Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR022854 |
Nonparametric Data Assimilation Scheme for Land Hydrological Applications | |
Khaki, M.1,2; Hamilton, F.3; Forootan, E.4; Hoteit, I.5; Awange, J.1; Kuhn, M.1 | |
2018-07-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:7页码:4946-4964 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia; USA; Wales; Saudi Arabia |
英文摘要 | Data assimilation, which relies on explicit knowledge of dynamical models, is a well-known approach that addresses models' limitations due to various reasons, such as errors in input and forcing data sets. This approach, however, requires intensive computational efforts, especially for high-dimensional systems such as distributed hydrological models. Alternatively, data-driven methods offer comparable solutions when the physics underlying the models are unknown. For the first time in a hydrological context, a nonparametric framework is implemented here to improve model estimates using available observations. This method uses Takens delay coordinate method to reconstruct the dynamics of the system within a Kalman filtering framework, called the Kalman-Takens filter. A synthetic experiment is undertaken to fully investigate the capability of the proposed method by comparing its performance with that of a standard assimilation framework based on an adaptive unscented Kalman filter (AUKF). Furthermore, using terrestrial water storage (TWS) estimates obtained from the Gravity Recovery And Climate Experiment mission, both filters are applied to a real case scenario to update different water storages over Australia. In situ groundwater and soil moisture measurements within Australia are used to further evaluate the results. The Kalman-Takens filter successfully improves the estimated water storages at levels comparable to the AUKF results, with an average root-mean-square error reduction of 37.30% for groundwater and 12.11% for soil moisture estimates. Additionally, the Kalman-Takens filter, while reducing estimation complexities, requires a fraction of the computational time, that is, similar to 8 times faster compared to the AUKF approach. |
英文关键词 | nonparametric filtering data assimilation Kalman-Takens adaptive unscented Kalman filtering (AUKF) hydrological modeling |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000442502100043 |
WOS关键词 | SURFACE SOIL-MOISTURE ; WATER STORAGE OBSERVATIONS ; ENSEMBLE KALMAN FILTER ; INTEGRATING GRACE DATA ; MURRAY-DARLING BASIN ; MODEL ; SIMULATION ; STREAMFLOW ; ERROR ; RETRIEVALS |
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/22059 |
专题 | 资源环境科学 |
作者单位 | 1.Curtin Univ, Sch Earth & Planetary Sci, Discipline Spatial Sci, Perth, WA, Australia; 2.Univ Newcastle, Sch Engn, Callaghan, NSW, Australia; 3.North Carolina State Univ Raleigh, Dept Math, Raleigh, NC USA; 4.Cardiff Univ, Sch Earth & Ocean Sci, Cardiff, S Glam, Wales; 5.King Abdullah Univ Sci & Technol, Div Phys Sci & Engn, Thuwal, Saudi Arabia |
推荐引用方式 GB/T 7714 | Khaki, M.,Hamilton, F.,Forootan, E.,et al. Nonparametric Data Assimilation Scheme for Land Hydrological Applications[J]. WATER RESOURCES RESEARCH,2018,54(7):4946-4964. |
APA | Khaki, M.,Hamilton, F.,Forootan, E.,Hoteit, I.,Awange, J.,&Kuhn, M..(2018).Nonparametric Data Assimilation Scheme for Land Hydrological Applications.WATER RESOURCES RESEARCH,54(7),4946-4964. |
MLA | Khaki, M.,et al."Nonparametric Data Assimilation Scheme for Land Hydrological Applications".WATER RESOURCES RESEARCH 54.7(2018):4946-4964. |
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