Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado

Los deslizamientos son fenómenos naturales cuya ocurrencia está estrechamente relacionada con los niveles de humedad en el suelo. Esta humedad se acumula no solo por la lluvia del día del evento, sino también por las precipitaciones registradas en los días o semanas previas. En este contexto, la llu...

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Autores:
Jimenez Giraldo, Hernan
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2025
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
spa
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/48458
Acceso en línea:
https://hdl.handle.net/10495/48458
Palabra clave:
Lluvia
Rain
Humedad del suelo
Soil moisture
Fenómeno natural
Natural phenomena
http://aims.fao.org/aos/agrovoc/c_330740
http://vocabularies.unesco.org/thesaurus/concept1218
http://vocabularies.unesco.org/thesaurus/concept12055
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openAccess
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
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network_name_str Repositorio UdeA
repository_id_str
dc.title.spa.fl_str_mv Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
title Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
spellingShingle Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
Lluvia
Rain
Humedad del suelo
Soil moisture
Fenómeno natural
Natural phenomena
http://aims.fao.org/aos/agrovoc/c_330740
http://vocabularies.unesco.org/thesaurus/concept1218
http://vocabularies.unesco.org/thesaurus/concept12055
title_short Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
title_full Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
title_fullStr Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
title_full_unstemmed Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
title_sort Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de Grado
dc.creator.fl_str_mv Jimenez Giraldo, Hernan
dc.contributor.advisor.none.fl_str_mv Gómez García, Derly Estefanny
dc.contributor.author.none.fl_str_mv Jimenez Giraldo, Hernan
dc.subject.unesco.none.fl_str_mv Lluvia
Rain
Humedad del suelo
Soil moisture
topic Lluvia
Rain
Humedad del suelo
Soil moisture
Fenómeno natural
Natural phenomena
http://aims.fao.org/aos/agrovoc/c_330740
http://vocabularies.unesco.org/thesaurus/concept1218
http://vocabularies.unesco.org/thesaurus/concept12055
dc.subject.agrovoc.none.fl_str_mv Fenómeno natural
Natural phenomena
dc.subject.agrovocuri.none.fl_str_mv http://aims.fao.org/aos/agrovoc/c_330740
dc.subject.unescouri.none.fl_str_mv http://vocabularies.unesco.org/thesaurus/concept1218
http://vocabularies.unesco.org/thesaurus/concept12055
description Los deslizamientos son fenómenos naturales cuya ocurrencia está estrechamente relacionada con los niveles de humedad en el suelo. Esta humedad se acumula no solo por la lluvia del día del evento, sino también por las precipitaciones registradas en los días o semanas previas. En este contexto, la lluvia antecedente efectiva (AEP) se define como la fracción de lluvia que se infiltra y permanece en el suelo, excluyendo aquella que se evapora, es interceptada por la vegetación o escurre superficialmente. En consecuencia, la adecuada estimación de la AEP es fundamental para anticipar condiciones de saturación que puedan generar inestabilidad, especialmente en territorios con alta susceptibilidad, como Colombia, debido a su elevada pluviosidad, topografía montañosa y limitada disponibilidad de datos sobre humedad del suelo. Frente a esta problemática, el presente trabajo se enfocó en el análisis de metodologías utilizadas para estimar AEP, así como en la forma en que distintos autores la han aplicado para evaluar la amenaza de deslizamientos. Para ello, se llevó a cabo una revisión de literatura global, incluyendo artículos de revistas científicas indexadas, tesis universitarias y proyectos ingenieriles, donde inicialmente, se identificaron más de doscientos artículos, los cuales fueron filtrados con base en dos criterios: año de publicación (entre 2000 y 2025, ya que es una revisión literaria reciente) y claridad en la descripción del método utilizado para estimar la AEP, obteniendo finalmente cuarenta y seis artículos clave. Posteriormente, la información recopilada fue agrupada en trece metodologías diferentes, evidenciando que el API clásico, pese a tener casi sesenta años de antigüedad, continúa siendo ampliamente utilizado a nivel global. Asimismo, se evidenció que las metodologías más recientes no reemplazan este modelo, sino que lo redefinen mediante la optimización, modificación o calibración del factor de decaimiento “k”, manteniendo su estructura conceptual. Por lo tanto, esta evolución metodológica demuestra la vigencia del API como base fundamental para estimar AEP.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-11-28T15:36:48Z
dc.date.issued.none.fl_str_mv 2025
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dc.relation.references.none.fl_str_mv Ali, S., Ghosh, N. C., & Singh, R. (2010). Rainfall-runoff simulation using a normalized antecedent precipitation index. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 55(2), 266–274. https://doi.org/10.1080/02626660903546175
Aparicio, J., Rodríguez, J. L., & Vázquez, J. A. (2000). Hidrología superficial y subterránea (2.ª ed.). Limusa Noriega.
Aristizábal Giraldo, E. V., Vélez Upegui, J. I., & Martínez Carvajal, H. E. (2016). INFLUENCIA DE LA LLUVIA ANTECEDENTE Y LA CONDUCTIVIDAD HIDRÁULICA EN LA OCURRENCIA DE DESLIZAMIENTOS DETONADOS POR LLUVIAS UTILIZANDO EL MODELO SHIA_LANDSLIDE. Revista EIA, 26, 31–46. https://doi.org/10.24050/reia.v13i26.863
Banco Mundial. (2019). Resilience to disaster risk and climate change in Colombia. https://www.bancomundial.org/es/results/2023/03/16/resilience-to-disaster-risk-and climate-change-in-colombia
Chang-wei, Z., Yan-xia, Z., Yi-feng, W., & Ping-cang, Z. (2015). Fractal Relation Between Precipitation and Sediment Yield and Calculation of Antecedent Effective Precipitation in Xiangxi River Watershed. Journal of Changjiang River Scientific Research Institute, 32(3), 121. https://doi.org/10.3969/J.ISSN.1001-5485.2015.03.024
Chik, L., Albrecht, D., & Kodikara, J. (2018). Modeling Failures in Water Mains Using the Minimum Monthly Antecedent Precipitation Index. Journal of Water Resources Planning and Management, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000926 144(4), 06018004.
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Descroix, L., Nouvelot, J. F., & Vauclin, M. (2002). Evaluation of an antecedent precipitation index to model runoff yield in the western Sierra Madre (North-west Mexico). Journal of Hydrology, 263(1–4), 114–130. https://doi.org/10.1016/S0022-1694(02)00047-1
Du, J., Fang, J., Xu, W., & Shi, P. (2013). Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China. Stochastic Environmental Research and Risk Assessment, 27(2), 377–387. https://doi.org/10.1007/S00477-012-0589-6/METRICS
Gariano, S. L., & Guzzetti, F. (2016). Landslides in a changing climate. Earth-Science Reviews, 162, 227–252. https://doi.org/10.1016/J.EARSCIREV.2016.08.011
Ghosh, N. C., Jaiswal, R. K., & Ali, S. (2021). Normalized Antecedent Precipitation Index Based Model for Prediction of Runoff from Un-Gauged Catchments. Water Resources Management, 35(4), W/METRICS 1211–1230. https://doi.org/10.1007/S11269-021-02775
Glade, T., Crozier, M., & Smith, P. (2000). Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model.” Pure and Applied https://doi.org/10.1007/S000240050017
Gómez, D., Aristizábal, E., García, E. F., Marín, D., Valencia, S., & Vásquez, M. (2023). Landslides forecasting using satellite rainfall estimations and machine learning in the Colombian Andean region. Journal of South American Earth Sciences, 125, 104293. https://doi.org/10.1016/J.JSAMES.2023.104293
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1–2), 42–66. https://doi.org/10.1016/J.EARSCIREV.2012.02.001
Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2008). The rainfall intensity-duration control of shallow landslides and debris flows: An update. Landslides, 5(1), 3–17. https://doi.org/10.1007/S10346-007-0112-1
Gwak, Y.-S., Kim, S.-J., Lee, E.-H., Hamm, S.-Y., Kim, S.-H., & Author, C. (2016). Soil Water Storage and Antecedent Precipitation Index at Gwangneung Humid-Forested Hillslope. Korean Journal of Agricultural and Forest Meteorology, 18(1), 30–41. https://doi.org/10.5532/KJAFM.2016.18.1.30
Heggen, R. J. (2001). Normalized Antecedent Precipitation Index. Journal of Hydrologic Engineering, 6(5), 377–381. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:5(377)
Huang, R., & Li, W. (2011). Formation, distribution and risk control of landslides in China. Journal of Rock Mechanics and Geotechnical Engineering, 3(2), 97–116. https://doi.org/10.3724/SP.J.1235.2011.00097
Hui, B., Zhan-ping, W., Li, L., Tao, Z., Hui, B., Zhan-ping, W., Li, L., & Tao, Z. (2013). The application of the daily meteorological drought indicator based on standardized antecedent precipitation index in Guizhou. Journal of Yunnan University: Natural Sciences Edition, 2013, Vol. 35, Issue 5, Pages: 661-, 35(5), 661-. https://doi.org/10.7540/J.YNU.20120483
IDEAM. (2020). Informe anual sobre el estado del medio ambiente y los recursos naturales renovables en Colombia. Instituto de Hidrología, Meteorología y Estudios Ambientales.
Jung, Y.-Y., Koh, D.-C., Han, H.-S., Kwon, H.-I., & Lim, E.-K. (2016). Estimation of Stream Discharge using Antecedent Precipitation Index Models in a Small Mountainous Forested Catchment: Upper Reach of Yongsucheon Stream, Gyeryongsan Mountain. Journal of Soil and Groundwater https://doi.org/10.7857/JSGE.2016.21.6.036
Konstantinos, N., & Antonakos, A. (2007). THE USE OF " ANTECEDENT PRECIPITATION INDEX " AND " DELAY FACTOR " TO ESTIMATE RUNOFF FROM RAINFALL; A CASE STUDY FROM EIGHT DRAINAGE BASINSACHAIA, PELOPONESSOS, GREECE. https://www.researchgate.net/publication/278683549_THE_USE_OF_ANTECEDENT_PRECIPITATION_INDEX_AND_DELAY_FACTOR_TO_ESTIMATE_RUNOFF_FROM_RAINFALL_A_CASE_STUDY_FROM_EIGHT_DRAINAGE_BASINS_ACHAIA_PELOPONESSOS_GREECE
Li, J., Wang, Z., Wu, X., Xu, C. Y., Guo, S., & Chen, X. (2020). Toward Monitoring Short Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index. Journal of Hydrometeorology, 21(5), 891–908. https://doi.org/10.1175/JHM-D-19-0298.1
Li, X., Wei, Y., & Li, F. (2021). Optimality of antecedent precipitation index and its application. Journal of Hydrology, 595, 126027. https://doi.org/10.1016/J.JHYDROL.2021.126027
Liang, J., Hu, Z., Liu, S., Zhong, G., Zhen, Y., Makhinov, A. N., & Araruna, J. T. (2022). Residual-Oriented Optimization of Antecedent Precipitation Index and Its Impact on Flood Prediction Uncertainty. Water 2022, Vol. 14, Page 3222, 14(20), 3222. https://doi.org/10.3390/W14203222
Liang, X., Segoni, S., Fan, W., Yin, K., Deng, L., Xiao, T., Barbadori, F., & Casagli, N. (2025). Integration of effective antecedent rainfall to improve the performance of rainfall thresholds for landslide early warning in Wanzhou District, China. International Journal of Disaster Risk Reduction, 119, 105317. https://doi.org/10.1016/J.IJDRR.2025.105317
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spelling Gómez García, Derly EstefannyJimenez Giraldo, Hernan2025-11-28T15:36:48Z2025https://hdl.handle.net/10495/48458Los deslizamientos son fenómenos naturales cuya ocurrencia está estrechamente relacionada con los niveles de humedad en el suelo. Esta humedad se acumula no solo por la lluvia del día del evento, sino también por las precipitaciones registradas en los días o semanas previas. En este contexto, la lluvia antecedente efectiva (AEP) se define como la fracción de lluvia que se infiltra y permanece en el suelo, excluyendo aquella que se evapora, es interceptada por la vegetación o escurre superficialmente. En consecuencia, la adecuada estimación de la AEP es fundamental para anticipar condiciones de saturación que puedan generar inestabilidad, especialmente en territorios con alta susceptibilidad, como Colombia, debido a su elevada pluviosidad, topografía montañosa y limitada disponibilidad de datos sobre humedad del suelo. Frente a esta problemática, el presente trabajo se enfocó en el análisis de metodologías utilizadas para estimar AEP, así como en la forma en que distintos autores la han aplicado para evaluar la amenaza de deslizamientos. Para ello, se llevó a cabo una revisión de literatura global, incluyendo artículos de revistas científicas indexadas, tesis universitarias y proyectos ingenieriles, donde inicialmente, se identificaron más de doscientos artículos, los cuales fueron filtrados con base en dos criterios: año de publicación (entre 2000 y 2025, ya que es una revisión literaria reciente) y claridad en la descripción del método utilizado para estimar la AEP, obteniendo finalmente cuarenta y seis artículos clave. Posteriormente, la información recopilada fue agrupada en trece metodologías diferentes, evidenciando que el API clásico, pese a tener casi sesenta años de antigüedad, continúa siendo ampliamente utilizado a nivel global. Asimismo, se evidenció que las metodologías más recientes no reemplazan este modelo, sino que lo redefinen mediante la optimización, modificación o calibración del factor de decaimiento “k”, manteniendo su estructura conceptual. Por lo tanto, esta evolución metodológica demuestra la vigencia del API como base fundamental para estimar AEP.Landslides are natural hazards whose occurrence is closely linked to soil moisture levels. This moisture accumulates not only from rainfall on the day of the event, but also from precipitation recorded during the days or weeks prior. In this context, effective antecedent precipitation (AEP) is defined as the fraction of water that infiltrates and remains in the soil, excluding the portion that evaporates, is intercepted by vegetation, or flows off as surface runoff. Accordingly, accurate estimation of AEP is essential to anticipate soil saturation conditions that may lead to slope instability, particularly in highly susceptible regions such as Colombia, characterized by high rainfall, mountainous topography, and limited availability of soil moisture data. To address this issue, the present study focused on analyzing the methodologies used to estimate AEP, as well as how various authors have applied them to assess landslide hazard. A global literature review was conducted, including indexed scientific articles, university theses, and engineering projects. Initially, over two hundred documents were identified and then filtered based on two criteria: year of publication (between 2000 and 2025, as it is a recent review) and clarity in the description of the method used to estimate AEP, resulting in a final selection of fourty six key articles. The collected information was subsequently grouped into thirteen different methodologies, revealing that the classical API, despite being nearly sixty years old, remains widely used worldwide. Furthermore, it was observed that recent methodologies do not replace the original model but rather refine it through optimization, modification, or calibration of the decay factor “k”, while maintaining its conceptual structure. This methodological evolution highlights the enduring relevance of the API as a fundamental tool for estimating AEP.PregradoIngeniero Civil54 páginasapplication/pdfspaUniversidad de AntioquiaIngeniería CivilMedellín, ColombiaFacultad de IngenieríaCampus Medellín - Ciudad Universitariahttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Lluvia antecedente efectiva: revisión literaria y aplicación en estudios de amenaza de deslizamientos. Trabajo de GradoTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/redcol/resource_type/TPTexthttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/draftAli, S., Ghosh, N. C., & Singh, R. (2010). Rainfall-runoff simulation using a normalized antecedent precipitation index. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 55(2), 266–274. https://doi.org/10.1080/02626660903546175Aparicio, J., Rodríguez, J. L., & Vázquez, J. A. (2000). Hidrología superficial y subterránea (2.ª ed.). Limusa Noriega.Aristizábal Giraldo, E. V., Vélez Upegui, J. I., & Martínez Carvajal, H. E. (2016). INFLUENCIA DE LA LLUVIA ANTECEDENTE Y LA CONDUCTIVIDAD HIDRÁULICA EN LA OCURRENCIA DE DESLIZAMIENTOS DETONADOS POR LLUVIAS UTILIZANDO EL MODELO SHIA_LANDSLIDE. Revista EIA, 26, 31–46. https://doi.org/10.24050/reia.v13i26.863Banco Mundial. (2019). 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Residual-Oriented Optimization of Antecedent Precipitation Index and Its Impact on Flood Prediction Uncertainty. Water 2022, Vol. 14, Page 3222, 14(20), 3222. https://doi.org/10.3390/W14203222Liang, X., Segoni, S., Fan, W., Yin, K., Deng, L., Xiao, T., Barbadori, F., & Casagli, N. (2025). Integration of effective antecedent rainfall to improve the performance of rainfall thresholds for landslide early warning in Wanzhou District, China. International Journal of Disaster Risk Reduction, 119, 105317. https://doi.org/10.1016/J.IJDRR.2025.105317Linsley, K. (1967). Hidrología para Ingenieros. https://www.academia.edu/37765494/Hidrolog%C3%ADa_para_Ingenieros_LINSLEY_KOHLER_y_PAULHUSMehaiguene, M., Touhari, F., & Zouidi, M. (2025). The role of the antecedent precipitation index on runoff of semi arid catchment (North-West Algeria). https://ojs.brazilianjournals.com.br/ojs/index.php/BJAER/article/view/76479/53186Moraes, M. A. E. de, Filho, W. M. M., Mendes, R. M., Bortolozo, C. A., Metodiev, D., Andrade, M. R. M. de, Egas, H. M., Mendes, T. S. G., Pampuch, L. A., Moraes, M. A. E. de, Filho, W. M. M., Mendes, R. M., Bortolozo, C. A., Metodiev, D., Andrade, M. R. M. de, Egas, H. M., Mendes, T. S. G., & Pampuch, L. A. (2024). Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides. International Journal of Geosciences, 15(1), 70–86. https://doi.org/10.4236/IJG.2024.151006Nguyen-Huy, T., Kath, J., Nagler, T., Khaung, Y., Su Aung, T. S., Mushtaq, S., Marcussen, T., & Stone, R. (2022). A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar. Remote Sensing Applications: Society and Environment, 26, 100733. https://doi.org/10.1016/J.RSASE.2022.100733Odin, M. (2021). 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Estimation of soil moisture using modified Antecedent Precipitation Index 1 with application in landslide predictions. https://doi.org/10.1007/s10346-019LluviaRainHumedad del sueloSoil moistureFenómeno naturalNatural phenomenahttp://aims.fao.org/aos/agrovoc/c_330740http://vocabularies.unesco.org/thesaurus/concept1218http://vocabularies.unesco.org/thesaurus/concept12055PublicationORIGINALJimenezHernan_2025_LluviaAntecedenteEfectiva.pdfJimenezHernan_2025_LluviaAntecedenteEfectiva.pdfTrabajo de grado de pregradoapplication/pdf1212090https://bibliotecadigital.udea.edu.co/bitstreams/3c1ffec1-4975-4b15-bc04-902f727df07e/downloadd3a1d4396cc37b5be710a62db356e399MD51trueAnonymousREADLICENSElicense.txtlicense.txttext/plain; charset=utf-814837https://bibliotecadigital.udea.edu.co/bitstreams/7095ed80-853a-4bbe-97ef-66339cf964ba/downloadb76e7a76e24cf2f94b3ce0ae5ed275d0MD54falseAnonymousREADCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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