Data assimilation in surface water quality modeling: a review

Authors: CHO, KYUNGHWA - Ulsan National Institute Of Science And Technology (UNIST); Pachepsky, Yakov; LIGARAY, MAYZONEE - Ulsan National Institute Of Science And Technology (UNIST); KWON, YONGSUNG - Ulsan National Institute Of Science And Technology (UNIST)

Submitted to: Water Research
Publication Type: Review Article
Publication Acceptance Date: 8/15/2020
Publication Date: 8/16/2020
Citation: Cho, K., Pachepsky, Y.A., Ligaray, M., Kwon, Y. 2020. Data assimilation in surface water quality modeling: a review. Water Research. https://doi.org/10.1016/j.watres.2020.116307.
DOI: https://doi.org/10.1016/j.watres.2020.116307

Interpretive Summary:

Technical Abstract: Data assimilation techniques allow to use data as soon as they appear to improve model predictions and reducing their uncertainty by correcting the state variables, model parameters, and boundary and initial conditions. The objective of the review is to explore existing approaches and advances in DA applications to the surface water quality modeling and identify the future research prospects. We first review the DA methods used in the water quality modeling literature, and then address observations and suggestions regarding various factors of DA performance, such as mismatch between both lateral and vertical spatial domains 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 is concluded with the outlook section that outlines current challenges and opportunities.