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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 10 | Copyright
Hydrol. Earth Syst. Sci., 22, 5243-5257, 2018
https://doi.org/10.5194/hess-22-5243-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 Oct 2018

Research article | 15 Oct 2018

Value of uncertain streamflow observations for hydrological modelling

Simon Etter1, Barbara Strobl1, Jan Seibert1,2, and H. J. Ilja van Meerveld1 Simon Etter et al.
  • 1Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
  • 2Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, 75007 Uppsala, Sweden.

Abstract. Previous studies have shown that hydrological models can be parameterised using a limited number of streamflow measurements. Citizen science projects can collect such data for otherwise ungauged catchments but an important question is whether these observations are informative given that these streamflow estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a simple bucket-type runoff model for six Swiss catchments. We pretended that only a few observations were available and that these were affected by different levels of inaccuracy. The level of inaccuracy was based on a log-normal error distribution that was fitted to streamflow estimates of 136 citizens for medium-sized streams. Two additional levels of inaccuracy, for which the standard deviation of the error distribution was divided by 2 and 4, were used as well. Based on these error distributions, random errors were added to the measured hourly streamflow data. New time series with different temporal resolutions were created from these synthetic streamflow time series. These included scenarios with one observation each week or month, as well as scenarios that are more realistic for crowdsourced data that generally have an irregular distribution of data points throughout the year, or focus on a particular season. The model was then calibrated for the six catchments using the synthetic time series for a dry, an average and a wet year. The performance of the calibrated models was evaluated based on the measured hourly streamflow time series. The results indicate that streamflow estimates from untrained citizens are not informative for model calibration. However, if the errors can be reduced, the estimates are informative and useful for model calibration. As expected, the model performance increased when the number of observations used for calibration increased. The model performance was also better when the observations were more evenly distributed throughout the year. This study indicates that uncertain streamflow estimates can be useful for model calibration but that the estimates by citizen scientists need to be improved by training or more advanced data filtering before they are useful for model calibration.

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To evaluate the potential value of streamflow estimates for hydrological model calibration, we created synthetic streamflow datasets in various temporal resolutions based on the errors in streamflow estimates of 136 citizens. Our results show that streamflow estimates of untrained citizens are too inaccurate to be useful for model calibration. If, however, the errors can be reduced by training or filtering, the estimates become useful if also a sufficient number of estimates are available.
To evaluate the potential value of streamflow estimates for hydrological model calibration, we...
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