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On the visual inspection of streamflow time series: distributions and impacts of non-natural flow records
Distributed under a Creative Commons Attribution 4.0 International License ;DOI: 10.5194/hess-2023-58
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Title:
On the visual inspection of streamflow time series: distributions and impacts of non-natural flow records
Author:
Strohmenger, Laurent
;
Thirel, Guillaume
Subjects:
Environmental Sciences
Description:
This poster presents the results of a large visual inspection campaign of 674 flow time series in France by 43 evaluators, who were asked to identify flaws belonging to five categories: linear interpolation, drops, noise, point anomaly, and others. We examined the individual behavior of evaluators in terms of severity and consistency with other raters, as well as the temporal distributions of flaws and their influence on commonly used hydrological indicators. We found that agreement among raters was surprisingly low, with an average of 12% of overlapping periods reported as flaws. The most common types of flaws identified were linear interpolation and noise, and they were most often reported during low-flow periods in summer. The impact of cleaning the reported flaws from the time series was higher for low-flow indicators than for high-flow indicators, with change rates of less than 5% most of the time. We conclude that flaws identification in streamflow time series is highly dependent on the goals and skills of individual evaluators, raising the need for better practices for data cleaning that could benefit from future advances in machine learning tools. Ce poster présente les résultats d'une campagne d'inspection visuelle de 674 séries temporelles de débits de cours d'eau en France. Cette inspection a été réalisée par43 évaluateurs dans le but d'identifier les périodes suspectées non-naturelles et de les classer d'an l'une de ces cinq catégories : interpolation linéaire, décrochements, bruit, anomalie ponctuelle, et autres. Nous avons examiné le comportement individuel des évaluateurs en termes de sévérité et de cohérence avec les autres évaluateurs, ainsi que la distribution temporelle de ces anomalies et leur influence sur les indicateurs hydrologiques. Les résultats montrent que l'accord entre les évaluateurs était étonnamment faible, avec une médiane de 12 % de taux d'accord sur les périodes signalées non-naturelles. Les types d'anomalies les plus reportés sont les interpolations linéaires et le bruit. Les périodes reportées sont plus fréquemment reportées pendant les périodes d'étiage en été. Inclure ces anomalies lors du calcul d'indicateurs hydrologique a un impact plus important sur les valeurs d'indicateurs de bas débit que pour sur indicateurs de haut débit. En conclusion, l'identification des périodes dites non-naturelles dans les séries temporelles débit dépend fortement des objectifs et des compétences des évaluateurs, ce qui soulève un besoin d'homogénéiser nos méthode de pré-traitement de données, qui pourraient potentiellement bénéficier des progrès futurs des outils d'apprentissage automatique.
Creation Date:
2023
Language:
English
Identifier:
DOI: 10.5194/hess-2023-58
Source:
Hyper Article en Ligne (HAL) (Open Access)
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