Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers

Pathak, Devanshi ORCID: https://orcid.org/0000-0003-3290-5149; Hutchins, Michael ORCID: https://orcid.org/0000-0003-3764-5331; Brown, Lee; Loewenthal, Matthew; Scarlett, Peter; Armstrong, Linda; Nicholls, David; Bowes, Michael ORCID: https://orcid.org/0000-0002-0673-1934; Edwards, Francois. 2021 Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers. Water Resources Research, 57 (3), e2020WR028773. 10.1029/2020WR028773

Before downloading, please read NORA policies.

Preview

Text
N529399JA.pdf - Published Version
Available under License Creative Commons Attribution 4.0.
Download (2MB) | Preview

Official URL: https://doi.org/10.1029/2020WR028773

Abstract/Summary

High‐resolution river modeling is valuable to study diurnal scale phytoplankton dynamics and understand biomass response to short‐term, rapid changes in its environmental controls. Based on theory contained in the Quality Evaluation and Simulation Tool for River‐systems model, a new river model is developed to simulate hourly scale phytoplankton growth and its environmental controls, thus allowing to study diurnal changes thereof. The model is implemented along a 62 km stretch in a lowland river, River Thames (England), using high‐frequency water quality measurements to simulate flow, water temperature, dissolved oxygen, nutrients, and phytoplankton concentrations for 2 years (2013–2014). The model satisfactorily simulates diurnal variability and transport of phytoplankton with Nash and Sutcliffe Efficiency (NSE) > 0.7 at all calibration sites. Even without high‐frequency data inputs, the model performs satisfactorily with NSE > 0.6. The model therefore can serve as a powerful tool both for predictive purposes and for hindcasting past conditions when hourly resolution water quality monitoring is unavailable. Model sensitivity analysis shows that the model with cool water diatoms as dominant species with an optimum growth temperature of 14°C performs the best for phytoplankton prediction. Phytoplankton blooms are mainly controlled by residence time, light and water temperature. Moreover, phytoplankton blooms develop within an optimum range of flow (21–63 m3 s−1). Thus, lowering river residence time with short‐term high flow releases could help prevent major bloom developments. The hourly model improves biomass prediction and represents a step forward in high‐resolution phytoplankton modeling and consequently, bloom management in lowland river systems.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1029/2020WR028773
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-) Water Resources (Science Area 2017-)
ISSN:	0043-1397
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	phytoplankton modeling, river water quality, River Thames, algal bloom, high‐frequency data, flow regulation
NORA Subject Terms:	Ecology and Environment
Date made live:	13 Jan 2021 17:06 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529399

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1029/2020WR028773

UKCEH and CEH Sections/Science Areas:

Pollution (Science Area 2017-)
Water Resources (Science Area 2017-)

ISSN:

0043-1397

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

phytoplankton modeling, river water quality, River Thames, algal bloom, high‐frequency data, flow regulation