Hydrological models are computer codes that determine the amount of streamflow for a given rainfall event. These models require some tuning to match the calculated streamflow with the observed streamflow. Once they have been tuned, these models can be used to predict high or low flows.

Seibert, J., van Meerveld, H.J., Etter, S., Strobl, B., Assendelft, R., Hummer, P.: Wasserdaten sammeln mit dem Smartphone – Wie können Menschen messen, was hydrologische Modelle brauchen? – Hydrologie & Wasserbewirtschaftung, 63, (2),, 2019

Link to the paper

Seibert, J., Strobl, B., Etter, S., Hummer, P., van Meerveld, H.J.: Virtual staff gauges for crowd-based stream level observations, Front. Earth Sci. – Hydrosphere,, 2019.

In this study, we test if estimates of streamflow from citizens (rather than actual measurements by government agencies) can be used for the tuning of a hydrological model. Because we didn’t have enough data from the CrowdWater app yet, we created artificial streamflow datasets with data points at different times (for example, one data point per week or one per month) and added different errors to the data. To determine the typical errors in streamflow estimates, we asked 136 people in the Zurich area to estimate the streamflow and compared their estimates to the measured streamflow. We determined for six catchments how the errors in the streamflow estimates and the number of data points affect how well we can tune the model for these catchments. The results show that the streamflow estimates of untrained citizens are too inaccurate to be useful for the tuning of a model. However, if the errors can be reduced (by about half) through training or filtering, their estimates of streamflow are useful when there is on average one streamflow estimate per week. Then, the model can be used, in combination with a weather forecast, for flood predictions.

Etter, S., Strobl, B., Seibert, J., and van Meerveld, H. J. I.: Value of uncertain streamflow observations for hydrological modelling, Hydrol. Earth Syst. Sci., 22, 5243-5257,, 2018.

Link to the paper

Kampf, S., B. Strobl, J. Hammond, A. Anenberg, S. Etter, C. Martin, K. Puntenney-Desmond, J. Seibert, and I. van Meerveld (2018), Testing the waters: Mobile apps for crowdsourced streamflow data, Eos, 99, Published on 12 April 2018.
Link to the paper

van Meerveld, H. J. I., Vis, M. J. P., and Seibert, J.: Information content of stream level class data for hydrological model calibration, Hydrol. Earth Syst. Sci., 21, 4895-4905,, 2017.
Link to the paper


Catchment Science Gordon Research Conference and Seminar – Ilja van Meerveld
Can citizens observe what models need? Evaluation of the potential value of crowd-sourced stream level observations for hydrological model calibration

Österreichische Citizen Science Konferenz 2018 – Barbara Strobl
CrowdWater als Bereicherung des Unterrichts?

Tag der Hydrologie 2018 – Jan Seibert
CrowdWater – Können Menschen messen was hydrologische Modelle brauchen?
This poster has won the poster price 2018 in the category “most innovative study”.

EGU 2018 – Simon Etter
Can citizens observe what models need?


MOOC stands for massive open online course. Like in a traditional university course, learners study a subject over a specific time period. However, students attain lectures, discuss problems and solve exercises online. In the MOOC «Water in Switzerland» learners can watch a choice of lectures and field films, as well as solve assessments and practical tasks. The MOOC is split up into seven modules, each of them taking approximately 3 – 4 hours of work each week.

CrowdWater participated in this MOOC.

Click here to see a trailer for this MOOC or the course website.