Copula-based projections of wind power: Ireland as a case study

Moradian, Sogol; Olbert, Agnieszka Indiana; Gharbia, Salem; Iglesias, G.

doi:10.1016/j.rser.2023.113147

Cited by 20 publications

(11 citation statements)

References 46 publications

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“…In the context of flood analysing, the joint probabilities identify the likelihood of combinations of coastal and river conditions occurring and capture relationships between variables (Wu et al, 2021;Olbert et al, 2023;Moradian et al, 2023).…”

Section: Estimating Joint Probabilitiesmentioning

confidence: 99%

“…The copula method was employed in this study to compute joint probabilities for extreme sea levels and river flows cooccurring in the Conwy for the first time. The joint probabilities were computed using the framework introduced by Sadegh et al ( 2017) and Moradian et al (2023). The proposed framework uses three main components: (i) 16 statistical distributions were employed to identify the best marginal distributions; (ii) 26 distinct copula functions were applied to sea level and river flows; and (iii) the Bayesian method was employed to compute the joint probabilities.…”

Section: Estimating Joint Probabilitiesmentioning

confidence: 99%

“…The proposed framework uses three main components: (i) 16 statistical distributions were employed to identify the best marginal distributions; (ii) 26 distinct copula functions were applied to sea level and river flows; and (iii) the Bayesian method was employed to compute the joint probabilities. The following sections provide a concise overview of the steps involved in this framework, while more comprehensive details can be found in Sadegh et al (2017Sadegh et al ( , 2018, Yazdandoost et al ( 2020), and Moradian et al (2023).…”

Section: Estimating Joint Probabilitiesmentioning

confidence: 99%

“…To identify the most suitable marginal distributions for the data, researchers commonly employ parametric or nonparametric distributions. It is important to note that each variable's marginal distribution is modelled using the best-fitted distribution, as shown in Table 6 of Moradian et al (2023).…”

Section: Statistical Marginal Distributionsmentioning

confidence: 99%

“…To assess the accuracy of the marginal distributions, their significance at a 5 % level is evaluated using the chi-squared goodness-of-fit test (Greenwood and Nikulin, 1996). Furthermore, various metrics are used for statistical evaluations, as detailed in Table 5 of Moradian et al (2023). These metrics include the Akaike information criterion (AIC), Bayesian information criterion (BIC), maximum likelihood estimation (MLE), Nash-Sutcliffe efficiency (NSE), and root mean square error (RMSE).…”

Section: Statistical Marginal Distributionsmentioning

confidence: 99%

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Thresholds for estuarine compound flooding using a combined hydrodynamic–statistical modelling approach

Lyddon,

Chien,

Vasilopoulos

et al. 2024

Nat. Hazards Earth Syst. Sci.

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Abstract. Estuarine compound flooding can happen when extreme sea level and river discharges occur concurrently, or in close succession, inundating low-lying coastal regions. Such events are hard to predict and amplify the hazard. Recent UK storms, including Storm Desmond (2015) and Ciara (2020), have highlighted the vulnerability of mountainous Atlantic-facing catchments to the impacts of compound flooding including risk to life and short- and long-term socio-economic damages. To improve prediction and early warning of compound flooding, combined sea and river thresholds need to be established. In this study, observational data and numerical modelling were used to reconstruct the historic flood record of an estuary particularly vulnerable to compound flooding (Conwy, North Wales). The record was used to develop a method for identifying combined sea level and river discharge thresholds for flooding using idealised simulations and joint-probability analyses. The results show how flooding extent responds to increasing total water level and river discharge, with notable amplification in flood extent due to the compounding drivers in some circumstances, and sensitivity (∼ 7 %) due to a 3 h time lag between the drivers. The influence of storm surge magnitude (as a component of total water level) on the flooding extent was only important for scenarios with minor flooding. There was variability as to when and where compound flooding occurred; it was most likely under moderate sea and river conditions (e.g. 60th–70th and 30th–50th percentiles) and only in the middle-estuary zone. For such cases, joint-probability analysis is important for establishing compound flood risk behaviour. Elsewhere in the estuary, either the sea state (lower estuary) or river flow (upper estuary) dominated the hazard, and single-value probability analysis is sufficient. These methods can be applied to estuaries worldwide to identify site-specific thresholds for flooding to support emergency response and long-term coastal management plans.

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Section: Estimating Joint Probabilitiesmentioning

confidence: 99%

Section: Estimating Joint Probabilitiesmentioning

confidence: 99%