Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography and determined the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.

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.

It has been 12 years since Bishop et al. (2008) wrote the Invited Commentary "Aqua Incognita: the unknown headwaters." They highlighted that "In most regions, the overwhelming majority of stream length lies beyond the frontiers of any systematic documentation and would have to be represented as a blank space on the assessment map. This means that for the majority of streams that support aquatic life, a systematic understanding is lacking on water quality, habitat, biota, specific discharge, or even how many kilometres of such streams are there. This blank space is so vast that it deserves a name to help us at least to remember that it is there. We propose calling it 'Aqua Incognita'" (Bishop et al., 2008, p. 1239). We continue to agree with this statement and the need to understand headwater streams better. In this commentary, we want to draw attention to a particular type of headwater stream that is even less frequently examined: headwater streams that flow intermittently, that is, the Aqua Temporaria Incognita. Question 3 of the 23 unsolved problems in hydrology (Blöschl et al., 2019) focuses on ephemeral dryland streams. We argue that this focus needs broadening to headwater temporary streams because they are ubiquitous in all climates. Headwater temporary streams feed larger perennial streams and are particularly sensitive to climate change and other human influences (Jaeger, Olden, &

Abstract. Some form of training is often necessary for citizen science projects. While in some citizen science projects, it is possible to keep tasks simple so that training requirements are minimal, other projects include more challenging tasks and, thus, require more extensive training. Training can be a hurdle to joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated whether an online game that was originally developed for data quality control in a citizen science project can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to place the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however, this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project-related tasks intuitively by observing actual gauge placements by other citizen scientists in the game and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants' performance or the performance increase through the training. These results demonstrate the value of an online game for training. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods, particularly when other information materials are not used extensively by citizen scientists.