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Storm surge proxies in a data-poor landscape: a practical monitoring method for under-surveyed and -studied communities vulnerable to climate change

Author

Listed:
  • Jon Rosales

    (St. Lawrence University)

  • Carol Cady

    (St. Lawrence University)

  • Glenn Juday

    (University of Alaska – Fairbanks)

  • Claire Alix

    (University of Alaska-Fairbanks)

  • Miho Morimoto

    (Alaska Department of Natural Resources)

  • Jessica Chapman

    (St. Lawrence University)

  • Dakota Casserly

    (St. Lawrence University)

  • Sophia Katchatag

    (Native Village of Shaktoolik)

Abstract

The central problem we investigate is how coastal communities in the Arctic can plan for future storms in the absence of continuous, long-term data and/or instrumentation to monitor climatic events. The native village of Shaktoolik, Alaska recognizes these limitations and seeks increased monitoring of life-threatening storms affecting their village in their adaptation planning documents. To address this situation, and with the consent of the Tribal Council, we establish a baseline to monitor storm intensity in this data-poor region by dating and mapping storm surges in the Shaktoolik area. A storm surge is a proxy of storm intensity. We use driftwood in two ways to reconstruct past storm surges. First, we plot GPS points of driftwood lines on remote sensing imagery and digital elevation model data to map the maximum extent of storm surge for the November 2011 and 2013, and August 2019 storms. Second, in order to demonstrate that a particular log could have been deposited by those storms, dendrochronological analysis of individual logs within those deposits provides an estimate of when those trees died and could have entered the water as driftwood. These techniques, however, cannot determine the date of when those logs were deposited on a given beach. To narrow the date of the driftwood deposits from past storms, that is, to determine when the driftwood landed on the beach, local knowledge and observations are coupled with newspaper accounts. From these three lines of evidence, we show that the maximum storm surge the village can withstand without inundation is equivalent to the 2011 and 2019 storms. Those storms can be used as baseline indicators for future storms. This method of monitoring storm surges can be scaled up to other locations that also have minimal storm monitoring infrastructure.

Suggested Citation

  • Jon Rosales & Carol Cady & Glenn Juday & Claire Alix & Miho Morimoto & Jessica Chapman & Dakota Casserly & Sophia Katchatag, 2021. "Storm surge proxies in a data-poor landscape: a practical monitoring method for under-surveyed and -studied communities vulnerable to climate change," Climatic Change, Springer, vol. 164(1), pages 1-17, January.
  • Handle: RePEc:spr:climat:v:164:y:2021:i:1:d:10.1007_s10584-021-02995-4
    DOI: 10.1007/s10584-021-02995-4
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    References listed on IDEAS

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    1. Jonathan Ignatowski & Jon Rosales, 2013. "Identifying the exposure of two subsistence villages in Alaska to climate change using traditional ecological knowledge," Climatic Change, Springer, vol. 121(2), pages 285-299, November.
    2. Adam Hatzikyriakou & Ning Lin, 2017. "Simulating storm surge waves for structural vulnerability estimation and flood hazard mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 939-962, November.
    3. Jianbin Huang & Xiangdong Zhang & Qiyi Zhang & Yanluan Lin & Mingju Hao & Yong Luo & Zongci Zhao & Yao Yao & Xin Chen & Lei Wang & Suping Nie & Yizhou Yin & Ying Xu & Jiansong Zhang, 2017. "Recently amplified arctic warming has contributed to a continual global warming trend," Nature Climate Change, Nature, vol. 7(12), pages 875-879, December.
    4. A. R. Siders, 2019. "Social justice implications of US managed retreat buyout programs," Climatic Change, Springer, vol. 152(2), pages 239-257, January.
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