English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In many coastal communities, the risks driven by storm surges are motivatingsubstantial investments in flood risk management. The design of adaptive riskmanagement strategies, however, hinges on the ability to detect future changesin storm surge statistics. Previous studies have used observations to identifychanges in past storm surge statistics. Here, we focus on the simple anddecision-relevant question: How fast can we learn from past and potentialfuture storm surge observations about changes in future statistics? UsingObserving System Simulation Experiments, we quantify the time required todetect changes in the probability of extreme storm surge events. We estimatelow probabilities of detection when substantial but gradual changes to the100-year storm surge occur. As a result, policy makers may underestimateconsiderable increases in storm surge risk over the typically long lifespans ofmajor infrastructure projects.

Understanding the detectability of potential changes to the 100-year peak storm surge