Lessons Learned from Remote Sensing of River Ice for Flood Early Warning

Rivers in regions with cold winters can seasonally freeze up. River ice breakup and freeze-up processes can lead to river ice jams, which are a major contributor to flood risk in cold regions (across most of the high latitudes of the northern hemisphere). In Canada, satellite remote sensing is used across the country to provide timely information on the status of river ice. Methods and algorithms to classify various stages of river ice from the Radarsat Constellation Mission (RCM) are available, but the operational implementation of these, especially the integration into larger flood forecasting and early warning systems, requires specific expertise, software and computational resources, and comes with its own set of challenges. In collaboration with various agencies across Canada we have set up operational monitoring systems with the purpose of assisting the daily tasks of forecasters on duty. These have been used in practice over multiple ice breakup and freeze-up seasons, which has highlighted both their usefulness and shortcomings. We will focus on various aspects of such a system and share lessons learned on its design, setup and operational use, as well as a framework to analyse various factors relevant for operational monitoring purposes (e.g. spatiotemporal coverage and latency of the data, critical elements in the support of decision-making relating to floods). In this, we do not shy away from problems and pitfalls, so that others can learn from these. While various challenges remain, this work is a good example of the value in the joint engagement of applied science and end users.