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Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 559342
Title Ensemble data assimilation methods for improving river water quality forecasting accuracy
Author(s) Loos, Sibren; Shin, Chang Min; Sumihar, Julius; Kim, Kyunghyun; Cho, Jaegab; Weerts, Albrecht H.
Source Water Research 171 (2020). - ISSN 0043-1354
Department(s) Hydrology and Quantitative Water Management
Publication type Refereed Article in a scientific journal
Publication year 2020
Keyword(s) Data assimilation - Ensemble kalman filter - River basin modelling - Water quality forecasting

River water quality is one of the main challenges that societies face during the 21st century. Accurate and reliable real-time prediction of water quality is an effective adaptation measure to counteract water quality issues such as accidental spill and harmful algae blooms. To improve accuracy and skill of water quality forecasts along the Yeongsan River in South Korea three different ensemble data assimilation (DA) methods have been investigated: the traditional Ensemble Kalman Filter (EnKF) and two related algorithms (Dud-EnKF and EnKF-GS) that offer either possibilities to improve initial conditions for non-linear models or reduce computation time (important for real-time forecasting) by using a (smaller) time-lagged ensemble to estimate the Kalman gain. Twin experiments, assimilating synthetic observations of three algae species and phosphate concentrations, with relatively small ensemble sizes showed that all three DA methods improved forecast accuracy and skill with only subtle difference between the methods. They all improved the model accuracy at downstream locations with very similar performances but due to spurious correlation, the accuracy at upstream locations was somewhat deteriorated. The experiments also showed no clear trend of improvement by increasing the ensemble size from 8 to 64. The real world experiments, assimilating real observations of three algae species and phosphate concentrations, showed that less improvement was achieved compared to the twin experiments. Further improvement of the model accuracy may be achieved with different state variable definitions, use of different perturbation and error modelling settings and/or better calibration of the deterministic water quality model.

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