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.advisor | Vélez Upegui, Jorge Julián | |
dc.contributor.author | Jiménez Duque, José Jacobo | |
dc.contributor.researchgroup | Grupo de Trabajo Académico en Ingeniería Hidráulica y Ambiental | spa |
dc.date.accessioned | 2024-04-16T13:56:23Z | |
dc.date.available | 2024-04-16T13:56:23Z | |
dc.date.issued | 2023 | |
dc.description | graficas, mapas, tablas | spa |
dc.description.abstract | Los 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.abstract | The 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.curriculararea | Ingeniería Civil.Sede Manizales | spa |
dc.description.degreelevel | Maestría | spa |
dc.description.degreename | Magíster en Ingeniería - Recursos Hidráulicos | spa |
dc.description.researcharea | Hidrología | spa |
dc.format.extent | 121 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.identifier.instname | Universidad Nacional de Colombia | spa |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/85922 | |
dc.language.iso | spa | spa |
dc.publisher | Universidad Nacional de Colombia | spa |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Manizales | spa |
dc.publisher.faculty | Facultad de Ingeniería y Arquitectura | spa |
dc.publisher.place | Manizales, Colombia | spa |
dc.publisher.program | Manizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Recursos Hidráulicos | spa |
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dc.relation.references | Teegavarapu, R. S. V., & Chandramouli, V. (2005). Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. Journal of Hydrology, 312(1–4), 191–206. https://doi.org/10.1016/j.jhydrol.2005.02.015 | spa |
dc.relation.references | Telesca, V., Caniani, D., Calace, S., Marotta, L., & Mancini, I. M. (2017). Daily temperature and precipitation prediction using neuro-fuzzy networks and weather generators. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10409 LNCS, 441–455. https://doi.org/10.1007/978-3-319-62407-5_31 | spa |
dc.relation.references | Tian, Y., & Peters-Lidard, C. D. (2010). A global map of uncertainties in satellite-based precipitation measurements. Geophysical Research Letters, 37(24). https://doi.org/10.1029/2010GL046008 | spa |
dc.relation.references | Toro 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.v6i3.640 | spa |
dc.relation.references | 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 | spa |
dc.relation.references | Twumasi, 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.2021.115039 | spa |
dc.relation.references | Urrea, V., Ochoa, A., & Mesa, O. (2016). Validación de la base de datos de precipitación CHIRPS para Colombia a escala diaria, mensual y anual en el período 1981-2014. XXVII Congreso Latinoamericano de Hidráulica, 11. http://ladhi2016.org/ | spa |
dc.relation.references | Valencia, S., Marín, D. E., Gómez, D., Hoyos, N., Salazar, J. F., & Villegas, J. C. (2023). Spatio-temporal assessment of Gridded precipitation products across topographic and climatic gradients in Colombia. Atmospheric Research, 285, 106643. https://doi.org/https://doi.org/10.1016/j.atmosres.2023.106643 | spa |
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dc.relation.references | Wilks, D. S. (1999). Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agricultural and Forest Meteorology, 93(3), 153–169. https://doi.org/10.1016/S0168-1923(98)00125-7 | spa |
dc.relation.references | World Meteorological Organization. (2003). OBSERVATION NETWORKS AND SYSTEMS (Issue 52). | spa |
dc.relation.references | Xia, Y., Fabian, P., Stohl, A., & Winterhalter, M. (1999). Forest climatology: Estimation of missing values for Bavaria, Germany. Agricultural and Forest Meteorology, 96(1–3), 131–144. https://doi.org/10.1016/S0168-1923(99)00056-8 | spa |
dc.relation.references | Xu, G., Xu, X., Liu, M., Sun, A. Y., & Wang, K. (2015). Spatial Downscaling of TRMM Precipitation Product Using a Combined Multifractal and Regression Approach: Demonstration for South China. Water, 7(6), 3083–3102. https://doi.org/10.3390/w7063083 | spa |
dc.relation.references | Young, 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;2 | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
dc.subject.ddc | 620 - Ingeniería y operaciones afines | spa |
dc.subject.proposal | Sensores remotos | spa |
dc.subject.proposal | Relleno de series | spa |
dc.subject.proposal | Precipitación diaria | spa |
dc.subject.proposal | Inteligencia artificial | spa |
dc.subject.proposal | Métodos estocásticos | spa |
dc.subject.proposal | Remote sensors | eng |
dc.subject.proposal | Data filling | eng |
dc.subject.proposal | Daily precipitation | eng |
dc.subject.proposal | Artificial intelligence | eng |
dc.subject.proposal | Stochastic methods | eng |
dc.subject.unesco | Hidrología | spa |
dc.subject.unesco | Hydrology | eng |
dc.title | Metodologías para el uso de la información de satélite como herramienta para rellenar series de datos diarios de lluvia en zonas de montaña colombianas | spa |
dc.title.translated | Methodologies for the use of satellite information as a tool for filling daily rainfall data series in Colombian mountainous areas | eng |
dc.type | Trabajo de grado - Maestría | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dcterms.audience.professionaldevelopment | Bibliotecarios | spa |
dcterms.audience.professionaldevelopment | Estudiantes | spa |
dcterms.audience.professionaldevelopment | Investigadores | spa |
dcterms.audience.professionaldevelopment | Maestros | spa |
dcterms.audience.professionaldevelopment | Público general | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
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