PMID- 29043571
OWN - NLM
STAT- MEDLINE
DCOM- 20180119
LR  - 20181202
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Linking)
VI  - 189
IP  - 11
DP  - 2017 Oct 17
TI  - An uncertainty-based framework to quantifying climate change impacts on coastal 
      flood vulnerability: case study of New York City.
PG  - 567
LID - 10.1007/s10661-017-6282-y [doi]
AB  - The continued development efforts around the world, growing population, and the 
      increased probability of occurrence of extreme hydrologic events have adversely 
      affected natural and built environments. Flood damages and loss of lives from the 
      devastating storms, such as Irene and Sandy on the East Coast of the USA, are 
      examples of the vulnerability to flooding that even developed countries have to 
      face. The odds of coastal flooding disasters have been increased due to 
      accelerated sea level rise, climate change impacts, and communities' interest to 
      live near the coastlines. Climate change, for instance, is becoming a major 
      threat to sustainable development because of its adverse impacts on the 
      hydrologic cycle. Effective management strategies are thus required for flood 
      vulnerability reduction and disaster preparedness. This paper is an extension to 
      the flood resilience studies in the New York City coastal watershed. Here, a 
      framework is proposed to quantify coastal flood vulnerability while accounting 
      for climate change impacts. To do so, a multi-criteria decision making (MCDM) 
      approach that combines watershed characteristics (factors) and their weights is 
      proposed to quantify flood vulnerability. Among the watershed characteristics, 
      potential variation in the hydrologic factors under climate change impacts is 
      modeled utilizing the general circulation models' (GCMs) outputs. The considered 
      factors include rainfall, extreme water level, and sea level rise that exacerbate 
      flood vulnerability through increasing exposure and susceptibility to flooding. 
      Uncertainty in the weights as well as values of factors is incorporated in the 
      analysis using the Monte Carlo (MC) sampling method by selecting the best-fitted 
      distributions to the parameters with random nature. A number of low impact 
      development (LID) measures are then proposed to improve watershed adaptive 
      capacity to deal with coastal flooding. Potential range of current and future 
      vulnerability to flooding is estimated with and without consideration of climate 
      change impacts and after implementation of LIDs. Results show that climate change 
      has the potential to increase rainfall intensity, flood volume, floodplain 
      extent, and flood depth in the watershed. The results also reveal that improving 
      system resilience by reinforcing the adaptation capacity through implementing 
      LIDs could mitigate flood vulnerability. Moreover, the results indicate the 
      significant effect of uncertainties, arising from the factors' weights as well as 
      climate change, impacts modeling approach, on quantifying flood vulnerability. 
      This study underlines the importance of developing applicable schemes to quantify 
      coastal flood vulnerability for evolving future responses to adverse impacts of 
      climate change.
FAU - Zahmatkesh, Zahra
AU  - Zahmatkesh Z
AD  - Department of Civil Engineering, Faculty of Engineering, McMaster University, 
      Hamilton, ON, Canada. zahmatkz@mcmaster.ca.
FAU - Karamouz, Mohammad
AU  - Karamouz M
AD  - School of Civil Engineering, University of Tehran, Tehran, Iran.
LA  - eng
PT  - Journal Article
DEP - 20171017
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
SB  - IM
MH  - *Climate Change
MH  - Conservation of Natural Resources/methods
MH  - Disasters
MH  - Environmental Monitoring/*methods/standards
MH  - Floods/*statistics & numerical data
MH  - Forecasting
MH  - New York City
MH  - Probability
MH  - Uncertainty
OTO - NOTNLM
OT  - Adaptive measures
OT  - Climate change
OT  - Coastal areas
OT  - New York City
OT  - Resilience
OT  - Uncertainty
OT  - Vulnerability
EDAT- 2017/10/19 06:00
MHDA- 2018/01/20 06:00
CRDT- 2017/10/19 06:00
PHST- 2017/04/21 00:00 [received]
PHST- 2017/10/05 00:00 [accepted]
PHST- 2017/10/19 06:00 [entrez]
PHST- 2017/10/19 06:00 [pubmed]
PHST- 2018/01/20 06:00 [medline]
AID - 10.1007/s10661-017-6282-y [pii]
AID - 10.1007/s10661-017-6282-y [doi]
PST - epublish
SO  - Environ Monit Assess. 2017 Oct 17;189(11):567. doi: 10.1007/s10661-017-6282-y.