How well can people observe the flow state of temporary streams?

Even though more than half of the global river network does not have continuous flow, temporary (i.e., non-perennial) streams are poorly represented in traditional monitoring networks. Therefore, new approaches are needed to monitor these streams. Citizen science provides an interesting opportunity as people, equipped with smartphones, can observe the flow state of temporary streams. Such observations can go beyond a simple classification of flow vs. no flow and include ecologically important states, such as standing water, isolated pools, or wet streambeds. However, the quality of citizen science data for temporary streams has so far not been thoroughly assessed. Therefore, we asked more than 1,200 people during 23 field days to visually determine the flow state of eight temporary streams based on six classes ranging from a dry streambed to flowing water. Participants could most clearly distinguish a flowing stream from a non-flowing stream. The overall agreement between participants was 66%; 83% of the selected flow states were within one class of the most frequently selected flow state. The agreement with the expert was lower (56% chose the same class, and 79% chose a state within one class). Inconsistencies between the selected flow state and answers to specific yes-no statements about the temporary stream were largest for the dry streambed and damp/wet streambed states. These discrepancies were partly caused by participants looking at different parts of the stream (i.e., participants considered the flow state for a location further upstream or downstream). To ensure that all participants determine the flow state comparably, we recommend clear definitions of the flow state classes, detailed information on the exact location for which the flow state needs to be determined, as well as more training

Self-guided smartphone excursions in university teaching—experiences from exploring “Water in the City”

Like many other university teachers, we were faced with an unprecedented situation in spring 2020, when we had to cancel on-site teaching and excursions due to the Covid-19 pandemic. However, we were in the fortunate position that we had already started to develop a smartphone-based self-guided excursion on the topic of “Water in the City”. We accelerated this development and used it to replace the traditional group excursion in our Bachelor level introductory course in Hydrology and Climatology. The excursion of this course is visited by around 150 students each year. Because the student feedback was overall very positive, we used the self-guided excursion again in 2021 and plan to continue to use it in the coming years. In this paper, we describe the excursion, discuss the experiences of the students and ourselves, and present recommendations and ideas that could be useful for similar excursions at other universities

Rainfall threshold for hillslope outflow: an emergent property of flow pathway connectivity

Nonlinear relations between rain input and hillslope outflow are common observations in hillslope hydrology field studies. In this paper we use percolation theory to model the threshold relationship between rainfall amount and outflow and show that this nonlinear relationship may arise from simple linear processes at the smaller scale. When the rainfall amount exceeds a threshold value, the underlying elements become connected and water flows out of the base of the hillslope. The percolation approach shows how random variations in storage capacity and connectivity at the small spatial scale cause a threshold relationship between rainstorm amount and hillslope outflow. <br><br> As a test case, we applied percolation theory to the well characterized experimental hillslope at the Panola Mountain Research Watershed. Analysing the measured rainstorm events and the subsurface stormflow with percolation theory, we could determine the effect of bedrock permeability, spatial distribution of soil properties and initial water content within the hillslope. The measured variation in the relationship between rainstorm amount and subsurface flow could be reproduced by modelling the initial moisture deficit, the loss of free water to the bedrock, the limited size of the system and the connectivity that is a function of bedrock topography and existence of macropores. The values of the model parameters were in agreement with measured values of soil depth distribution and water saturation

Citizen science approaches for water quality measurements

Citizen science has become a widely used approach in water quality studies. Although there are literature reviews about citizen science and water quality assessments, an overview of the most commonly used methods and their strengths and weaknesses is still lacking. Therefore, we reviewed the scientific literature on citizen science for surface water quality assessments and examined the methods and strategies used by the 72 studies that fulfilled our search criteria. Special attention was given to the parameters monitored, the monitoring tools, and the spatial and temporal resolution of the data collected in these studies. In addition, we discuss the advantages and disadvantages of the different approaches used in water quality assessments and their potential to complement traditional hydrological monitoring and research

Accuracy of crowdsourced streamflow and stream level class estimates

Streamflow data are important for river management and the calibration of hydrological models. However, such data are only available for gauged catchments. Citizen science offers an alternative data source, and can be used to estimate streamflow at ungauged sites. We evaluated the accuracy of crowdsourced streamflow estimates for 10 streams in Switzerland by asking citizens to estimate streamflow either directly, or based on the estimated width, depth and velocity of the stream. Additionally, we asked them to estimate the stream level class by comparing the current stream level with a picture that included a virtual staff gauge. To compare the different estimates, the stream level class estimates were converted into streamflow. The results indicate that stream level classes were estimated more accurately than streamflow, and more accurately represented high and low flow conditions. Based on this result, we suggest that citizen science projects focus on stream level class estimates instead of streamflow estimates

Value of crowd‐based water level class observations for hydrological model calibration

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 data on the model validation performance. Our results indicate that on average one observation per week for a one‐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

A multi‐scale study of the dominant catchment characteristics impacting low‐flow metrics

Low flows can impact water use and instream ecology. Therefore, reliable predictions of low-flow metrics are crucial. In this study, we assess which catchment characteristics (climate, topography, geology and landcover) can explain the spatial variability of low-flow metrics at two different scales: the regional scale and the small headwater catchment scale. For the regional-scale analysis, we calculated the mean 7-day annual minimum flow (qmin), the mean of the flow that is exceeded 95% of the year (q95), and the master recession constant (C) for 280 independent gauging stations across the Swiss Plateau and the Swiss Alps for the 2000–2018 period. We assessed the relation between 44 catchment characteristics and the three low-flow metrics based on correlation analysis and a random forest model. Low-flow magnitudes across the Swiss Plateau were positively correlated with the fraction of the area covered by sandstone bedrock or alluvium, and with the area that has a slope between 10° and 30°. Across the Swiss Alps, low-flow magnitudes were positively correlated with the fraction of area with slopes between 30° and 60°, and the area with glacial deposits and debris cover. There was good agreement between observations and predictions by the random forest regression model with the top 11 catchment characteristics for both regions: for 80% of the Swiss Plateau catchments and 60% of the Swiss Alpine catchments, we could predict the three low-flow metrics within an error of 30%. The residuals of the regression model, however, varied across short distances, suggesting that local catchment characteristics affect the variability of low-flow metrics. For the local-scale headwater catchments, we conducted 1-day snapshot field campaigns in 16 catchments during low-flow periods in 2015 and 2016. The measurements in these sub-catchments also showed that areas with sandstone bedrock and a good storage-to-river connectivity had above average low-flow magnitudes. Including knowledge on local catchment characteristics may help to improve regional low-flow predictions, however, not all local catchment characteristics were useful descriptors at larger scales

Assessment of the value of remotely sensed surface water extent data for the calibration of a lumped hydrological model

For many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat‐based surface water extent observations can inform the calibration of a lumped bucket‐type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April–October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely‐available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher‐resolution imagery will be helpful for model calibration for more sites, especially when time series length increases