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The beaches of the Hawaiian Islands attract nearly 9 million visitors each year, who inject around $15.6 billion into the state’s economy and support almost 200,000 jobs. Beyond their economic importance, Hawaiian beaches are also culturally and ecologically valuable. However, climate change driven sea-level rise is causing many beaches to disappear, endangering property, infrastructure, and critical habitats. The goal of this project was to develop a method for forecasting erosion-vulnerable beach areas that could be used in coastal management planning. Researchers focused on the island of Kauaʻi, modeling beach response to rising sea level over the next century and producing maps that provide information about...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2014,
CASC,
Completed,
Data Visualization & Tools,
Data Visualization & Tools, All tags...
Drought, Fire and Extreme Weather,
Drought, Fire and Extreme Weather,
Extreme Weather,
Extreme Weather,
Landscapes,
Landscapes,
Other Landscapes,
Other Landscapes,
Pacific Islands,
Pacific Islands CASC,
Projects by Region,
Science Tools for Managers,
Science Tools for Managers,
Sea-Level Rise and Coasts,
Sea-Level Rise and Coasts,
Water, Coasts and Ice,
Water, Coasts and Ice,
beach,
erosion,
model,
sea-level rise, Fewer tags
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Planning community resilience to sea level rise (SLR) requires information about where, when, and how SLR hazards will impact the coastal zone. We augment passive flood mapping (the so-called “bathtub” approach) by simulating physical processes posing recurrent threats to coastal infrastructure, communities, and ecosystems in Hawai‘i (including tidally-forced direct marine and groundwater flooding, seasonal wave inundation, and chronic coastal erosion). We find that the “bathtub” approach, alone, ignores 35–54 percent of the total land area exposed to one or more of these hazards, depending on location and SLR scenario. We conclude that modeling dynamic processes, including waves and erosion, is essential to robust...
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