%0 Journal Article %1 Rey20191041 %A Rey, W. %A Mendoza, E.T. %A Salles, P. %A Zhang, K. %A Teng, Y.-C. %A Trejo-Rangel, M.A. %A Franklin, G.L. %D 2019 %I Springer Netherlands %J Natural Hazards %K 2019 FloodHazard FloodModeling FloodRisk FloodVulnerability MEOW YucatanPeninsula from:erick007 %N 3 %P 1041-1065 %R 10.1007/s11069-019-03587-3 %T Hurricane flood risk assessment for the Yucatan and Campeche State coastal area %U https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062771879&doi=10.1007%2fs11069-019-03587-3&partnerID=40&md5=baede3dca317a89ec5fee8fb01d9152d %V 96 %X In this study, the first ever Sea, Lake, Overland Surges from Hurricanes (SLOSH) grid was built for the Yucatan Peninsula. The SLOSH model was used to simulate storm surges in the coastal area of the states of Yucatan and Campeche (Mexico). Based on climatology, more than 39,900 hypothetical hurricanes covering all possible directions of motion were synthesized. The storm intensity (category), forward speed, radius of maximum winds and the tide anomaly were varied for each hypothetical track. According to these scenarios, the potential storm surge and associated inundation threat were computed. Subsequently, the Maximum Envelope of Water (MEOW) and the Maximum of the MEOWs (MOMs) were calculated to assess the flood hazard induced by tropical cyclones under varying conditions. In addition, for each MOM, the socioeconomic vulnerability aspects were taken into account in order to assess the hurricane flood risk for the states of Yucatan and Campeche. Results show that the most vulnerable areas are the surroundings of Terminos lagoon, Campeche City and its neighboring areas in the state of Campeche. For Yucatan, the towns located in the Northwest (Celestun, Hunucma and Progreso) and the eastern part of the state presented the highest risk values. The methodology used in this study can be applied to other coastal zones of Mexico as well as places with similar attributes. Furthermore, the MEOW and MOM are very useful as a decision-making tool for prevention, preparedness, evacuation plans, mitigation of the flood hazard and its associated risk, and also for insurance companies. © 2019, Springer Nature B.V.

@article{Rey20191041, abbrev_source_title = {Nat. Hazards}, abstract = {In this study, the first ever Sea, Lake, Overland Surges from Hurricanes (SLOSH) grid was built for the Yucatan Peninsula. The SLOSH model was used to simulate storm surges in the coastal area of the states of Yucatan and Campeche (Mexico). Based on climatology, more than 39,900 hypothetical hurricanes covering all possible directions of motion were synthesized. The storm intensity (category), forward speed, radius of maximum winds and the tide anomaly were varied for each hypothetical track. According to these scenarios, the potential storm surge and associated inundation threat were computed. Subsequently, the Maximum Envelope of Water (MEOW) and the Maximum of the MEOWs (MOMs) were calculated to assess the flood hazard induced by tropical cyclones under varying conditions. In addition, for each MOM, the socioeconomic vulnerability aspects were taken into account in order to assess the hurricane flood risk for the states of Yucatan and Campeche. Results show that the most vulnerable areas are the surroundings of Terminos lagoon, Campeche City and its neighboring areas in the state of Campeche. For Yucatan, the towns located in the Northwest (Celestun, Hunucma and Progreso) and the eastern part of the state presented the highest risk values. The methodology used in this study can be applied to other coastal zones of Mexico as well as places with similar attributes. Furthermore, the MEOW and MOM are very useful as a decision-making tool for prevention, preparedness, evacuation plans, mitigation of the flood hazard and its associated risk, and also for insurance companies. © 2019, Springer Nature B.V.}, added-at = {2021-05-28T04:12:09.000+0200}, affiliation = {Dirección General Marítima, Centro de Investigaciones Oceanográficas e Hidrográficas del Caribe, Barrio Bosque, Sector Manzanillo Escuela Naval, Cartagena de Indias, Colombia; Laboratorio de Ingeniería y Procesos Costeros, Instituto de Ingeniería, UNAM, Puerto de Abrigo S/N, Sisal, 92718, Mexico; Laboratorio Nacional de Resiliencia Costera, CONACyT, Mérida, Yucatán, Mexico; International Hurricane Research Center, Florida International University, Miami, FL 33199, United States; Graduate Institute of Hydrological and Oceanic Sciences, National Central University, Taoyuan, Taiwan; Michael Baker International, Hamilton, NJ, United States}, author = {Rey, W. and Mendoza, E.T. and Salles, P. and Zhang, K. and Teng, Y.-C. and Trejo-Rangel, M.A. and Franklin, G.L.}, biburl = {https://www.bibsonomy.org/bibtex/2b27ee09a1217478846ed6e2aca0649ec/publ_del_lipc}, correspondence_address1 = {Rey, W.; Dirección General Marítima, Colombia; email: w.reysanchez@gmail.com}, document_type = {Article}, doi = {10.1007/s11069-019-03587-3}, funding_details = {2014-01-115561, 252354, 271544}, funding_text 1 = {W.R. was supported by a doctoral scholarship (CVU 308,087) from the Mexican National Council for Science and Technology (CONACYT), and from the Council of the National Research Training (COLCIENCIAS). This research was supported by the Coordinación de Estudios de Posgrado and Posgrado de Ingeniería of the UNAM, FOBESII, CONACYT INFR projects 2014-01-115561, 252354 and 271544, as well as Engineering Institute project 5341. W.R. thanks Jamie Rhome and Keqi Zhang for the opportunity to carry out an internship at the National Hurricane Center in collaboration with the International Hurricane Research Center-IHRC as part of his Ph.D. research. Cody Fritz and Tarah M. Sharon provided assistance in setting up the SLOSH grid and modeling the inundation threat, respectively. The first author also wishes to acknowledge contributions from Brian C. Zachry and Cristina Forbes for their suggestions on validating the SLOSH model and ideas for improving this research as well as to DHI Water and Environment for facilitating a student license of MIKE 21 hydrodynamic model. Niels Van Kuik and Pablo Ruíz Salcines are acknowledged for reviewing and making suggestions to enhance this paper, and Gonzalo U. Martín-Ruiz for computational support.}, funding_text 2 = {W.R. was supported by a doctoral scholarship (CVU 308,087) from the Mexican National Council for Science and Technology (CONACYT), and from the Council of the National Research Training (COLCIENCIAS). This research was supported by the Coordinaci?n de Estudios de Posgrado and Posgrado de Ingenier?a of the UNAM, FOBESII, CONACYT INFR projects 2014-01-115561, 252354 and 271544, as well as Engineering Institute project 5341. W.R. thanks Jamie Rhome and Keqi Zhang for the opportunity to carry out an internship at the National Hurricane Center in collaboration with the International Hurricane Research Center-IHRC as part of his Ph.D. research. Cody Fritz and Tarah M. Sharon provided assistance in setting up the SLOSH grid and modeling the inundation threat, respectively. The first author also wishes to acknowledge contributions from Brian C. Zachry and Cristina Forbes for their suggestions on validating the SLOSH model and ideas for improving this research as well as to DHI Water and Environment for facilitating a student license of MIKE 21 hydrodynamic model. Niels Van Kuik and Pablo Ru?z Salcines are acknowledged for reviewing and making suggestions to enhance this paper, and Gonzalo U. Mart?n-Ruiz for computational support.}, interhash = {b742d458d40323b5f31156471cacdf5d}, intrahash = {b27ee09a1217478846ed6e2aca0649ec}, issn = {0921030X}, journal = {Natural Hazards}, keywords = {2019 FloodHazard FloodModeling FloodRisk FloodVulnerability MEOW YucatanPeninsula from:erick007}, language = {English}, number = 3, pages = {1041-1065}, publisher = {Springer Netherlands}, source = {Scopus}, timestamp = {2021-06-23T06:18:27.000+0200}, title = {Hurricane flood risk assessment for the Yucatan and Campeche State coastal area}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062771879&doi=10.1007%2fs11069-019-03587-3&partnerID=40&md5=baede3dca317a89ec5fee8fb01d9152d}, volume = 96, year = 2019 }