Articles | Volume 27, issue 18
https://doi.org/10.5194/hess-27-3375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2023
Research article |
| 22 Sep 2023
| 22 Sep 2023
On the visual detection of non-natural records in streamflow time series: challenges and impacts
Laurent Strohmenger
CORRESPONDING AUTHOR
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Eric Sauquet
INRAE, UR RiverLy, Villeurbanne, France
Claire Bernard
Chambre d'agriculture du Vaucluse, Avignon, France
Jérémie Bonneau
INRAE, UR RiverLy, Villeurbanne, France
Flora Branger
INRAE, UR RiverLy, Villeurbanne, France
Amélie Bresson
EPIDOR, Castelnaud-la-Chapelle, France
Pierre Brigode
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Observatoire de la Côte d'Azur, CNRS, OCA, IRD, Géoazur, Université Côte d'Azur, Sophia-Antipolis, France
Rémy Buzier
URA IRSTEA, University of Limoges, Limoges, France
Olivier Delaigue
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Alexandre Devers
INRAE, UR RiverLy, Villeurbanne, France
Guillaume Evin
INRAE, CNRS, IRD, Grenoble INP, IGE, Univ. Grenoble Alpes, Grenoble, France
Maïté Fournier
ACTeon – Environment, Research & Consultancy, Grenoble, France
Shu-Chen Hsu
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Sandra Lanini
BRGM, unité EAU-RMD, Montpellier, France
G-EAU, UMR 183, INRAE, CIRAD, IRD, AgroParisTech, Supagro, BRGM, Montpellier, France
Alban de Lavenne
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Thibault Lemaitre-Basset
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
UMR 7619 METIS, Sorbonne Université, CNRS, EPHE, Paris, France
Claire Magand
Office français de la biodiversité (OFB), Vincennes, France
Guilherme Mendoza Guimarães
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Max Mentha
Safege-Suez Consulting, Paris, France
Simon Munier
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Charles Perrin
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Tristan Podechard
CEREG, Montpellier, France
Léo Rouchy
INRAE, UR RiverLy, Villeurbanne, France
Malak Sadki
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Myriam Soutif-Bellenger
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
AgroParisTech, 75005, Paris, France
François Tilmant
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Yves Tramblay
HSM, University of Montpellier, CNRS, IRD, IMT, Montpellier, France
Anne-Lise Véron
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
Jean-Philippe Vidal
INRAE, UR RiverLy, Villeurbanne, France
Guillaume Thirel
CORRESPONDING AUTHOR
INRAE, HYCAR Research Unit, Université Paris-Saclay, Antony, France
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Cited
3 citations as recorded by crossref.
- Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
- Évolution du manteau neigeux pendant la sécheresse de 2022 en France S. Gascoin et al. 10.1080/27678490.2024.2314174
- On the visual detection of non-natural records in streamflow time series: challenges and impacts L. Strohmenger et al. 10.5194/hess-27-3375-2023
1 citations as recorded by crossref.
Latest update: 17 Jul 2024
Short summary
We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
We present the results of a large visual inspection campaign of 674 streamflow time series in...
