A methodology to estimate wave‐induced coastal flooding hazard maps in Spain

Tomás, Antonio; Méndez, Fernando J.; Medina, Raúl; Jaime, Francisco F.; Higuera, Pablo; Lara, Javier L.; Ortiz, Modesto; Eulate, María Fuentes Álvarez de

doi:10.1111/jfr3.12198

Cited by 17 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The significant total swash S tot and the significant IG swash S IG are estimated from the swash energy spectra E RU ( f ) as

S_{IG} = 4 \sqrt{\int_{0}^{f_{p} false/ 2} E_{RU} false(f false) normald f},

S_{tot} = 4 \sqrt{\int_{0}^{\infty} E_{RU} false(f false) normald f} .

A significant dependence of S IG (equation ) and S tot (equation ) on the parameter

β \sqrt{H_{s 0} L_{0}}

is observed (squared correlation coefficients r 2 are, respectively, 0.70 and 0.75); see Figures a and b. The best linear fits forced to pass through the origin are

S_{IG} = 2.03 β \sqrt{H_{s 0} L_{0}},

S_{tot} = 2.11 β \sqrt{H_{s 0} L_{0}} .

This dependence implies that the vertical scaling of swash is proportional to the Iribarren number as first suggested by Hunt, (; see also equation (3) of Tomas et al, ) and then confirmed by several studies (Cohn & Ruggiero, ; Holman & Sallenger, ; Ruggiero et al, ; Stockdon et al, ). Moreover, the dependence of S IG on

β \sqrt{H_{s 0} L_{0}}

is higher for broad‐banded runs with γ =1 ( r 2 =0.81) with respect to more narrow‐banded runs (e.g., r 2 =0.59 for runs with γ =13).…”

Section: Resultsmentioning

confidence: 99%

Numerical Assessment of Infragravity Swash Response to Offshore Wave Frequency Spread Variability

2019

View full text Add to dashboard Cite

We use a numerical model, already validated for this purpose, to simulate the effect of wave frequency spread on wave transformation and swash amplitudes. Simulations are performed for planar beach slope cases and for offshore wave spectra whose frequency spread changes over realistic values. Results indicate that frequency spread, under normally approaching waves, affects swash amplitudes. For moderately dissipative conditions, the significant infragravity swash increases for increasing values of the offshore frequency spread. The opposite occurs under extremely dissipative conditions. The numerical analysis suggests that this inverted pattern is driven by the effect that different distributions of incoming long-wave energy have on low-frequency wave propagation and dissipation. In fact, with large frequency spreads, wave groups force relatively short subharmonic waves that are strongly enhanced in the shoaling zone. This process leads to an infragravity swash increase for increasing frequency spread under moderately dissipative conditions in which low-frequency energy dissipation in shallow water is negligible or small. However, under extremely dissipative conditions, the significant low-frequency energy dissipation associated with large frequency spreads overturns the strong energy growth in the shoaling zone eventually yielding an infragravity swash decrease for increasing frequency spread.

show abstract

“…The significant total swash S tot and the significant IG swash S IG are estimated from the swash energy spectra E RU ( f ) as

S_{IG} = 4 \sqrt{\int_{0}^{f_{p} false/ 2} E_{RU} false(f false) normald f},

S_{tot} = 4 \sqrt{\int_{0}^{\infty} E_{RU} false(f false) normald f} .

A significant dependence of S IG (equation ) and S tot (equation ) on the parameter

β \sqrt{H_{s 0} L_{0}}

is observed (squared correlation coefficients r 2 are, respectively, 0.70 and 0.75); see Figures a and b. The best linear fits forced to pass through the origin are

S_{IG} = 2.03 β \sqrt{H_{s 0} L_{0}},

S_{tot} = 2.11 β \sqrt{H_{s 0} L_{0}} .

β \sqrt{H_{s 0} L_{0}}

is higher for broad‐banded runs with γ =1 ( r 2 =0.81) with respect to more narrow‐banded runs (e.g., r 2 =0.59 for runs with γ =13).…”

Section: Resultsmentioning

confidence: 99%

Numerical Assessment of Infragravity Swash Response to Offshore Wave Frequency Spread Variability

2019

View full text Add to dashboard Cite

show abstract

“…Concerning storm dynamics in the Gulf of Lion, focusing, respectively, on surges and waves, Ullmann et al (2008) showed that marine storms do not last longer than 3 days. We therefore decided to select the maximum H s values per 3-day block, with a minimum of 1.5 days between peaks to make sure of their independence.…”

Section: Data Preparationmentioning

confidence: 99%

High-resolution marine flood modelling with coupled overflow and overtopping processes: framing the hazard based on historical and statistical approaches

Lerma¹,

Bulteau²,

Elineau³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. A modelling chain was implemented in order to propose a realistic appraisal of the risk in coastal areas affected as well by overflowing as overtopping processes. Simulations are performed through a nested downscaling strategy from regional to local scale at high spatial resolution with explicit buildings, urban structures such as sea front walls and hydraulic structures liable to affect the propagation of water in urban areas. Validation of the model performance is based on hard and soft available data analysis and conversion of qualitative to quantitative information to reconstruct the area affected by flooding and the succession of events during two recent storms. Two joint probability statistical approaches (joint exceedance probability and environmental contour) are used to define 100 years off-shore conditions scenarios and to investigate the flood response to each scenario in term of: (1) maximum spatial extent of flooded areas, (2) volumes of water propagation inland and (3) water level in flooded areas. Scenarios of sea level rise are also considerate in order to evaluate the potential hazard evolution. Our simulations show that for a maximising 100-year hazard scenario, for the municipality as a whole, 38 % of the zones are prone to overflow flooding and 62 % to flooding by propagation of overtopping water volume along the seafront. Results also reveal that for the two kind of statistic scenarios a difference of about 5 % in the forcing conditions (water level, wave height and period) can produce significant differences responses in terms of flooding like +13.5 % of water volumes propagating inland or +11.3 % of affected surfaces. In some areas, flood response appears to be very sensible to the scenario chosen with differences of 0.3 to 0.5 m in water level. The approach developed enable to bracket the 100-year hazard and to characterise spatially the robustness or the uncertainty over the results. Considering a 100-year scenario with mean sea level rise (0.6 m), hazard characteristics are dramatically changed with an evolution of the overtopping/overflowing process ratio and an increase of 384 % in volumes of water propagating inland and 247 % in flooded surfaces.

show abstract

“…It can also be integrated into a static flood mapping approach (Gallien et al 2014;Didier et al 2015), where the flood elevation is assumed to propagate toward the coast and overland until it reaches a homogeneous and equivalent contour elevation (Vitousek et al 2009;Patrick et al 2015). Wave runup can be calculated at different points along the coast to give a TWL at each cross-shore profile where the slope has been calculated (Tomás et al 2015). On platform-beaches however, the morphodynamic context is different to sandy beaches.…”

Section: Introductionmentioning

confidence: 99%

Wave runup estimations on platform-beaches for coastal flood hazard assessment

et al. 2016

View full text Add to dashboard Cite

Wave runup in a shore platform environment has been acquired by in situ measurements at high tide and along field debris following the December 6, 2010, flood near Rimouski (Quebec, Canada), south coast of the St. Lawrence estuary. Using offshore wave data and beach slopes, a linear empirical runup relationship has been adjusted to the study site and showed good predictive results. Two types of beach slopes, the upper foreshore and foreshore slopes, have been assessed in order to calculate the surf similarity parameter. It appears that the foreshore slope, located between the coastline and the end of the foreshore at the seaward edge of the platform, shows best results. This slope was thus used to calculate the wave runup on the shore platform. A static flood map, adding wave runup to observed peak tidal level including the storm surge during the December 6, 2010, flood, has been realized on a LiDAR base layer for a specific site in Sainte-Luce. A second flood map has been applied to validate the approach on an external site in Sainte-Flavie, outside the study area. In both cases, predicted flood extents highly correspond to observed flooded area. High underpredictions occur when using the observed storm tide level only (a 182-year event) without runup, where only 5 and 4 % of the studied area are considered as flooded, respectively. It appears that in a shore platform environment, a wider foreshore reduces wave runup and flood levels.

show abstract

A methodology to estimate wave‐induced coastal flooding hazard maps in Spain

Cited by 17 publications

References 28 publications

Numerical Assessment of Infragravity Swash Response to Offshore Wave Frequency Spread Variability

Numerical Assessment of Infragravity Swash Response to Offshore Wave Frequency Spread Variability

High-resolution marine flood modelling with coupled overflow and overtopping processes: framing the hazard based on historical and statistical approaches

Wave runup estimations on platform-beaches for coastal flood hazard assessment

Contact Info

Product

Resources

About