, Netherlands; Portland State University, Department of Civil and Environmental Engineering, 1930 S. W. 4th Avenue, Portland, OR, United States
Recommended Citation:
Demirel M,C,, Booij M,et al. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models[J]. Hydrology and Earth System Sciences,2015-01-01,19(1)