Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping

Alipour, Atieh; Jafarzadegan, Keighobad; Moradkhani, Hamid

doi:10.1016/j.envsoft.2022.105398

Cited by 57 publications

(34 citation statements)

References 97 publications

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“…Overall, the two main components of model structural uncertainty in HD models are the formulation and numerical schemes used to route the flow throughout the domain and the spatiotemporal discretization of the domain ( Willis et al., 2019 ). Although diverse numerical schemes (or methods) have been developed to solve the momentum and mass balance equations (e.g., Eulerian, Eulerian-Lagrangian, and diffusion-wave, among others), we argue that grid-cell size is the most influential factor in accurately simulating flood dynamics based on global-sensitivity analyses ( Alipour et al., 2022 ). Nevertheless, there is always a trade-off between model accuracy and computation burden (or run time) that should be evaluated before any attempts of large-scale mesh refinement.…”

Section: Hydrological and Hydrodynamic Systems For Modeling Compound ...mentioning

confidence: 99%

“…In the last two decades, sensitivity analysis ( Alipour et al., 2022 ; Hall et al., 2005 ; Pappenberger et al., 2008 ; Thomas Steven Savage et al., 2016 ), Monte Carlo Based methods ( Apel et al., 2004 ; Domeneghetti et al., 2013 ), Generalized Likelihood Uncertainty Estimation ( Aronica et al., 2002 ; Domeneghetti et al., 2013 ; Romanowicz and Beven, 2003 ), and Data Assimilation ( Brêda et al., 2019 ; Durand et al., 2008 ; Xu et al., 2017a , 2017b ) have been commonly applied for uncertainty quantification and reduction in HD models and flood inundation mapping. Compared to DA in HL modeling, the integration of DA in HD modeling has received much less attention in scientific literature.…”

Section: Quantifying and Reducing Uncertaintiesmentioning

confidence: 99%

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Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

Abbaszadeh¹,

Muñoz²,

Moftakhari³

et al. 2022

iScience

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Section: Hydrological and Hydrodynamic Systems For Modeling Compound ...mentioning

confidence: 99%

Section: Quantifying and Reducing Uncertaintiesmentioning

confidence: 99%

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

Abbaszadeh¹,

Muñoz²,

Moftakhari³

et al. 2022

iScience

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“…Uncertainties surrounding flood-hazard projections can stem from river flow conditions, river and floodplain geometric and hydraulic characteristics, as well as the topography of the region [8][9][10][11][12][13] . Several studies highlight the dominating influence of digital elevation model (DEM) resolution, upstream discharge, and river and floodplain roughness coefficients on the uncertainty of flood hazard projections [8][9][10][11][12][13] . Uncertainties surrounding flood exposure and vulnerability can further add uncertainty to flood risk projections 14 .…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies [8][9][10][11][12][13] have broken important new ground in characterizing the uncertainties surrounding riverine flood hazards and risks and their drivers. For example, Pappenberger et al (2008) uses five different sensitivity analysis approaches to rank the sensitivity indices of flood hazard parameters in a 1D flood inundation model 11 .…”

Section: Introductionmentioning

confidence: 99%

Hazards are the key drivers of uncertainties surrounding riverine flood risks

Hosseini‐Shakib

Alipour

Lee

et al. 2023

Preprint

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Flooding drives considerable risks. Designing strategies to manage these risks is complicated by the often-large uncertainty surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem, for example, from choices regarding boundary conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. A quantitative understanding of which factors drive uncertainties surrounding flood hazards and risks can inform the design of mission-oriented research. Here we analyze a case study to (i) characterize key uncertainties impacting flood risk projections and (ii) characterize the most important drivers of the uncertainties surrounding riverine flood hazards and risks. We find that the projected flood risk is most sensitive to factors associated with flood hazards: upstream discharge, riverbed elevation, channel roughness, and the digital elevation model resolution. Our framework and results can help to improve flood-hazard and risk projections.

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“…Studies indicated that people's decision to evacuate is heavily related to the SSHWS category (Lazo et al., 2010; Morss & Hayden, 2010). While intensive winds associated with TC cause severe property damage and casualty, torrential rainfall accompanied with weakened wind speed, cause extensive flooding (Alipour et al., 2022; Oldenborgh et al., 2017; Touma et al., 2019). Slow moving TCs in coastal areas result in massive flooding due to heavy rainfall amount and destructive surges (Kossin, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Scaling System Is Essential to Characterize the Tropical Cyclones' Risk

et al. 2022

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The current Tropical Cyclones (TCs) scaling system, Saffir‐Simpson Hurricane Wind Scale (SSHWS), characterizes the hazardousness of these events solely based on wind speed. This is despite the fact that TCs are classic examples of compound hazards during which multiple hazard drivers that are wind, storm surge, and intense rainfall interact and yield in impacts greater than the sum of individuals. Studies have shown that people's decision to evacuate is highly related to the estimated SSHWS category. Thus, the current SSHWS ‐based classification of TCs yields an underestimation of the hazardousness of TCs and so may misguide the threatened communities. Here, we propose a new scaling system that uses Copulas for categorizing TCs based on the likelihood of a given set of severity for rainfall, surge, and wind speed. We use a variety of data sources to obtain the timing and intensity of wind speed, rainfall along the track, and the associated maximum surge for 102 TCs that have made landfall in the United States' Atlantic and Gulf coasts between 1979 and 2020. Comparing the outputs of our scaling system with official damage reporting for the costliest TCs in the history of the United States, we show that the proposed approach significantly improves TC hazard communication and can be useful for informing decision makers and emergency responders.

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Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping

Cited by 57 publications

References 97 publications

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

Hazards are the key drivers of uncertainties surrounding riverine flood risks

A Multivariate Scaling System Is Essential to Characterize the Tropical Cyclones' Risk

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