Observational evidence of change in extreme wind along the Eastern Seaboard of the United States
Chung, Jai Seoung.
2018
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Abstract: Extreme
winds cause significant damage to infrastructure in the United States. Climate change
effects to extreme winds including increasing trends have been predicted in climate
change scenarios; however, little observational evidence exists to support the
hypothesis of increasing winds in U.S. coastal communities due to climate change. In
this study, we use the historical record of ... read morepeak 3-s gust winds at sites along the
Eastern Seaboard to determine if nonstationarity exists in the historic wind record. We
evaluate nonstationarity at individual stations and within regional "superstation"
clusters. In order to evaluate nonstationarity, both parametric (Student's t-test) and
non-parametric (Mann-Kendall) trend tests are used. As Lombardo and Ayyub (2014)
separated winds by storm types, we observe evidence of nonstationarity by storm types
(commingled, nonthunderstorms, thunderstorms and tropical storms). For commingled data,
23 out of 108 stations exhibit evidence of nonstationarity. Roughly 16% of these
stations show a positive trend from Florida to NY. In New England, 6% of stations
exhibit a negative trend. In addition to the single station results, we cluster similar
wind sites together using the k-means algorithm, to extend observation records and
observe nonstationary behavior of extreme winds regionally. Incorporating L-moments in
regional frequency analysis for clustering purposes requires regional standardized
L-moment parameters (Eslamian et al., 2012; Parida et al., 1998). A combination of three
parameters consisting of latitude, longitude and L-CV is used to define the k-means
clustering. Regional frequency analysis (Hosking and Wallis, 1997) is carried out using
L-moments to confirm homogeneity of the cluster prior to evaluation of nonstationarity.
Once the clusters are defined, we employ the "superstation" (Peterka, 1992; Peterka and
Shahid., 1993) method for each cluster to extend and regionalize the record. By creating
a virtual "superstation", we have a single database with extended records and reduced
sample errors. Using trend tests, we find evidence of statistically significant regional
nonstationarity of 3-s gust wind speeds in 4 out of 7 clusters, all resulting in a
positive trend. Two clusters are in Florida, one is along the mid-coast and the final
one is New Englnad. The cluster in New England exhibits heterogeneity according to the
L-moment homogeneity tests. The trend tests for three of the regional clusters do not
exhibit a statistically significant trend. For the two statistically significant
clusters in Florida, we apply a nonstationary homoscedastic trend model to identify
return levels with time of interest at years 2010, 2030,
2050.
Thesis (M.S.)--Tufts University, 2018.
Submitted to the Dept. of Civil Engineering.
Advisor: Laurie Baise.
Committee: Richard Vogel, Franklin Lombardo, and Jonathan Lamontagne.
Keyword: Civil engineering.read less - ID:
- n009wd47r
- Component ID:
- tufts:24309
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- TARC Citation Guide EndNote
