A Bayesian copula-based nonstationary framework for compound flood risk assessment along US coastlines

Naseri, Kasra; Hummel, Michelle A.

doi:10.1016/j.jhydrol.2022.128005

Cited by 29 publications

(15 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nonstationarity of individual flooding drivers and their compounding effects that are evolving, if ignored, lead to underestimation of flooding probability estimates (Hao & Singh, 2020;H. R. Moftakhari, Salvadori, et al, 2017;Naseri & Hummel, 2022;Tebaldi et al, 2021).…”

Section: Sea Level Risementioning

confidence: 99%

Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling

Jafarzadegan

Moradkhani

Pappenberger

et al. 2023

Reviews of Geophysics

View full text Add to dashboard Cite

Flooding is one of the most frequent natural disasters that endanger people's livelihoods and causes serious economic losses and damages annually. According to reports provided by the United Nations Office for Disaster Risk Reduction, flooding accounted for 43.4% of all 7,255 disaster events recorded globally between 1998 and 2017 (UNDRR, 2020). Recent studies have shown that climate change associated with global warming has

show abstract

Section: Sea Level Risementioning

confidence: 99%

Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling

Jafarzadegan

Moradkhani

Pappenberger

et al. 2023

Reviews of Geophysics

View full text Add to dashboard Cite

show abstract

“…不断增加 [54] 。通过条件概率表征变量间的因果关系或条件依赖关系 [55] 。贝叶斯网络在刻画随机变量相依关系和事件过程的级联风险方面有广泛的应用，近年来也应用于复合事件风险的研究。Couasnon 等 [56] 结合高斯 Copula 构建了贝叶斯网络模型，对美国休斯顿海湾地区洪潮复合洪水进行了模拟研究； Naseri 等 [57] 利用基于 Copula 的贝叶斯网络模型，评价了美国沿海地区极端降水和高潮位复合洪水的相依关系、概率风险和不确定性特征。复杂网络(complex network)是系统科学中的重要概念和理论，借助复杂网络的关联特性，可对极端事件的空间结构进行刻画和分析。Boers 等 [58] 基于复杂网络理论提出事件同步性的方法和指标，揭示了全球极端降水的遥相关特征和格局； de Michele 等 [59] 以观测站点为节点对构建了干湿二值…”

Section: 基于 Copula 的建模unclassified

Progress of research on compound extreme event and hazard assessment

Fang¹,

TAO²,

MU³

et al. 2023

Progress in Geography

View full text Add to dashboard Cite

“…MCMC algorithms can provide samples from high-dimensional complex distributions for estimating posterior distributions of parameters (Naseri and Hummel 2022). Thus, the posterior distributions of parameters in copula functions can be estimated through hybrid-evolution MCMC algorithm, based on Multivariate Copula Analysis Toolbox (Sadegh et al 2017).…”

Section: Uncertainty Analysis Based On Copula Functions 221 Correlate...mentioning

confidence: 99%

Gaseous reactive nitrogen losses of agricultural systems in China influenced by crop trade

Yue

et al. 2022

Environ. Res. Lett.

View full text Add to dashboard Cite

China plays an important role in the international trade of agricultural commodities. Considering the dynamic reactive nitrogen (Nr) losses of agricultural systems in China, a hypothesis was proposed that crop conversion in China would be correlated with the extent of crop trade, influencing Nr losses in agricultural systems. The objective of this study was to verify the hypothesis based on a hybrid approach, which incorporated life cycle analysis (LCA), copula-Markov Chain Monte Carlo (MCMC) simulation, and copula sampling. The approach was proven to be of benefit in (a) evaluating Nr losses in crop planting based on a LCA framework, (b) identifying dependencies and co-movements of the correlated variables in planting structures and crop trade using copula-MCMC simulations, and (c) recognizing fluctuations in Nr losses of crop planting in the future using copula-based sampling method. The planting structures and international trade of four types of crops (i.e., wheat, soybeans, maize, and rice) in twenty provinces of China indicated significant correlations, thus supporting the initial hypothesis. With the improvement of self-sufficiency in crop production, especially soybeans, Nr losses from the crop production of China in 2025 and 2030 would decrease by 8.43% and 4.26%, compared with those in 2018 (i.e., 1916.74 kt N).

show abstract

A Bayesian copula-based nonstationary framework for compound flood risk assessment along US coastlines

Cited by 29 publications

References 84 publications

Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling

Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling

Progress of research on compound extreme event and hazard assessment

Gaseous reactive nitrogen losses of agricultural systems in China influenced by crop trade

Contact Info

Product

Resources

About