Improving Operational Water Quality Forecasting with Ensemble Data Assimilation

Accurately predicting water quality in river systems is critical to enabling decision makers to respond more quickly to emergencies such as oil spills and protecting public health from harmful water quality conditions such as algal blooms and bacterial pollution.

Educate yourself on this topic today by reading the new JWMM paper by Riazi, Kim and Seo from the University of Texas at Arlington, and Shin and Kim from the National Institute of Environmental Research (NIER).

Improving Operational Water Quality Forecasting with Ensemble Data Assimilation