DOI: 10.5194/hess-19-1-2015
Scopus记录号: 2-s2.0-84920809106
论文题名: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts
作者: Li M ; , Wang Q ; J ; , Bennett J ; C ; , Robertson D ; E
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 1 起始页码: 1
结束页码: 15
语种: 英语
Scopus关键词: Climate models
; Forecasting
; Hydrology
; Stream flow
; Adverse effect
; Auto regressive models
; Auto-regressive
; Hydrological modeling
; Rainfall-runoff models
; Stand -alone
; Streamflow forecast
; Streamflow forecasting
; Errors
; forecasting method
; hydrological modeling
; performance assessment
; rainfall-runoff modeling
; streamflow
英文摘要: For streamflow forecasting, rainfall-runoff models are often augmented with updating procedures that correct forecasts based on the latest available streamflow observations of streamflow. A popular approach for updating forecasts is autoregressive (AR) models, which exploit the "memory" in hydrological model simulation errors. AR models may be applied to raw errors directly or to normalised errors. In this study, we demonstrate that AR models applied in either way can sometimes cause over-correction of forecasts. In using an AR model applied to raw errors, the over-correction usually occurs when streamflow is rapidly receding. In applying an AR model to normalised errors, the over-correction usually occurs when streamflow is rapidly rising. In addition, when parameters of a hydrological model and an AR model are estimated jointly, the AR model applied to normalised errors sometimes degrades the stand-alone performance of the base hydrological model. This is not desirable for forecasting applications, as forecasts should rely as much as possible on the base hydrological model, with updating only used to correct minor errors. To overcome the adverse effects of the conventional AR models, a restricted AR model applied to normalised errors is introduced. We show that the new model reduces over-correction and improves the performance of the base hydrological model considerably. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78653
Appears in Collections: 气候变化事实与影响
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作者单位: CSIRO Digital Productivity Flagship, Floreat, WA, Australia; CSIRO Land and Water Flagship, Highett, VIC, Australia
Recommended Citation:
Li M,, Wang Q,J,et al. A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts[J]. Hydrology and Earth System Sciences,2015-01-01,19(1)