Distinguished Student Work

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Steve Condie, PhD; Illinois Mathematics and Science Academy


With over 400 units, between them covering almost 850 million acres of carefully preserved land, the National Park Service (NPS) acts as steward to the nation’s natural treasures. In the move to the Twenty-First century, the NPS faces numerous looming challenges, particularly those related to a rapidly changing climate. It was our task to strategize with the Service in addressing three such issues, leveraging our experience in mathematical modelling and data analysis to aid them in the quest to protect and to preserve.

The first problem under consideration was determining the risk associated with sea-level change for five different coastal locations. Risk was categorized as being “low,” “medium,” and “high” over a period of 10, 20, and 50 years. The lines between the three intensities were determined by the intermediate-low and intermediate-high predictions of global sea-level rise, as given by the National Oceanic and Atmospheric Administration (NOAA). For example, if a location’s predicted rise in sea-level fell below the intermediate-low prediction for the rise in global sea-levels, it was deemed a “low” risk. If it fell in the middle, a “medium” risk. And above the intermediate-high line, a “high risk.” Together with other considerations like the elevation of a park, the final valuations are presented on page 8. Given the nature of the model and the inherent unpredictability of climatology, the model cannot be extrapolated to 100 years, but works fairly well in the given time frame.

The next challenge involved assigning climate vulnerability scores to coastal locations based on the susceptibility of each location to natural disasters. Such scores were determined as a product of the severity of a particular disaster with its frequency. By plumbing datasets provided, vulnerability scores for each of the five locations under analysis were determined and are presented on page 13.

The final task sought to determine where the NPS’s financial resources should go based on the value of each park. By leveraging the vector-like nature of the vulnerability scores along with the popularity of each location and sea-level rise considerations, a graphical model was generated grouping parks of higher and lower values together in a distinguishable manner, as presented on page 16. From this graphic, our final recommendation to the NPS would be, in times of tight revenues, to fund Olympic National Park, consider funding Acadia and Kenai Fjords, and avoid funding the seashore locations.


The team received an Honorable Mention at Moody’s Mega Math Challenge 2017.

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Mathematics Commons



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