What can data assimilation do for water quality forecasting?

Abstract:

Proactive water quality management through preventive actions requires predictive information. Water quality forecasting can provide such information, e.g., to protect public health from harmful water quality conditions such as algal blooms or bacterial pollution and to allow the decision makers to respond more quickly to emergency situations such as oil spills for protection of water resources systems. Operational water quality forecasting is a large challenge due to the complexities and large uncertainties associated with various physiobiochemical processes involved. As such, there is an added impetus to utilize real-time observations effectively in the forecast process. In this work, we apply data assimilation (DA) to the Hydrologic Simulation Program – Fortran (HSPF) model to improve accuracy of watershed water quality forecast. The DA technique used is based on maximum likelihood ensemble filter (MLEF).The resulting DA module, MLEF-HSPF, has been implemented in the Water Quality Forecast System at the National Institute of Environmental Research (WQFS-NIER) in Korea. In this presentation, we describe MLEF-HSPF, share multi-catchment evaluation results for the Nakdong River Basin in Korea, and identify science and operational challenges.