Joint probability distribution of coastal winds and waves using a log-transformed kernel density estimation and mixed copula approach

Bai, Xiaoyu; Jiang, Hui; Li, Chen; Lei, Huang

doi:10.1016/j.oceaneng.2020.107937

Cited by 29 publications

(6 citation statements)

References 22 publications

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“…Compared with three commonly used single copulas, the mixed copula is more suitable for describing the bivariate complex dependency structure between drought variables and can yield more realistic joint probability estimations. Our findings are consistent with the researches of Qian et al (2020) and Bai et al (2020). They reported that the mixed copula performs better than single copula in modeling the dependence pattern between precipitation variables and between coastal winds and waves data.…”

Section: Discussionsupporting

confidence: 93%

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Mixed copula-based bivariate drought risk analysis combined with a composite index in the Fenhe River Basin, China

Wei,

Zhao,

Chen

2024

Preprint

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In the context of climate change, drought events were likely to be frequent and severe in Fenhe river basin (FRB). This study developed a multivariate composite drought index (CDI) combining standardized precipitation index (SPI), standardized runoff index (SRI), and agricultural standardized precipitation index (aSPI) based on entropy weight method for comprehensive characterizing drought in the FRB. A mixed copula was then developed for comprehensively modeling the dependent structures of drought variables (duration, severity, peak), through coupling three single copulas (Gumbel, Clayton and Frank copulas). The drought variation was examined by modified Mann-Kendall test (MMK) method and the historical droughts of the basin were examined in three periods: the first period (1951–1974), the second period (1975–1998), and the third period (1999–2022). The bivariate return periods and bivariate risk of drought variables were finally analyzed based on mixed copula. The results indicate that (1) the constructed CDI is consistent with SPI, SRI and aSPI series, and it is sensitive and effective to capture historical drought events. (2) the CDI, SPI, SRI and aSPI present significant decreasing trend indicating the increasingly serious drought condition in the basin. (3) drought variables are higher in the third period than in other periods and the basin experienced an increasingly serious dry condition. (4) the mixed copula provides more reliable return periods and drought risk results.

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Section: Discussionsupporting

confidence: 93%

“…The mixed copula function has six unknown parameters: 𝜔 𝑘 and 𝜃 𝑘 (k=1,2,3). The MATLAB function "fmincon" was adopted to obtain the values of the parameters that minimize the deviation square (ordinary least square, OLS), which can be expressed as (Bai et al 2020):…”

Section: 𝑘=1mentioning

confidence: 99%

Mixed copula-based bivariate drought risk analysis combined with a composite index in the Fenhe River Basin, China

Wei,

Zhao,

Chen

2024

Preprint

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“…Thus, it is more robust to assess the joint behavior of

μ

and

τ_{2}

, and of

τ_{3}

and

τ_{4}

as shape properties. We treated (

μ, τ_{2}

) and

(τ_{3}, τ_{4})

as bivariate random variables and estimate their probability density function (PDF) by using bivariate kernel densities, which are widely applied as alternatives to parametric models (Bai, 2020). First, we highlighted the 0.25, 0.50, and 0.75 highest probability regions (HPR) in the bivariate densities (defined as the smallest possible regions containing 0.25, 0.50, and 0.75 of the bivariate points) of the L‐moments in GPCC (see Figure 4).…”

Section: Methodsmentioning

confidence: 99%

Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation

et al. 2021

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Extreme environmental phenomena are part of natural variability. Yet, anthropogenic activities are expected to intensify extreme events, affecting the natural and built environment (e.g., AghaKouchak et al., 2020;Moustakis et al., 2021;Raymond et al., 2020). Extreme events have severe effects on human health, ecology, biodiversity, and economy. For example, globally, from 1980 to 2009, floods caused more than 500,000 deaths and adversely affected more than 2.8 billion people (Doocy et al., 2013). Therefore, analyzing and predicting the behavior of extreme events, especially under climate change, is of great importance. Meanwhile, a clear definition of extreme events does not exist and we typically relate extremes with low exceedance probabilities . However, in the literature, popular methods focus on analyzing

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“…According to Bai et al . (2020), ensemble copula could help improve the projection accuracy up to 4.67 and 54.23%.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…The results obtained by ensemble copula show that the colour bands are located between the three single copulas, indicating that the ensemble can yield better simulation results for the bivariate risk compared with single copula. According to Bai et al (2020), ensemble copula could help improve the projection accuracy up to 4.67 and 54.23%.…”

Section: Bivariate Features Under Uncertaintiesmentioning

confidence: 99%

Development of an ensemble Bayesian inference‐based copula approach for bivariate risk evaluation of extreme precipitation under climate change

Sun

et al. 2022

Intl Journal of Climatology

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In this study, an ensemble Bayesian inference-based copula approach (EBICA) has been proposed to evaluate the joint risk of extreme precipitation indices under climate change. EBICA incorporates GCMs, quantile delta mappingbased bias correction method, Monte Carlo Markov Chain (MCMC)-based Bayesian inference method, and copula functions into an integration, where 17 GCMs and 3 copulas are explored EBICA can reflect the inherent uncertainties in GCM and copula selections, as well as deal with the uncertainty caused by copula parameters when modelling the dependent structures.Ensemble results of GCMs and copulas are also analysed to promote the projection accuracy of dynamic features of extreme precipitation (characterized by precipitation intensity, very heavy precipitation amount and frequency, and maximum 1 or 5-day precipitation amount). EBICA is then applied to Fujian Province to investigate climate change impacts on extreme precipitation during 2021-2099. Major findings can be summarized as (a) GCM is the primary source of uncertainties that impacts extreme precipitation projection, and the ensemble of GCMs suggests that climate change would exacerbate future precipitation intensity, amount, and frequency over Fujian Province; (b) mountainous area in Fujian is more easily encountered with very heavy precipitation amount and coastal area is more easily faced with maximum 1-day or consecutive 5-day precipitation under climate change; (c) Fujian Province would averagely encounter with shorter co-occurrence period of [SDII, R95p] and [R20mm, R95p], corresponding to higher risk level in very heavy precipitation amount and frequency, especially in the north part. Generally, these findings could help lay the foundation for forecasting and warning of extreme precipitation events and reducing associated risk losses.

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Joint probability distribution of coastal winds and waves using a log-transformed kernel density estimation and mixed copula approach

Cited by 29 publications

References 22 publications

Mixed copula-based bivariate drought risk analysis combined with a composite index in the Fenhe River Basin, China

Mixed copula-based bivariate drought risk analysis combined with a composite index in the Fenhe River Basin, China

Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation

Development of an ensemble Bayesian inference‐based copula approach for bivariate risk evaluation of extreme precipitation under climate change

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