A scalable flood-resilience-index for measuring climate change adaptation: Munich city.
Clicks: 198
ID: 94235
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
198 views
33 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #125 of 168 articles by views in Water research
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 168 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Climate change is affecting the frequency and intensity of rainfall extreme events worldwide. Despite the growing global awareness, developing flood resilient cities has proven to be a major challenge. This paper investigates the application of an event-based scalable Flood Resilience Index (FRI) for assessing climate change adaptation. Flood resilience is represented by three dimensions: physical, social and economic. A household climate adaptation is adopted consisting of a combination of a flood-proof gate with an indoor tank and a submersible pump system implemented in all houses. The climate related impact under a high-emission scenario (RCP8.5) is analysed for Munich with the CRCM5 Large-Ensemble. Results show that for Munich extreme heavy rainfall events are increasing. The FRI can successfully identify households and districts which: a) are mostly affected by heavy rainfall, b) benefit the most from the climate adaptation, and c) are the most resilient. For the most severe future scenario investigated the climate adaptation measure was able to improve 57% of all affected buildings within Maxvorstadt to an FRI equal to 1.0 during the event and recovery phase.
| Reference Key |
leandro2020awater
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Leandro, Jorge;Chen, Kai-Feng;Wood, Raul R;Ludwig, Ralph; |
| Journal | Water research |
| Year | 2020 |
| DOI |
S0043-1354(20)30038-5
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.