Data assimilation in surface water quality modeling: A review

WATER RESEARCH

卷:186

Review

Cho, Kyung Hwa¹  Pachepsky, Yakov²  Ligaray, Mayzonee³  Kwon, Yongsung⁴  Kim, Kyung Hyun⁵ 

[1] Ulsan Natl Inst Sci & Technol, Sch Urban & Environm Engn, Ulsan 689798, South Korea

[2] USDA ARS, Environm Microbial & Food Safety Lab, Beltsville, MD 20705 USA

[3] Univ Philippines Diliman, Coll Sci, Inst Environm Sci & Meteorol, Quezon City 1101, Philippines

[4] Natl Inst Ecol, Div Ecol Assessment Res, Seocheon 33657, South Korea

[5] Natl Inst Environm Res, Watershed & Total Load Management Res Div, Hwangyong Ro 42, Incheon, South Korea

关键词: Water quality model;    Data assimilation;    Variational data assimilation;    Extended Kalman filter;    Ensemble Kalman filter;   

DOI  :  10.1016/j.watres.2020.116307

来源: Elsevier

【 摘 要 】

Data assimilation (DA) techniques are powerful means of dynamic natural system modeling that allow for the use of data as soon as it appears to improve model predictions and reduce prediction uncertainty by correcting state variables, model parameters, and boundary and initial conditions. The objectives of this review are to explore existing approaches and advances in DA applications for surface water qual-ity modeling and to identify future research prospects. We first reviewed the DA methods used in water quality modeling as reported in literature. We then addressed observations and suggestions regarding various factors of DA performance, such as the mismatch between both lateral and vertical spatial detail of measurements and modeling, subgrid heterogeneity, presence of temporally stable spatial patterns in water quality parameters and related biases, evaluation of uncertainty in data and modeling results, mismatch between scales and schedules of data from multiple sources, selection of parameters to be updated along with state variables, update frequency and forecast skill. The review concludes with the outlook section that outlines current challenges and opportunities related to growing role of novel data sources, scale mismatch between model discretization and observation, structural uncertainty of models and conversion of measured to simulated vales, experimentation with DA prior to applications, using DA performance or model selection, the role of sensitivity analysis, and the expanding use of DA in water quality management. Published by Elsevier Ltd.

【 授权许可】

Free