On the visual detection of non-natural records in streamflow time series: challenges and impacts

Strohmenger, Laurent ; Sauquet, Eric ; Bernard, Claire ; Bonneau, Jérémie ; Branger, Flora ; Bresson, Amélie ; Brigode, Pierre ; Buzier, Rémy ; Delaigue, Olivier ; Devers, Alexandre ; Evin, Guillaume ; Fournier, Maïté ; Hsu, Shu-Chen ; Lanini, Sandra ; de Lavenne, Alban ; Lemaitre-Basset, Thibault ; Magand, Claire ; Mendoza Guimarães, Guilherme ; Mentha, Max ; Munier, Simon ; Perrin, Charles ; Podechard, Tristan ; Rouchy, Léo ; Sadki, Malak ; Soutif-Bellenger, Myriam ; Tilmant, François ; Tramblay, Yves ; Véron, Anne-Lise ; Vidal, Jean-Philippe ; Thirel, Guillaume

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<p align=justify>Large datasets of long-term streamflow measurements are widely used to infer and model hydrological processes. However, streamflow measurements may suffer from what users can consider anomalies, i.e. non-natural records that may be erroneous streamflow values or anthropogenic influences that can lead to misinterpretation of actual hydrological processes. Since identifying anomalies is time consuming for humans, no study has investigated their proportion, temporal distribution, and influence on hydrological indicators over large datasets. This study summarizes the results of a large visual inspection campaign of 674 streamflow time series in France made by 43 evaluators, who were asked to identify anomalies falling under five categories, namely, linear interpolation, drops, noise, point anomalies, and other. We examined the evaluators' individual behaviour in terms of severity and agreement with other evaluators, as well as the temporal distributions of the anomalies and their influence on commonly used hydrological indicators. We found that inter-evaluator agreement was surprisingly low, with an average of 12 % of overlapping periods reported as anomalies. These anomalies were mostly identified as linear interpolation and noise, and they were more frequently reported during the low-flow periods in summer. The impact of cleaning data from the identified anomaly values was higher on low-flow<span id="page3376"/> indicators than on high-flow indicators, with change rates lower than 5 % most of the time. We conclude that the identification of anomalies in streamflow time series is highly dependent on the aims and skills of each evaluator, which raises questions about the best practices to adopt for data cleaning.</p>
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