CrowdWater: Citizen Science to Hydrological Forecasting

This thesis is part of the CrowdWater project. The CrowdWater project has developed a data collection method that makes it easy for anyone to monitor water bodies using a mobile app. In use since 2017, the CrowdWater mobile app has been used to collect over 70,000 data points across over 12,000 sites. Some of these sites are located in ungauged basins, meaning they lack the high-resolution in-situ discharge observations required for conventional calibration of conceptual hydrological models. Current approaches to predictions in ungauged basins primarily rely on parameter regionalization from gauged catchments or calibration using remotely sensed hydrological fluxes and states. The incorporation of in-situ data to these approaches remains a challenge.  This project explores whether water-level class observations collected by citizen scientists can provide useful information on discharge dynamics, and how best to integrate this information with ex-situ data sources. 

Contact: Linnaea Cahill

Supervisors: Jan Seibert und Ilja van Meerveld