Characterizing compound flooding potential and the corresponding driving mechanisms across coastal environments

Pirani, Farshad Jalili; Najafi, Mohammad Reza

doi:10.1007/s00477-022-02374-0

Cited by 11 publications

(6 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, due to the introduction of copula-based multi-variate frameworks, this limitation is no longer applicable [155][156][157]. These frameworks are also useful in dealing with compound flooding, which results from the interaction of different flood-generating sources, such as river-borne floods and storm surges [16,124,[158][159][160][161], as well as heavy precipitation combined with snowmelt or saturated soil conditions [162,163], all of which contribute to the magnification of the hazardous situation. The definition of conventionally used single return period becomes inadequate in the case of compound flooding since it involves the joint probability of occurrence of two or more hazardous events.…”

Section: • Importance Of Data Length In Ffa and Surrogatesmentioning

confidence: 99%

Climate-resilience of dams and levees in Canada: a review

Islam,

Fereshtehpour,

Najafi

et al. 2024

Discov Appl Sci

View full text Add to dashboard Cite

Increasing frequency and intensification of flooding pose significant threats to critical structures, such as dams and levees. Failure of these structures can lead to substantial economic losses and significant adverse environmental and social consequences. Improving the resilience of these structures against climate-related impacts is important to avoid future risks of failure due to the potential intensification of flooding. National-level guidance on integrating resilience-based frameworks and addressing climate risks and uncertainties in existing design flood estimation methodologies for dams and levees are lacking. To address these gaps, this study first reviews projected climate change patterns for Canada and then discusses regional vulnerabilities of dams by considering significant historical floods and their consequences. Subsequently, a review of existing design flood estimation procedures, with a focus on frequency- and probable maximum flood-based approaches, is conducted to identify areas where climate change-related aspects can be integrated. By examining the challenges associated with various stages of design flood estimation procedures, the review discusses a framework for enhancing climate resiliency of dams and levees considering four pillars of resilience. Furthermore, Canadian design flood estimation practices are compared with international practices to identify areas that require attention. The study highlights the importance of a resilience-based framework in providing design and operation guidance to ensure that dams and levees are resilient to climate impacts. Policymakers and engineers can prioritize consideration of climate-resilience in the design and operation of these structures in order to safeguard communities and infrastructure from the growing risks of future floods associated with climate change.

show abstract

Section: • Importance Of Data Length In Ffa and Surrogatesmentioning

confidence: 99%

Climate-resilience of dams and levees in Canada: a review

Islam,

Fereshtehpour,

Najafi

et al. 2024

Discov Appl Sci

View full text Add to dashboard Cite

show abstract

“…Hydrologic risk is evaluated by FP, defined as the likelihood of a potential flood event taking place at least once over the lifespan of a project [8,22,35,62]. The equation for calculating the FP is as follows:…”

Section: Failure Probabilitymentioning

confidence: 99%

“…Beyond flood frequency, recent studies [8,[33][34][35] have introduced the concept of FP to quantify hydrologic event risks. They argue that the return period (RP) inadequately represents the risk throughout the whole project lifetime.…”

Section: Introductionmentioning

confidence: 99%

Compound Impact of Storm Surge and Flood Characteristics in Coastal Area Based on Copula

Zhu,

Zhang,

Zhu

2024

Water

View full text Add to dashboard Cite

In low-lying coastal areas, the interplay of various factors including precipitation, river flow, and storm surge can lead to greater influence on floods when they occur simultaneously. The copula method was used in this study to investigate the bivariate flood risk of compounding storm surge and river discharge events in the Pearl River Delta (PRD). Our results indicate that while the correlation between storm surge and flood peak (S-Q) was weak, there was a strong dependence between the pairs of storm surge–flood volume (S-V) and storm surge–flood duration (S-D). For these three pairs, the Clayton copula was the optimal function for S-Q, while the Frank copula was the optimal function for S-V and S-D, respectively. When the flood volume exceeds 2.0 × 104 m3/s and the flood duration is more than 10 days, the bivariate hydrologic risk for S-V and S-D is observed to decrease rapidly. Furthermore, the failure probability (FP) would be underestimated when the combined impact of river flow and storm surge is ignored in coastal flood risk assessment. Such bivariate hydrologic risk analysis implies that when determining design values in coastal flood risk assessment, the combined impact of river flow and storm surge should be taken into account.

show abstract

“…CF modeling can be performed via multivariate statistical analysis (Bensi et al, 2020;Jalili Pirani and Najafi, 2023;Sadegh et al, 2018), process-based modeling (Bates et al, 2021;Sanders et al, 2023;Santiago-Collazo et al, 2019), and even "hybrid" methods (Gori et al, 2020;Moftakhari et al, 2019;Serafin et al, 2019). Statistical analyses enable the prediction of future CF events, the reliability of which largely depends on the length of data records.…”

Section: Introductionmentioning

confidence: 99%

Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

Muñoz,

Moftakhari,

Moradkhani

2024

Preprint

View full text Add to dashboard Cite

Abstract. Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in which concurrent or successive flood drivers synergize, producing larger impacts than those from individual drivers. CF modeling is yet subject to four main sources of uncertainty including (i) initial condition, (ii) forcing (or boundary) conditions, (iii) model parameters, and (iv) model structure. These sources of uncertainty, if not quantified and effectively reduced, cascade in series throughout the modeling chain and compromise the accuracy of CF hazard assessments. Here, we characterize cascading uncertainty using linked process-based and machine learning (PB-ML) models for a well-known CF event, namely Hurricane Harvey in Galveston Bay, TX. For this, we run a set of hydrodynamic model scenarios to quantify isolated and cascading uncertainty in terms of maximum water level residuals, and additionally, track the evolution of residuals during the onset, peak, and dissipation of Hurricane Harvey. We then develop multiple-linear regression (MLR) and PB-ML models to estimate the relative and cumulative contribution of the four sources of uncertainty to total uncertainty over time. Results from this study show that the proposed PB-ML model capture “hidden” nonlinear associations and interactions among the sources of uncertainty, thereby outperforming conventional MLR models. Model structure and forcing conditions are the main sources of uncertainty in CF modeling and their corresponding model scenarios, or input features, contribute to (56 %) 49 % of variance reduction in the estimation of (maximum) water level residuals. Following these results, we conclude that PB-ML models are a feasible alternative for quantifying cascading uncertainty in CF modeling.

show abstract

Characterizing compound flooding potential and the corresponding driving mechanisms across coastal environments

Cited by 11 publications

References 61 publications

Climate-resilience of dams and levees in Canada: a review

Climate-resilience of dams and levees in Canada: a review

Compound Impact of Storm Surge and Flood Characteristics in Coastal Area Based on Copula

Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

Contact Info

Product

Resources

About