Streamflow and stream temperature forecasting based on real-time citizen science data

Small stream networks often lack traditional sources of data (e.g. government monitored gauges), which limits the application of complex hydrological models. Low-cost sensor systems and crowd-sourced data have the potential to bridge that data gap; however, only few studies have exploited this capability. This study explores the potential for real-time crowd-sourced data to improve complex computational hydrologic models. We selected the Boyne River basin (185 km²) in northern Michigan as a case study to demonstrate crowd-source data assimilation in distributed hydrological models. We utilized CrowdHydrology, a citizen science network that collects hydrologic data throughout the United States, to obtain local stream stage and stream temperature measurements. CrowdHydrology provides an infrastructure for citizen scientists to voluntarily send a text message with the current water stage height and stream temperature to a server located at the University at Buffalo. Our approach retrieves CrowdHydrology observations and weather data on a weekly basis from a nearby weather station. Stream stage citizen science observations are processed to obtain streamflow discharge based on field-derived stage-discharge relationships at five locations along the river. This database of observations was used as input to a Soil and Water Assessment Tool (SWAT) model of the Boyne River Watershed. Within this framework, the hydrologic model was re-calibrated on a weekly schedule using AMALGAM, a genetically adaptive evolutionary optimization method. Each new calibrated model provides a more accurate estimate of a 7-day forecast of streamflow and stream temperature throughout the river basin. This novel approach can potentially benefit small communities by providing information on local water resources derived from complex hydrological models.