3 min
Expert Insight: Dampening the Data Desert: A First Step Toward Improving Space Coast Climate Resilience
By Steven Lazarus Like many coastal regions, Florida’s Space Coast faces significant climate resilience challenges and risks. According to the National Oceanic and Atmospheric Administration (NOAA), Florida has over 8,000 miles of shoreline, more than any other state in the contiguous U.S. In addition, the 2020 census indicates that that there are 21 million Florida residents, 75-80% of which live in coastal counties. This makes our state particularly vulnerable to rising sea levels, which are directly responsible for a host of coastal impacts, such as saltwater intrusion, sunny-day (high-tide) flooding, worsening surge, etc. There is growing evidence that storms are becoming wetter as the atmosphere warms— increasing the threat associated with compound flooding, which involves the combined effects of storm surge, rainfall, tides and river flow. Inland flooding events are also increasing due to overdevelopment, heavy precipitation and aging and/or inadequate infrastructure. The economic ramifications of these problems are quite evident, as area residents are confronted with the rising costs of their homeowners and flood insurance policies. As the principal investigator on a recently funded Department of Energy grant, Space Coast ReSCUE (Resilience Solutions for Climate, Urbanization, and Environment), I am working with Argonne National Laboratory, Florida Tech colleagues, community organizations and local government to improve our climate resilience in East Central Florida. It is remarkable that, despite its importance for risk management, urban planning and evaluating the environmental impacts of runoff, official data regarding local flooding is virtually nonexistent! Working alongside a local nonprofit, we have installed 10 automated weather stations and manual rain gauges in what was previously a “data desert” east of the Florida Tech campus: one at Stone Magnet Middle School and others at local homes. “We think that a ‘best methods’ approach is proactive, informed and cost-effective. The foundation of good decision-making, assessment and planning is built on data (model and observations), which are critical to adequately addressing the impact of climate on our communities.” – steven lazarus, meteorology professor, ocean engineering and marine sciences Data from these stations are available, in real-time, from two national networks: CoCoRaHS and Weather Underground. The citizen science initiative involving the rain gauge measurements is designed to document flooding in a neighborhood with limited resources. In addition to helping residents make informed choices, these data will also provide a means by which we can evaluate our flood models that will be used to create highly detailed flood maps of the neighborhood. We are working with two historic extreme-precipitation events: Hurricane Irma (2017) and Tropical Storm Fay (2008)—both of which produced excessive flooding in the area. What might the local flooding look like, in the future, as storms become wetter? To find out, we plan to simulate these two storms in both present-day and future climate conditions. What will heat stress, a combination of temperature and humidity, feel like in the future? What impact will this have on energy consumption? The station data will also be used develop and test building energy-efficiency tools designed to help the community identify affordable ways to reduce energy consumption, as well as to produce high-precision urban heat island (heat stress) maps that account for the impact of individual buildings. The heat island and building energy modeling will be complemented by a drone equipped with an infrared camera, which will provide an observation baseline. We think that a “best methods” approach is proactive, informed and cost-effective. The foundation of good decision-making, assessment and planning is built on data (model and observations), which are critical to adequately addressing the impact of climate on our communities.
