Collaborative Research: ABI Innovation: Improving high performance super computer aquatic ecosystem models with the integration of real-time citizen science data

This research is designed to engage public participation in data collection and the development of a stream discharge, stream temperature, and aquatic species habitat forecasting model. Through the use of citizen-based observations of stream height and stream temperature, this approach will demonstrate how citizen-derived observations can contribute to forecasts of stream discharge, stream temperature, and identification of freshwater fish habitat. Freshwater fishes have significant ecological, economic, and recreational importance across the United States. However, freshwater species are among the most endangered groups of organisms in North America, largely due to the impact of human activities. Accurate representations of freshwater species habitat are needed to develop approaches to balance the needs of society with the conservation of freshwater resources. This can be accomplished through the collection of observed data by government and research organizations or by computer modeling of habitat, whereby the quality of the model depends upon the availability of observed data. However, the amount of observed data for freshwater systems has been declining due to decreases in funding. The data that do exist are generally focused on large rivers that are important for urban communities (i.e., flooding, water supply), which are locations not always relevant to freshwater species whose habitat often occurs in smaller headwater streams. Local communities of recreational users and their mobile phones offer an opportunity to close this data-availability gap through citizen science. As regular users of shared resources, like streams and waterways, outdoor enthusiasts have valuable knowledge of specific locations. This knowledge is vastly underutilized by scientific communities. This project will harness information and data collected by members of local communities and develop an approach for data collection, storage, and integration with computer models that can predict streamflow, stream temperature, and freshwater species habitat, which can then aid in sustainable management of these resources. The Boyne River Basin in Michigan, USA will be used as a test location, but the techniques can be used in watersheds throughout the world. The goal of this research is to develop techniques that integrate citizen science hydrology and stream temperature data with eco-hydrological models. Specifically, this research is designed to fully couple citizen participation in the development of a real-time stream discharge, temperature, and aquatic species habitat forecasting model framework. The project will install CrowdHydrology (a citizen science network that collects hydrologic data) equipment throughout the Boyne River Basin. The local community can then text (via cellphone) stream level and stream temperature data to the CrowdHydrology platform. These citizen science data will then be transformed and input into an eco-hydrological model for near real-time simulations of streamflow, stream temperature, and aquatic species habitat. This approach will demonstrate how citizen-derived observations can contribute to the modeling of stream discharge, stream temperature, and aquatic species habitat. The model simulations and forecasts (one week ahead), including stream flows, temperatures, and habitat distributions, will be presented in tables and simple spatial plots available for download on the CrowdHydrology website (http://www.crowdhydrology.com)