Ensemble data assimilation methods for improving river water quality forecasting accuracy

Sibren Loos*, Chang Min Shin, Julius Sumihar, Kyunghyun Kim, Jaegab Cho, Albrecht H. Weerts

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article number115343
JournalWater Research
Volume171
DOIs
Publication statusPublished - 15 Mar 2020

Fingerprint

data assimilation
Water quality
river water
Rivers
water quality
Algae
Kalman filter
Kalman filters
alga
Phosphates
experiment
Experiments
phosphate
twenty first century
Hazardous materials spills
algal bloom
perturbation
method
Calibration
calibration

Keywords

  • Data assimilation
  • Ensemble kalman filter
  • River basin modelling
  • Water quality forecasting

Cite this

Loos, Sibren ; Shin, Chang Min ; Sumihar, Julius ; Kim, Kyunghyun ; Cho, Jaegab ; Weerts, Albrecht H. / Ensemble data assimilation methods for improving river water quality forecasting accuracy. In: Water Research. 2020 ; Vol. 171.
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Ensemble data assimilation methods for improving river water quality forecasting accuracy. / Loos, Sibren; Shin, Chang Min; Sumihar, Julius; Kim, Kyunghyun; Cho, Jaegab; Weerts, Albrecht H.

In: Water Research, Vol. 171, 115343, 15.03.2020.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Ensemble data assimilation methods for improving river water quality forecasting accuracy

AU - Loos, Sibren

AU - Shin, Chang Min

AU - Sumihar, Julius

AU - Kim, Kyunghyun

AU - Cho, Jaegab

AU - Weerts, Albrecht H.

PY - 2020/3/15

Y1 - 2020/3/15

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AB - 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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