High-resolution water-quality and ecosystem-metabolism modelling 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, Mike ORCID: https://orcid.org/0000-0002-0673-1934; Edwards, Francois; Old, Gareth ORCID: https://orcid.org/0000-0002-4713-1070. 2022 High-resolution water-quality and ecosystem-metabolism modelling in lowland rivers. Limnology and Oceanography, 67 (6). 1313-1327. https://doi.org/10.1002/lno.12079

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Abstract/Summary

High-resolution monitoring of water quality and ecosystem functioning over large spatial scales in expansive lowland river catchments is challenging. Therefore, we need modeling tools to predict these processes at locations where observations are absent. Here, we present a new approach to estimate ecosystem metabolism underpinned by a high-resolution, process-based model of in-stream flows and water quality. The model overcomes the current challenges in metabolism modeling by accounting for oxygen transport under varying flows and oxygen transformations due to biogeochemical processes. We implement the model in a 62-km-long stretch of the River Thames, England, using observations spanning 2 yr. Model outputs suggest that the river is primarily autotrophic from mid-spring to mid-summer due to high biomass during low-flow periods, and is heterotrophic during the rest of the year. Ecosystem respiration in upstream reaches is driven mainly by biochemical oxygen demand, autotrophic respiration, and nitrification processes, whereas downstream sites also show a control of benthic oxygen demand in addition to the aforementioned processes. Using empirical modeling, we analyze the sensitivity of our estimated metabolism rates to multiple environmental stressors. Results demonstrate that empirical models could be useful for rapid river health assessments, but need improvements to reproduce peak metabolism rates. The process-based model, although more complex than existing in situ approaches to metabolism quantification, allows inference when gaps in continuous observations are present. The model offers additional benefits for predicting metabolism rates under future scenarios of environmental change incorporating multiple stressor effects.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/lno.12079
UKCEH and CEH Sections/Science Areas:	Hydro-climate Risks (Science Area 2017-) Pollution (Science Area 2017-) Water Resources (Science Area 2017-)
ISSN:	0024-3590
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Ecology and Environment
Date made live:	26 Apr 2022 11:10 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532536

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