Variable Physical Drivers of Near-Surface Turbulence in a Regulated River

Sofya Guseva; M. Aurela; Alicia Cortés Cortés; R. Kivi; E. Lotsari; S. MacIntyre; I. Mammarella; A. Ojala; V. Stepanenko; Petteri Uotila; Aki Vähä; Timo Vesala; Marcus Wallin; Andreas Lorke

doi:10.1029/2020WR027939

Variable Physical Drivers of Near-Surface Turbulence in a Regulated River

Sofya Guseva^*
, M. Aurela
, Alicia Cortés Cortés
, R. Kivi
, E. Lotsari
, S. MacIntyre
, I. Mammarella
, A. Ojala
, V. Stepanenko
, Petteri Uotila
, Aki Vähä
, Timo Vesala
, Marcus Wallin
, Andreas Lorke

^*Corresponding author for this work

Not published at Aalto University

Research output: Contribution to journal › Article › Scientific › peer-review

19 Citations (Scopus)

Abstract

Inland waters, such as lakes, reservoirs and rivers, are important sources of climate forcing trace gases. A key parameter that regulates the gas exchange between water and the atmosphere is the gas transfer velocity, which itself is controlled by near-surface turbulence in the water. While in lakes and reservoirs, near-surface turbulence is mainly driven by atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of gravity-forced flow. Large rivers represent a transition between these two cases. Near-surface turbulence has rarely been measured in rivers and the drivers of turbulence have not been quantified. We analyzed continuous measurements of flow velocity and quantified turbulence as the rate of dissipation of turbulent kinetic energy over the ice-free season in a large regulated river in Northern Finland. Measured dissipation rates agreed with predictions from bulk parameters, including mean flow velocity, wind speed, surface heat flux, and with a one-dimensional numerical turbulence model. Values ranged from (Formula presented.) to (Formula presented.). Atmospheric forcing or gravity was the dominant driver of near-surface turbulence for similar fraction of the time. Large variability in near-surface dissipation rate occurred at diel time scales, when the flow velocity was strongly affected by downstream dam operation. By combining scaling relations for boundary-layer turbulence at the river bed and at the air-water interface, we derived a simple model for estimating the relative contributions of wind speed and bottom friction of river flow as a function of depth.

Original language	English
Article number	e2020WR027939
Journal	Water Resources Research
Volume	57
Issue number	11
DOIs	https://doi.org/10.1029/2020WR027939
Publication status	Published - Nov 2021
MoE publication type	A1 Journal article-refereed

Funding

We thank Falk Feddersen for consulting us and Alexander Shamanskiy for mathematical advice. We thank David Bastviken and John Melack for assistance with editing. We are grateful to Daniela Franz, Christoph Bors, Risto Taipale, Anders Lindroth and John Melack for their significant help during field campaign in 2018. We thank Marko Kärkkäinen and Pasi Korpelainen (University of Eastern Finland) for assisting in field work related to the aerial photography of the study area. We thank all people at the field station for organizing the accommodation and food and helping with the instruments and transportation. This work was supported by several funding agencies. Sofya Guseva and Andreas Lorke were supported by the German Research Foundation (DFG) under the grant LO1150/12-1. Mika Aurela was supported by the Academy of Finland (project 296888). Alicia Cortés and Sally MacIntyre were supported by the U.S. N.S.F. 1737411. Eliisa Lotsari was supported by The Department of Geographical and Historical Studies, University of Eastern Finland. Ivan Mammarella and Timo Vesala thank the European Union for supporting the RINGO project funded by the Horizon 2020 Research and Innovation Programme under Grant Agreement 730944. Aki Vähä and Timo Vesala were supported by the University of Helsinki ICOS-Finland. In addition, Timo Vesala was supported by the Tyumen region government in accordance with the Program of the World-Class West Siberian Interregional Scientific and Educational Center (National Project “Nauka”). Simulations of river turbulence by the k − ɛ model have been carried out according to the research program of Moscow Center for Fundamental and Applied Mathematics. Victor Stepanenko is grateful to Andrey Glazunov and Andrey Debolskiy for advice in setup of simulations with k − ɛ model; his work was supported by the Russian Foundation for Basic Research (grant 20-05-00773), Russian Ministry for Science and Higher Education (contract No. 075-15-2019-1621), and Grant Council of the President of Russia (grant MD-1850.2020.5). Marcus Bo Wallin was supported by the King Carl-Gustaf XVI award for environmental science. Open access funding enabled and organized by Projekt DEAL. We thank Falk Feddersen for consulting us and Alexander Shamanskiy for mathematical advice. We thank David Bastviken and John Melack for assistance with editing. We are grateful to Daniela Franz, Christoph Bors, Risto Taipale, Anders Lindroth and John Melack for their significant help during field campaign in 2018. We thank Marko Kärkkäinen and Pasi Korpelainen (University of Eastern Finland) for assisting in field work related to the aerial photography of the study area. We thank all people at the field station for organizing the accommodation and food and helping with the instruments and transportation. This work was supported by several funding agencies. Sofya Guseva and Andreas Lorke were supported by the German Research Foundation (DFG) under the grant LO1150/12‐1. Mika Aurela was supported by the Academy of Finland (project 296888). Alicia Cortés and Sally MacIntyre were supported by the U.S. N.S.F. 1737411. Eliisa Lotsari was supported by The Department of Geographical and Historical Studies, University of Eastern Finland. Ivan Mammarella and Timo Vesala thank the European Union for supporting the RINGO project funded by the Horizon 2020 Research and Innovation Programme under Grant Agreement 730944. Aki Vähä and Timo Vesala were supported by the University of Helsinki ICOS‐Finland. In addition, Timo Vesala was supported by the Tyumen region government in accordance with the Program of the World‐Class West Siberian Interregional Scientific and Educational Center (National Project “Nauka”). Simulations of river turbulence by the − model have been carried out according to the research program of Moscow Center for Fundamental and Applied Mathematics. Victor Stepanenko is grateful to Andrey Glazunov and Andrey Debolskiy for advice in setup of simulations with − model; his work was supported by the Russian Foundation for Basic Research (grant 20‐05‐00773), Russian Ministry for Science and Higher Education (contract No. 075‐15‐2019‐1621), and Grant Council of the President of Russia (grant MD‐1850.2020.5). Marcus Bo Wallin was supported by the King Carl‐Gustaf XVI award for environmental science. Open access funding enabled and organized by Projekt DEAL. k ɛ k ɛ

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

gas exchange
river
turbulence
wind

Access to Document

10.1029/2020WR027939Licence: CC BY-NC-ND

Cite this

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title = "Variable Physical Drivers of Near-Surface Turbulence in a Regulated River",

abstract = "Inland waters, such as lakes, reservoirs and rivers, are important sources of climate forcing trace gases. A key parameter that regulates the gas exchange between water and the atmosphere is the gas transfer velocity, which itself is controlled by near-surface turbulence in the water. While in lakes and reservoirs, near-surface turbulence is mainly driven by atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of gravity-forced flow. Large rivers represent a transition between these two cases. Near-surface turbulence has rarely been measured in rivers and the drivers of turbulence have not been quantified. We analyzed continuous measurements of flow velocity and quantified turbulence as the rate of dissipation of turbulent kinetic energy over the ice-free season in a large regulated river in Northern Finland. Measured dissipation rates agreed with predictions from bulk parameters, including mean flow velocity, wind speed, surface heat flux, and with a one-dimensional numerical turbulence model. Values ranged from (Formula presented.) to (Formula presented.). Atmospheric forcing or gravity was the dominant driver of near-surface turbulence for similar fraction of the time. Large variability in near-surface dissipation rate occurred at diel time scales, when the flow velocity was strongly affected by downstream dam operation. By combining scaling relations for boundary-layer turbulence at the river bed and at the air-water interface, we derived a simple model for estimating the relative contributions of wind speed and bottom friction of river flow as a function of depth.",

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AU - Guseva, Sofya

AU - Aurela, M.

AU - Cortés Cortés, Alicia

AU - Kivi, R.

AU - Lotsari, E.

AU - MacIntyre, S.

AU - Mammarella, I.

AU - Ojala, A.

AU - Stepanenko, V.

AU - Uotila, Petteri

AU - Vähä, Aki

AU - Vesala, Timo

AU - Wallin, Marcus

AU - Lorke, Andreas

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N2 - Inland waters, such as lakes, reservoirs and rivers, are important sources of climate forcing trace gases. A key parameter that regulates the gas exchange between water and the atmosphere is the gas transfer velocity, which itself is controlled by near-surface turbulence in the water. While in lakes and reservoirs, near-surface turbulence is mainly driven by atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of gravity-forced flow. Large rivers represent a transition between these two cases. Near-surface turbulence has rarely been measured in rivers and the drivers of turbulence have not been quantified. We analyzed continuous measurements of flow velocity and quantified turbulence as the rate of dissipation of turbulent kinetic energy over the ice-free season in a large regulated river in Northern Finland. Measured dissipation rates agreed with predictions from bulk parameters, including mean flow velocity, wind speed, surface heat flux, and with a one-dimensional numerical turbulence model. Values ranged from (Formula presented.) to (Formula presented.). Atmospheric forcing or gravity was the dominant driver of near-surface turbulence for similar fraction of the time. Large variability in near-surface dissipation rate occurred at diel time scales, when the flow velocity was strongly affected by downstream dam operation. By combining scaling relations for boundary-layer turbulence at the river bed and at the air-water interface, we derived a simple model for estimating the relative contributions of wind speed and bottom friction of river flow as a function of depth.

AB - Inland waters, such as lakes, reservoirs and rivers, are important sources of climate forcing trace gases. A key parameter that regulates the gas exchange between water and the atmosphere is the gas transfer velocity, which itself is controlled by near-surface turbulence in the water. While in lakes and reservoirs, near-surface turbulence is mainly driven by atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of gravity-forced flow. Large rivers represent a transition between these two cases. Near-surface turbulence has rarely been measured in rivers and the drivers of turbulence have not been quantified. We analyzed continuous measurements of flow velocity and quantified turbulence as the rate of dissipation of turbulent kinetic energy over the ice-free season in a large regulated river in Northern Finland. Measured dissipation rates agreed with predictions from bulk parameters, including mean flow velocity, wind speed, surface heat flux, and with a one-dimensional numerical turbulence model. Values ranged from (Formula presented.) to (Formula presented.). Atmospheric forcing or gravity was the dominant driver of near-surface turbulence for similar fraction of the time. Large variability in near-surface dissipation rate occurred at diel time scales, when the flow velocity was strongly affected by downstream dam operation. By combining scaling relations for boundary-layer turbulence at the river bed and at the air-water interface, we derived a simple model for estimating the relative contributions of wind speed and bottom friction of river flow as a function of depth.

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KW - turbulence

KW - wind

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JF - Water Resources Research

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ER -

Variable Physical Drivers of Near-Surface Turbulence in a Regulated River

Abstract

Funding

UN SDGs

Keywords

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