19577268

Source:http://linkedlifedata.com/resource/pubmed/id/19577268

Download in:

Switch to

Custom View

Named Graph Language Inference

Statements in which the resource exists as a subject.
Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0002035, umls-concept:C0016538, umls-concept:C0180860, umls-concept:C0231449, umls-concept:C0581406, umls-concept:C1522664, umls-concept:C1546637, umls-concept:C1550638, umls-concept:C1704449, umls-concept:C1704684
pubmed:issue	17
pubmed:dateCreated	2009-9-14
pubmed:abstractText	A 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 Deltat=1 day, 2h, 1h, 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 microg/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.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0105072
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Sep
pubmed:issn	1879-2448
pubmed:author	pubmed-author:ChoiK WKW, pubmed-author:LamK MKM, pubmed-author:LeeJoseph H WJH
pubmed:issnType	Electronic
pubmed:volume	43
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	4214-24
pubmed:dateRevised	2010-11-18
pubmed:meshHeading	pubmed-meshheading:19577268-Ecosystem, pubmed-meshheading:19577268-Eukaryota, pubmed-meshheading:19577268-Filtration, pubmed-meshheading:19577268-Forecasting, pubmed-meshheading:19577268-Models, Theoretical
pubmed:year	2009
pubmed:articleTitle	The extended Kalman filter for forecast of algal bloom dynamics.
pubmed:affiliation	Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong.
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't