Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes

Leijnse, Tim; Ormondt, Maarten van; Nederhoff, Kees; Dongeren, Ap van

doi:10.1016/j.coastaleng.2020.103796

Cited by 75 publications

(76 citation statements)

References 46 publications

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“…Insights from such local studies can aid engineers and risk management officials in performing more robust flood risk assessments that consider compounding effects in the planning, design, and operation of flood risk reduction measures. Approaches for assessing compound flooding include the implementation of hydrodynamic and/or hydrologic models (e.g., Gori et al, 2020;Leijnse et al, 2021;Santiago-Collazo et al, 2019), application of statistical models capable of modelling dependence structures between flooding drivers (Heffernan & Tawn, 2004;Sklar, 1959), or a combination of both (e.g., Moftakhari et al, 2019;Muñoz et al, 2020;Serafin et al, 2019;van den Hurk et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

Santos

Wahl

Jane

et al. 2021

J Flood Risk Management

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Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine-copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre-processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.

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Section: Introductionmentioning

confidence: 99%

Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

Santos

Wahl

Jane

et al. 2021

J Flood Risk Management

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“…Moreover, Coriolis as well as advection terms can be turned off, resulting in the local inertial equations. The SFINCS model was successfully applied to reproduce the wave-driven flooding of the village of Hernani in the Philippines during typhoon Haiyan in 2013 [6]. In this study, we apply SFINCS to model onshore inundation forced by the FM model and also-for the first time-to simulate offshore tsunami propagation.…”

Section: Sfincs Modelmentioning

confidence: 99%

“…For this, the water level time-series computed by the overall FM/SFINCS models for each observation point during the simulation time of 6 h 15 min was separated into incoming and outgoing wave components and only the incoming tsunami wave was applied at the boundaries of the nested SFINCS model. The nested SFINCS model is forced with a slowly and a fast-varying component using a 10-min moving mean using the method of [41] as described in [6], to keep the front of the nested tsunami wave as steep as forced.…”

Section: Sfincs Modelmentioning

confidence: 99%

Rapid Assessment of Tsunami Offshore Propagation and Inundation with D-FLOW Flexible Mesh and SFINCS for the 2011 Tōhoku Tsunami in Japan

Röbke

Leijnse

Winter

et al. 2021

JMSE

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This study demonstrates the skills of D-FLOW Flexible Mesh (FM) and SFINCS (Super-Fast INundation of CoastS) in combination with the Delft Dashboard Tsunami Toolbox to numerically simulate tsunami offshore propagation and inundation based on the example of the 2011 Tōhoku tsunami in Japan. Caused by a megathrust earthquake, this is one of the most severe tsunami events in recent history, resulting in vast inundation and devastation of the Japanese coast. The comparison of the simulated with the measured offshore water levels at four DART buoys located in the Northwestern Pacific Ocean shows that especially the FM but also the SFINCS model accurately reproduce the observed tsunami propagation. The inundation observed at the Sendai coast is well reproduced by both models. All in all, the model outcomes are consistent with the findings gained in earlier simulation studies. Depending on the specific needs of future tsunami simulations, different possibilities for the application of both models are conceivable: (i) the exclusive use of FM to achieve high accuracy of the tsunami offshore propagation, with the option to use an all-in-one model domain (no nesting required) and to add tsunami sediment dynamics, (ii) the combined use of FM for the accurate simulation of the tsunami propagation and of SFINCS for the accurate and time efficient simulation of the onshore inundation and (iii) the exclusive use of SFINCS to get a reliable picture of the tsunami propagation and accurate results for the onshore inundation within seconds of computational time. This manuscript demonstrates the suitability of FM and SFINCS for the rapid and reliable assessment of tsunami propagation and inundation and discusses use cases of the three model combinations that form an important base for tsunami risk management.

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“…Therefore the key to the successful application of MLMF is choosing an accurate high fidelity model and an appropriate lower fidelity model, which reasonably approximates the high fidelity one. Coastal flood modelling is therefore an ideal field on which to apply MLMF because there exist a large number of high fidelity but computationally expensive full physics models such as XBeach (Roelvink et al, 2009), SWASH (Zijlema et al, 2011), or MIKE21 (Warren and Bach, 1992), and lower fidelity computationally cheaper reduced physics models such as SFINCS (Leijnse et al, 2021), LISFLOOD-FP (Bates et al, 2010) or SBeach (Larson and Kraus, 1989). Furthermore, this work provides an interesting example of a framework for combining lower and high fidelity models in an area where there is already a lot of research into combining different fidelity models (for example Callaghan et al, 2013;Leijnse et al, 2021).…”

mentioning

confidence: 99%

“…In our work, we choose the depth-averaged finite-volume based coastal ocean model XBeach as our high fidelity model because it can parameterise unresolved wave propagation, such as wind-driven wave fields, and has been successfully used numerous times in the coastal zone to simulate wave propagation and flow including, for example, in Roelvink et al (2018) and de Beer et al (2020). For our lower fidelity model, we use the hydrodynamic model SFINCS (Super-Fast INundation of CoastS) because of its ability to simulate the relevant processes for compound coastal flooding (Leijnse et al, 2021). Note that to maximise computational efficiency, SFINCS does not explicitly solve for short wavelength wind-driven waves internally but instead these can be provided in the form of a prescribed forcing.…”

mentioning

confidence: 99%

Multilevel multifidelity Monte Carlo methods for assessing coastal flood risk

Clare¹,

Leijnse²,

McCall³

et al. 2022

Preprint

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When choosing an appropriate hydrodynamic model, there is always a compromise between accuracy and computational cost, with high fidelity models being more expensive than low fidelity ones. However, when assessing uncertainty, we can use a multifidelity approach to take advantage of the accuracy of high fidelity models and the computational efficiency of low fidelity models. Here, we apply the multilevel multifidelity Monte Carlo method (MLMF) to quantify uncertainty by computing statistical estimators of key output variables with respect to uncertain inputs, using the high fidelity hydrodynamic model XBeach and the lower fidelity coastal flooding model SFINCS. The multilevel aspect opens up the further advantageous possibility of applying each of these models at multiple resolutions. This work represents the first application of MLMF in the coastal zone and one of its first applications in any field. For both idealised and real-world test cases, MLMF can significantly reduce computational cost for the same accuracy compared to both the standard Monte Carlo method and to a multilevel approach utilising only a single model (the multilevel Monte Carlo method). In particular, here we demonstrate using the case of Myrtle Beach, USA, that this improvement in computational efficiency allows in-depth uncertainty analysis to be conducted in the case of real-world coastal environments -- a task that would previously have been practically unfeasible. Moreover, for the first time, we show how an inverse transform sampling technique can be used to accurately estimate the cumulative distribution function (CDF) of variables from the MLMF outputs. MLMF based estimates of the expectations and the CDFs of the variables of interest are of significant value to decision makers when assessing risk.

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Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes

Cited by 75 publications

References 46 publications

Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

Rapid Assessment of Tsunami Offshore Propagation and Inundation with D-FLOW Flexible Mesh and SFINCS for the 2011 Tōhoku Tsunami in Japan

Multilevel multifidelity Monte Carlo methods for assessing coastal flood risk

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