Metodologías para el uso de la información de satélite como herramienta para rellenar series de datos diarios de lluvia en zonas de montaña colombianas

dc.contributor.advisorVélez Upegui, Jorge Julián
dc.contributor.authorJiménez Duque, José Jacobo
dc.contributor.researchgroupGrupo de Trabajo Académico en Ingeniería Hidráulica y Ambientalspa
dc.date.accessioned2024-04-16T13:56:23Z
dc.date.available2024-04-16T13:56:23Z
dc.date.issued2023
dc.descriptiongraficas, mapas, tablasspa
dc.description.abstractLos datos faltantes de lluvia diaria afectan la estimación de eventos extremos, los resultados en la gestión del agua y la planificación de los recursos hídricos. Se han implementado diferentes técnicas para abordar este problema, en donde las estrategias más utilizadas implican análisis estadísticos, enfoques estocásticos y técnicas de inteligencia artificial, así como los métodos híbridos que mezclan las estrategias mencionadas anteriormente. La precipitación en la zona andina tropical se ve afectada por este efecto de datos faltantes, ya que apenas se monitorea hasta cierta altitud, y la distribución espacial de las estaciones medidoras de lluvia en tierra es muy dispersa espacialmente y en algunos casos con muchos datos faltantes. Sin embargo, los productos satelitales de precipitación cubren toda el área, dan valores continuos en el tiempo y esta información se puede descargar de forma gratuita desde diferentes bases de datos. Por lo tanto, esta tesis explora el uso de los productos satelitales de precipitación combinados con metodologías tradicionales para mejorar las estrategias de relleno de datos faltantes de las series de lluvia diaria en el departamento de Caldas. Se propone un análisis que compara la eficiencia de los diferentes métodos como las redes neuronales artificiales, los análisis estadísticos clásicos y los métodos estocásticos, con o sin datos de productos satelitales de precipitación y con la combinación de ambas fuentes de información (estaciones en tierra y productos satelitales de precipitación). Los resultados principales revelan que la incorporación de CHIRPS V2.0 junto con la información proveniente de las estaciones en tierra en la zona de estudio no produce cambios significativos en los resultados obtenidos mediante las metodologías aplicadas. Además, se observa que el desempeño de las metodologías con el uso exclusivo la información de CHIRPS V2.0 es inferior en comparación con el uso de las estaciones en tierra. Se destaca como mejor metodología para el relleno de series de lluvia diaria en el departamento de Caldas, las redes de perceptrones multicapa (MLP) cuando se utilizan únicamente las estaciones en tierra como fuente de información (Texto tomado de la fuente)spa
dc.description.abstractThe gap-filling rainfall data affects the estimation of extreme events, the results in water management and water resource planning. Different techniques have been implemented to address this problem, where the most used strategies involve statistical analysis, stochastic approaches and artificial intelligence techniques, as well as hybrid methods that mix the strategies mentioned above. Precipitation in the tropical Andean zone is affected by this missing data effect, since it is barely monitored up to a certain altitude, and the spatial distribution of ground-based rain gauge stations is very spatially dispersed and in some cases with many missing data. However, satellite precipitation products cover the entire area, provide continuous values over time, and can be freely downloaded from different databases. Therefore, this thesis explores the use of use of satellite precipitation products combined with traditional methodologies to improve strategies to fill missing data in the daily rainfall series in the Department of Caldas. An analysis is proposed to compare the effectiveness of different methods, such as artificial neural networks, classical statistical analyses, and stochastic methods, both with and without satellite precipitation data, as well as the combination of both data sources (ground-based stations and satellite precipitation products). The main results indicate that the incorporation of CHIRPS V2.0 along with information from ground-based stations in the study area does not yield significant changes in the results obtained from the applied methodologies. Furthermore, it is observed that the performance of methodologies using exclusively CHIRPS V2.0 data is inferior compared to the use of ground-based stations. Notably, the multilayer perceptron (MLP) neural networks with only ground-based stations as the source of information emerge as the superior methodology for filling in daily rainfall series in the Department of Caldas.eng
dc.description.curricularareaIngeniería Civil.Sede Manizalesspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Recursos Hidráulicosspa
dc.description.researchareaHidrologíaspa
dc.format.extent121 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85922
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Recursos Hidráulicosspa
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dc.relation.referencesTian, Y., & Peters-Lidard, C. D. (2010). A global map of uncertainties in satellite-based precipitation measurements. Geophysical Research Letters, 37(24). https://doi.org/10.1029/2010GL046008spa
dc.relation.referencesToro Trujillo, A. M., Arteaga Ramírez, R., Vázquez Peña, M. A., & Ibáñez Castillo, L. A. (2017). Relleno de series diarias de precipitación, temperatura mínima, máxima de la región norte del Urabá Antioqueño. Revista Mexicana de Ciencias Agrícolas, 6(3), 577–588. https://doi.org/10.29312/remexca.v6i3.640spa
dc.relation.referencesToté, 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/rs70201758spa
dc.relation.referencesTwumasi, Y. A., Annan, J. B., Merem, E. C., Namwamba, J. B., Ayala-Silva, T., Ning, Z. H., Asare-Ansah, A. B., Oppong, J., Frimpong, D. B., Loh, P. M., Owusu, F., Kangwana, L. A., Mwakimi, O. S., Petja, B. M., Okwemba, R., Akinrinwoye, C. O., Mosby, H. J., & McClendon-Peralta, J. (2021). Time Series Analysis on Selected Rainfall Stations Data in Louisiana Using ARIMA Approach. In Open Journal of Statistics (Vol. 11, Issue 05, pp. 655–672). https://doi.org/10.4236/ojs.2021.115039spa
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dc.relation.referencesYoung, K. C. (1992). A three-way model for interpolating for monthly precipitation values. Monthly Weather Review, 120(11), 2561–2569. https://doi.org/10.1175/1520-0493(1992)120<2561:ATWMFI>2.0.CO;2spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc620 - Ingeniería y operaciones afinesspa
dc.subject.proposalSensores remotosspa
dc.subject.proposalRelleno de seriesspa
dc.subject.proposalPrecipitación diariaspa
dc.subject.proposalInteligencia artificialspa
dc.subject.proposalMétodos estocásticosspa
dc.subject.proposalRemote sensorseng
dc.subject.proposalData fillingeng
dc.subject.proposalDaily precipitationeng
dc.subject.proposalArtificial intelligenceeng
dc.subject.proposalStochastic methodseng
dc.subject.unescoHidrologíaspa
dc.subject.unescoHydrologyeng
dc.titleMetodologías para el uso de la información de satélite como herramienta para rellenar series de datos diarios de lluvia en zonas de montaña colombianasspa
dc.title.translatedMethodologies for the use of satellite information as a tool for filling daily rainfall data series in Colombian mountainous areaseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentBibliotecariosspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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