Demonstrating the value of community-based (‘citizen science’) observations for catchment modelling and characterisation

This research demonstrates the value of community-based (‘citizen science’) observations for modelling and understanding catchment response as a contribution to catchment science. Community-based rainfall, river level and flood observations were successfully collected and quality-checked, and used to build and run a physically-based, spatially-distributed catchment model, SHETRAN.

The results show how the local network of community-based observations alongside traditional sources of hydro-information supports characterisation of catchment response more accurately than using traditional observations alone. The researchers demonstrate that these community-derived datasets are most valuable during local flash flood events, particularly towards peak discharge.

While community-based observations are less valuable during prolonged and widespread floods, or over longer hydrological periods of interest, they can still ground-truth existing traditional sources of catchment data to increase confidence during characterisation and management activities. Involvement of the public in data collection activities also encourages wider community engagement, and provides important information for catchment management.

Journal of Hydrology. Shared under a Creative Commons license (CC BY 4.0).