I would like to start by thanking the committee for inviting us to attend today.
The visual aids that we shared with the committee weren't available in time, but they'll be available to you later and are intended to provide a sampling of existing data integration and risk communication tools based on validated data collection and/or predictive risk models that can be used to improve public risk communication. My remarks today will focus on how a science-based approach to risk planning adds valuable insights to mitigation and preparedness, and can inform better response plans.
Building on the existing knowledge base, we can improve how disaster is measured, visualized, communicated and understood before the disaster even strikes. To model future risks, environmental scientists and emergency planners should integrate existing science-based predictive models with GIS-based operational environmental maps that include water quality, land use, financial liability, economic risk, watershed characteristics, dams, hydrometric data, snow survey sites, and climate change in order to forecast the risk of floods, fires, and ice storms, and to complement or add to that knowledge with spatial analysis and/or data trends obtained from past disasters and regional traditional knowledge.
The results would be an integrated GIS-based predictive map that would use available data and knowledge on terrain and weather to simulate a range of possible outcomes based on a series of inputs. The current risk assessments generated are generally well understood by emergency planners and environmental scientists. However, the specifics of what that means to homeowners and the public are not well understood. Specifically, there's a gap in translating that risk assessment into information the public can understand. The outputs of science-based risk models should be presented to the public in a way that allows people to better understand the risks. This means using integrated predictive maps that clearly show high-risk areas and how the risk will manifest. This could include showing how high the water will rise in a flood, where the fire could burn, and how long the power could be knocked out based on the distance from the main power lines. The outcome of the natural disaster should be made clear to the public to emphasize exactly how they will be affected.
Any risk communication to the public is not a one-time activity. Therefore, accumulated GIS-based data on things such as snowpack, land-use activity, forest cover and expected water levels should be integrated into predictive models to clearly forecast and show the public the pending or immediate risk.
Once the extreme weather event has passed, the data required should then be used to update the model and risk profile to ensure that the model is and continues to be credible. For emergency managers and emergency planners, scenario-based experimentation is essential to planning for a range of possible outcomes. By manipulating the variables like expected rainfalls, warmer weather—