Using A Coastal Storm Hazard Index to Assess Storm Impacts in Lisbon

Using A Coastal Storm Hazard Index to Assess Storm Impacts in Lisbon

Bogdan Jaranovic Jorge Trindade  João Ribeiro Adélio Silva 

Centre of Geographical Studies - University of Lisbon, Universidade Aberta and Hidromod - Modelling & IT Specialists

Page: 
221-233
|
DOI: 
https://doi.org/10.2495/SAFE-V7-N2-221-233
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

Coastal areas are among the most dynamic earth systems as they are exposed to powerful agents. Near-shore wave energy is one of the most important triggering factors for erosion and flooding and is often neglected for severe infrastructure damaging, property losses and loss of life. These consequences are amplified with high population density and heavy infrastructure implantation as it happens in Lisbon (Portugal). In this context, it is of great importance for coastal stakeholders, decision-makers and civil protection entities to estimate precisely the spatial distribution of storm hazard for prevention and mitigation purposes, as well as to design adjusted answers for calamity responses. We apply a coastal storm hazard index (CSHI) considering triggering and conditioning variables involved in the effects of an extreme storm, namely: 100-year return period of SWAN modelled Hs, and its spatial distribution across the study area, land use, number of buildings, height, slope, geology, geomorphology, erosion/ accretion rates, width of the systems, exposure of the coastline, bathymetry and legally protected areas.

The variables were weighted according to a hierarchical analysis process and classified into five classes of exposure. A validation process was then implemented by comparing the occurrences identified in the last two decades newspapers and the storm hazard classification, showing a satisfactory validation results. The results show a classified storm hazard map that identifies the most and the less exposed areas. High values of CSHI occur in areas with excessive human pressure, low heights sandy systems with significant costal erosion rates. The main type of consequences identified are associated with inland flooding and erosion, resulting in the destruction of coastal protection infrastructures, and population displacement leading to great economic and social impacts and loss of life.

Keywords: 

Coast, hazard index, numerical modelling, return period, waves

  References

[1] Sousa, N., Dinâmica da linha de costa e vulnerabilidade à erosão no setor não artificializado do arco Caparica-Espichel. Masters dissertation, Institute of Geography and territorial planning, Lisbon, 2015.

[2] Dodet, G., Bertin, X. & Taborda, R., Wave climate variability in the North-East Atlantic Ocean over the last six decades. Ocean Modelling, 31, pp. 120–131, 2010. https://doi.org/10.1016/j.ocemod.2009.10.010

[3] Gornitz, V.M., Daniels, R.C., White, T.W. & Birdwell, K.R., The development of a coastal risk assessment database: vulnerability to sea-level rise in the U.S. Southeast. Journal of Coast Research, 12, pp. 327–338, 1994.

[4] Shaw, J., Taylor, R.B., Forbes, D.L., Ruz, M.H., & Solomon, S., Sensitivity of the coasts of Canada to sea-level rise. Bulletin – Geological Survey Canada, 505, pp. 1–79, 1998. https://doi.org/10.4095/210075

[5] Silva, S.F., Martinho, M., Capitão, R., Reis, T., Fortes, C.J. & Ferreira, J.C., An indexbased method for coastal-flood risk assessment in low-lying areas (Costa de Caparica, Portugal). Ocean & Coastal Management, 144, pp. 90–104, 2017. https://doi.org/10.1016/j.ocecoaman.2017.04.010

[6] Kumar, T.S., Mahendra, R.S., Nayak, S., Radhakrishnan, K. & Sahu, K., Coastal vulnerability assessment for Orissa state, East Coast of India. Journal of Coastal Research, 3, pp. 523–534, 2010. https://doi.org/10.2112/09-1186.1

[7] Islam, A., Mitra, D., Dewan, A., & Akhter S.H., Coastal multi-hazard vulnerability assessment along the Ganges deltaic coast of Bangladesh:a geospatial approach. Ocean & Coastal Management, 127, pp. 1–15, 2016. https://doi.org/10.1016/j.ocecoaman.2016.03.012

[8] Seenath, A., Wilson, M. & Miller, K., Hydrodynamic versus GIS modelling for coastal flood vulnerability assessment: Which is better for guiding coastal management? Ocean & Coastal Management, 120, pp. 99–109, 2016. https://doi.org/10.1016/j.ocecoaman.2015.11.019

[9] Dickson, M. & Perry, G., Identifying the controls on coastal cliff landslides using machine-learning approaches. Environmental Modelling & Software, 76, pp. 117–127, 2016. https://doi.org/10.1016/j.envsoft.2015.10.029

[10] Booij, N., Ris R.C. & Holthuijsen, L.H., A third-generation wave model for coastal regions, Part I, Model description and validation. Journal of Geophysical Research, 104, pp. 7649–7666, 1999. https://doi.org/10.1029/98jc02622

[11] Holthuijsen, L.H., Waves in Oceanic and Coastal Waters, Delft University of Technology and UNESCO-IHE, pp. 387, 2007.

[12] Rangel-Buitrago, N. & Anfuso G., Risk assessment of storms in coastal zones: case studies from Cartagena (Colombia) and Cadiz (Spain), Springer Briefs in Earth Sciences, pp. 63, 2015.

[13] Huang, Y., Weisberg, R., Zheng, L. & Zijlema, M., Gulf of Mexico hurricane wave simulations using SWAN: Bulk formula-based drag coefficient sensitivity for Hurricane Ike. Journal of Geophysical Research: Oceans, 118, pp. 3916–3938, 2012. https://doi.org/10.1002/jgrc.20283

[14] Gorrell. L., Raubenheimer, B., Elgar, S. & Guza, R., SWAN predictions of waves observed in shallow water onshore of complex bathymetry. Coastal Engineering, 58, pp. 510–516, 2011. https://doi.org/10.1016/j.coastaleng.2011.01.013

[15] Rusu, E., Pilar, P. & Soares, G.S., Evaluation of the wave conditions in Madeira Archipelago with spectral models. Ocean Engineering, 35, pp. 1357–1371, 2008. https://doi.org/10.1016/j.oceaneng.2008.05.007

[16] Pires, H.O. & Pessanha, V.E., Wave power climate of Portugal. Proceedings IUTAM Symposium, Hidrodynamics of Ocean Wave-Energy Utilization, pp.157–167, 1986. https://doi.org/10.1007/978-3-642-82666-5_12

[17] Davidson-Arnott R., An introduction to coastal processes and geomorphology. Cambridge University press, pp. 396–400, 2010.

[18] Veloso-Gomes, F., Pinto, F.P., Barbosa, J.P., Costa, J. & Rodrigues, A., The Defensive Works at Costa da Caparica. 2. as Jornadas de Hidráulica, Recursos Hídricos e Ambiente, pp. 23–32, 2007.

[19] Rocha, J., Ferreira, J.C., Simões, J. & Tenedório, J.A., Modelling coastal and land use evolution patterns through neural network and cellular automata integration. Journal of Coastal Research, 50, pp. 827–831, 2007.

[20] Coelho, C.R., Silva, F., Veloso-Gomes, F. & Taveira-Pinto, F., A vulnerability analysis approach for the Portuguese west coast. Risk Analysis V: Simulation and Hazard Mitigation, 91, pp. 251–262, 2009. https://doi.org/10.2495/risk060241

[21] Gilleland, E. & Katz, R., extRemes 2.0: an extreme value analysis package inR. Journal of Statistical Software, 72(8), pp. 1–39, 2016. https://doi.org/10.18637/jss.v072.i08

[22] Ribeiro, J., Silva, A.J.R. & Leitão, J.C., Modelos Operacionais de previsão da agitação para suporte à navegação e gestão de riscos, 6as Jornadas Portuguesas de Engenharia Costeira e Portuária, pp. 22–38, 2009.

[23] Tolman, H. – User manual and system documentation of WAVEWATCH-III version 2.22, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, pp. 133, 2002.

[24] Denner, K., Phillips, M.R., Jenkins, R.E. & Thomas, T., A coastal vulnerability and environmental risk assessment of Loughor Estuary, South Wales. Ocean & Coastal Management, 116, pp. 478–490, 2015. https://doi.org/10.1016/j.ocecoaman.2015.09.002

[25] Saaty, R.W., The analytic hierarchy process – What it is and how it is used. Math Modelling, 9, pp. 161–176, 1987. https://doi.org/10.1016/0270-0255(87)90473-8

[26] Thieler E.R. & Hammar-Klose E.S., National Assessment of Coastal Vulnerability to Sea Level Rise: preliminary results for the US. Atlanta coast USGS, pp. 99 – 593, 1999.

[27] Nageswara, K., Subraelu, P., Venkateswara, T., Hema, B., Ratheesh, R., Bhattacharya, S. & Ajai, A.S.R., Sea-level rise and coastal vulnerability: an assessment of Andhra Pradesh coast, India through remote sensing and GIS. Journal of Coastal Conservation. 12, pp. 195–207, 2008. https://doi.org/10.1007/s11852-009-0042-2