Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER
Incluye índice de figuras y tablas
- Autores:
-
Pérez-Montiel, Jhonny Isaac
CARDENAS MERCADO, LEYNER
Galindo Montero, Andres Alfonso
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2024
- Institución:
- Universidad de la Guajira
- Repositorio:
- Repositorio Uniguajira
- Idioma:
- spa
- OAI Identifier:
- oai:repositoryinst.uniguajira.edu.co:uniguajira/1541
- Acceso en línea:
- https://repositoryinst.uniguajira.edu.co/handle/uniguajira/1541
- Palabra clave:
- Inundaciones urbanas
Adaptación urbana verde
Modelo matemático
IBER
MODCEL
Comparación
Escorrentías
Urban flooding
Urban green adaptation
Mathematical modeling
Comparison
Runoff
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-sa/4.0/
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| dc.title.spa.fl_str_mv |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER |
| title |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER |
| spellingShingle |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER Inundaciones urbanas Adaptación urbana verde Modelo matemático IBER MODCEL Comparación Escorrentías Urban flooding Urban green adaptation Mathematical modeling Comparison Runoff |
| title_short |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER |
| title_full |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER |
| title_fullStr |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER |
| title_full_unstemmed |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER |
| title_sort |
Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBER |
| dc.creator.fl_str_mv |
Pérez-Montiel, Jhonny Isaac CARDENAS MERCADO, LEYNER Galindo Montero, Andres Alfonso |
| dc.contributor.author.none.fl_str_mv |
Pérez-Montiel, Jhonny Isaac CARDENAS MERCADO, LEYNER Galindo Montero, Andres Alfonso |
| dc.subject.proposal.spa.fl_str_mv |
Inundaciones urbanas Adaptación urbana verde Modelo matemático IBER MODCEL Comparación Escorrentías |
| topic |
Inundaciones urbanas Adaptación urbana verde Modelo matemático IBER MODCEL Comparación Escorrentías Urban flooding Urban green adaptation Mathematical modeling Comparison Runoff |
| dc.subject.proposal.eng.fl_str_mv |
Urban flooding Urban green adaptation Mathematical modeling Comparison Runoff |
| description |
Incluye índice de figuras y tablas |
| publishDate |
2024 |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2025-04-01T21:30:20Z |
| dc.date.available.none.fl_str_mv |
2025-04-01T21:30:20Z |
| dc.type.none.fl_str_mv |
Libro |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2f33 |
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Text |
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info:eu-repo/semantics/book |
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http://purl.org/coar/resource_type/c_2f33 |
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979-628-7718-48-7 |
| dc.identifier.uri.none.fl_str_mv |
https://repositoryinst.uniguajira.edu.co/handle/uniguajira/1541 |
| identifier_str_mv |
979-628-7718-48-7 |
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https://repositoryinst.uniguajira.edu.co/handle/uniguajira/1541 |
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spa |
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spa |
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Abebe, Y. A., Ghorbani, A., Nikolic, I., Vojinovic, Z., & Sanchez, A. (2019). A cou pled flood-agent-institution modelling (CLAIM) framework for urban flood risk management. Environmental Modelling & Software, 111(October), 483 492. https://doi.org/10.1016/j.envsoft.2018.10.015 Adams, T. E., & Pagano, T. C. (2016). Flood Forecasting - A Global Perspective. Elsevier. https://doi.org/10.1016/C2014-0-01361-5 Alcaldía Municipal de Riohacha. (2020). Plan de desarrollo RIOHACHA CAMBIA LA HISTORIA 2020-2023 (p. 292). Alcaldía Municipal de Riohacha. https:// www.riohacha-laguajira.gov.co/MiMunicipio/Paginas/Presentacion.aspx Anh, P. T., & Ngoc, N. T. B. (2019). Public Participatory Role in Urban Flood Risk Management of Ho Chi Minh City - Vietnam: From Awareness to Action. South Asian Journal of Social Studies and Economics, 4(4), 1–10. https://doi. org/10.9734/sajsse/2019/v4i430133 Apel, H., Aronica, G. T., Kreibich, H., & Thieken, A. H. (2009). Flood risk analyses— how detailed do we need to be? Natural Hazards, 49(1), 79–98. https://doi. org/10.1007/s11069-008-9277-8 Arduino, G., Reggiani, P., & Todini, E. (2005). Recent advances in flood forecas ting and flood risk assessment. Hydrology and Earth System Sciences, 9(4), 280–284. https://doi.org/10.5194/hess-9-280-2005 Arroyo, Á. G. (2015). Análisis del comportamiento espacio temporal de la preci pitación en la región pacífica colombiana en presencia de los fenómenos de El Niño y La Niña, en el periodo 1983 - 2009 [Universidad de Nariño]. In Universidad de Nariño. https://sired.udenar.edu.co/1584/ Avila Brito, W., Nardini, A., & Pérez Montiel, J. (2017). Alternativa de solución para las inundaciones en Riohacha. “mi casa se inunda” (Editorial). Editorial Gen te Nueva. https://d11260b5-22e5-4724-8a81-25a390c8aa00.filesusr.com/ ugd/dad4f8_1e2f98d6a68a4bc58e0ea9937255f0d1.pdf Baro, J., Díaz Delgado, C., Esteller, M., & Calderon, G. (2007). Curvas de daños provocados por inundaciones en zonas habitacionales y agrícolas de Mé Bhuiyan, M. J. A. N., & Dutta, D. (2012). Analysis of flood vulnerability and assess ment of the impacts in coastal zones of Bangladesh due to potential sea-le vel rise. Natural Hazards, 61(2), 729–743. https://doi.org/10.1007/s11069 011-0059-3 Bladé, E., Cea, L., Corestein, G., Escolano, E., Puertas, J., Vázquez-Cendón, E., Dolz, J., & Coll, A. (2014). Iber: herramienta de simulación numérica del flujo en ríos. Revista Internacional de Métodos Numéricos Para Cálculo y Diseño En Ingeniería, 30(1), 1–10. https://doi.org/10.1016/j.rimni.2012.07.004 Bomers, A. (2020). Hydraulic modelling approaches to decrease uncer tainty in flood frequency relations [University of Twente]. https://doi. org/10.3990/1.9789036549288 Cai, W., Wang, G., Santoso, A., McPhaden, M. J., Wu, L., Jin, F.-F., Timmermann, A., Collins, M., Vecchi, G., Lengaigne, M., England, M. H., Dommenget, D., Takahashi, K., & Guilyardi, E. (2015). Increased frequency of extreme La Niña events under greenhouse warming. Nature Climate Change, 5(2), 132–137. https://doi.org/10.1038/nclimate2492 Cardenas Mercado, L., Nardini, A., & Pérez, M. J. (2019). Análisis comparativos de dos modelos para estimar nivel de inundación. Caso: Distrito de Rioha cha, La Guajira-Colombia. In M. L. C. Martínez & M. J. B. Barranco (Eds.), I congreso internacional gestión integral frente al cambio climático (pp. 367–372). Universidad de La Guajira. https://www.researchgate.net/publica tion/349671236_Analisis_comparativos_de_dos_modelos_para_estimar_ nivel_de_inundacion_Caso_Distrito_de_Riohacha_La_Guajira-Colombia Climate-data. (2023). Clima Riohacha. Climate Data. https://es.climate-data.org/ america-del-sur/colombia/la-guajira/riohacha-714990/ CREACUA. (2013). Proyecto: “Adaptación Urbana Verde frente a inundaciones con el soporte de la modelación matemática y del software MODCEL en Rioha cha, La Guajira, Colombia”; Convenio de cooperación No 9677-04-1047 2013; CREACUA: Riohacha, Colombia. http://modcelrhcdatos.wixsite.com/ modcel-riohacha/avance CREACUA, & UFRJ. (2014). Proyecto: Adaptación urbana “verde” frente a inunda ciones. Modelo de simulación de la cuenca urbana de Riohacha considerada en el proyecto: conceptualización, calibración e indicaciones de diseño de alternativas de solución. http://docs.wixstatic.com/ugd/dad4f8_26e8648aa cb44bdfbde9e709e6b11655.pdf CRED, (Centre for Research on the Epidemiology of Disasters). (2015). The Hu man Cost of Natural Disaters 2015: A Global Perspective. https://reliefweb. int/sites/rehttps://www.researchgate.net/publication/317645955_The_Hu man_Cost_of_Natural_Disasters_-_A_global_perspective Cruz, R. K. S. (2012). GESTIÓN INTEGRADA DEL RIESGO DE INUNDACIONES EN COLOMBIA [Universidad politécnica de Valencia]. https://riunet.upv.es/ bitstream/handle/10251/27223/TFM_Gestión_ Inundaciones_Colombia_Ka rime_Sedano.pdf?sequence=1 Dahri, N., & Abida, H. (2020). Causes and impacts of flash floods: case of Gabes City, Southern Tunisia. Arabian Journal of Geosciences, 13(4), 176. https:// doi.org/10.1007/s12517-020-5149-7 Dazzi, S., Shustikova, I., Domeneghetti, A., Castellarin, A., & Vacondio, R. (2021). Comparison of two modelling strategies for 2D large-scale flood simulations. Environmental Modelling & Software, 146, 105225. https://doi.org/10.1016/j. envsoft.2021.105225 Dos Santos, F. M., de Souza Pelinson, N., de Oliveira, R. P., & Di Lollo, J. A. (2023). Using the SWAT model to identify erosion prone areas and to estimate soil loss and sediment transport in Mogi Guaçu River basin in Sao Paulo State, Brazil. CATENA, 222, 106872. https://doi.org/10.1016/j.catena.2022.106872 Dutta, D., Teng, J., Vaze, J., Lerat, J., Hughes, J., & Marvanek, S. (2013). Storage-ba sed approaches to build floodplain inundation modelling capability in river system models for water resources planning and accounting. Journal of Hy drology, 504, 12–28. https://doi.org/10.1016/j.jhydrol.2013.09.033 Erickson, K. (2019). ¡Prepara un poco de budín de El Niño! https://spaceplace. nasa.gov/el-nino/sp/ Erickson, K. (2020). ¿Qué es La Niña? https://spaceplace.nasa.gov/la-nina/sp/ Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019). DEM Gene ration from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205 Fernández López, C., & Bladé, E. I. C. (2011). Modelización de la ruptura de una balsa de laminación y análisis según el esquema de cálculo [Universidad Politécnica de Catalunya]. In PROJECTE O T Figueiredo, R., Schröter, K., Weiss-Motz, A., Martina, M. L. V., & Kreibich, H. (2018). Multi-model ensembles for assessment of flood losses and associated un certainty. Natural Hazards and Earth System Sciences, 18(5), 1297–1314. https://doi.org/10.5194/nhess-18-1297-2018 Fletcher, T. D., Shuster, W., Hunt, W. F., Ashley, R., Butler, D., Arthur, S., Trowsda le, S., Barraud, S., Semadeni-Davies, A., Bertrand-Krajewski, J.-L., Mikkelsen, P. S., Rivard, G., Uhl, M., Dagenais, D., & Viklander, M. (2015). SUDS, LID, BMPs, WSUD and more – The evolution and application of terminology su rrounding urban drainage. Urban Water Journal, 12(7), 525–542. https://doi. org/10.1080/1573062X.2014.916314 Frías-Paredes, L., Mallor, F., Gastón-Romeo, M., & León, T. (2017). Assessing ener gy forecasting inaccuracy by simultaneously considering temporal and ab solute errors. Energy Conversion and Management, 142, 533–546. https:// doi.org/10.1016/j.enconman.2017.03.056 Gallegos, H. A., Schubert, J. E., & Sanders, B. F. (2009). Two-dimensional, high-re solution modeling of urban dam-break flooding: A case study of Baldwin Hills, California. Advances in Water Resources, 32(8), 1323–1335. https://doi. org/10.1016/j.advwatres.2009.05.008 Gerl, T., Kreibich, H., Franco, G., Marechal, D., & Schröter, K. (2016). A Review of Flood Loss Models as Basis for Harmonization and Benchmarking. PLOS ONE, 11(7), e0159791. https://doi.org/10.1371/journal.pone.0159791 Gibson, M. J., Savic, D. A., Djordjevic, S., Chen, A. S., Fraser, S., & Watson, T. (2016). Accuracy and Computational Efficiency of 2D Urban Surface Flood Modelling Based on Cellular Automata. Procedia Engineering, 154, 801–810. https://doi.org/10.1016/j.proeng.2016.07.409 Gomes Miguez, M., Peres Battemarco, B., Martins De Sousa, M., Moura Rezende, O., Pires Veról, A., & Gusmaroli, G. (2017). Urban Flood Simulation Using MODCEL—An Alternative Quasi-2D Conceptual Model. Water, 9(6), 445. ht tps://doi.org/10.3390/w9060445 González-Cao, J., Fernández-Nóvoa, D., García-Feal, O., Figueira, J. R., Vaquero, J. M., Trigo, R. M., & Gómez-Gesteira, M. (2022). The Rivillas flood of 5–6 No vember 1997 (Badajoz, Spain) revisited: An approach based on Iber+ mode lling. Journal of Hydrology, 610(November 2021). https://doi.org/10.1016/j. jhydrol.2022.127883 Hall, J. (2015). Direct Rainfall Flood Modelling: The Good, the Bad and the Ugly. Australasian Journal of Water Higgins, S. A., Overeem, I., Steckler, M. S., Syvitski, J. P. M., Seeber, L., & Akhter, S. H. (2014). InSAR measurements of compaction and subsidence in the Gan ges-Brahmaputra Delta, Bangladesh. Journal of Geophysical Research: Earth Surface, 119(8), 1768–1781. https://doi.org/10.1002/2014JF003117 Hu, C., Xia, J., She, D., Song, Z., Zhang, Y., & Hong, S. (2021). A new urban hydro logical model considering various land covers for flood simulation. Journal of Hydrology, 603, 126833. https://doi.org/10.1016/j.jhydrol.2021.126833 IDIGER. (2022). Caracterización General del Escenario de Riesgo por Inundación. Instituto Distrital de Gestión de Riesgo y Cambio Climático. https://www. idiger.gov.co/rinundacion IPCC. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Ro berts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr. https://doi. org/10.1017/9781009325844. IPCC. (2023). El cambio climático está ocurriendo por todas partes. CLIMATE SCIENCE 2030. https://climatescience2030.com/es/ Jiang, W., Yu, J., Wang, Q., & Yue, Q. (2022). Understanding the effects of digital elevation model resolution and building treatment for urban flood mode lling. Journal of Hydrology: Regional Studies, 42(January), 101122. https:// doi.org/10.1016/j.ejrh.2022.101122 Karim, F., Dutta, D., Marvanek, S., Petheram, C., Ticehurst, C., Lerat, J., Kim, S., & Yang, A. (2015). Assessing the impacts of climate change and dams on floo dplain inundation and wetland connectivity in the wet–dry tropics of nor thern Australia. Journal of Hydrology, 522, 80–94. https://doi.org/10.1016/j. jhydrol.2014.12.005 Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., Mach, K., Muir-Wood, R., Braken ridge, G. R., Kron, W., Benito, G., Honda, Y., Takahashi, K., & Sherstyukov, B. (2014). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1–28. https://doi.org/10.1080/0262666 7.2013.857411 Li, J. (2016). Assessing spatial predictive models in the environmental sciences: Accuracy measures, data variation and variance explained. Environmental Modelling & Software, 80, 1–8. https://doi.org/10.1016/j.envsoft.2016.02.004 Makondo, C. C., & Thomas, D. S. G. (2018). Climate change adaptation: Lin king indigenous knowledge with western science for effective adaptation. Environmental Science & Policy, 88, 83–91. https://doi.org/10.1016/j.envs ci.2018.06.014 Mark, O., Weesakul, S., Apirumanekul, C., Aroonnet, S. B., & Djordjević, S. (2004). Potential and limitations of 1D modelling of urban flooding. Journal of Hy drology, 299(3–4), 284–299. https://doi.org/10.1016/j.jhydrol.2004.08.014 Mascarenhas, F. C. B., & Miguez, M. G. (2002). Urban flood control through a mathematical cell model. Water International, 27(2), 208–218. https://doi. org/10.1080/02508060208686994 Melo Leon, S. F., Riveros Salcedo, L. C., Romero Otalora, G., Álvarez, A. C., Diaz Giraldo, C., & Calderon Diaz, S. L. (2017). Efectos economicos de futuras se quias en colombia estimacion a partir del fenomeno El niño 2015. Archivos de Economia, DPN, 1–76. https://colaboracion.dnp.gov.co/CDT/Estudios Econmicos/469.pdf Mentaschi, L., Besio, G., Cassola, F., & Mazzino, A. (2013). Problems in RMSE-ba sed wave model validations. Ocean Modelling, 72, 53–58. https://doi.or g/10.1016/j.ocemod.2013.08.003 Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article & quot;Assessment of economic flood damage" Natural Hazards and Earth System Sciences, 10(8), 1697–1724. https://doi.org/10.5194/ nhess-10-1697-2010 Mignot, E., Li, X., & Dewals, B. (2019). Experimental modelling of urban floo ding: A review. Journal of Hydrology, 568(November), 334–342. https://doi. org/10.1016/j.jhydrol.2018.11.001 Miguez, Marcelo Gomes;, & Mascarenhas, F. C. B. (1999). Modelação matemática de cheias urbanas através de um esquema de células de escoamento. Revista Brasileira de Recursos Hídricos - RBRH, 4, 119–140. https://abrh.s3.sa-east-1. amazonaws.com/Sumarios/50/2f0edba5277f45c60b75a31947675b55_fc0c d24479700710eb97dcc1830c1b1e.pdf Miguez, Marcelo Gomes., Mascarenhas, F. C. B., Prodanoff, J. H. A., & Magalhães, L. P. C. (2007). Simulating floods in urban watersheds: hydrodynamic mo delling of macro, micro-drainage and flows over streets. Water Resources Management IV, I, 645–654. https://doi.org/10.2495/WRM070601 Morgan, A., Olivier, D., Nathalie, B., Claire-Marie, D., & Philippe, G. (2016). Hi gh-resolution Modelling With Bi-dimensional Shallow Water Equations Ba sed Codes – High-Resolution Topographic Data Use for Flood Hazard As sessment Over Urban and Industrial Environments. Procedia Engineering, 154, 853–860. https://doi.org/10.1016/j.proeng.2016.07.453 Moura Rezende, O., Ribeiro da Cruz de Franco, A. B., Beleño de Oliveira, A. K., Pitzer Jacob, A. C., & Gourban flood resilience into the design of flood control alternatives. Journal of Hydrology, 576, 478–493. https://doi.org/10.1016/j.jhydrol.2019.06.063mes Miguez, M. (2019). A framework to introduce Municipio de Riohacha. (2012). Plan de Ordenamiento Territorial del municipio de Riohacha, La Guajira- Colombia 2002-2105. https://goo.gl/NQaMUZ Nardini, A., & Gomes Miguez, M. (2016). An Integrated Plan to Sustainably Enable the City of Riohacha (Colombia) to Cope with Increasing Urban Flooding, while Improving Its Environmental Setting. Sustainability, 8(3), 198. https:// doi.org/10.3390/su8030198 Navarro Cerrillo, R. M., Sanchez de la Orden, M., Gomez Bonilla, J., Garcia-Ferrer, A., Hernandez Clemente, R., & Lanjeri, S. (2010). LIDAR-based estimation of leaf area index on Holm oak [Quercus ilex L. subsp. ballota (Desf.) Samp.] trees. Forest Systems, 19(1), 61. https://doi.org/10.5424/fs/2010191-01167 Neal, J., Villanueva, I., Wright, N., Willis, T., Fewtrell, T., & Bates, P. (2012). How much physical complexity is needed to model flood inundation? Hydrologi cal Processes, 26(15), 2264–2282. https://doi.org/10.1002/hyp.8339 Nkwunonwo, U. C., Whitworth, M., & Baily, B. (2020). A review of the current sta tus of flood modelling for urban flood risk management in the developing countries Nwe Nwe Aung, San San Htwe, Kyi Kyi Sein, & Lei Lei Aung. (2019). The Impact of Floods on the Socio-Economic Activities of Yangon. In Population, Develo pment, and the Environment (pp. 255–271). Springer Singapore. https://doi. org/10.1007/978-981-13-2101-6_15 ONU. (2023). ¿Qué es el cambio climático? Acción Por El Clima. https://www. un.org/es/climatechange/what-is-climate-change Ozdemir, H., Sampson, C. C., de Almeida, G. A. M., & Bates, P. D. (2013). Evalua ting scale and roughness effects in urban flood modelling using terrestrial LIDAR data. Hydrology and Earth System Sciences, 17(10), 4015–4030. ht tps://doi.org/10.5194/hess-17-4015-2013 Özgen, I., Zhao, J., Liang, D., & Hinkelmann, R. (2016). Urban flood modeling using shallow water equations with depth-dependent anisotropic porosity. Journal of Hydrology, 541, 1165–1184. https://doi.org/10.1016/j.jhydrol.2016.08.025 Pérez-Montiel, J. I., Cardenas-Mercado, L., & Nardini, A. G. C. (2022). Flood Mode ling in a Coastal Town in Northern Colombia: Comparing MODCEL vs. IBER. Water, 14(23), 3866. https://doi.org/10.3390/w14233866 Pérez, J., Escobar, J., & Fragozo, J. (2018). Modelación Hidráulica 2D de Inundacio nes en Regiones con Escasez de Datos. El Caso del Delta del Río Ranchería, Pérez, J. I., Nardini, A., & Zuñiga, Y. P. (2018). Identificación Multiatributo de Tipo logías de Viviendas Vulnerables a Inundaciones en Riohacha, La Guajira-Co lombia. Información Tecnológica, 29(5), 187–202. https://doi.org/10.4067/ S0718-07642018000500187 Pérez, J. I., Nardini, A., & Zuñiga, Y. P. (2018). Identificación Multiatributo de Tipo logías de Viviendas Vulnerables a Inundaciones en Riohacha, La Guajira-Co lombia. Información Tecnológica, 29(5), 187–202. https://doi.org/10.4067/ S0718-07642018000500187 Roe, P. L. (1986). Discrete models for the numerical analysis of time-dependent multidimensional gas dynamics. Journal of Computational Physics, 63(2), 458–476. https://doi.org/10.1016/0021-9991(86)90204-4 Rouf, T. (2015). Flood inundation map of Sirajgonj district using mathematical mo del [Bangladesh University of Engineering and Technology]. http://lib.buet. ac.bd:8080/xmlui/handle/123456789/966 Russo, B., Sunyer, D., Velasco, M., & Djordjević, S. (2015). Analysis of extreme flooding events through a calibrated 1D/2D coupled model: the case of Barcelona (Spain). Journal of Hydroinformatics, 17(3), 473–491. https://doi. org/10.2166/hydro.2014.063 Sadrehaghighi, I. (2022). Unstructured Meshing for CFD. https://www.academia. edu/44184149/Unstructured_Meshing_for_CFD Salvan, L., Abily, M., Gourbesville, P., & Schoorens, J. (2016). Drainage System and Detailed Urban Topography: Towards Operational 1D-2D Modelling for Stormwater Management. Procedia Engineering, 154, 890–897. https://doi. org/10.1016/j.proeng.2016.07.469 Santillan, J. R., & Makinano-Santillan, M. (2016). Vertical accuracy assessment of 30-M resolution ALOS, ASTER, and SRTM global DEMS over Northeastern Mindanao, Philippines. International Archives of the Photogrammetry, Re mote Sensing and Spatial Information Sciences - ISPRS Archives, 41(Sep tember), 149–156. https://doi.org/10.5194/isprsarchives-XLI-B4-149-2016 Sanz-Ramos, M., Cea, L., Bladé, E., López-Gómez, D., Sañudo, E., Corestein, G., García-Alén, G., & Aragón-Hernández, J. (2022). Iber v3. Reference ma nual and user’s interface of the new implementations. CIMNE. https://doi. org/10.23967/iber.2022.01 Sarzynski, A. (2015). Public participation, civic capacity, and climate change adaptation in cities. Urban Climate, 14, 52–67. https://doi.org/10.1016/j. uclim.2015.08.002 Shen, Y., Morsy, M. M., Huxley, C., Tahvildari, N., & Goodall, J. L. (2019). Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall. Journal of Hydrology, 579(June), 124159. https://doi.org/10.1016/j.jhydrol.2019.124159 Shrestha, B. B., Kawasaki, A., & Zin, W. W. (2021). Development of flood damage assessment method for residential areas considering various house types for Bago Region of Myanmar. International Journal of Disaster Risk Reduc tion, 66, 102602. https://doi.org/10.1016/j.ijdrr.2021.102602 Sreedevi, S., Eldho, T. I., & Jayasankar, T. (2022). Physically-based distributed modelling of the hydrology and soil erosion under changes in landuse and climate of a humid tropical river basin. CATENA, 217, 106427. https://doi. org/10.1016/j.catena.2022.106427 Srivastav, R. K., Schardong, A., & Simonovic, S. P. (2014). Equidistance Quantile Mat ching Method for Updating IDFCurves under Climate Change. Water Resources Management, 28(9), 2539–2562. https://doi.org/10.1007/s11269-014-0626-y Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, recent advances and uncertain ty analysis. Environmental Modelling & Software, 90, 201–216. https://doi. org/10.1016/j.envsoft.2017.01.006 Timbe Castro, L. M., & Willems, P. (2011). Desempeño de modelos hidráulicos 1D y 2D para la simulación de inundaciones Lus Timbe. MASKANA, 2(1), 91–98. https://doi.org/10.18537/mskn.02.01.07 Timbe, L., & Willems, P. (2011). Desempeño de modelos hidráulicos 1D y 2D para la simulación de inundaciones. MASKANA, 2(1), 69–76. https://doi. org/10.18537/mskn.02.01.06 Toté, C., Patricio, D., Boogaard, H., van der Wijngaart, R., Tarnavsky, E., & Funk, C. (2015). Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique. Remote Sensing, 7(2), 1758–1776. https://doi. org/10.3390/rs70201758 Upadhyay, S. (2023). What Is An Algorithm? Characteristics, Types and How to write it. Simplilearn. https://www.simplilearn.com/tutorials/data-structu re-tutorial/what-is-an-algorithm#:~:text=An algorithm is a set,to perform a particular task. Valderrama, J. O., & Alvarez, V. H. (2008). Correct Way of Reporting Results when Modelling Supercritical Phase Equilibria using Equations of State. The Canadian Journal of Chemical Engineering, 83(3), 578–581. https://doi. org/10.1002/cjce.5450830323 Valdivieso Taborga, C. E., Valdivieso Castellón, R., & Valdivieso Taborga, O. Á. (2011). Determinación del Tamaño Muestral Mediante el uso de Árboles de Decisión. INVESTIGACION & DESARROLLO, 11(1), 53–80. https://doi. org/10.23881/idupbo.011.1-4e Vojinovic, Z., & Tutulic, D. (2009). On the use of 1D and coupled 1D-2D modelling approaches for assessment of flood damage in urban areas. Urban Water Journal, 6(3), 183–199. https://doi.org/10.1080/15730620802566877 Vozinaki, A.-E. K., Morianou, G. G., Alexakis, D. D., & Tsanis, I. K. (2017). Com paring 1D and combined 1D/2D hydraulic simulations using high-resolution topographic data: a case study of the Koiliaris basin, Greece. Hydrological Sciences Journal, 62(4), 642–656. https://doi.org/10.1080/02626667.2016.1 255746 Wang, Y.-M., & Luo, Y. (2010). Integration of correlations with standard devia tions for determining attribute weights in multiple attribute decision ma king. Mathematical and Computer Modelling, 51(1–2), 1–12. https://doi.or g/10.1016/j.mcm.2009.07.016 Weber, J. F. (2014). Parámetros del modelo de infiltración de Horton obtenidos mediante el uso de un simulador de lluvia, Córdoba, Argentina. Ambiente e Agua - An Interdisciplinary Journal of Applied Science, 9(1). https://doi. org/10.4136/ambi-agua.1320 Xia, X., Liu, Z., & Yang, H. (2016). Regularized estimation for the least absolu te relative error models with a diverging number of covariates. Computa tional Statistics & Data Analysis, 96, 104–119. https://doi.org/10.1016/j. csda.2015.10.012 Yang, Y., Guan, W., Deleersnijder, E., & He, Z. (2022). Hydrodynamic and sediment transport modelling in the Pearl River Estuary and adjacent Chinese coastal zone during Typhoon Mangkhut. Continental Shelf Research, 233, 104645. https://doi.org/10.1016/j.csr.2022.104645 Yoshida, H., & Dittrich, A. (2002). 1D unsteady-state flow simulation of a sec tion of the upper Rhine. Journal of Hydrology, 269(1–2), 79–88. https://doi. org/10.1016/S0022-1694(02)00196-8 Zhang, Y., Ge, J., Qi, J., Liu, H., Zhang, X., Marek, G. W., Yuan, C., Ding, B., Feng, P., Liu, D. L., Srinivasan, R., & Chen, Y. (2023). Evaluating the effects of single and integrated extreme climate events on hydrology in the Liao River Basin, China using a modified SWAT-BSR model. Journal of Hydrology, 623, 129772. https://doi.org/10.1016/j.jhydrol.2023.129772 |
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Pérez-Montiel, Jhonny Isaacvirtual::627-1CARDENAS MERCADO, LEYNERvirtual::628-1Galindo Montero, Andres Alfonsovirtual::629-1Distrito Especial, Turístico y Cultural de Riohacha2025-04-01T21:30:20Z2025-04-01T21:30:20Z2024979-628-7718-48-7https://repositoryinst.uniguajira.edu.co/handle/uniguajira/1541Incluye índice de figuras y tablasSe comparan dos modelos de simulación de inundaciones (MODCEL e IBER) aplica dos en Riohacha, ciudad costera ubicada al norte de Colombia, donde el fenómeno de La Niña genera lluvias intensas con consecuente inundaciones graves. MODCEL es un modelo de tipo conceptual que ya se había calibrado y validado en un trabajo anterior. IBER es un modelo hidráulico de tipo físicamente basado 2D que se ha calibrado y validado en este estudio con los mismos datos usados para MODCEL, tanto para la topografía, como para las alturas de inundación. Los hietogramas de precipitación utilizados para la calibración y validación corresponden al evento del 18 de septiembre del 2011 y 29 de noviembre del 2011 con tiempo de retorno de 84 años y 10 años respectivamente. Se ha comparado el desempeño de los modelos considerando las bondades de ajuste, versatilidad, robustez y sencillez de uso. Para determinar los indicadores de ajuste se consideró la profundidad del agua en la parte más baja de la celda y en las viviendas ubicadas en las celdas de transporte (calles) y celdas tipo tanque (humedales). En general, MODCEL presentó mejor desempeño según varios indicadores de bondad tanto en la calibración, como en validación. El rendimiento de los dos modelos fue similar en los centros de celdas tipo tanque (hu medales), donde la topografía fue detallada manualmente. Los mejores resultados de MODCEL es posiblemente justificado por la falta de una topografía más refinada que incluya incluso la complejidad del tejido urbano. Sin embargo, posiblemente ni siquiera ese elemento alcanzaría a cambiar el éxito de la comparación porque existen muchas obras hidráulicas que difícilmente pueden ser representadas adecuadamen te en IBER (al menos en su versión 2.3.1 utilizada en este estudio). En MODCEL esta información pudo incorporarse explorando las indicaciones cualitativas obtenidas de la encuesta que han permitido construir un mapa real de las direcciones de flujo a través de la ciudad punto base para establecer la esquematización del territorio en celdas y sus conexiones. MODCEL tiene un mejor desempeño técnico que IBER, su aplicación es difícil porque requiere una profunda comprensión del territorio, mucho esfuerzo y tiempo para la esquematización y una sólida experiencia que práctica mente lo limita a sus creadores; además, MODCEL es mucho menos amigable que IBER. A pesar que MODCEL es cuasi 2D no permite obtener mapas de inundación ni comportamiento de la velocidad, punto fuerte de IBER que además tiene una interfaz sencilla y fácil de utilizar. De todos modos, los dos modelos capturan suficientemen te bien el comportamiento de las inundaciones urbanas y sus cambios en relación a posibles intervenciones, por lo que constituyen herramientas de planificación clave para la gestión de riesgo de desastres frente al probleTwo flood simulation models (MODCEL and IBER) applied in Riohacha, a coastal city located in northern Colombia, are compared where the La Niña phenomenon generates intense rainfalls with harsh flooding. MODCEL is a conceptual model that had already been calibrated and validated in a previous work. IBER is a 2D physically based hydraulic model that has been calibrated and validated in this study with the same data used for MODCEL, both for topography and flood heights.The precipi tation hyetograms used for calibration and validation correspond to the events of September 18, 2011 and November 29, 2011 with return period of 84 years and 10 years, respectively. The performance of the models has been compared considering the indicators of fitting goodness. versatility, robustness and simplicity of use. To determine the adjustment indicators, the depth of water in the lowest part of the cell and in the houses located in the transport cells (streets) and tank cells (wet lands) were considered. Generally speaking, MODCEL performs better according to a plethora of goodness indicators, both in calibration and validation. The perfor mance of the two models was similar in the tank cell sites (wetlands), where the to pography was manually detailed. The better results of MODCEL is possibly justified by the lack of a more refined topography including even the complexity of the urban fabric. However, we suspect that neither that element would change the outcome of the comparison; there are indeed several hydraulic works spread across the town that hardly could adequately be represented in IBER (at least version 2.3.1 used in this study). In MODCEL this information could be to incorporate taking advantage of the indications provided by the enquired people, which allowed us to build a map of the real flow directions across the town (a key element to set up the cells schematization needed by MODCEL). MODCEL has a better technical performance than IBER, Its application is difficult because it requires a deep understanding of the territory, a lot of effort and time for the schematization and a solid experience that practically limits its creators; moreover, MODCEL is much less user-friendly than IBER. Although MODCEL is quasi 2D, it does not allow to obtain flood maps or velocity behavior, a strong point of IBER, which also has a simple and easy to use interface. In any case, the two models capture sufficiently well the behavior of ur ban flooding and its changes in relation to possible interveIntroducción 1. Fundamentación teórico – conceptual 1.1 Cambio climático 1.2 Variabilidad climática 1.3 Fenómeno de el niño 1.4 Fenómeno de la niña 1.5 Inundaciones 1.6 Modelación de inundaciones 2. Procedimiento metodológico 2.1 Zona de estudio 2.2 Población y muestra 2.3 Descripción de las herramientas de modelación 2.3.1 MODCEL (modelación en celdas) 2.3.2 IBER (modelización hidráulica bidimensional) 2.4 Indicadores de bondad de ajuste 2.5 Información de partida para configurar IBER 2.5.1 División del área de estudio en celdas 2.5.2 Topografía 2.5.3 Condiciones iniciales y de borde 2.5.4 Precipitación 2.5.5 Usos del suelo 2.5.6 Infiltración 2.5.7 Estructuras hidráulicas 2.5.8 Selección de celdas para la comparación de los modelos 53 2.5.9 Interpretación y configuración datos iniciales de MODCEL Configuración y simulación en IBER - evento de calibración 2.7 Validación de los modelos 2.8 Escenarios de inundaciones futuras 2.9 Análisis comparativo de los modelos MODCEL E IBER 3. Análisis y discusión de los resultados 3.1 Mapa de inundación en la calibración de IBER 3.2 comparación de los modelos en el evento de calibración 3.3 Comparación de los modelos en el evento de validación 3.4 Mapas de inundación 3.5 Escenarios de inundación futuros 3.6 Características de los modelos 3.6.1 Sencillez 3.6.2 Versatilidad 3.6.3 Robustez y requerimiento de cómputo Conclusiones Recomendaciones ReferenciasIncluye mapas a colorPrimera edición131 páginasapplication/pdfspaUniversidad de La GuajiraDistrito Especial, Turistico y Cultural de Riohachahttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)http://purl.org/coar/access_right/c_abf2Modelación de inundaciones en zona urbana: caso de estudio Riohacha, ciudad costera al norte de Colombia comparando los modelos MODCEL e IBERLibrohttp://purl.org/coar/resource_type/c_2f33Textinfo:eu-repo/semantics/bookhttp://purl.org/coar/version/c_970fb48d4fbd8a85Abebe, Y. A., Ghorbani, A., Nikolic, I., Vojinovic, Z., & Sanchez, A. (2019). A cou pled flood-agent-institution modelling (CLAIM) framework for urban flood risk management. Environmental Modelling & Software, 111(October), 483 492. https://doi.org/10.1016/j.envsoft.2018.10.015Adams, T. E., & Pagano, T. C. (2016). Flood Forecasting - A Global Perspective. Elsevier. https://doi.org/10.1016/C2014-0-01361-5Alcaldía Municipal de Riohacha. (2020). Plan de desarrollo RIOHACHA CAMBIA LA HISTORIA 2020-2023 (p. 292). Alcaldía Municipal de Riohacha. https:// www.riohacha-laguajira.gov.co/MiMunicipio/Paginas/Presentacion.aspxAnh, P. T., & Ngoc, N. T. B. (2019). Public Participatory Role in Urban Flood Risk Management of Ho Chi Minh City - Vietnam: From Awareness to Action. South Asian Journal of Social Studies and Economics, 4(4), 1–10. https://doi. org/10.9734/sajsse/2019/v4i430133Apel, H., Aronica, G. T., Kreibich, H., & Thieken, A. H. (2009). Flood risk analyses— how detailed do we need to be? Natural Hazards, 49(1), 79–98. https://doi. org/10.1007/s11069-008-9277-8Arduino, G., Reggiani, P., & Todini, E. (2005). Recent advances in flood forecas ting and flood risk assessment. Hydrology and Earth System Sciences, 9(4), 280–284. https://doi.org/10.5194/hess-9-280-2005Arroyo, Á. G. (2015). Análisis del comportamiento espacio temporal de la preci pitación en la región pacífica colombiana en presencia de los fenómenos de El Niño y La Niña, en el periodo 1983 - 2009 [Universidad de Nariño]. In Universidad de Nariño. https://sired.udenar.edu.co/1584/Avila Brito, W., Nardini, A., & Pérez Montiel, J. (2017). Alternativa de solución para las inundaciones en Riohacha. “mi casa se inunda” (Editorial). Editorial Gen te Nueva. https://d11260b5-22e5-4724-8a81-25a390c8aa00.filesusr.com/ ugd/dad4f8_1e2f98d6a68a4bc58e0ea9937255f0d1.pdfBaro, J., Díaz Delgado, C., Esteller, M., & Calderon, G. (2007). Curvas de daños provocados por inundaciones en zonas habitacionales y agrícolas de MéBhuiyan, M. J. A. N., & Dutta, D. (2012). Analysis of flood vulnerability and assess ment of the impacts in coastal zones of Bangladesh due to potential sea-le vel rise. Natural Hazards, 61(2), 729–743. https://doi.org/10.1007/s11069 011-0059-3Bladé, E., Cea, L., Corestein, G., Escolano, E., Puertas, J., Vázquez-Cendón, E., Dolz, J., & Coll, A. (2014). Iber: herramienta de simulación numérica del flujo en ríos. Revista Internacional de Métodos Numéricos Para Cálculo y Diseño En Ingeniería, 30(1), 1–10. https://doi.org/10.1016/j.rimni.2012.07.004Bomers, A. (2020). Hydraulic modelling approaches to decrease uncer tainty in flood frequency relations [University of Twente]. https://doi. org/10.3990/1.9789036549288Cai, W., Wang, G., Santoso, A., McPhaden, M. J., Wu, L., Jin, F.-F., Timmermann, A., Collins, M., Vecchi, G., Lengaigne, M., England, M. H., Dommenget, D., Takahashi, K., & Guilyardi, E. (2015). Increased frequency of extreme La Niña events under greenhouse warming. Nature Climate Change, 5(2), 132–137. https://doi.org/10.1038/nclimate2492Cardenas Mercado, L., Nardini, A., & Pérez, M. J. (2019). Análisis comparativos de dos modelos para estimar nivel de inundación. Caso: Distrito de Rioha cha, La Guajira-Colombia. In M. L. C. Martínez & M. J. B. Barranco (Eds.), I congreso internacional gestión integral frente al cambio climático (pp. 367–372). Universidad de La Guajira. https://www.researchgate.net/publica tion/349671236_Analisis_comparativos_de_dos_modelos_para_estimar_ nivel_de_inundacion_Caso_Distrito_de_Riohacha_La_Guajira-ColombiaClimate-data. (2023). Clima Riohacha. Climate Data. https://es.climate-data.org/ america-del-sur/colombia/la-guajira/riohacha-714990/CREACUA. (2013). Proyecto: “Adaptación Urbana Verde frente a inundaciones con el soporte de la modelación matemática y del software MODCEL en Rioha cha, La Guajira, Colombia”; Convenio de cooperación No 9677-04-1047 2013; CREACUA: Riohacha, Colombia. http://modcelrhcdatos.wixsite.com/ modcel-riohacha/avanceCREACUA, & UFRJ. (2014). Proyecto: Adaptación urbana “verde” frente a inunda ciones. Modelo de simulación de la cuenca urbana de Riohacha considerada en el proyecto: conceptualización, calibración e indicaciones de diseño de alternativas de solución. http://docs.wixstatic.com/ugd/dad4f8_26e8648aa cb44bdfbde9e709e6b11655.pdfCRED, (Centre for Research on the Epidemiology of Disasters). (2015). The Hu man Cost of Natural Disaters 2015: A Global Perspective. https://reliefweb. int/sites/rehttps://www.researchgate.net/publication/317645955_The_Hu man_Cost_of_Natural_Disasters_-_A_global_perspectiveCruz, R. K. S. (2012). GESTIÓN INTEGRADA DEL RIESGO DE INUNDACIONES EN COLOMBIA [Universidad politécnica de Valencia]. https://riunet.upv.es/ bitstream/handle/10251/27223/TFM_Gestión_ Inundaciones_Colombia_Ka rime_Sedano.pdf?sequence=1Dahri, N., & Abida, H. (2020). Causes and impacts of flash floods: case of Gabes City, Southern Tunisia. Arabian Journal of Geosciences, 13(4), 176. https:// doi.org/10.1007/s12517-020-5149-7Dazzi, S., Shustikova, I., Domeneghetti, A., Castellarin, A., & Vacondio, R. (2021). Comparison of two modelling strategies for 2D large-scale flood simulations. Environmental Modelling & Software, 146, 105225. https://doi.org/10.1016/j. envsoft.2021.105225Dos Santos, F. M., de Souza Pelinson, N., de Oliveira, R. P., & Di Lollo, J. A. (2023). Using the SWAT model to identify erosion prone areas and to estimate soil loss and sediment transport in Mogi Guaçu River basin in Sao Paulo State, Brazil. CATENA, 222, 106872. https://doi.org/10.1016/j.catena.2022.106872Dutta, D., Teng, J., Vaze, J., Lerat, J., Hughes, J., & Marvanek, S. (2013). Storage-ba sed approaches to build floodplain inundation modelling capability in river system models for water resources planning and accounting. Journal of Hy drology, 504, 12–28. https://doi.org/10.1016/j.jhydrol.2013.09.033Erickson, K. (2019). ¡Prepara un poco de budín de El Niño! https://spaceplace. nasa.gov/el-nino/sp/Erickson, K. (2020). ¿Qué es La Niña? https://spaceplace.nasa.gov/la-nina/sp/Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019). DEM Gene ration from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205Fernández López, C., & Bladé, E. I. C. (2011). Modelización de la ruptura de una balsa de laminación y análisis según el esquema de cálculo [Universidad Politécnica de Catalunya]. In PROJECTE O TFigueiredo, R., Schröter, K., Weiss-Motz, A., Martina, M. L. V., & Kreibich, H. (2018). Multi-model ensembles for assessment of flood losses and associated un certainty. Natural Hazards and Earth System Sciences, 18(5), 1297–1314. https://doi.org/10.5194/nhess-18-1297-2018Fletcher, T. D., Shuster, W., Hunt, W. F., Ashley, R., Butler, D., Arthur, S., Trowsda le, S., Barraud, S., Semadeni-Davies, A., Bertrand-Krajewski, J.-L., Mikkelsen, P. S., Rivard, G., Uhl, M., Dagenais, D., & Viklander, M. (2015). SUDS, LID, BMPs, WSUD and more – The evolution and application of terminology su rrounding urban drainage. Urban Water Journal, 12(7), 525–542. https://doi. org/10.1080/1573062X.2014.916314Frías-Paredes, L., Mallor, F., Gastón-Romeo, M., & León, T. (2017). Assessing ener gy forecasting inaccuracy by simultaneously considering temporal and ab solute errors. Energy Conversion and Management, 142, 533–546. https:// doi.org/10.1016/j.enconman.2017.03.056Gallegos, H. A., Schubert, J. E., & Sanders, B. F. (2009). Two-dimensional, high-re solution modeling of urban dam-break flooding: A case study of Baldwin Hills, California. Advances in Water Resources, 32(8), 1323–1335. https://doi. org/10.1016/j.advwatres.2009.05.008Gerl, T., Kreibich, H., Franco, G., Marechal, D., & Schröter, K. (2016). A Review of Flood Loss Models as Basis for Harmonization and Benchmarking. PLOS ONE, 11(7), e0159791. https://doi.org/10.1371/journal.pone.0159791Gibson, M. J., Savic, D. A., Djordjevic, S., Chen, A. S., Fraser, S., & Watson, T. (2016). Accuracy and Computational Efficiency of 2D Urban Surface Flood Modelling Based on Cellular Automata. Procedia Engineering, 154, 801–810. https://doi.org/10.1016/j.proeng.2016.07.409Gomes Miguez, M., Peres Battemarco, B., Martins De Sousa, M., Moura Rezende, O., Pires Veról, A., & Gusmaroli, G. (2017). Urban Flood Simulation Using MODCEL—An Alternative Quasi-2D Conceptual Model. Water, 9(6), 445. ht tps://doi.org/10.3390/w9060445González-Cao, J., Fernández-Nóvoa, D., García-Feal, O., Figueira, J. R., Vaquero, J. M., Trigo, R. M., & Gómez-Gesteira, M. (2022). The Rivillas flood of 5–6 No vember 1997 (Badajoz, Spain) revisited: An approach based on Iber+ mode lling. Journal of Hydrology, 610(November 2021). https://doi.org/10.1016/j. jhydrol.2022.127883Hall, J. (2015). Direct Rainfall Flood Modelling: The Good, the Bad and the Ugly. Australasian Journal of WaterHiggins, S. A., Overeem, I., Steckler, M. S., Syvitski, J. P. M., Seeber, L., & Akhter, S. H. (2014). InSAR measurements of compaction and subsidence in the Gan ges-Brahmaputra Delta, Bangladesh. Journal of Geophysical Research: Earth Surface, 119(8), 1768–1781. https://doi.org/10.1002/2014JF003117Hu, C., Xia, J., She, D., Song, Z., Zhang, Y., & Hong, S. (2021). A new urban hydro logical model considering various land covers for flood simulation. Journal of Hydrology, 603, 126833. https://doi.org/10.1016/j.jhydrol.2021.126833IDIGER. (2022). Caracterización General del Escenario de Riesgo por Inundación. Instituto Distrital de Gestión de Riesgo y Cambio Climático. https://www. idiger.gov.co/rinundacionIPCC. (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Ro berts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr. https://doi. org/10.1017/9781009325844.IPCC. (2023). El cambio climático está ocurriendo por todas partes. CLIMATE SCIENCE 2030. https://climatescience2030.com/es/Jiang, W., Yu, J., Wang, Q., & Yue, Q. (2022). Understanding the effects of digital elevation model resolution and building treatment for urban flood mode lling. Journal of Hydrology: Regional Studies, 42(January), 101122. https:// doi.org/10.1016/j.ejrh.2022.101122Karim, F., Dutta, D., Marvanek, S., Petheram, C., Ticehurst, C., Lerat, J., Kim, S., & Yang, A. (2015). Assessing the impacts of climate change and dams on floo dplain inundation and wetland connectivity in the wet–dry tropics of nor thern Australia. Journal of Hydrology, 522, 80–94. https://doi.org/10.1016/j. jhydrol.2014.12.005Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., Mach, K., Muir-Wood, R., Braken ridge, G. R., Kron, W., Benito, G., Honda, Y., Takahashi, K., & Sherstyukov, B. (2014). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1–28. https://doi.org/10.1080/0262666 7.2013.857411Li, J. (2016). Assessing spatial predictive models in the environmental sciences: Accuracy measures, data variation and variance explained. Environmental Modelling & Software, 80, 1–8. https://doi.org/10.1016/j.envsoft.2016.02.004Makondo, C. C., & Thomas, D. S. G. (2018). Climate change adaptation: Lin king indigenous knowledge with western science for effective adaptation. Environmental Science & Policy, 88, 83–91. https://doi.org/10.1016/j.envs ci.2018.06.014Mark, O., Weesakul, S., Apirumanekul, C., Aroonnet, S. B., & Djordjević, S. (2004). Potential and limitations of 1D modelling of urban flooding. Journal of Hy drology, 299(3–4), 284–299. https://doi.org/10.1016/j.jhydrol.2004.08.014Mascarenhas, F. C. B., & Miguez, M. G. (2002). Urban flood control through a mathematical cell model. Water International, 27(2), 208–218. https://doi. org/10.1080/02508060208686994Melo Leon, S. F., Riveros Salcedo, L. C., Romero Otalora, G., Álvarez, A. C., Diaz Giraldo, C., & Calderon Diaz, S. L. (2017). Efectos economicos de futuras se quias en colombia estimacion a partir del fenomeno El niño 2015. Archivos de Economia, DPN, 1–76. https://colaboracion.dnp.gov.co/CDT/Estudios Econmicos/469.pdfMentaschi, L., Besio, G., Cassola, F., & Mazzino, A. (2013). Problems in RMSE-ba sed wave model validations. Ocean Modelling, 72, 53–58. https://doi.or g/10.1016/j.ocemod.2013.08.003Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article & quot;Assessment of economic flood damage" Natural Hazards and Earth System Sciences, 10(8), 1697–1724. https://doi.org/10.5194/ nhess-10-1697-2010Mignot, E., Li, X., & Dewals, B. (2019). Experimental modelling of urban floo ding: A review. Journal of Hydrology, 568(November), 334–342. https://doi. org/10.1016/j.jhydrol.2018.11.001Miguez, Marcelo Gomes;, & Mascarenhas, F. C. B. (1999). Modelação matemática de cheias urbanas através de um esquema de células de escoamento. Revista Brasileira de Recursos Hídricos - RBRH, 4, 119–140. https://abrh.s3.sa-east-1. amazonaws.com/Sumarios/50/2f0edba5277f45c60b75a31947675b55_fc0c d24479700710eb97dcc1830c1b1e.pdfMiguez, Marcelo Gomes., Mascarenhas, F. C. B., Prodanoff, J. H. A., & Magalhães, L. P. C. (2007). Simulating floods in urban watersheds: hydrodynamic mo delling of macro, micro-drainage and flows over streets. Water Resources Management IV, I, 645–654. https://doi.org/10.2495/WRM070601Morgan, A., Olivier, D., Nathalie, B., Claire-Marie, D., & Philippe, G. (2016). Hi gh-resolution Modelling With Bi-dimensional Shallow Water Equations Ba sed Codes – High-Resolution Topographic Data Use for Flood Hazard As sessment Over Urban and Industrial Environments. Procedia Engineering, 154, 853–860. https://doi.org/10.1016/j.proeng.2016.07.453Moura Rezende, O., Ribeiro da Cruz de Franco, A. B., Beleño de Oliveira, A. K., Pitzer Jacob, A. C., & Gourban flood resilience into the design of flood control alternatives. Journal of Hydrology, 576, 478–493. https://doi.org/10.1016/j.jhydrol.2019.06.063mes Miguez, M. (2019). A framework to introduceMunicipio de Riohacha. (2012). Plan de Ordenamiento Territorial del municipio de Riohacha, La Guajira- Colombia 2002-2105. https://goo.gl/NQaMUZNardini, A., & Gomes Miguez, M. (2016). An Integrated Plan to Sustainably Enable the City of Riohacha (Colombia) to Cope with Increasing Urban Flooding, while Improving Its Environmental Setting. Sustainability, 8(3), 198. https:// doi.org/10.3390/su8030198Navarro Cerrillo, R. M., Sanchez de la Orden, M., Gomez Bonilla, J., Garcia-Ferrer, A., Hernandez Clemente, R., & Lanjeri, S. (2010). LIDAR-based estimation of leaf area index on Holm oak [Quercus ilex L. subsp. ballota (Desf.) Samp.] trees. Forest Systems, 19(1), 61. https://doi.org/10.5424/fs/2010191-01167Neal, J., Villanueva, I., Wright, N., Willis, T., Fewtrell, T., & Bates, P. (2012). How much physical complexity is needed to model flood inundation? Hydrologi cal Processes, 26(15), 2264–2282. https://doi.org/10.1002/hyp.8339Nkwunonwo, U. C., Whitworth, M., & Baily, B. (2020). A review of the current sta tus of flood modelling for urban flood risk management in the developing countriesNwe Nwe Aung, San San Htwe, Kyi Kyi Sein, & Lei Lei Aung. (2019). The Impact of Floods on the Socio-Economic Activities of Yangon. In Population, Develo pment, and the Environment (pp. 255–271). Springer Singapore. https://doi. org/10.1007/978-981-13-2101-6_15ONU. (2023). ¿Qué es el cambio climático? Acción Por El Clima. https://www. un.org/es/climatechange/what-is-climate-changeOzdemir, H., Sampson, C. C., de Almeida, G. A. M., & Bates, P. D. (2013). Evalua ting scale and roughness effects in urban flood modelling using terrestrial LIDAR data. Hydrology and Earth System Sciences, 17(10), 4015–4030. ht tps://doi.org/10.5194/hess-17-4015-2013Özgen, I., Zhao, J., Liang, D., & Hinkelmann, R. (2016). Urban flood modeling using shallow water equations with depth-dependent anisotropic porosity. Journal of Hydrology, 541, 1165–1184. https://doi.org/10.1016/j.jhydrol.2016.08.025Pérez-Montiel, J. I., Cardenas-Mercado, L., & Nardini, A. G. C. (2022). Flood Mode ling in a Coastal Town in Northern Colombia: Comparing MODCEL vs. IBER. Water, 14(23), 3866. https://doi.org/10.3390/w14233866Pérez, J., Escobar, J., & Fragozo, J. (2018). Modelación Hidráulica 2D de Inundacio nes en Regiones con Escasez de Datos. El Caso del Delta del Río Ranchería,Pérez, J. I., Nardini, A., & Zuñiga, Y. P. (2018). Identificación Multiatributo de Tipo logías de Viviendas Vulnerables a Inundaciones en Riohacha, La Guajira-Co lombia. Información Tecnológica, 29(5), 187–202. https://doi.org/10.4067/ S0718-07642018000500187Pérez, J. I., Nardini, A., & Zuñiga, Y. P. (2018). Identificación Multiatributo de Tipo logías de Viviendas Vulnerables a Inundaciones en Riohacha, La Guajira-Co lombia. Información Tecnológica, 29(5), 187–202. https://doi.org/10.4067/ S0718-07642018000500187Roe, P. L. (1986). Discrete models for the numerical analysis of time-dependent multidimensional gas dynamics. Journal of Computational Physics, 63(2), 458–476. https://doi.org/10.1016/0021-9991(86)90204-4Rouf, T. (2015). Flood inundation map of Sirajgonj district using mathematical mo del [Bangladesh University of Engineering and Technology]. http://lib.buet. ac.bd:8080/xmlui/handle/123456789/966Russo, B., Sunyer, D., Velasco, M., & Djordjević, S. (2015). Analysis of extreme flooding events through a calibrated 1D/2D coupled model: the case of Barcelona (Spain). Journal of Hydroinformatics, 17(3), 473–491. https://doi. org/10.2166/hydro.2014.063Sadrehaghighi, I. (2022). Unstructured Meshing for CFD. https://www.academia. edu/44184149/Unstructured_Meshing_for_CFDSalvan, L., Abily, M., Gourbesville, P., & Schoorens, J. (2016). Drainage System and Detailed Urban Topography: Towards Operational 1D-2D Modelling for Stormwater Management. Procedia Engineering, 154, 890–897. https://doi. org/10.1016/j.proeng.2016.07.469Santillan, J. R., & Makinano-Santillan, M. (2016). Vertical accuracy assessment of 30-M resolution ALOS, ASTER, and SRTM global DEMS over Northeastern Mindanao, Philippines. International Archives of the Photogrammetry, Re mote Sensing and Spatial Information Sciences - ISPRS Archives, 41(Sep tember), 149–156. https://doi.org/10.5194/isprsarchives-XLI-B4-149-2016Sanz-Ramos, M., Cea, L., Bladé, E., López-Gómez, D., Sañudo, E., Corestein, G., García-Alén, G., & Aragón-Hernández, J. (2022). Iber v3. Reference ma nual and user’s interface of the new implementations. CIMNE. https://doi. org/10.23967/iber.2022.01Sarzynski, A. (2015). Public participation, civic capacity, and climate change adaptation in cities. Urban Climate, 14, 52–67. https://doi.org/10.1016/j. uclim.2015.08.002Shen, Y., Morsy, M. M., Huxley, C., Tahvildari, N., & Goodall, J. L. (2019). Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall. Journal of Hydrology, 579(June), 124159. https://doi.org/10.1016/j.jhydrol.2019.124159Shrestha, B. B., Kawasaki, A., & Zin, W. W. (2021). Development of flood damage assessment method for residential areas considering various house types for Bago Region of Myanmar. International Journal of Disaster Risk Reduc tion, 66, 102602. https://doi.org/10.1016/j.ijdrr.2021.102602Sreedevi, S., Eldho, T. I., & Jayasankar, T. (2022). Physically-based distributed modelling of the hydrology and soil erosion under changes in landuse and climate of a humid tropical river basin. CATENA, 217, 106427. https://doi. org/10.1016/j.catena.2022.106427Srivastav, R. K., Schardong, A., & Simonovic, S. P. (2014). Equidistance Quantile Mat ching Method for Updating IDFCurves under Climate Change. Water Resources Management, 28(9), 2539–2562. https://doi.org/10.1007/s11269-014-0626-yTeng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, recent advances and uncertain ty analysis. Environmental Modelling & Software, 90, 201–216. https://doi. org/10.1016/j.envsoft.2017.01.006Timbe Castro, L. M., & Willems, P. (2011). Desempeño de modelos hidráulicos 1D y 2D para la simulación de inundaciones Lus Timbe. MASKANA, 2(1), 91–98. https://doi.org/10.18537/mskn.02.01.07Timbe, L., & Willems, P. (2011). Desempeño de modelos hidráulicos 1D y 2D para la simulación de inundaciones. MASKANA, 2(1), 69–76. https://doi. org/10.18537/mskn.02.01.06Toté, C., Patricio, D., Boogaard, H., van der Wijngaart, R., Tarnavsky, E., & Funk, C. (2015). Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique. Remote Sensing, 7(2), 1758–1776. https://doi. org/10.3390/rs70201758Upadhyay, S. (2023). What Is An Algorithm? Characteristics, Types and How to write it. Simplilearn. https://www.simplilearn.com/tutorials/data-structu re-tutorial/what-is-an-algorithm#:~:text=An algorithm is a set,to perform a particular task.Valderrama, J. O., & Alvarez, V. H. (2008). Correct Way of Reporting Results when Modelling Supercritical Phase Equilibria using Equations of State. The Canadian Journal of Chemical Engineering, 83(3), 578–581. https://doi. org/10.1002/cjce.5450830323Valdivieso Taborga, C. E., Valdivieso Castellón, R., & Valdivieso Taborga, O. Á. (2011). Determinación del Tamaño Muestral Mediante el uso de Árboles de Decisión. INVESTIGACION & DESARROLLO, 11(1), 53–80. https://doi. org/10.23881/idupbo.011.1-4eVojinovic, Z., & Tutulic, D. (2009). On the use of 1D and coupled 1D-2D modelling approaches for assessment of flood damage in urban areas. Urban Water Journal, 6(3), 183–199. https://doi.org/10.1080/15730620802566877Vozinaki, A.-E. K., Morianou, G. G., Alexakis, D. D., & Tsanis, I. K. (2017). Com paring 1D and combined 1D/2D hydraulic simulations using high-resolution topographic data: a case study of the Koiliaris basin, Greece. Hydrological Sciences Journal, 62(4), 642–656. https://doi.org/10.1080/02626667.2016.1 255746Wang, Y.-M., & Luo, Y. (2010). Integration of correlations with standard devia tions for determining attribute weights in multiple attribute decision ma king. Mathematical and Computer Modelling, 51(1–2), 1–12. https://doi.or g/10.1016/j.mcm.2009.07.016Weber, J. F. (2014). Parámetros del modelo de infiltración de Horton obtenidos mediante el uso de un simulador de lluvia, Córdoba, Argentina. Ambiente e Agua - An Interdisciplinary Journal of Applied Science, 9(1). https://doi. org/10.4136/ambi-agua.1320Xia, X., Liu, Z., & Yang, H. (2016). Regularized estimation for the least absolu te relative error models with a diverging number of covariates. Computa tional Statistics & Data Analysis, 96, 104–119. https://doi.org/10.1016/j. csda.2015.10.012Yang, Y., Guan, W., Deleersnijder, E., & He, Z. (2022). Hydrodynamic and sediment transport modelling in the Pearl River Estuary and adjacent Chinese coastal zone during Typhoon Mangkhut. Continental Shelf Research, 233, 104645. https://doi.org/10.1016/j.csr.2022.104645Yoshida, H., & Dittrich, A. (2002). 1D unsteady-state flow simulation of a sec tion of the upper Rhine. Journal of Hydrology, 269(1–2), 79–88. https://doi. org/10.1016/S0022-1694(02)00196-8Zhang, Y., Ge, J., Qi, J., Liu, H., Zhang, X., Marek, G. W., Yuan, C., Ding, B., Feng, P., Liu, D. L., Srinivasan, R., & Chen, Y. (2023). Evaluating the effects of single and integrated extreme climate events on hydrology in the Liao River Basin, China using a modified SWAT-BSR model. Journal of Hydrology, 623, 129772. https://doi.org/10.1016/j.jhydrol.2023.129772Inundaciones urbanasAdaptación urbana verdeModelo matemáticoIBERMODCELComparaciónEscorrentíasUrban floodingUrban green adaptationMathematical modelingComparisonRunoffPublication74e929e2-b0a6-48da-9291-57476c1e3bb7virtual::627-1833eda09-d99f-4f00-9f09-8de1b8e57dbcvirtual::628-1982637ce-96f7-44ba-970f-f9cd336459bbvirtual::629-174e929e2-b0a6-48da-9291-57476c1e3bb7virtual::627-1833eda09-d99f-4f00-9f09-8de1b8e57dbcvirtual::628-1982637ce-96f7-44ba-970f-f9cd336459bbvirtual::629-10000-0003-0826-5452virtual::627-10000-0002-0193-373Xvirtual::628-10000-0001-8383-2512virtual::629-1LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositoryinst.uniguajira.edu.co/bitstreams/c11feac7-2498-43eb-a0b6-1259e0ad6ad9/download73a5432e0b76442b22b026844140d683MD51ORIGINAL83. Modelación de inundaciones en zona urbana.pdf83. 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 autor) para ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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