Extreme value analysis of wave climate in Chesapeake Bay Arash Niroomandi a , Gangfeng Ma a, * , Xinyu Ye b , Sha Lou c , Pengfei Xue b a Department of Civil and Environmental Engineering, Old Dominion University, Norfolk, 23455, VA, USA b Department of Civil and Environmental Engineering, Michigan Technological University, Houghton, 49931, MI, USA c Department of Hydraulic Engineering, Tongji University, Shanghai, 200092, China ARTICLE INFO Keywords: Chesapeake bay Design wave height Generalized extreme value distribution Generalized Pareto distribution Empirical orthogonal function ABSTRACT A thirty-seven year wave hindcast (1979–2015) in Chesapeake bay using NCEP's Climate Forecast System Reanalysis (CFSR) wind is presented. The long-term significant wave heights are generated by the third- generation nearshore wave model SWAN, which is validated using the wave height measurements at buoy sta- tions in the bay. The simulated wave heights are analyzed to characterize their temporal and spatial variabilities as well as long-term changing trends by using an Empirical Orthogonal Function (EOF) analysis and an empirical cumulative distribution function approach. Seasonal variability as well as extreme storm effects on significant wave heights are revealed in the first mode of principle component. Then, an extreme value analysis based on generalized extreme value and generalized Pareto distribution functions is applied to evaluate design wave heights with different return periods. The effects of key parameters including threshold value, time span and data length on the design wave heights are extensively studied. Through the comparisons of different distribution functions evaluated by Bayesian Information Criterion and Akaike Information Criterion, it is found that Gamma distribution function and generalized extreme value analysis provide the best fit for annual and monthly data, while generalized Pareto distribution function gives the best fit when peak-over-threshold analysis is conducted. 1. Introduction Coastal planners and engineers increasingly require information about wave climate to make better planning decisions and minimize future coastal hazards and economic loss, because coastal waves play a significant role in coastal flooding and damage of coastal infrastructure. Wave studies in the field of ocean and coastal engineering have usually focused on characterizing the spatial and temporal variabilities of char- acteristic wave height, typically the significant wave height, and deter- mining the design wave heights for structure design purposes. To study spatial and temporal variabilities of significant wave height, statistical analysis of long-term wave climate data could be performed. For example, Empirical Orthogonal Function (EOF) analysis provides useful information regarding possible spatial patterns of variability within the data and how they change with time. EOF analysis has been widely used in oceanography to study major modes of climate variability such as the El Nino/Southern Oscillation (ENSO) (Roundy, 2015; Lian and Chen, 2012; Messie and Chavez, 2011), and in coastal engineering to identify spreading and seasonal variability in shoreline and slope data (Lemke and Miller, 2017). The long-term changing trends of wave height can be revealed by means of a regression analysis and an empirical cumulative distribution function approach, which have been applied in a number of recent studies on extreme wave height in different ocean and coastal regions (Komar and Allan, 2007, 2008; Ruggiero et al., 2010). Long-term trend of extreme wave height is of considerable interest in recent wave studies because significant changes in wave heights have been found in many coastal and ocean regions. For instance, Mendez et al. (2006) and Menendez et al. (2008) revealed significant long-term variability of extreme wave height in the Northeast Pacific ocean using buoy measurements and a time-dependent peak over threshold (POT) model. In the North Atlantic ocean near the coast of England (Carter and Draper, 1988; Bacon and Carter, 1991) and east coast of U.S. (Komar and Allan, 2007, 2008), researchers have found significant increases in wave height generated by extreme storms during the past decades. Similar results have also been reported in other locations such as west coast of U.S. using measurements from NOAA buoy stations (Komar et al., 2009; Allan and Komar, 2000; 2006; Ruggiero et al., 2010) and by analysis of storm intensities and hindcasted wave heights (Graham and Diaz, 2001). To determine the design wave heights, extreme value analysis of significant wave height is always performed. Extreme value analysis (EVA) has broad applications in many disciplines such as coastal engi- neering, weather and climate, finance and traffic prediction. The theory * Corresponding author. E-mail address: gma@odu.edu (G. Ma). Contents lists available at ScienceDirect Ocean Engineering journal homepage: www.elsevier.com/locate/oceaneng https://doi.org/10.1016/j.oceaneng.2018.03.094 Received 17 November 2017; Received in revised form 29 March 2018; Accepted 31 March 2018 0029-8018/© 2018 Elsevier Ltd. All rights reserved. Ocean Engineering 159 (2018) 22–36