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While hydrological models generally rely on continuous streamflow data for calibration, previous studies have shown that a few measurements can be sufficient to constrain model parameters. Other studies have shown that continuous water level or water level class (WL-class) data can be informative for model calibration. In this study, we combined these approaches and explored the potential value of a limited number of WL-class observations for calibration of a bucket-type runoff model (HBV) for four catchments in Switzerland. We generated synthetic data to represent citizen science data and examined the effects of the temporal resolution of the observations, the numbers of WL-classes, and the magnitude of the errors in the WL-class observations on the model validation performance. Our results indicate that on average one observation per week for a 1-year period can significantly improve model performance compared to the situation without any streamflow data. Furthermore, the validation performance for model parameters calibrated with WL-class observations was similar to the performance of the calibration with precise water level measurements. The number of WL-classes did not influence the validation performance noticeably when at least four WL-classes were used. The impact of typical errors for citizen science-based estimates of WL-classes on the model performance was small. These results are encouraging for citizen science projects where citizens observe water levels for otherwise ungauged streams using virtual or physical staff gauges.

Plain Language Summary Normally, multiple years of streamflow measurements are used to calibrate a hydrological model for a specific catchment so that it can be used to, for instance, predict floods or droughts. Taking these measurements is expensive and requires a lot of effort. Therefore, such data are often missing, especially in remote areas and developing countries. We investigated the potential value of water level class (WL-class) data for model calibration. WL-classes can be observed by citizens with the help of a virtual ruler with different classes that is pasted onto a picture of a stream bank as a sticker (see Figure 2). We show that one WL-class observation per week for 1 year improves model calibration compared to situations without streamflow data. The model results for the WL-class observations were as good as precise water level observations that require a physical staff gauge or continuous water level data measurements that can be obtained from a water level sensor that is installed in the stream. However, the results were not as good as when streamflow data were used for model calibration, but these are more expensive to collect. Errors in the WL-class observations did in most cases not affect the model performance noticeably.