Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025237 |
Recursive Bayesian Estimation of Conceptual Rainfall-Runoff Model Errors in Real-Time Prediction of Streamflow | |
Tajiki, M.1; Schoups, G.2; Hendricks Franssen, H. J.3; Najafinejad, A.1; Bahremand, A.1 | |
2020-02-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | Iran; Netherlands; Germany |
英文摘要 | Conceptual rainfall-runoff models account for the spatial dynamics of hydrological processes in a basin using simple spatially lumped storage-flow relations. Such rough approximations introduce model errors that are often difficult to characterize. Here, we develop and apply a methodology that recursively estimates and accounts for model errors in real-time streamflow prediction settings by adding time-dependent random noise to the internal states (storages) of the hydrological model. Magnitude of the added noise depends on a precision (inverse variance) parameter that is estimated from rainfall-runoff data. A recursive Bayesian technique is used for estimation: posteriors of hydrological parameters and states are updated through time with an ensemble Kalman filter, whereas the posterior of the precision parameter is updated recursively using a novel gamma density approximation technique. Applying this algorithm to different model error scenarios allows identification of the main source of model errors. The methodology is applied to short-term streamflow prediction with the Hymod rainfall-runoff model in a semi-cold, semi-humid basin in Iran. Results show that (i) streamflow prediction in this snow-dominated basin is more affected by model errors in the slow flow than the quick flow component of the model, (ii) accounting for model errors in the slow flow component improves both low and high flow predictions, and (iii) predictive performance further improves by accounting for Hymod parameter uncertainty in addition to model errors. Overall, accounting for model errors increased Nash-Sutcliffe efficiency (by 1-5%), reduced mean absolute error (by 2-43%), and improved probabilistic predictive performance (by 50-80%). |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000535672800052 |
WOS关键词 | ENSEMBLE KALMAN FILTER ; ADAPTIVE COVARIANCE INFLATION ; STATE-PARAMETER ESTIMATION ; DATA ASSIMILATION ; SEQUENTIAL STATE ; VARIABLES ; SCHEME |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/280552 |
专题 | 资源环境科学 |
作者单位 | 1.Gorgan Univ Agr Sci & Nat Resources, Dept Watershed Management, Gorgan, Golestan, Iran; 2.Delft Univ Technol, Dept Water Management, Delft, Netherlands; 3.Res Ctr Julich, Inst Bio & Geosci Agrosphere, Julich, Germany |
推荐引用方式 GB/T 7714 | Tajiki, M.,Schoups, G.,Hendricks Franssen, H. J.,et al. Recursive Bayesian Estimation of Conceptual Rainfall-Runoff Model Errors in Real-Time Prediction of Streamflow[J]. WATER RESOURCES RESEARCH,2020,56(2). |
APA | Tajiki, M.,Schoups, G.,Hendricks Franssen, H. J.,Najafinejad, A.,&Bahremand, A..(2020).Recursive Bayesian Estimation of Conceptual Rainfall-Runoff Model Errors in Real-Time Prediction of Streamflow.WATER RESOURCES RESEARCH,56(2). |
MLA | Tajiki, M.,et al."Recursive Bayesian Estimation of Conceptual Rainfall-Runoff Model Errors in Real-Time Prediction of Streamflow".WATER RESOURCES RESEARCH 56.2(2020). |
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