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Article: The extended Kalman filter for forecast of algal bloom dynamics

TitleThe extended Kalman filter for forecast of algal bloom dynamics
Authors
KeywordsAlgal blooms
Data assimilation
Dissolved oxygen
Ecosystem model
Eutrophication
Extended Kalman filter
Fish culture zone
Short term forecast
Issue Date2009
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/watres
Citation
Water Research, 2009, v. 43 n. 17, p. 4214-4224 How to Cite?
AbstractA deterministic ecosystem model is combined with an extended Kalman filter (EKF) to produce short term forecasts of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly flushed FCZ is modelled as a well-mixed system; the tidal exchange with the outer bay is lumped into a flushing rate that is numerically determined from a three-dimensional hydrodynamic model. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. High frequency field observations of chlorophyll, dissolved oxygen (DO) and hydro-meteorological parameters (sampling interval Δt = 1 day, 2 h, 1 h, respectively) and bi-weekly nutrient data are assimilated into the model to produce the combined state estimate accounting for the uncertainties. In addition to the water quality state variables, the EKF incorporates dynamic estimation of algal growth rate and settling velocity. The effectiveness of the EKF data assimilation is studied for a wide range of sampling intervals and prediction lead-times. The chlorophyll and dissolved oxygen estimated by the EKF are compared with field data of seven algal bloom events observed at Lamma Island, Hong Kong. The results show that the EKF estimate well captures the nonlinear error evolution in time; the chlorophyll level can be satisfactorily predicted by the filtered model estimate with a mean absolute error of around 1-2 μg/L. Predictions with 1-2 day lead-time are highly correlated with the observations (r = 0.7-0.9); the correlation stays at a high level for a lead-time of 3 days (r = 0.6-0.7). Estimated algal growth and settling rates are in accord with field observations; the more frequent DO data can compensate for less frequent algal biomass measurements. The present study is the first time the EKF is successfully applied to forecast an entire algal bloom cycle, suggesting the possibility of using EKF for real time forecast of algal bloom dynamics. © 2009 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/124593
ISSN
2015 Impact Factor: 5.991
2015 SCImago Journal Rankings: 2.772
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grants CouncilHKU 7110/04E
University Grants Committee of the Hong Kong Special Administrative Region, ChinaAoE/P-04/04
Agriculture, Fisheries and Conservation Department (AFCD)
Environmental Protection Department (EPD) of the Hong Kong Government
Funding Information:

This work is supported by the Hong Kong Research Grants Council (project no. HKU 7110/04E) and partially by a grant from the University Grants Committee of the Hong Kong Special Administrative Region, China (project no. AoE/P-04/04). The support of the Agriculture, Fisheries and Conservation Department (AFCD) and the Environmental Protection Department (EPD) of the Hong Kong Government are gratefully acknowledged.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorMao, JQen_HK
dc.contributor.authorLee, JHWen_HK
dc.contributor.authorChoi, KWen_HK
dc.date.accessioned2010-10-31T10:43:07Z-
dc.date.available2010-10-31T10:43:07Z-
dc.date.issued2009en_HK
dc.identifier.citationWater Research, 2009, v. 43 n. 17, p. 4214-4224en_HK
dc.identifier.issn0043-1354en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124593-
dc.description.abstractA deterministic ecosystem model is combined with an extended Kalman filter (EKF) to produce short term forecasts of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly flushed FCZ is modelled as a well-mixed system; the tidal exchange with the outer bay is lumped into a flushing rate that is numerically determined from a three-dimensional hydrodynamic model. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. High frequency field observations of chlorophyll, dissolved oxygen (DO) and hydro-meteorological parameters (sampling interval Δt = 1 day, 2 h, 1 h, respectively) and bi-weekly nutrient data are assimilated into the model to produce the combined state estimate accounting for the uncertainties. In addition to the water quality state variables, the EKF incorporates dynamic estimation of algal growth rate and settling velocity. The effectiveness of the EKF data assimilation is studied for a wide range of sampling intervals and prediction lead-times. The chlorophyll and dissolved oxygen estimated by the EKF are compared with field data of seven algal bloom events observed at Lamma Island, Hong Kong. The results show that the EKF estimate well captures the nonlinear error evolution in time; the chlorophyll level can be satisfactorily predicted by the filtered model estimate with a mean absolute error of around 1-2 μg/L. Predictions with 1-2 day lead-time are highly correlated with the observations (r = 0.7-0.9); the correlation stays at a high level for a lead-time of 3 days (r = 0.6-0.7). Estimated algal growth and settling rates are in accord with field observations; the more frequent DO data can compensate for less frequent algal biomass measurements. The present study is the first time the EKF is successfully applied to forecast an entire algal bloom cycle, suggesting the possibility of using EKF for real time forecast of algal bloom dynamics. © 2009 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/watresen_HK
dc.relation.ispartofWater Researchen_HK
dc.subjectAlgal bloomsen_HK
dc.subjectData assimilationen_HK
dc.subjectDissolved oxygenen_HK
dc.subjectEcosystem modelen_HK
dc.subjectEutrophicationen_HK
dc.subjectExtended Kalman filteren_HK
dc.subjectFish culture zoneen_HK
dc.subjectShort term forecasten_HK
dc.subject.meshEcosystemen_HK
dc.subject.meshEukaryota - growth & developmenten_HK
dc.subject.meshFiltrationen_HK
dc.subject.meshForecastingen_HK
dc.subject.meshModels, Theoreticalen_HK
dc.titleThe extended Kalman filter for forecast of algal bloom dynamicsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0043-1354&volume=43&spage=4214&epage=4224&date=2009&atitle=The+extended+Kalman+filter+for+forecast+of+algal+bloom+dynamicsen_HK
dc.identifier.emailLee, JHW: hreclhw@hku.hken_HK
dc.identifier.emailChoi, KW: choidkw@hkucc.hku.hken_HK
dc.identifier.authorityLee, JHW=rp00061en_HK
dc.identifier.authorityChoi, KW=rp00107en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.watres.2009.06.012en_HK
dc.identifier.pmid19577268-
dc.identifier.scopuseid_2-s2.0-69949085086en_HK
dc.identifier.hkuros175620en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-69949085086&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume43en_HK
dc.identifier.issue17en_HK
dc.identifier.spage4214en_HK
dc.identifier.epage4224en_HK
dc.identifier.isiWOS:000270629500014-
dc.publisher.placeUnited Kingdomen_HK
dc.relation.projectData assimilation for forecasting of coastal water quality-
dc.identifier.scopusauthoridMao, JQ=15042639700en_HK
dc.identifier.scopusauthoridLee, JHW=36078318900en_HK
dc.identifier.scopusauthoridChoi, KW=25627214800en_HK
dc.identifier.citeulike5065229-

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