Case Study: What are the most effective ways to relay glacial outburst hazard information to stakeholder groups in Juneau?
Dina Abdel-Fattah is a Natural Resources and Sustainability Ph.D. Student at UAF and she is being advised by Dr. Sarah Trainor. She is also an ACCAP research assistant and works on Dr. Nathan Kettle’s Arctic Domain Awareness Center (ADAC) funded project, Developing sea ice and weather forecasting tools to improve situational awareness and crisis response in the Arctic.
The purpose of the case study is to understand what are a few of the most effective ways to relay glacial outburst hazard information to different stakeholder groups in Juneau. This work is specifically interested in understanding and relaying how decision-making under uncertainty takes place with regards to predictive modeling of glacial hazards. This case study will therefore investigate how presenting information in different formats, to different stakeholders, changes 1) their perception of, if at all, the utility of predictive modeling and 2) their use of, if at all, predictive modeling. Dina will work in collaboration with both UAS and NWS while down in Juneau to refine the research questions and methodology that will be utilized in this case study.
The data used in this case study will be:
- Suicide Basin discharge prediction model produced by the University of Alaska Southeast (UAS)
- US Geological Survey (USGS) water sensor data in Suicide Basin
- The National Weather Service’s Alaska-Pacific River Forecast Center (APRFC) hydrological model of Suicide Basin release in Mendenhall Valley
- The City and Borough of Juneau inundation maps of Mendenhall Valley
- The City and Borough of Juneau infrastructure maps of Mendenhall Valley
A beta data product that combines the updated NWS APRFC hydrological model with the new Suicide Basin discharge prediction against infrastructure and the flood plain in Mendenhall Valley will be created as part of this study, as a way to test data format and presentation. This study might also potentially create a beta product that combines the USGS water sensor data and UAS discharge model together, if feasible.