In the agricultural state of Iowa, water quality research is of great importance for monitoring and managing the health of aquatic systems. Among many water quality parameters, water temperature is a critical variable that governs the rates of chemical and biological processes which affect river health. The main objective of this thesis is to develop a real-time high resolution predictive stream temperature model for the entire state of Iowa. A statistical model based solely on the water-air temperature relationship was developed using logistic regression approach. With hourly High Resolution Rapid Refresh (HRRR) air temperature estimations, the implemented stream temperature model produces current state-wide estimations. The results are updated hourly in real-time and presented on a web-based visualization platform: the Iowa Water Quality Information System, Beta version (IWQIS Beta). Streams of 4th order and up are color-coded according to the estimated temperatures. Hourly forecasts for lead time of up to 18 hours are also available.
A model was developed separately for spring (March to May), summer (June to August), and autumn (September to November) seasons. 2016 model estimation results generate less than 3 °C average RMSE for the three seasons, with a summer season RMSE of below 2 °C. The model is transferrable to basins of different catchment sizes within the state of Iowa and requires hourly air temperature as the only input variable. The product will assist Iowa water quality research and provide information to support public management decisions.