Adaptive forecasting of phytoplankton communities

Page, Trevor; Smith, Paul J.; Beven, Keith J.; Jones, Ian D.; Elliott, J. Alex; Maberly, Stephen C. ORCID: https://orcid.org/0000-0003-3541-5903; Mackay, Eleanor B. ORCID: https://orcid.org/0000-0001-5697-7062; De Ville, Mitzi; Feuchtmayr, Heidrun. 2018 Adaptive forecasting of phytoplankton communities. Water Research, 134. 74-85. https://doi.org/10.1016/j.watres.2018.01.046

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Official URL: https://doi.org/10.1016/j.watres.2018.01.046

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.watres.2018.01.046
UKCEH and CEH Sections/Science Areas:	Water Resources (Science Area 2017-)
ISSN:	0043-1354
Additional Keywords:	phytoplankton model, forecasting, data assimilation, ensemble Kalman filter, cyanobacteria, PROTECH
NORA Subject Terms:	Ecology and Environment Management
Date made live:	08 Feb 2018 12:28 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/519210

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.watres.2018.01.046

UKCEH and CEH Sections/Science Areas:

Water Resources (Science Area 2017-)

ISSN:

0043-1354

Additional Keywords:

phytoplankton model, forecasting, data assimilation, ensemble Kalman filter, cyanobacteria, PROTECH

NORA Subject Terms:

Ecology and Environment
Management