Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic shallow lake governed by extreme meteorological events

Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic... •We combined powerful statistical tools and physically-based models to analyze and predict key processes of phytoplankton dynamics in a eutrophic shallow lake destined for drinking water.•An 11-year time series of meteorological, hydrological, physicochemical and biological variables, continuously measured by water managers was considered.•The models were able to explain a significant part of the variability of phytoplankton biomass and composition and presented acceptable predictive accuracy.•In the current water quality situation, predictive models are crucial to anticipate non-desirable water quality conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic shallow lake governed by extreme meteorological events

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/j.ecolmodel.2017.06.017
Publisher site
See Article on Publisher Site

Abstract

•We combined powerful statistical tools and physically-based models to analyze and predict key processes of phytoplankton dynamics in a eutrophic shallow lake destined for drinking water.•An 11-year time series of meteorological, hydrological, physicochemical and biological variables, continuously measured by water managers was considered.•The models were able to explain a significant part of the variability of phytoplankton biomass and composition and presented acceptable predictive accuracy.•In the current water quality situation, predictive models are crucial to anticipate non-desirable water quality conditions.

Journal

Ecological ModellingElsevier

Published: Sep 24, 2017

References

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