Data assimilation in surface water quality modeling: A review

• Published in: Water research (Oxford) Vol. 186; p. 116307
• Main Authors: Cho, Kyung Hwa, Pachepsky, Yakov, Ligaray, Mayzonee, Kwon, Yongsung, Kim, Kyung Hyun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2020
• Subjects: Data assimilation; Ensemble Kalman filter; Extended Kalman filter; Variational data assimilation; Water quality model; Water quality model; Ensemble Kalman filter; Extended Kalman filter; Data assimilation; Variational data assimilation
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2020.116307

•Sources, magnitudes, and controls of the uncertainty is the critical DA research field.•New and multiple sources of data for the DA are becoming available•More needs to be learned with simultaneous water quality state and parameter updates•DA assessment is of interest to changes in the forecast skill as related to the update scheduling.•Need and feasibility of expanding the DA applications exist and can be explored 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 quality 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. [Display omitted]