Run uphill for a tsunami, downhill for a landslide
Following fatal landslides, the town of Sitka, Alaska, worked with scientists to create a new, individualized hazard warning system, revealing the complexities of coproducing knowledge.
Historic Sitka, Alaska, a town of 9,000 residents, is located on an island southwest of the state capital of Juneau, in the heart of the Tongass National Forest. The Pacific Ocean borders one side of the town, with steep mountains rising above the other. The community has long recognized the threat of tsunamis, but heavy rains in recent years have now caused landslides to be a concern as well. This is the story of how Sitka came to build its own innovative landslide warning system, as told by members of the team working to develop it.
August 18, 2015: Rain all morning
Sitka Sound Science Center’s Lisa Busch:
When four inches of rain fell on Sitka before 9:00 a.m., we knew it was not going to be a typical rainy day. The town has a well-functioning tsunami warning system, but had previously experienced few landslides, even though we get about 100 inches of rain a year. So when mud began to move down the hillsides around the town, there were no warnings. Three people died, a house was destroyed, and a lot of city infrastructure was damaged. Volunteers and city workers immediately went to work with shovels.
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In the weeks and months immediately following the August 2015 landslide, new anxieties emerged in our community and prompted new questions. Should we send our children to school on heavy-rain days? Whose responsibility would it be to call a “rain day” and cancel school? What information would we need to be able to predict landslides? How safe are the housing developments in steeply sloped neighborhoods? What will this new concern mean to land values and the availability of insurance?
Angst and anxiety, we soon came to realize, would drive the questions, methodology, and forward momentum of the entire project.
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We planned to use geoscience to identify risky moments and deliver a medium-term warning, so people had time to evacuate. We also saw an opportunity to significantly improve landslide prediction by using newly inexpensive Internet of Things moisture sensors placed directly in the hills to determine when local conditions suggested a slide was imminent. Citizen science efforts would provide additional data and further involve the community in the project. And to make sure the word got to all of Sitka’s citizens, we planned to use social network analysis to understand how to send warnings by text or social media without leaving anyone out.
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This meant that achieving a comprehensive sample of all Sitkans and their social networks would be nearly impossible. And given that, could we be sufficiently confident that any insights we gained from a partial survey were up to the task of hazard warnings? Many researchers consider partial network data to be a trade-off. We could either have genuine conversations with Sitkans about their networks and theoretically accept holes in the network, or we could leverage existing data sources and build synthetic models and compromise the human component. In order to apply community-partnered research and social science theory in a meaningful way to Sitkans, building trust was foundational to ensuring quality and relevant insights. Consequently, instead of viewing this as a trade-off, we opted for a third way that did not strictly follow either of the two options. Holding genuine conversations was both a means and an end: by building trust with Sitkans, we were able to unlock a better understanding of the unique social dynamics of Sitka that no model could have ever estimated.
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