Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá
Ilustraciones, gráficos, fotografías
- Autores:
-
Arenas González, Brayan Andrés
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86269
- Palabra clave:
- 500 - Ciencias naturales y matemáticas
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Cobertura de suelos - Valle de Aburrá (Antioquia, Colombia)
Acuíferos - Valle de Aburrá (Antioquia, Colombia)
Recarga de aguas subterráneas - Valle de Aburrá (Antioquia, Colombia)
Aguas subterráneas - Valle de Aburrá (Antioquia, Colombia)
Recarga directa
modelación
clasificación de imágenes satelitales
coberturas de la tierra
SWB 2.0
Acuífero
Groundwater recharge
hydrologic modeling
satellite image classification
LULC
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
id |
UNACIONAL2_56ea5ca11cd1124fdb3c8a48ee7d39bd |
---|---|
oai_identifier_str |
oai:repositorio.unal.edu.co:unal/86269 |
network_acronym_str |
UNACIONAL2 |
network_name_str |
Universidad Nacional de Colombia |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá |
dc.title.translated.eng.fl_str_mv |
Effect of land cover changes on potential direct recharge in the Aburrá Valley unconfined Aquifer |
title |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá |
spellingShingle |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá 500 - Ciencias naturales y matemáticas 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Cobertura de suelos - Valle de Aburrá (Antioquia, Colombia) Acuíferos - Valle de Aburrá (Antioquia, Colombia) Recarga de aguas subterráneas - Valle de Aburrá (Antioquia, Colombia) Aguas subterráneas - Valle de Aburrá (Antioquia, Colombia) Recarga directa modelación clasificación de imágenes satelitales coberturas de la tierra SWB 2.0 Acuífero Groundwater recharge hydrologic modeling satellite image classification LULC |
title_short |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá |
title_full |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá |
title_fullStr |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá |
title_full_unstemmed |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá |
title_sort |
Efecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de Aburrá |
dc.creator.fl_str_mv |
Arenas González, Brayan Andrés |
dc.contributor.advisor.none.fl_str_mv |
Ortiz Pimienta, Carolina |
dc.contributor.author.none.fl_str_mv |
Arenas González, Brayan Andrés |
dc.contributor.researcher.none.fl_str_mv |
Betancur Vargas, Teresita |
dc.subject.ddc.spa.fl_str_mv |
500 - Ciencias naturales y matemáticas 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología |
topic |
500 - Ciencias naturales y matemáticas 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Cobertura de suelos - Valle de Aburrá (Antioquia, Colombia) Acuíferos - Valle de Aburrá (Antioquia, Colombia) Recarga de aguas subterráneas - Valle de Aburrá (Antioquia, Colombia) Aguas subterráneas - Valle de Aburrá (Antioquia, Colombia) Recarga directa modelación clasificación de imágenes satelitales coberturas de la tierra SWB 2.0 Acuífero Groundwater recharge hydrologic modeling satellite image classification LULC |
dc.subject.agrovoc.none.fl_str_mv |
Cobertura de suelos - Valle de Aburrá (Antioquia, Colombia) Acuíferos - Valle de Aburrá (Antioquia, Colombia) Recarga de aguas subterráneas - Valle de Aburrá (Antioquia, Colombia) Aguas subterráneas - Valle de Aburrá (Antioquia, Colombia) |
dc.subject.proposal.spa.fl_str_mv |
Recarga directa modelación clasificación de imágenes satelitales coberturas de la tierra SWB 2.0 Acuífero |
dc.subject.proposal.eng.fl_str_mv |
Groundwater recharge hydrologic modeling satellite image classification LULC |
description |
Ilustraciones, gráficos, fotografías |
publishDate |
2024 |
dc.date.accessioned.none.fl_str_mv |
2024-06-19T14:10:20Z |
dc.date.available.none.fl_str_mv |
2024-06-19T14:10:20Z |
dc.date.issued.none.fl_str_mv |
2024-06-18 |
dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
status_str |
acceptedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/86269 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
url |
https://repositorio.unal.edu.co/handle/unal/86269 https://repositorio.unal.edu.co/ |
identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.indexed.spa.fl_str_mv |
LaReferencia |
dc.relation.references.spa.fl_str_mv |
Abdullah, A. Y. M., Masrur, A., Gani Adnan, M. S., Al Baky, M. A., Hassan, Q. K., & Dewan, A. (2019). Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7). https://doi.org/10.3390/rs11070790 Adhikari, R. K., Mohanasundaram, S., & Shrestha, S. (2020). Impacts of land-use changes on the groundwater recharge in the Ho Chi Minh city, Vietnam. Environmental Research, 185(February), 109440. https://doi.org/10.1016/j.envres.2020.109440 Ahmed, S. A., & N, H. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 1–17. Al Atawneh, D., Cartwright, N., & Bertone, E. (2021). Climate change and its impact on the projected values of groundwater recharge: A review. Journal of Hydrology, 601, 126602. AMVA. (2018). Actualización del Plan de Ordenación y Manejo de la Cuenca Hidrográfica el Río Aburrá. AMVA, & UdeA. (2013). Determinación y protección de las potenciales zonas de recarga en el norte del Valle de Aburrá. 295. AMVA, & Universidad Nacional de Colombia. (2017). Proyecto Planeción Estratégica para el Área Metropolitana del Valle de Aburrá. 56–200. AMVA, & UPB. (2007). Estudio de la forma y el crecimiento urbano de la Región Metropolitana. Aschonitis, V. G., Antonopoulos, V. Z., Lekakis, E. H., Litskas, V. D., Kotsopoulos, S. A., & Karamouzis, D. N. (2013). Estimation of field capacity for aggregated soils using changes of the water retention curve under the effects of compaction. European Journal of Soil Science, 64(5), 688–698. https://doi.org/10.1111/ejss.12058 Attard, G., Rossier, Y., Winiarski, T., & Eisenlohr, L. (2017). Urban underground development confronted by the challenges of groundwater resources: Guidelines dedicated to the construction of underground structures in urban aquifers. Land Use Policy, 64, 461–469. https://doi.org/10.1016/j.landusepol.2017.03.015 Barrera-De-Calderón, M. L., Garfias, J., Martel, R., & Salas-García, J. (2022). Estimation of spatiotemporal groundwater recharge distribution in humid regions with tropical climate. In Tecnologia y Ciencias del Agua (Vol. 13, Issue 2). https://doi.org/10.24850/J-TYCA-2022-02-07 Barua, S., Cartwright, I., Evan Dresel, P., & Daly, E. (2021). Using multiple methods to investigate the effects of land-use changes on groundwater recharge in a semi-arid area. Hydrology and Earth System Sciences, 25(1), 89–104. https://doi.org/10.5194/hess-25-89-2021 Bastidas, B. (2019). Modelo Conceptual de la Recarga de Aguas Subterráneas en el Nivel Somero del Sistema Hidrogeológico Golfo de Urabá, Evaluando su Magnitud y Variabilidad Espacio – Temporal. 198. Bastidas, B., Betancur, T., Vélez, M. V., Londoño, R., & Dulce, K. (2021). Aproximación al balance hídrico de un acuífero en un ambiente urbano: acuífero libre del valle de Aburrá. XXIV Seminario Nacional de Hidráulica e Hidrología, September, 22–39. Batey, T. (2009). Soil compaction and soil management - A review. Soil Use and Management, 25(4), 335–345. https://doi.org/10.1111/j.1475-2743.2009.00236.x Beegam, S., & Arulraj, P. (2018). A REVIEW ARTICLE ON IMPACT OF URBANIZATION ON HYDROLOGICAL PARAMETERS. International Journal of Civil Engineering and Technology, 9(12), 199–208. Betancur, T., Bocanegra, E., Custodio, E., Manzano, M., & Cardoso da Silva, G. (2016). Estado y factores de cambio de los servicios ecosistémicos de aprovisionamiento en humedales relacionados con aguas sunterráneas en Iberoamérica y España. Biota Colombiana, 16(3), 106–119. https://doi.org/10.21068/c2016s01a06 Betancur-Vargas, T., Martínez-Uribe, C., García-Aristizábal, E. F., & Escobar-Martínez, J. F. (2017). Identification and characterization of regional water flows contributing to the recharge of an unconfined aquifer. Revista Facultad de Ingenieria, 85, 70–85. https://doi.org/10.17533/udea.redin.n85a07 Bradbury, K., & Dripps, W. (2000). Groundwater recharge rates. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. Bremer, L. L., Elshall, A. S., Wada, C. A., Brewington, L., Delevaux, J. M. S., El-Kadi, A. I., Voss, C. I., & Burnett, K. M. (2021). Effects of land-cover and watershed protection futures on sustainable groundwater management in a heavily utilized aquifer in Hawai‘i (USA). Hydrogeology Journal, 29(5), 1749–1765. https://doi.org/10.1007/s10040-021-02310-6 Bresciani, E., Cranswick, R. H., Banks, E. W., Batlle-Aguilar, J., Cook, P. G., & Batelaan, O. (2018). Using hydraulic head, chloride and electrical conductivity data to distinguish between mountain-front and mountain-block recharge to basin aquifers. Hydrology and Earth System Sciences, 22(2), 1629–1648. https://doi.org/10.5194/hess-22-1629-2018 Campillo, A., Taupin, J. D., Patris, N., & Betancur, T. (2015). Uso de la geoquímica y de los isótopos estables del agua en el estudio de un sistema acuífero superficial en el complejo urbanizado andino (Valle de Aburrá, Colombia). Revista Peruana Geo Atmosférica, 4(4), 62–79. Carrera-Hernández, J. J., & Gaskin, S. J. (2008). Spatio-temporal analysis of potential aquifer recharge: Application to the Basin of Mexico. Journal of Hydrology, 353(3–4), 228–246. https://doi.org/10.1016/j.jhydrol.2008.02.012 Censi, A. M., Ienco, D., Gbodjo, Y. J. E., Pensa, R. G., Interdonato, R., & Gaetano, R. (2021a). Attentive Spatial Temporal Graph CNN for Land Cover Mapping from Multi Temporal Remote Sensing Data. IEEE Access, 9, 23070–23082. https://doi.org/10.1109/ACCESS.2021.3055554 Censi, A. M., Ienco, D., Gbodjo, Y. J. E., Pensa, R. G., Interdonato, R., & Gaetano, R. (2021b). Attentive Spatial Temporal Graph CNN for Land Cover Mapping from Multi Temporal Remote Sensing Data. IEEE Access, 9, 23070–23082. https://doi.org/10.1109/ACCESS.2021.3055554 Chai, B., & Li, P. (2023). An ensemble method for monitoring land cover changes in urban areas using dense Landsat time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 29–42. Chaves Córdoba, B.; Jaramillo Robledo, A. (1998). Regionalización de la temperatura del aire en Colombia. In Cenicafé (Colombia) (Vol. 49, pp. 224–230). Cheng, Y., Ogden, F. L. and Z., & Jianting. (2017). Earthworms and tree roots: A model study of the effect of preferential flow paths on runoff generation and groundwater recharge in steep, saprolitic, tropical lowland catchments. Water Resources Research, 53(7), 5400--5419. https://doi.org/10.1111/j.1752-1688.1969.tb04897.x Chuvieco, E. (1995). Fundamentos De Teledeteccion Espacial (EDICIONES & SA. RIALP, Eds.; Segunda). Clerici, N., Cote-Navarro, F., Escobedo, F. J., Rubiano, K., & Villegas, J. C. (2019). Spatio-temporal and cumulative effects of land use-land cover and climate change on two ecosystem services in the Colombian Andes. Science of the Total Environment, 685, 1181–1192. https://doi.org/10.1016/j.scitotenv.2019.06.275 Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. Dripps, W. R., & Bradbury, K. R. (2007). A simple daily soil-water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas (p. 15). Springer-Verlag, P.O. Box 2485 Secaucus NJ 07096-2485 USA, [mailto:orders@springer-ny.com], [URL:http://www.springer-ny.com/]. Ebrahimian, A., Gulliver, J. S., & Wilson, B. N. (2018). Estimating effective impervious area in urban watersheds using land cover, soil character and asymptotic curve number. Hydrological Sciences Journal, 63(4), 513–526. https://doi.org/10.1080/02626667.2018.1440562 Escobar, J., Betancur, T., García, E., Martínez, C., & Palacio, P. (2017). Análisis jerárquico ponderado aplicado a la identificación de recarga y flujos regionales en acuíferos. Revista Politécnica, 13(24), 37–48. García-Leoz, V., Villegas, J. C., Suescún, D., Flórez, C. P., Merino-Martín, L., Betancur, T., & León, J. D. (2018). Land cover effects on water balance partitioning in the Colombian Andes: improved water availability in early stages of natural vegetation recovery. Regional Environmental Change, 18(4), 1117–1129. https://doi.org/10.1007/s10113-017-1249-7 Ghimire, U., Shrestha, S., Neupane, S., Mohanasundaram, S., & Lorphensri, O. (2021). Climate and land-use change impacts on spatiotemporal variations in groundwater recharge: A case study of the Bangkok Area, Thailand. Science of the Total Environment, 792, 148370. https://doi.org/10.1016/j.scitotenv.2021.148370 Gómez-Moncada, R. A., Mora, A., Jaramillo, M., Mayorga, H., Martínez, A., Parra, M., Suárez, D., Sandoval, J., Sandoval, J., Caballero, V. M., Jiménez, M., Bueno, R., & Saylor, J. E. (2022). Decoding of Groundwater Recharge in Deep Aquifers of Foreland Basins Using Stable Isotopes (Δ18o and Δd) And Anion-Cation Analysis: A Case Study in the Southern Llanos Basin, Colombia. SSRN Electronic Journal, 1–54. https://doi.org/10.2139/ssrn.4093802 González-Ortigoza, S., Hernández-Espriú, A., & Arciniega-Esparza, S. (2023). Regional modeling of groundwater recharge in the Basin of Mexico: new insights from satellite observations and global data sources. Hydrogeology Journal, 31(7), 1971–1990. https://doi.org/10.1007/s10040-023-02667-w Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2). Springer. Healy, R. W. (2010). Estimating Groundwater Recharge (C. university Press, Ed.). Cambridge university press. Healy, R. W., & Cook, P. G. (2002). Using groundwater levels to estimate recharge. Hydrogeology Journal, 10(1), 91–109. https://doi.org/10.1007/s10040-001-0178-0 Hoffmann, J. (2005). The future of satellite remote sensing in hydrogeology. Hydrogeology Journal, 13, 247–250. Holzbecher, E. (2012). Environmental modeling: using MATLAB. Springer Science & Business Media. Horbath, J. E. (2016). Tendencias y proyecciones de la población del área metropolitana del Valle de Aburrá en Colombia, 2010-2030. Notas de Población, 43(102), 37–65. https://doi.org/10.18356/9d3ffa22-es Huang, T., Pang, Z., Yang, S., & Yin, L. (2020). Impact of Afforestation on Atmospheric Recharge to Groundwater in a Semiarid Area. Journal of Geophysical Research: Atmospheres, 125(9), 1–19. https://doi.org/10.1029/2019JD032185 IGAC, & GOBERNACIÓN DE ANTIOQUIA. (2007). Estudio General de Suelos y Zonificación de Tierras del Departamento de Antioquia. Ingenieria, C., AMVA, CORNARE, & CORANTIOQUIA. (2015). Plan de Ordenación y Manejo de la Cuenca del Río Aburrá. Jaramillo-Llorente, M. F., Rengifo, R., & Retrepo, I. (2021). PRZ index for identifying potential areas of recharge in alluvial aquifers and for land use planning. Ingeniería Y Competitividad, 24(1). https://doi.org/10.25100/iyc.v24i1.11001 Kovačič, G., Petrič, M., & Ravbar, N. (2020). Evaluation and quantification of the effects of climate and vegetation cover change on karst water sources: Case studies of two springs in south-western slovenia. Water (Switzerland), 12(11), 1–20. https://doi.org/10.3390/w12113087 Lall, U., Josset, L., & Russo, T. (2020). A snapshot of the world’s groundwater challenges. Annual Review of Environment and Resources, 45, 171–194. Lamichhane, S., & Shakya, N. M. (2019). Alteration of groundwater recharge areas due to land use/cover change in Kathmandu Valley, Nepal. Journal of Hydrology: Regional Studies, 26(October 2019), 100635. https://doi.org/10.1016/j.ejrh.2019.100635 Lerner, D. (2002). Identifying and quantifying urban recharge: a review. In Hydrogeology Journal (Vol. 10, pp. 143–152). Lerner, D. N. (1990). Groundwater recharge in urban areas. Li, H., Wan, W., Fang, Y., Zhu, S., Chen, X., Liu, B., & Hong, Y. (2019). A Google Earth Engine-enabled software for efficiently generating high-quality user-ready Landsat mosaic images. Environmental Modelling and Software, 112(March 2018), 16–22. https://doi.org/10.1016/j.envsoft.2018.11.004 Mariño-Martínez, J. E., & Martínez-Sánchez, A. (2018). Analysis of precipitation and recharge of aquifers in Tota and Ibagué (Colombia) from stable isotopes (18O and 2H). Revista Facultad de Ingeniería, 27(47), 61–71. https://doi.org/10.19053/01211129.v27.n47.2018.7752 Mattos, T. S., de Oliveira, P. T. S., Lucas, M. C., & Wendland, E. (2019). Groundwater recharge decrease replacing pasture by Eucalyptus plantation. Water (Switzerland), 11(6), 1–13. https://doi.org/10.3390/w11061213 Mensah, J. K., Ofosu, E. A., Yidana, S. M., Akpoti, K., & Kabo-bah, A. T. (2022). Integrated modeling of hydrological processes and groundwater recharge based on land use land cover, and climate changes: A systematic review. Environmental Advances, 8(March), 100224. https://doi.org/10.1016/j.envadv.2022.100224 Minnig, M., Moeck, C., Radny, D., & Schirmer, M. (2018). Impact of urbanization on groundwater recharge rates in Dübendorf, Switzerland. Journal of Hydrology, 563, 1135–1146. https://doi.org/10.1016/j.jhydrol.2017.09.058 Moeck, C., Grech-Cumbo, N., Podgorski, J., Bretzler, A., Gurdak, J. J., Berg, M., & Schirmer, M. (2020). A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships. Science of the Total Environment, 717, 137042. https://doi.org/10.1016/j.scitotenv.2020.137042 Nychka, D., Furrer, R., Paige, J., & Sain, S. (2016). Tools for Spatial Data. O’Callaghan, J. F., & Mark, D. M. (1984). The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, and Image Processing, 28(3), 323–344. Ossa, J., Campillo, A. K., Omar, C., & Betancur, T. (2021). Recharge área maps from precipitation isoscapes. Case study: Aburrá valley, colombia. Boletin Geologico y Minero, 132(1–2), 65–75. https://doi.org/10.21701/bolgeomin.132.1-2.007 Ouyang, Y., Jin, W., Grace, J. M., Obalum, S. E., Zipperer, W. C., & Huang, X. (2019). Estimating impact of forest land on groundwater recharge in a humid subtropical watershed of the Lower Mississippi River Alluvial Valley. Journal of Hydrology: Regional Studies, 26(October), 100631. https://doi.org/10.1016/j.ejrh.2019.100631 Oviedo Aleman, L. M. (2020). Variaciones de la recarga de agua subterránea bajo escenarios de cambio climático en el nivel somero del sistema acuífero bajo cauca antioqueño. Universidad Nacional de Colombia Patiño, D. (2018). Respuesta hidrológica ante los cambios de uso y cobertura del suelo en la cuenca del río Chinchiná. Universidad Nacional de Colombia. Patiño, D. A. (2018). Respuesta hidrológica ante los cambios de uso y cobertura del suelo en la cuenca del río Chinchiná. Universidad Nacional de Colombia. Patiño Rojas, S. M., & Jaramillo, M. (2022). Estimación espaciotemporal de la recarga potencial en un sistema pseudokárstico tropical. Revista de La Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 46(178), 261–278. https://doi.org/10.18257/raccefyn.1512 Patiño, S. M., Jaramillo, M., Espinosa-Espinosa, C., & Arias-Lopez, M. F. (2021). Preferential groundwater flow directions in a pseudokarst system in Colombia, South America. Journal of South American Earth Sciences, 112(September). https://doi.org/10.1016/j.jsames.2021.103572 Pérez Fonseca, A. L. (2018). Las periferias en disputa. Procesos de poblamiento urbano popular en Medellín. Estudios Políticos (Medellín), 53, 148–170. https://doi.org/10.17533/udea.espo.n53a07 Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling and Software, 79, 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008 Posada-Marín, J. A., & Salazar, J. F. (2022). River flow response to deforestation: Contrasting results from different models. Water Security, 15(March). https://doi.org/10.1016/j.wasec.2022.100115 Ribeiro, L., Kretschmer, N., Nascimento, J., Buxo, A., Rötting, T., Soto, G., Señoret, M., Oyarzún, J., Maturana, H., & Oyarzún, R. (2015). Evaluating piezometric trends using the Mann-Kendall test on the alluvial aquifers of the Elqui River basin, Chile. Hydrological Sciences Journal, 60(10), 1840–1852. Rios Rivera, M. A. (2019). Upscaling of point-scale groundwater recharge measurements using machine learning: A case study in New Zealand and Colombia (Doctoral dissertation, Lincoln University). Rodríguez-Campero, C., Garfias, J., Martel, R., & Navarro-de León, I. (2023). Estimación espacio-temporal de la distribución de la recarga potencial en el Valle de Toluca. Boletín de La Sociedad Geológica Mexicana, 75(2), A080523. https://doi.org/10.18268/bsgm2023v75n2a080523 Sargemt, R. (2015). Modeling and Simulation in the Systems Engineering. In Modeling and Simulation in the Systems Engineering (Vol. 1, pp. 52–58). https://doi.org/10.1007/978-1-4615-0863-2_17 Scanlon, B. R., Jolly, I., Sophocleous, M., & Zhang, L. (2007). Global impacts of conversions from natural to agricultural ecosystems on water resources: Quantity versus quality. Water Resources Research, 43(3). https://doi.org/10.1029/2006WR005486 Scanlon, B. R., Kelley, E. K., Alan, L. F., & Lorraine, E. F. (2006). Global synthesis of groundwater recharge in semiarid and arid regions. Hydrological Processes: An International Journal, 20, 3335–3370. https://doi.org/10.1002/hyp Scanlon, B. R., Reedy, R. C., Stonestrom, D. A., Prudic, D. E., & Dennehy, K. F. (2005). Impact of land use and land cover change on groundwater recharge and quality in the southwestern US. Global Change Biology, 11(10), 1577–1593. https://doi.org/10.1111/j.1365-2486.2005.01026.x Shahfahad, Kumari, B., Tayyab, M., Hang, H. T., Khan, M. F., & Rahman, A. (2019). Assessment of public open spaces (POS) and landscape quality based on per capita POS index in Delhi, India. SN Applied Sciences, 1, 1–13. Shi, D., Wang, W., Jiang, G., Peng, X., Yu, Y., Li, Y., & Ding, W. (2016). Effects of disturbed landforms on the soil water retention function during urbanization process in the Three Gorges Reservoir Region, China. Catena, 144, 84–93. https://doi.org/10.1016/j.catena.2016.04.010 Sicard, P., Coulibaly, F., Lameiro, M., Araminiene, V., De Marco, A., Sorrentino, B., Anav, A., Manzini, J., Hoshika, Y., & Moura, B. B. (2023). Object-based classification of urban plant species from very high-resolution satellite imagery. Urban Forestry & Urban Greening, 81, 127866. Siddik, M. S., Tulip, S. S., Rahman, A., Islam, M. N., Haghighi, A. T., & Mustafa, S. M. T. (2022). The impact of land use and land cover change on groundwater recharge in northwestern Bangladesh. Journal of Environmental Management, 315, 115–130. Siqueira, V. A., Fleischmann, A., Jardim, P. F., Fan, F. M., & Collischonn, W. (2016). IPH-Hydro Tools: a GIS coupled tool for watershed topology acquisition in an open-source environment. Rbrh, 21, 274–287. Stocker, O., & Le Bris, A. (2020). Can Spot-6/7 Cnn Semantic Segmentation Improve Sentinel-2 Based Land Cover Products? Sensor Assessment and Fusion. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(2), 557–564. https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020 Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135. Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations-A review. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071135 Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review, 38, 55–94. Thornthwaite, C. W., & Mather, J. R. (1957). Instructions and tables for computing potential evapotranspiration and the water balance. Centerton. UdeA. (2018). AUNAR ESFUERZOS PARA LA APROPIACIÓN TECNOLÓGICA Y DEL CONOCIMIENTO PARA LA GESTIÓN INTEGRAL DEL RECURSO HÍDRICO SUPERFICIAL Y SUBTERRÁNEO, EN EL CONTEXTO DE LA OPERACIÓN DE LA RED DE MONITOREO AMBIENTAL EN LA CUENCA HIDROGRÁFICA DEL RÍO ABURRÁ-MEDELLIN Y . UdeA, & AMVA. (2018). FORMULACIÓN DEL PLAN DE MANEJO AMBIENTAL DE ACUÍFERO DELVALLE DE ABURRÁ. Universidad Pontificia Bolivariana, & AMVA. (2007). Estudio de la forma y el crecimiento urbano de la región metropolitana. UPB, & AMVA. (2015). Política Pública de Construcción Sostenible. Linea Base. Velez, J. (2001). DESARROLLO DE UN MODELO DISTRIBUIDO DE PREDICCIÓN EN TIEMPO REAL PARA EVENTOS DE CRECIDAS (Issue January) [UNIVERSIDAD POLITÉCNICA DE VALENCIA]. https://doi.org/10.13140/2.1.4996.1288 Vélez, M. V., Botero, V., Salazar, J. F., & Gómez, J. (2005). Esctimación de la recarga en una región colombiana mediante un modelo iterativo. Ingenieria Hidraulica En Mexico, 20(2), 61–77. Wakode, H. B., Baier, K., Jha, R., & Azzam, R. (2018). Impact of urbanization on groundwater recharge and urban water balance for the city of Hyderabad, India. International Soil and Water Conservation Research, 6(1), 51–62. https://doi.org/10.1016/j.iswcr.2017.10.003 Westenbroek, J. A., Stephen, M., Engott, V. A., Kelson, & Hunt, R. J. (2018). Water Availability and Use Science Program National Water Quality Program SWB Version 2.0-A Soil-Water-Balance Code for Estimating Net Infiltration and Other Water-Budget Components Book 6, Modeling Techniques. https://pubs.usgs.gov/tm/06/a59/tm6a59.pdf Westenbroek, M. S., Kelson, V. a., Dripps, W. R., Hunt, R. J., & Bradbury, K. R. (2010). SWB — A Modified Thornthwaite-Mather Soil-Water- Balance Code for Estimating Groundwater Recharge. U.S. Geological Survey Techniques and Methods 6-A31, 60. Westenbroek, S. M., Kelson, V. A., Dripps, W. R., Hunt, R. J., & Bradbury, K. R. (2010). SWB--a modified Thornthwaite-Mather Soil-Water-Balance Code for estimating groundwater recharge. US Department of the Interior, US Geological Survey, Ground Resources …. White, J. C., Wulder, M. A., Hobart, G. W., Luther, J. E., Hermosilla, T., Griffiths, P., Coops, N. C., Hall, R. J., Hostert, P., Dyk, A., & Guindon, L. (2014). Pixel-based image compositing for large-area dense time series applications and science. Canadian Journal of Remote Sensing, 40(3), 192–212. https://doi.org/10.1080/07038992.2014.945827 Wu, N., Crusiol, L. G. T., Liu, G., Wuyun, D., & Han, G. (2023). Comparing machine learning algorithms for pixel/object-based classifications of semi-arid grassland in northern China using multisource medium resolution imageries. Remote Sensing, 15(3), 750. Yacouby, R., & Axman, D. (2020). Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 79–91. Yan, C., Fan, X., Fan, J., Yu, L., Wang, N., Chen, L., & Li, X. (2023). HyFormer: Hybrid Transformer and CNN for Pixel-Level Multispectral Image Land Cover Classification. International Journal of Environmental Research and Public Health, 20(4), 3059. https://doi.org/10.3390/ijerph20043059 Yang, Y., Lerner, D. N., Barrett, M. H., & Tellam, J. H. (1999). Quantification of groundwater recharge in the city of Nottingham, UK. Environmental Geology, 38(3), 183–198. https://doi.org/10.1007/s002540050414 Zerrouki, N., & Bouchaffra, D. (2014). Pixel-based or object-based: Which approach is more appropriate for remote sensing image classification? 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 864–869. Zhang, X., Han, L., Han, L., & Zhu, L. (2020). How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery? Remote Sensing, 12(3), 1–29. https://doi.org/10.3390/rs12030417 Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384. Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.license.spa.fl_str_mv |
Reconocimiento 4.0 Internacional |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Reconocimiento 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.spa.fl_str_mv |
139 páginas |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.coverage.region.none.fl_str_mv |
Valle de Aburrá (Antioquia, Colombia) |
dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
dc.publisher.program.spa.fl_str_mv |
Medellín - Minas - Maestría en Ingeniería - Recursos Hidráulicos |
dc.publisher.faculty.spa.fl_str_mv |
Facultad de Minas |
dc.publisher.place.spa.fl_str_mv |
Medellín, Colombia |
dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Medellín |
institution |
Universidad Nacional de Colombia |
bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/86269/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/86269/2/1059709866.2024.pdf https://repositorio.unal.edu.co/bitstream/unal/86269/3/1059709866.2024.pdf.jpg |
bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a 698eee1cc9bdd625676947877dea151a c0156cadbd5620898ac82df6cf2e7bc4 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
_version_ |
1814089618490916864 |
spelling |
Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ortiz Pimienta, Carolina31b8d9e74b2bdc5d8ae834c57773e8e9Arenas González, Brayan Andrésf795355c6e9f4a49602643a7f49aa17bBetancur Vargas, Teresita2024-06-19T14:10:20Z2024-06-19T14:10:20Z2024-06-18https://repositorio.unal.edu.co/handle/unal/86269Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones, gráficos, fotografíasEs de gran importancia entender los efectos que tienen los cambios en la cobertura de la tierra en la recarga potencial directa (RPD), especialmente en zonas en las que se presenta crecimiento urbano acelerado y presiones por los cambios de las coberturas de la tierra. En este estudio se analizaron los efectos de los cambios de la cobertura de la tierra en la RPD del Acuífero Libre del Valle de Aburrá, un acuífero ubicado en un entorno urbano, con importantes intervenciones antrópicas y rápido crecimiento urbano, localizado la región andina de Colombia. Para representar los cambios de la cobertura de la tierra se generaron mapas de coberturas a partir de la clasificación de imágenes satelitales mediante el algoritmo Random Forest, se definieron cuatro (4) escenarios correspondientes a los años 1990, 2000, 2010 y 2020, el modelo de clasificación fue meticulosamente calibrado y validado, logrando coeficientes de kappa superiores a 0.87 en todos los escenarios. Se evaluaron los efectos de los cambios de la cobertura de la tierra en la RPD mediante el balance de humedad del suelo, implementando el modelo SWB 2.0 en los cuatro (4) escenarios de la cobertura de la tierra predefinidos, con un periodo de modelación hidrológico de 1990 a 2020 y un paso de tiempo diario. El modelo fue adecuadamente parametrizado y validado, sometiéndose a un análisis de sensibilidad para asegurar la fiabilidad de las estimaciones. Los resultados muestran una RPD promedio de 78.2 Hm3/año para el escenario de 1990, en cambio, para el escenario de 2020 se estima una RPD de 68.7 Hm3/año, reflejando así una disminución de 9.5 Hm3/año, equivalente al 12% de la RPD del escenario de 1990. La disminución de la RPD se atribuye principalmente al aumento de los territorios artificializados y la disminución de las coberturas vegetales en la zona de estudio, estos cambios de cobertura favorecen la escorrentía superficial y disminuyen la RPD de la zona de estudio. (Tomado de la fuente)Understand the effects that changes in land cover have on Direct Potential Recharge (DPR) is of great importance, especially in areas where accelerated urban growth and pressures from land cover changes occur. In this study, the effects of land cover changes on the DPR of the Aburrá Valley unconfined Aquifer were analyzed, it is located in an urban environment, with important anthropogenic interventions and rapid urban growth, located in the Andean region of Colombia. To represent the changes in land cover, coverage maps were generated from the classification of satellite images using the Random Forest algorithm, four (4) scenarios were defined corresponding to the years 1990, 2000, 2010 and 2020. The classification model was meticulously calibrated and validated, achieving kappa coefficients greater than 0.87 in all scenarios. The effects of land cover changes on the RPD were evaluated through soil moisture balance, implementing the SWB 2.0 model in the four (4) predefined land cover scenarios, with a hydrological modeling period from 1990 to 2020 and a daily time step. The model was adequately parameterized and validated, a sensitivity analysis was also carried out to ensure the reliability of the estimates. The results show an average DPR of 78.2 Hm3/year for the 1990 scenario, however, for the 2020 scenario a DPR of 68.7 Hm3/year is estimated, thus reflecting a decrease of 9.5 Hm3/year, equivalent to 12% of the DPR of the 1990 scenario. The decrease in the DPR is mainly attributed to the increase in artificialized territories and the decrease in vegetation cover in the study area. These changes favor surface runoff and reduce the DPR of the study area. studyMaestríaMagíster en Ingeniería - Recursos HidráulicosMedio Ambiente.Sede Medellín139 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Recursos HidráulicosFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín500 - Ciencias naturales y matemáticas550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaCobertura de suelos - Valle de Aburrá (Antioquia, Colombia)Acuíferos - Valle de Aburrá (Antioquia, Colombia)Recarga de aguas subterráneas - Valle de Aburrá (Antioquia, Colombia)Aguas subterráneas - Valle de Aburrá (Antioquia, Colombia)Recarga directamodelaciónclasificación de imágenes satelitalescoberturas de la tierraSWB 2.0AcuíferoGroundwater rechargehydrologic modelingsatellite image classificationLULCEfecto de los cambios de la cobertura de la tierra en la recarga potencial directa en el Acuífero Libre del Valle de AburráEffect of land cover changes on potential direct recharge in the Aburrá Valley unconfined AquiferTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMValle de Aburrá (Antioquia, Colombia)LaReferenciaAbdullah, A. Y. M., Masrur, A., Gani Adnan, M. S., Al Baky, M. A., Hassan, Q. K., & Dewan, A. (2019). Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7). https://doi.org/10.3390/rs11070790Adhikari, R. K., Mohanasundaram, S., & Shrestha, S. (2020). Impacts of land-use changes on the groundwater recharge in the Ho Chi Minh city, Vietnam. Environmental Research, 185(February), 109440. https://doi.org/10.1016/j.envres.2020.109440Ahmed, S. A., & N, H. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 1–17.Al Atawneh, D., Cartwright, N., & Bertone, E. (2021). Climate change and its impact on the projected values of groundwater recharge: A review. Journal of Hydrology, 601, 126602.AMVA. (2018). Actualización del Plan de Ordenación y Manejo de la Cuenca Hidrográfica el Río Aburrá.AMVA, & UdeA. (2013). Determinación y protección de las potenciales zonas de recarga en el norte del Valle de Aburrá. 295.AMVA, & Universidad Nacional de Colombia. (2017). Proyecto Planeción Estratégica para el Área Metropolitana del Valle de Aburrá. 56–200.AMVA, & UPB. (2007). Estudio de la forma y el crecimiento urbano de la Región Metropolitana.Aschonitis, V. G., Antonopoulos, V. Z., Lekakis, E. H., Litskas, V. D., Kotsopoulos, S. A., & Karamouzis, D. N. (2013). Estimation of field capacity for aggregated soils using changes of the water retention curve under the effects of compaction. European Journal of Soil Science, 64(5), 688–698. https://doi.org/10.1111/ejss.12058Attard, G., Rossier, Y., Winiarski, T., & Eisenlohr, L. (2017). Urban underground development confronted by the challenges of groundwater resources: Guidelines dedicated to the construction of underground structures in urban aquifers. Land Use Policy, 64, 461–469. https://doi.org/10.1016/j.landusepol.2017.03.015Barrera-De-Calderón, M. L., Garfias, J., Martel, R., & Salas-García, J. (2022). Estimation of spatiotemporal groundwater recharge distribution in humid regions with tropical climate. In Tecnologia y Ciencias del Agua (Vol. 13, Issue 2). https://doi.org/10.24850/J-TYCA-2022-02-07Barua, S., Cartwright, I., Evan Dresel, P., & Daly, E. (2021). Using multiple methods to investigate the effects of land-use changes on groundwater recharge in a semi-arid area. Hydrology and Earth System Sciences, 25(1), 89–104. https://doi.org/10.5194/hess-25-89-2021Bastidas, B. (2019). Modelo Conceptual de la Recarga de Aguas Subterráneas en el Nivel Somero del Sistema Hidrogeológico Golfo de Urabá, Evaluando su Magnitud y Variabilidad Espacio – Temporal. 198.Bastidas, B., Betancur, T., Vélez, M. V., Londoño, R., & Dulce, K. (2021). Aproximación al balance hídrico de un acuífero en un ambiente urbano: acuífero libre del valle de Aburrá. XXIV Seminario Nacional de Hidráulica e Hidrología, September, 22–39.Batey, T. (2009). Soil compaction and soil management - A review. Soil Use and Management, 25(4), 335–345. https://doi.org/10.1111/j.1475-2743.2009.00236.xBeegam, S., & Arulraj, P. (2018). A REVIEW ARTICLE ON IMPACT OF URBANIZATION ON HYDROLOGICAL PARAMETERS. International Journal of Civil Engineering and Technology, 9(12), 199–208.Betancur, T., Bocanegra, E., Custodio, E., Manzano, M., & Cardoso da Silva, G. (2016). Estado y factores de cambio de los servicios ecosistémicos de aprovisionamiento en humedales relacionados con aguas sunterráneas en Iberoamérica y España. Biota Colombiana, 16(3), 106–119. https://doi.org/10.21068/c2016s01a06Betancur-Vargas, T., Martínez-Uribe, C., García-Aristizábal, E. F., & Escobar-Martínez, J. F. (2017). Identification and characterization of regional water flows contributing to the recharge of an unconfined aquifer. Revista Facultad de Ingenieria, 85, 70–85. https://doi.org/10.17533/udea.redin.n85a07Bradbury, K., & Dripps, W. (2000). Groundwater recharge rates.Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.Bremer, L. L., Elshall, A. S., Wada, C. A., Brewington, L., Delevaux, J. M. S., El-Kadi, A. I., Voss, C. I., & Burnett, K. M. (2021). Effects of land-cover and watershed protection futures on sustainable groundwater management in a heavily utilized aquifer in Hawai‘i (USA). Hydrogeology Journal, 29(5), 1749–1765. https://doi.org/10.1007/s10040-021-02310-6Bresciani, E., Cranswick, R. H., Banks, E. W., Batlle-Aguilar, J., Cook, P. G., & Batelaan, O. (2018). Using hydraulic head, chloride and electrical conductivity data to distinguish between mountain-front and mountain-block recharge to basin aquifers. Hydrology and Earth System Sciences, 22(2), 1629–1648. https://doi.org/10.5194/hess-22-1629-2018Campillo, A., Taupin, J. D., Patris, N., & Betancur, T. (2015). Uso de la geoquímica y de los isótopos estables del agua en el estudio de un sistema acuífero superficial en el complejo urbanizado andino (Valle de Aburrá, Colombia). Revista Peruana Geo Atmosférica, 4(4), 62–79.Carrera-Hernández, J. J., & Gaskin, S. J. (2008). Spatio-temporal analysis of potential aquifer recharge: Application to the Basin of Mexico. Journal of Hydrology, 353(3–4), 228–246. https://doi.org/10.1016/j.jhydrol.2008.02.012Censi, A. M., Ienco, D., Gbodjo, Y. J. E., Pensa, R. G., Interdonato, R., & Gaetano, R. (2021a). Attentive Spatial Temporal Graph CNN for Land Cover Mapping from Multi Temporal Remote Sensing Data. IEEE Access, 9, 23070–23082. https://doi.org/10.1109/ACCESS.2021.3055554Censi, A. M., Ienco, D., Gbodjo, Y. J. E., Pensa, R. G., Interdonato, R., & Gaetano, R. (2021b). Attentive Spatial Temporal Graph CNN for Land Cover Mapping from Multi Temporal Remote Sensing Data. IEEE Access, 9, 23070–23082. https://doi.org/10.1109/ACCESS.2021.3055554Chai, B., & Li, P. (2023). An ensemble method for monitoring land cover changes in urban areas using dense Landsat time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 29–42.Chaves Córdoba, B.; Jaramillo Robledo, A. (1998). Regionalización de la temperatura del aire en Colombia. In Cenicafé (Colombia) (Vol. 49, pp. 224–230).Cheng, Y., Ogden, F. L. and Z., & Jianting. (2017). Earthworms and tree roots: A model study of the effect of preferential flow paths on runoff generation and groundwater recharge in steep, saprolitic, tropical lowland catchments. Water Resources Research, 53(7), 5400--5419. https://doi.org/10.1111/j.1752-1688.1969.tb04897.xChuvieco, E. (1995). Fundamentos De Teledeteccion Espacial (EDICIONES & SA. RIALP, Eds.; Segunda).Clerici, N., Cote-Navarro, F., Escobedo, F. J., Rubiano, K., & Villegas, J. C. (2019). Spatio-temporal and cumulative effects of land use-land cover and climate change on two ecosystem services in the Colombian Andes. Science of the Total Environment, 685, 1181–1192. https://doi.org/10.1016/j.scitotenv.2019.06.275Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.Dripps, W. R., & Bradbury, K. R. (2007). A simple daily soil-water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas (p. 15). Springer-Verlag, P.O. Box 2485 Secaucus NJ 07096-2485 USA, [mailto:orders@springer-ny.com], [URL:http://www.springer-ny.com/].Ebrahimian, A., Gulliver, J. S., & Wilson, B. N. (2018). Estimating effective impervious area in urban watersheds using land cover, soil character and asymptotic curve number. Hydrological Sciences Journal, 63(4), 513–526. https://doi.org/10.1080/02626667.2018.1440562Escobar, J., Betancur, T., García, E., Martínez, C., & Palacio, P. (2017). Análisis jerárquico ponderado aplicado a la identificación de recarga y flujos regionales en acuíferos. Revista Politécnica, 13(24), 37–48.García-Leoz, V., Villegas, J. C., Suescún, D., Flórez, C. P., Merino-Martín, L., Betancur, T., & León, J. D. (2018). Land cover effects on water balance partitioning in the Colombian Andes: improved water availability in early stages of natural vegetation recovery. Regional Environmental Change, 18(4), 1117–1129. https://doi.org/10.1007/s10113-017-1249-7Ghimire, U., Shrestha, S., Neupane, S., Mohanasundaram, S., & Lorphensri, O. (2021). Climate and land-use change impacts on spatiotemporal variations in groundwater recharge: A case study of the Bangkok Area, Thailand. Science of the Total Environment, 792, 148370. https://doi.org/10.1016/j.scitotenv.2021.148370Gómez-Moncada, R. A., Mora, A., Jaramillo, M., Mayorga, H., Martínez, A., Parra, M., Suárez, D., Sandoval, J., Sandoval, J., Caballero, V. M., Jiménez, M., Bueno, R., & Saylor, J. E. (2022). Decoding of Groundwater Recharge in Deep Aquifers of Foreland Basins Using Stable Isotopes (Δ18o and Δd) And Anion-Cation Analysis: A Case Study in the Southern Llanos Basin, Colombia. SSRN Electronic Journal, 1–54. https://doi.org/10.2139/ssrn.4093802González-Ortigoza, S., Hernández-Espriú, A., & Arciniega-Esparza, S. (2023). Regional modeling of groundwater recharge in the Basin of Mexico: new insights from satellite observations and global data sources. Hydrogeology Journal, 31(7), 1971–1990. https://doi.org/10.1007/s10040-023-02667-wHastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2). Springer.Healy, R. W. (2010). Estimating Groundwater Recharge (C. university Press, Ed.). Cambridge university press.Healy, R. W., & Cook, P. G. (2002). Using groundwater levels to estimate recharge. Hydrogeology Journal, 10(1), 91–109. https://doi.org/10.1007/s10040-001-0178-0Hoffmann, J. (2005). The future of satellite remote sensing in hydrogeology. Hydrogeology Journal, 13, 247–250.Holzbecher, E. (2012). Environmental modeling: using MATLAB. Springer Science & Business Media.Horbath, J. E. (2016). Tendencias y proyecciones de la población del área metropolitana del Valle de Aburrá en Colombia, 2010-2030. Notas de Población, 43(102), 37–65. https://doi.org/10.18356/9d3ffa22-esHuang, T., Pang, Z., Yang, S., & Yin, L. (2020). Impact of Afforestation on Atmospheric Recharge to Groundwater in a Semiarid Area. Journal of Geophysical Research: Atmospheres, 125(9), 1–19. https://doi.org/10.1029/2019JD032185IGAC, & GOBERNACIÓN DE ANTIOQUIA. (2007). Estudio General de Suelos y Zonificación de Tierras del Departamento de Antioquia.Ingenieria, C., AMVA, CORNARE, & CORANTIOQUIA. (2015). Plan de Ordenación y Manejo de la Cuenca del Río Aburrá.Jaramillo-Llorente, M. F., Rengifo, R., & Retrepo, I. (2021). PRZ index for identifying potential areas of recharge in alluvial aquifers and for land use planning. Ingeniería Y Competitividad, 24(1). https://doi.org/10.25100/iyc.v24i1.11001Kovačič, G., Petrič, M., & Ravbar, N. (2020). Evaluation and quantification of the effects of climate and vegetation cover change on karst water sources: Case studies of two springs in south-western slovenia. Water (Switzerland), 12(11), 1–20. https://doi.org/10.3390/w12113087Lall, U., Josset, L., & Russo, T. (2020). A snapshot of the world’s groundwater challenges. Annual Review of Environment and Resources, 45, 171–194.Lamichhane, S., & Shakya, N. M. (2019). Alteration of groundwater recharge areas due to land use/cover change in Kathmandu Valley, Nepal. Journal of Hydrology: Regional Studies, 26(October 2019), 100635. https://doi.org/10.1016/j.ejrh.2019.100635Lerner, D. (2002). Identifying and quantifying urban recharge: a review. In Hydrogeology Journal (Vol. 10, pp. 143–152).Lerner, D. N. (1990). Groundwater recharge in urban areas.Li, H., Wan, W., Fang, Y., Zhu, S., Chen, X., Liu, B., & Hong, Y. (2019). A Google Earth Engine-enabled software for efficiently generating high-quality user-ready Landsat mosaic images. Environmental Modelling and Software, 112(March 2018), 16–22. https://doi.org/10.1016/j.envsoft.2018.11.004Mariño-Martínez, J. E., & Martínez-Sánchez, A. (2018). Analysis of precipitation and recharge of aquifers in Tota and Ibagué (Colombia) from stable isotopes (18O and 2H). Revista Facultad de Ingeniería, 27(47), 61–71. https://doi.org/10.19053/01211129.v27.n47.2018.7752Mattos, T. S., de Oliveira, P. T. S., Lucas, M. C., & Wendland, E. (2019). Groundwater recharge decrease replacing pasture by Eucalyptus plantation. Water (Switzerland), 11(6), 1–13. https://doi.org/10.3390/w11061213Mensah, J. K., Ofosu, E. A., Yidana, S. M., Akpoti, K., & Kabo-bah, A. T. (2022). Integrated modeling of hydrological processes and groundwater recharge based on land use land cover, and climate changes: A systematic review. Environmental Advances, 8(March), 100224. https://doi.org/10.1016/j.envadv.2022.100224Minnig, M., Moeck, C., Radny, D., & Schirmer, M. (2018). Impact of urbanization on groundwater recharge rates in Dübendorf, Switzerland. Journal of Hydrology, 563, 1135–1146. https://doi.org/10.1016/j.jhydrol.2017.09.058Moeck, C., Grech-Cumbo, N., Podgorski, J., Bretzler, A., Gurdak, J. J., Berg, M., & Schirmer, M. (2020). A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships. Science of the Total Environment, 717, 137042. https://doi.org/10.1016/j.scitotenv.2020.137042Nychka, D., Furrer, R., Paige, J., & Sain, S. (2016). Tools for Spatial Data.O’Callaghan, J. F., & Mark, D. M. (1984). The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, and Image Processing, 28(3), 323–344.Ossa, J., Campillo, A. K., Omar, C., & Betancur, T. (2021). Recharge área maps from precipitation isoscapes. Case study: Aburrá valley, colombia. Boletin Geologico y Minero, 132(1–2), 65–75. https://doi.org/10.21701/bolgeomin.132.1-2.007Ouyang, Y., Jin, W., Grace, J. M., Obalum, S. E., Zipperer, W. C., & Huang, X. (2019). Estimating impact of forest land on groundwater recharge in a humid subtropical watershed of the Lower Mississippi River Alluvial Valley. Journal of Hydrology: Regional Studies, 26(October), 100631. https://doi.org/10.1016/j.ejrh.2019.100631Oviedo Aleman, L. M. (2020). Variaciones de la recarga de agua subterránea bajo escenarios de cambio climático en el nivel somero del sistema acuífero bajo cauca antioqueño. Universidad Nacional de ColombiaPatiño, D. (2018). Respuesta hidrológica ante los cambios de uso y cobertura del suelo en la cuenca del río Chinchiná. Universidad Nacional de Colombia.Patiño, D. A. (2018). Respuesta hidrológica ante los cambios de uso y cobertura del suelo en la cuenca del río Chinchiná. Universidad Nacional de Colombia.Patiño Rojas, S. M., & Jaramillo, M. (2022). Estimación espaciotemporal de la recarga potencial en un sistema pseudokárstico tropical. Revista de La Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 46(178), 261–278. https://doi.org/10.18257/raccefyn.1512Patiño, S. M., Jaramillo, M., Espinosa-Espinosa, C., & Arias-Lopez, M. F. (2021). Preferential groundwater flow directions in a pseudokarst system in Colombia, South America. Journal of South American Earth Sciences, 112(September). https://doi.org/10.1016/j.jsames.2021.103572Pérez Fonseca, A. L. (2018). Las periferias en disputa. Procesos de poblamiento urbano popular en Medellín. Estudios Políticos (Medellín), 53, 148–170. https://doi.org/10.17533/udea.espo.n53a07Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling and Software, 79, 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008Posada-Marín, J. A., & Salazar, J. F. (2022). River flow response to deforestation: Contrasting results from different models. Water Security, 15(March). https://doi.org/10.1016/j.wasec.2022.100115Ribeiro, L., Kretschmer, N., Nascimento, J., Buxo, A., Rötting, T., Soto, G., Señoret, M., Oyarzún, J., Maturana, H., & Oyarzún, R. (2015). Evaluating piezometric trends using the Mann-Kendall test on the alluvial aquifers of the Elqui River basin, Chile. Hydrological Sciences Journal, 60(10), 1840–1852.Rios Rivera, M. A. (2019). Upscaling of point-scale groundwater recharge measurements using machine learning: A case study in New Zealand and Colombia (Doctoral dissertation, Lincoln University).Rodríguez-Campero, C., Garfias, J., Martel, R., & Navarro-de León, I. (2023). Estimación espacio-temporal de la distribución de la recarga potencial en el Valle de Toluca. Boletín de La Sociedad Geológica Mexicana, 75(2), A080523. https://doi.org/10.18268/bsgm2023v75n2a080523Sargemt, R. (2015). Modeling and Simulation in the Systems Engineering. In Modeling and Simulation in the Systems Engineering (Vol. 1, pp. 52–58). https://doi.org/10.1007/978-1-4615-0863-2_17Scanlon, B. R., Jolly, I., Sophocleous, M., & Zhang, L. (2007). Global impacts of conversions from natural to agricultural ecosystems on water resources: Quantity versus quality. Water Resources Research, 43(3). https://doi.org/10.1029/2006WR005486Scanlon, B. R., Kelley, E. K., Alan, L. F., & Lorraine, E. F. (2006). Global synthesis of groundwater recharge in semiarid and arid regions. Hydrological Processes: An International Journal, 20, 3335–3370. https://doi.org/10.1002/hypScanlon, B. R., Reedy, R. C., Stonestrom, D. A., Prudic, D. E., & Dennehy, K. F. (2005). Impact of land use and land cover change on groundwater recharge and quality in the southwestern US. Global Change Biology, 11(10), 1577–1593. https://doi.org/10.1111/j.1365-2486.2005.01026.xShahfahad, Kumari, B., Tayyab, M., Hang, H. T., Khan, M. F., & Rahman, A. (2019). Assessment of public open spaces (POS) and landscape quality based on per capita POS index in Delhi, India. SN Applied Sciences, 1, 1–13.Shi, D., Wang, W., Jiang, G., Peng, X., Yu, Y., Li, Y., & Ding, W. (2016). Effects of disturbed landforms on the soil water retention function during urbanization process in the Three Gorges Reservoir Region, China. Catena, 144, 84–93. https://doi.org/10.1016/j.catena.2016.04.010Sicard, P., Coulibaly, F., Lameiro, M., Araminiene, V., De Marco, A., Sorrentino, B., Anav, A., Manzini, J., Hoshika, Y., & Moura, B. B. (2023). Object-based classification of urban plant species from very high-resolution satellite imagery. Urban Forestry & Urban Greening, 81, 127866.Siddik, M. S., Tulip, S. S., Rahman, A., Islam, M. N., Haghighi, A. T., & Mustafa, S. M. T. (2022). The impact of land use and land cover change on groundwater recharge in northwestern Bangladesh. Journal of Environmental Management, 315, 115–130.Siqueira, V. A., Fleischmann, A., Jardim, P. F., Fan, F. M., & Collischonn, W. (2016). IPH-Hydro Tools: a GIS coupled tool for watershed topology acquisition in an open-source environment. Rbrh, 21, 274–287.Stocker, O., & Le Bris, A. (2020). Can Spot-6/7 Cnn Semantic Segmentation Improve Sentinel-2 Based Land Cover Products? Sensor Assessment and Fusion. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(2), 557–564. https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135.Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations-A review. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071135 Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review, 38, 55–94.Thornthwaite, C. W., & Mather, J. R. (1957). Instructions and tables for computing potential evapotranspiration and the water balance. Centerton.UdeA. (2018). AUNAR ESFUERZOS PARA LA APROPIACIÓN TECNOLÓGICA Y DEL CONOCIMIENTO PARA LA GESTIÓN INTEGRAL DEL RECURSO HÍDRICO SUPERFICIAL Y SUBTERRÁNEO, EN EL CONTEXTO DE LA OPERACIÓN DE LA RED DE MONITOREO AMBIENTAL EN LA CUENCA HIDROGRÁFICA DEL RÍO ABURRÁ-MEDELLIN Y .UdeA, & AMVA. (2018). FORMULACIÓN DEL PLAN DE MANEJO AMBIENTAL DE ACUÍFERO DELVALLE DE ABURRÁ.Universidad Pontificia Bolivariana, & AMVA. (2007). Estudio de la forma y el crecimiento urbano de la región metropolitana.UPB, & AMVA. (2015). Política Pública de Construcción Sostenible. Linea Base.Velez, J. (2001). DESARROLLO DE UN MODELO DISTRIBUIDO DE PREDICCIÓN EN TIEMPO REAL PARA EVENTOS DE CRECIDAS (Issue January) [UNIVERSIDAD POLITÉCNICA DE VALENCIA]. https://doi.org/10.13140/2.1.4996.1288Vélez, M. V., Botero, V., Salazar, J. F., & Gómez, J. (2005). Esctimación de la recarga en una región colombiana mediante un modelo iterativo. Ingenieria Hidraulica En Mexico, 20(2), 61–77.Wakode, H. B., Baier, K., Jha, R., & Azzam, R. (2018). Impact of urbanization on groundwater recharge and urban water balance for the city of Hyderabad, India. International Soil and Water Conservation Research, 6(1), 51–62. https://doi.org/10.1016/j.iswcr.2017.10.003Westenbroek, J. A., Stephen, M., Engott, V. A., Kelson, & Hunt, R. J. (2018). Water Availability and Use Science Program National Water Quality Program SWB Version 2.0-A Soil-Water-Balance Code for Estimating Net Infiltration and Other Water-Budget Components Book 6, Modeling Techniques. https://pubs.usgs.gov/tm/06/a59/tm6a59.pdfWestenbroek, M. S., Kelson, V. a., Dripps, W. R., Hunt, R. J., & Bradbury, K. R. (2010). SWB — A Modified Thornthwaite-Mather Soil-Water- Balance Code for Estimating Groundwater Recharge. U.S. Geological Survey Techniques and Methods 6-A31, 60.Westenbroek, S. M., Kelson, V. A., Dripps, W. R., Hunt, R. J., & Bradbury, K. R. (2010). SWB--a modified Thornthwaite-Mather Soil-Water-Balance Code for estimating groundwater recharge. US Department of the Interior, US Geological Survey, Ground Resources ….White, J. C., Wulder, M. A., Hobart, G. W., Luther, J. E., Hermosilla, T., Griffiths, P., Coops, N. C., Hall, R. J., Hostert, P., Dyk, A., & Guindon, L. (2014). Pixel-based image compositing for large-area dense time series applications and science. Canadian Journal of Remote Sensing, 40(3), 192–212. https://doi.org/10.1080/07038992.2014.945827Wu, N., Crusiol, L. G. T., Liu, G., Wuyun, D., & Han, G. (2023). Comparing machine learning algorithms for pixel/object-based classifications of semi-arid grassland in northern China using multisource medium resolution imageries. Remote Sensing, 15(3), 750.Yacouby, R., & Axman, D. (2020). Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 79–91.Yan, C., Fan, X., Fan, J., Yu, L., Wang, N., Chen, L., & Li, X. (2023). HyFormer: Hybrid Transformer and CNN for Pixel-Level Multispectral Image Land Cover Classification. International Journal of Environmental Research and Public Health, 20(4), 3059. https://doi.org/10.3390/ijerph20043059Yang, Y., Lerner, D. N., Barrett, M. H., & Tellam, J. H. (1999). Quantification of groundwater recharge in the city of Nottingham, UK. Environmental Geology, 38(3), 183–198. https://doi.org/10.1007/s002540050414Zerrouki, N., & Bouchaffra, D. (2014). Pixel-based or object-based: Which approach is more appropriate for remote sensing image classification? 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 864–869.Zhang, X., Han, L., Han, L., & Zhu, L. (2020). How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery? Remote Sensing, 12(3), 1–29. https://doi.org/10.3390/rs12030417Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384.Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94.EstudiantesInvestigadoresMaestrosResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86269/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1059709866.2024.pdf1059709866.2024.pdfTesis de Maestría en Ingeniería - Recursos Hidráulicosapplication/pdf6916578https://repositorio.unal.edu.co/bitstream/unal/86269/2/1059709866.2024.pdf698eee1cc9bdd625676947877dea151aMD52THUMBNAIL1059709866.2024.pdf.jpg1059709866.2024.pdf.jpgGenerated Thumbnailimage/jpeg5347https://repositorio.unal.edu.co/bitstream/unal/86269/3/1059709866.2024.pdf.jpgc0156cadbd5620898ac82df6cf2e7bc4MD53unal/86269oai:repositorio.unal.edu.co:unal/862692024-06-19 23:05:12.981Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |