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Adaptive forecasting of phytoplankton communities

Research output: Contribution to journalJournal article

E-pub ahead of print
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<mark>Journal publication date</mark>1/05/2018
<mark>Journal</mark>Water Research
Volume134
Number of pages12
Pages (from-to)74-85
<mark>State</mark>E-pub ahead of print
Early online date5/02/18
<mark>Original language</mark>English

Abstract

The global proliferation of harmful algal blooms poses an increasing threat to water resources, recreation and ecosystems. Predicting the occurrence of these blooms is therefore needed to assist water managers in making management decisions to mitigate their impact. Evaluation of the potential for forecasting of algal blooms using the phytoplankton community model PROTECH was undertaken in pseudo-real-time. This was achieved within a data assimilation scheme using the Ensemble Kalman Filter to allow uncertainties and model nonlinearities to be propagated to forecast outputs. Tests were made on two mesotrophic lakes in the English Lake District, which differ in depth and nutrient regime. Some forecasting success was shown for chlorophyll a, but not all forecasts were able to perform better than a persistence forecast. There was a general reduction in forecast skill with increasing forecasting period but forecasts for up to four or five days showed noticeably greater promise than those for longer periods. Associated forecasts of phytoplankton community structure were broadly consistent with observations but their translation to cyanobacteria forecasts was challenging owing to the interchangeability of simulated functional species.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Water Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Water Research, 134, 2018 DOI: 10.1016/j.watres.2018.01.046