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Ecosystems

, Volume 22, Issue 4, pp 892–911 | Cite as

Estimating Ecosystem Metabolism to Entire River Networks

  • Tamara Rodríguez-CastilloEmail author
  • Edurne Estévez
  • Alexia María González-Ferreras
  • José Barquín
Article

Abstract

River ecosystem metabolism (REM) is a promising cost-effective measure of ecosystem functioning, as it integrates many different ecosystem processes and is affected by both rapid (primary productivity) and slow (organic matter decomposition) energy channels of the riverine food web. We estimated REM in 41 river reaches in Deva-Cares catchment (northern Spain) during the summer period. We used oxygen mass-balance techniques in which primary production and ecosystem respiration were calculated from oxygen concentration daily curves. Then, we used recently developed spatial statistical methods for river networks based on covariance structures to model REM to all river reaches within the river network. From the observed data and the modeled values, we show how REM spatial patterns are constrained by different river reach characteristics along the river network. In general, the autotrophy increases downstream, although there are some reaches associated to groundwater discharges and to different human activities (deforestation or sewage outflows) that disrupt this pattern. GPP was better explained by a combination of ecosystem size, nitrate concentration and amount of benthic chlorophyll a, whereas ER was better explained by spatial patterns of GPP plus minimum water temperatures. The presented methodological approach improves REM predictions for river networks compared to currently used methods and provides a good framework to orientate spatial measures for river functioning restoration and for global change mitigation. To reduce uncertainty and model errors, a higher density of sampling points should be used and especially in the smaller tributaries.

Keywords

spatial modeling river ecosystem metabolism primary production ecosystem respiration ecosystem functioning river network virtual watershed SSN model 

Notes

Acknowledgements

This study was partly funded by the Spanish Ministry of Economy and Competitiveness (MINECO) as part of the project HYDRA (REF: BIA2015-71197). José Barquín was supported by a Ramón y Cajal grant (Ref: RYC-2011-08313) of the Ministry of Economy and Science of the Total Environment Competitiveness. We are also grateful to the University of Cantabria for the funding to Tamara Rodríguez-Castillo through a Postgraduate Grant.

Supplementary material

10021_2018_311_MOESM1_ESM.docx (3 mb)
Supplementary material 1 (DOCX 3067 kb)

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Authors and Affiliations

  1. 1.Environmental Hydraulics InstituteUniversidad de CantabriaSantanderSpain

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