Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025727 |
A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification | |
Vinh Ngoc Tran1; Dwelle, M. Chase2; Sargsyan, Khachik3; Ivanov, Valeriy Y.2; Kim, Jongho1 | |
2020-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH
![]() |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | South Korea; USA |
英文摘要 | A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so-called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to similar to 80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high-fidelity hydrologic and hydraulic models should be increasingly adopted for real-time and ensemble flood forecasting. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000538000800031 |
WOS关键词 | CHAIN MONTE-CARLO ; HYDROLOGICAL DATA ASSIMILATION ; SEQUENTIAL DATA ASSIMILATION ; POLYNOMIAL CHAOS EXPANSION ; STATE-PARAMETER ESTIMATION ; KALMAN FILTER ; STREAMFLOW OBSERVATIONS ; INTERNAL VARIABILITY ; SENSITIVITY-ANALYSIS ; INITIAL CONDITION |
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/280580 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Ulsan, Sch Civil & Environm Engn, Ulsan, South Korea; 2.Univ Michigan, Dept Civil & Environm Engn, Ann Arbor, MI 48109 USA; 3.Sandia Natl Labs, Livermore, CA USA |
推荐引用方式 GB/T 7714 | Vinh Ngoc Tran,Dwelle, M. Chase,Sargsyan, Khachik,et al. A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification[J]. WATER RESOURCES RESEARCH,2020,56(3). |
APA | Vinh Ngoc Tran,Dwelle, M. Chase,Sargsyan, Khachik,Ivanov, Valeriy Y.,&Kim, Jongho.(2020).A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification.WATER RESOURCES RESEARCH,56(3). |
MLA | Vinh Ngoc Tran,et al."A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification".WATER RESOURCES RESEARCH 56.3(2020). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论