Ensemble data assimilation methods for improving river water quality forecasting accuracy. (15th March 2020)

• Record Type: Journal Article
• Authors: Loos, Sibren; Shin, Chang Min; Sumihar, Julius; Kim, Kyunghyun; Cho, Jaegab; Weerts, Albrecht H.
• Is Part Of: Water research. Volume 171(2020)
• Publication Date: 2020-03-15
• Subjects: Water quality forecasting -- Data assimilation -- Ensemble kalman filter -- River basin modelling; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2019.115343
• Languages: English
• ISSNs: 0043-1354

Abstract

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. Graphical abstract: Image 1 Highlights: State updating has been tested using an operational water quality model in Korea. A small EnKF ensemble, practical in operational use, shows improved forecast accuracy. Twin experiments provide insight in the performance of data assimilation methods. All ensemble DA methods improve model results downstream of the assimilated location. At upstream locations the model state updates may suffer from spurious correlation.