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.
Data assimilation in surface water quality modeling: A review.
K. Cho,Y. Pachepsky,Mayzonee Ligaray,Y. Kwon,Kyunghyun Kim
Published 2020 in Water Research
ABSTRACT
PUBLICATION RECORD
- Publication year
2020
- Venue
Water Research
- Publication date
2020-08-16
- Fields of study
Medicine, Computer Science, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- data assimilation
A modeling approach that incorporates observations into a dynamic system model to update states, parameters, or conditions as data become available.
Aliases: DA
- novel data sources
New observational inputs that can be incorporated into data assimilation workflows for water quality modeling.
Aliases: new data sources
- sensitivity analysis
A method for identifying which inputs or parameters most strongly influence model outputs.
- spatial measurement-model mismatch
The lack of alignment between the spatial resolution or dimensional detail of observations and the discretization used in a model.
Aliases: scale mismatch between measurements and model discretization
- structural uncertainty
Uncertainty caused by the model form, governing equations, or process representation rather than by parameter values alone.
- subgrid heterogeneity
Variation within an unresolved model cell or scale that is not explicitly represented by the grid.
- surface water quality modeling
Simulation of water-quality processes in surface-water systems such as rivers, lakes, and reservoirs.
- temporally stable spatial patterns
Persistent spatial structure in water-quality variables that remains relatively constant over time.
Aliases: stable spatial patterns
- uncertainty evaluation
Assessment of uncertainty in data, model states, and simulation results.
Aliases: uncertainty in data and modeling results
- water quality management
The use of water-quality model outputs to support operational or policy decisions.
REFERENCES
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