Our objectives for forecasting water quality are the same for our forecasts of water quantity: to provide the decision-support tools necessary to proactively manage watersheds. Through this research, we have identified compelling capabilities of HydroForecast to be extended into a water quality forecast service.
An example prediction demonstrating the probability distributions (displayed as confidence intervals) predicted by the model for a single variable. The model is able to indicate the varying certainty of its predictions as conditions change throughout the year.
In a feasibility study conducted in 2019, we sought to:
This study, conducted in the western basin of Lake Erie and the Sacramento River, confirmed that it is feasible to forecast water quality metrics using satellite data and machine learning.
High predictive accuracy was achieved for water temperature and dissolved oxygen. The average R² across all tested sites is 0.96 for water temperature and 0.90 for dissolved oxygen. Other metrics, such as blue green algae presence, pH, conductivity, turbidity, and nitrogen showed promising results and potential for operational usage with further research and development.
The power behind HydroForecast's architecture is its ability to flexibly support multiple outputs, variable horizons, and new inputs with ease. All of our water quality investigations leverage the existing breakthroughs achieved by HydroForecast.
Satellite sensed daytime land surface temperature data for the last five years. Land surface temperature provides useful information to our model about soil moisture, snow cover and evaporation conditions.
September 2017. Images show an algal bloom in Lake Erie. The NDCI image on the right shows blue and green values. This indicates a high amount of chlorophyll on the water's surface.