Zhaoqing Yang

Dr. Zhaoqing Yang

1 Pacific Northwest National Laboratory, 1100 Dexter Ave North, Suite 500, Seattle, Washington 98109, USA;
+1
orcid

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 April 2015 in Estuarine, Coastal and Shelf Science
Verified
Journal Article
Closed access
Zhaoqing YangZhaoqing Yang
Zhaoqing Yang
Taiping WangTaiping Wang
Taiping Wang
NVNathalie Voisin
Nathalie Voisin
ACAndrea Copping
Andrea Copping

30 July 2021 in Frontiers in Marine Science
Verified
Journal Article
Open access
ZXZiyu Xiao
Ziyu Xiao
Zhaoqing YangZhaoqing Yang
Zhaoqing Yang
Taiping WangTaiping Wang
Taiping Wang
NSNing Sun
Ning Sun
MWMark Wigmosta
Mark Wigmosta
DJDavid Judi
David Judi
Low-lying coastal areas in the mid-Atlantic region are prone to compound flooding resulting from the co-occurrence of river floods and coastal storm surges. To better understand the contribution of non-linear tide-surge-river interactions to compound flooding, the unstructured-grid Finite Volume Community Ocean Model was applied to simulate coastal storm surge and flooding in the Delaware Bay Estuary in the United States. The model was validated with tide gauge data in the estuary for selected hurricane events. Non-linear interactions between tide-surge-river were investigated using a non-stationary tidal analysis method, which decomposes the interactions’ components at the frequency domain. Model results indicated that tide-river interactions damped semidiurnal tides, while the tide-surge interactions mainly influenced diurnal tides. Tide-river interactions suppressed the water level upstream while tide-surge interaction increased the water level downstream, which resulted in a transition zone of damping and enhancing effects where the tide-surge-river interaction was prominent. Evident compound flooding was observed as a result of non-linear tide-surge-river interactions. Furthermore, sensitivity analysis was carried out to evaluate the effect of river flooding on the non-linear interactions. The transition zone of damping and enhancing effects shifted downstream as the river flow rate increased.

8 December 2013 in Natural Hazards
Verified
Journal Article
Closed access
PDF file
Zhaoqing YangZhaoqing Yang
Zhaoqing Yang
Taiping WangTaiping Wang
Taiping Wang
RLRuby Leung
Ruby Leung
KHKathy Hibbard
Kathy Hibbard
TJTony Janetos
Tony Janetos
TWTom Wilbanks
Tom Wilbanks

1 June 2021 in Renewable Energy
Verified
Journal Article
Closed access
BRBryson Robertson
Bryson Robertson
GDGabrielle Dunkle
Gabrielle Dunkle
JGJonah Gadasi
Jonah Gadasi
GMGabriel García Medina
Gabriel García Medina
Zhaoqing YangZhaoqing Yang
Zhaoqing Yang

1 June 2020 in Renewable Energy
Verified
Journal Article
Closed access
WWWEi-Cheng Wu
WEi-Cheng Wu
TWTaiping Wang
Taiping Wang
Zhaoqing YangZhaoqing Yang
Zhaoqing Yang
GMGabriel García Medina
Gabriel García Medina

Verified
Journal Article
Open access
PDF file
VNVincent Neary
Vincent Neary
seongho ahnseongho ahn
seongho ahn
BSBibiana Seng
Bibiana Seng
Nabi AllahdadiNabi Allahdadi
Nabi Allahdadi
Taiping WangTaiping Wang
Taiping Wang
Ruoying HeRuoying He
Ruoying He
Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.