Sergio Aurelio Zamora-Castro1 ,
Graciela Elizabeth Nani-González2 ,
Joaquín Sangabriel-Lomelí3 ,
Mayerlin Sandoval-Herazo3 ,
Saúl Rivera4 ,
Luis Carlos Sandoval-Herazo5
1 Facultad de Ingeniería, Universidad Veracruzana, Veracruz 93164, México
2 Department of Civil Engineering, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla, Veracruz 93164, México.
3 Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla
4 Estudiante de Doctorado en Ciencias de la Ingeniería, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla, Veracruz 93164, México.
5 Instituto Tecnológico Superior de Misantla
Abstract
El riesgo de inundaciones en ciudades cercanas a los cauces de los ríos es una preocupación latente, especialmente durante la temporada de lluvias. Un caso específico es el de Misantla, ubicada en el Estado de Veracruz-México, donde las inundaciones ya han causado daños sociales y económicos. Para la prevención de inundaciones, este estudio propone una metodología digital basada en los softwares especializados ArcGIS, HEC-RAS y HEC-GeoRAS, tomando como caso de estudio las áreas urbanas ubicadas en zonas montañosas de Misantla, Veracruz; asimismo, se utilizaron modelos hidrodinámicos apoyados en estudios de campo y el uso de sistemas de información geográfica (SIG) para conocer las zonas vulnerables. La simulación muestra las zonas probables de inundación y los niveles máximos de profundidad que pueden alcanzar los caudales en eventos extraordinarios de lluvia, lo cual es un elemento importante para la evaluación de riesgos. Al pronosticar las zonas inundables, las resoluciones espaciales utilizadas también repercuten directamente en la mitigación de los daños causados por las inundaciones. El estudio da como resultado un mapa de riesgo que muestra los sectores de la población más vulnerables al problema de las inundaciones e información para el diseño de obras de protección. En la ciudad de Misantla, el caso de este estudio, el mapa de riesgo simulado muestra que el 57,3 % de la superficie de la ciudad, en particular la zona centro, está en riesgo de inundación.
Keywords
modelo digital del terreno,mapa de inundaciones,llanura inundable,sistema de información geográfica,río
How to Cite
Zamora-Castro, S. A., Nani-González, G. E., Sangabriel-Lomelí, J., Sandoval-Herazo, M., Rivera, S., & Sandoval-Herazo, L. C. (2022). Metodología digital para detectar áreas vulnerables a las inundaciones en zonas urbanas situadas en la montaña. Renewable Energy, Biomass & Sustainability, 4(2), 10–23. https://doi.org/10.56845/rebs.v4i2.73
📄Amir, M. S. I. I., Khan, M. M. K., Rasul, M. G., Sharma, R. H., & Akram, F. (2018). Hydrologic and hydrodynamic modelling of extreme flood events to assess the impact of climate change in a large basin with limited data. Journal of Flood Risk Management, 11, S147-S157. https://doi.org/10.1111/jfr3.12189
📄Archer, D. R., & Fowler, H. J. (2018). Characterising flash flood response to intense rainfall and impacts using historical information and gauged data in Britain: Flash flood response to intense rainfall in Britain. Journal of Flood Risk Management, 11, S121–S133. https://doi.org/10.1111/jfr3.12187
📄Brody, S. D., Sebastian, A., Blessing, R., & Bedient, P. B. (2018). Case study results from southeast Houston, Texas: identifying the impacts of residential location on flood risk and loss: Residential location impact on flood risk and loss. Journal of Flood Risk Management, 11, S110–S120. https://doi.org/10.1111/jfr3.12184
📄Combes, J.-L., Kinda, T., Ouedraogo, R., & Plane, P. (2019). Financial flows and economic growth in developing countries. Economic Modelling, 83, 195–209. https://doi.org/10.1016/j.econmod.2019.02.010
📄Dyhouse, Gary, Benn JA, David Ford Consulting, Hatchett J, Rhee H (2003) Floodplain modeling using HEC-RAS, 1st edn. Haestad Press Waterbury.
📄Dottori, F., Martina, M. L. V., & Figueiredo, R. (2018). A methodology for flood susceptibility and vulnerability analysis in complex flood scenarios. Journal of Flood Risk Management, 11, S632-S645. https://doi.org/10.1111/jfr3.12234
📄Erdlenbruch, K., & Grelot, F. (2017). Economic assessment of flood prevention projects. En Floods (pp. 321–335). Elsevier.
📄Fitton, S. L., Moncaster, A., & Guthrie, P. (2016). Investigating the social value of theRipon rivers flood alleviation scheme: Ripon rivers flood alleviation scheme. Journal of Flood Risk Management, 9(4), 370–378. https://doi.org/10.1111/jfr3.12176
📄García, Y. C., Ramírez-Herrera, M. T., Delgado-Trejo, C., Legorreta-Paulin, G., & Corona, N. (2015). Modeling sea-level change, inundation scenarios, and their effect on the Colola Beach Reserve – a nesting-habitat of the black sea turtle, Michoacán, Mexico. Geofisica Internacional, 54(2), 179–190. https://doi.org/10.1016/j.gi.2015.04.013
📄Gillard, J. H., Barker, P. B., van Zijl, P. C., Bryan, R. N., & Oppenheimer, S. M. (1996). Proton MR spectroscopy in acute middle cerebral artery stroke. AJNR. American Journal of Neuroradiology, 17(5), 873–886.
📄Guo, K., Guan, M., & Yu, D. (2021). Urban surface water flood modelling – a comprehensive review of current models and future challenges. Hydrology and Earth System Sciences, 25(5), 2843–2860. https://doi.org/10.5194/hess-25-2843-2021
📄Grabs, W. (2016). Benchmarking flood risk reduction in the Elbe River: Benchmarking flood reduction. Journal of Flood Risk Management, 9(4), 335–342. https://doi.org/10.1111/jfr3.12217
📄Güçlü, Y. S., Şişman, E., & Yeleğen, M. Ö. (2018). Climate change and frequency-intensity-duration (FID) curves for Florya station, Istanbul: Climate change and FID curves. Journal of Flood Risk Management, 11, S403–S418. https://doi.org/10.1111/jfr3.12229
📄Huang, S., & Hattermann, F. F. (2018). Coupling a global hydrodynamic algorithm and a regional hydrological model for large-scale flood inundation simulations. Hydrology Research, 49(2), 438–449. https://doi.org/10.2166/nh.2017.061
📄Khan, D. M., Veerbeek, W., Chen, A. S., Hammond, M. J., Islam, F., Pervin, I., Djordjević, S., & Butler, D. (2018). Back to the future: assessing the damage of 2004 Dhaka flood in the 2050 urban environment: Assessing the damage of 2004 Dhaka flood. Journal of Flood Risk Management, 11, S43–S54. https://doi.org/10.1111/jfr3.12220
📄Kourgialas, N. N., & Karatzas, G. P. (2011). Flood management and a GIS modelling method to assess flood-hazard areas—a case study. Hydrological Sciences Journal–Journal des Sciences Hydrologiques, 56(2), 212-225. https://doi.org/10.1080/02626667.2011.555836
📄Kundzewicz, Z. W., Pińskwar, I., & Brakenridge, G. R. (2018). Changes in river flood hazard in Europe: a review. Hydrology Research, 49(2), 294–302. https://doi.org/10.2166/nh.2017.016
📄Lee, K.-H., Kim, S.-W., & Kim, S.-H. (2018). Simulating floods triggered by volcanic activities in the Cheon-ji caldera lake for hazards and risk analysis: Simulating floods triggered by volcanic activities in the Cheon-ji caldera lake. Journal of Flood Risk Management, 11, S479–S488. https://doi.org/10.1111/jfr3.12245
📄Luo, P., Apip, He, B., Duan, W., Takara, K., & Nover, D. (2018). Impact assessment of rainfall scenarios and land-use change on hydrologic response using synthetic Area IDF curves: Hydrological impact assessment of rainfall scenario. Journal of Flood Risk Management, 11, S84–S97. https://doi.org/10.1111/jfr3.12164
📄Macchione, F., Costabile, P., Costanzo, C., & De Santis, R. (2019). Moving to 3-D flood hazard maps for enhancing risk communication. Environmental Modelling & Software: With Environment Data News, 111, 510–522. https://doi.org/10.1016/j.envsoft.2018.11.005
📄Mark, O., Jørgensen, C., Hammond, M., Khan, D., Tjener, R., Erichsen, A., & Helwigh, B. (2018). A new methodology for modelling of health risk from urban flooding exemplified by cholera - case Dhaka, Bangladesh: A new model of health risk from urban flooding. Journal of Flood Risk Management, 11, S28–S42. https://doi.org/10.1111/jfr3.12182
📄Merwade, V., Cook, A., & Coonrod, J. (2008). GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping. Environmental Modelling & Software: With Environment Data News, 23(10–11), 1300–1311. https://doi.org/10.1016/j.envsoft.2008.03.005
📄Morris, S. E., Cobby, D., Zaidman, M., & Fisher, K. (2018). Modelling and mapping groundwater flooding at the ground surface in Chalk catchments: Modelling and mapping groundwater flooding. Journal of Flood Risk Management, 11, S251–S268. https://doi.org/10.1111/jfr3.12201
📄Patrikaki, O., Kazakis, N., Kougias, I., Patsialis, T., Theodossiou, N., & Voudouris, K. (2018). Assessing flood hazard at river basin scale with an index-based approach: The case of mouriki, Greece. Geosciences, 8(2), 50. https://doi.org/10.3390/geosciences8020050
📄Romanescu, G., Hapciuc, O. E., Minea, I., & Iosub, M. (2018). Flood vulnerability assessment in the mountain-plateau transition zone: a case study of Marginea village (Romania): Flood vulnerability assessment in the mountain-plateau transition zone. Journal of Flood Risk Management, 11, S502–S513. https://doi.org/10.1111/jfr3.12249
📄Rodríguez-Hernández, L. D., Valdés-Rodríguez, O. A., Ellis, E. A., & Armenta-Montero, S. (2020). Análisis de vulnerabilidad de la cuenca del río Misantla ante fenómenos hidrometeorológicos extremos. Revista Bio Ciencias, 7, 14.
📄Rufat, S., Tate, E., Burton, C. G., & Maroof, A. S. (2015). Social vulnerability to floods: Review of case studies and implications for measurement. International journal of disaster risk reduction: IJDRR, 14, 470–486. https://doi.org/10.1016/j.ijdrr.2015.09.013
📄Sandink, D. (2016). Urban flooding and ground-related homes in Canada: an overview: Urban flooding and ground-related homes in Canada. Journal of Flood Risk Management, 9(3), 208–223. https://doi.org/10.1111/jfr3.12168
📄Sandoval-Herazo, L., Alvarado-Lassman, A., Marín-Muñiz, J., Méndez-Contreras, J., & Zamora-Castro, S. A. (2018). Effects of the use of ornamental plants and different substrates in the removal of wastewater pollutants through microcosms of Constructed Wetlands. Sustainability, 10(5), 1594. https://doi.org/10.3390/su10051594
📄Shah, M. A. R., Rahman, A., & Chowdhury, S. H. (2018). Challenges for achieving sustainable flood risk management: Challenges for achieving sustainable flood risk management. Journal of Flood Risk Management, 11, S352–S358. https://doi.org/10.1111/jfr3.12211
📄Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034
📄Wang, Y., Li, Z., Tang, Z., & Zeng, G. (2011). A GIS-based spatial multi-criteria approach for flood risk assessment in the Dongting lake region, Hunan, central China. Water Resources Management, 25(13), 3465–3484. https://doi.org/10.1007/s11269-011-9866-2
📄Xu, J., Wang, Z., Shen, F., Ouyang, C., & Tu, Y. (2016). Natural disasters and social conflict: A systematic literature review. International Journal of Disaster Risk Reduction: IJDRR, 17, 38–48. https://doi.org/10.1016/j.ijdrr.2016.04.001
📄Ye, X., Xu, C.-Y., Li, X., & Zhang, Q. (2018). Comprehensive evaluation of multiple methods for assessing water resources variability of a lake–river system under the changing environment. Hydrology Research, 49(2), 332–343. https://doi.org/10.2166/nh.2017.006
,
Graciela Elizabeth Nani-González2