Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales

ilustraciones, gráficas, tablas

Autores:
García Echeverri, Camila Andrea
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/81573
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/81573
https://repositorio.unal.edu.co/
Palabra clave:
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Hydrology - Mathematical models
Runoff
Escorrentía
Cuencas hidrográficas
Hidrología - Modelos matemáticos
Watersheds
Modelación matemática
DWB
Clasificación no supervisada
K-means
CDC
DDS-AU
Mathematical modeling
DWB
Unsupervised classification
K-means
FDC
DDS-AU
Rights
openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_c037505c61687b097c12da383b148c17
oai_identifier_str oai:repositorio.unal.edu.co:unal/81573
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
dc.title.translated.eng.fl_str_mv Evaluation of the Dynamic Water Balance hydrological model at daily scale in tropical watersheds
title Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
spellingShingle Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Hydrology - Mathematical models
Runoff
Escorrentía
Cuencas hidrográficas
Hidrología - Modelos matemáticos
Watersheds
Modelación matemática
DWB
Clasificación no supervisada
K-means
CDC
DDS-AU
Mathematical modeling
DWB
Unsupervised classification
K-means
FDC
DDS-AU
title_short Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
title_full Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
title_fullStr Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
title_full_unstemmed Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
title_sort Evaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicales
dc.creator.fl_str_mv García Echeverri, Camila Andrea
dc.contributor.advisor.spa.fl_str_mv Mancipe Munoz, Nestor Alonso
Zamora Ávila, David Andrés
dc.contributor.author.spa.fl_str_mv García Echeverri, Camila Andrea
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación en Ingeniería de Recursos Hidrícos Gireh
dc.subject.ddc.spa.fl_str_mv 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
topic 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Hydrology - Mathematical models
Runoff
Escorrentía
Cuencas hidrográficas
Hidrología - Modelos matemáticos
Watersheds
Modelación matemática
DWB
Clasificación no supervisada
K-means
CDC
DDS-AU
Mathematical modeling
DWB
Unsupervised classification
K-means
FDC
DDS-AU
dc.subject.armarc.eng.fl_str_mv Hydrology - Mathematical models
dc.subject.lemb.spa.fl_str_mv Runoff
Escorrentía
Cuencas hidrográficas
Hidrología - Modelos matemáticos
dc.subject.lemb.eng.fl_str_mv Watersheds
dc.subject.proposal.spa.fl_str_mv Modelación matemática
DWB
Clasificación no supervisada
K-means
CDC
DDS-AU
dc.subject.proposal.eng.fl_str_mv Mathematical modeling
DWB
Unsupervised classification
K-means
FDC
DDS-AU
description ilustraciones, gráficas, tablas
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-06-13T19:47:02Z
dc.date.available.none.fl_str_mv 2022-06-13T19:47:02Z
dc.date.issued.none.fl_str_mv 2022-03-17
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/81573
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/81573
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.references.spa.fl_str_mv Anshuman, A., Kunnath-Poovakka, A., & Eldho, T. I. (2021). Performance evaluation of conceptual rainfall-runoff models GR4J and AWBM. ISH Journal of Hydraulic Engineering, 27(4), 365–374. https://doi.org/10.1080/09715010.2018.1556124
Arboleda-Obando, P. (2018). Determinando los efectos del cambio climático y del cambio en usos del suelo en la Macro Cuenca Magdalena Cauca utilizando el modelo de suelo-superficie e hidrológico MESH. 253.
Bai, P., Liu, W., & Guo, M. (2014). Impacts of climate variability and human activities on decrease in streamflow in the Qinhe River, China. Theoretical and Applied Climatology, 117(1–2), 293–301. https://doi.org/10.1007/s00704-013-1009-7
Bai, Y., Wagener, T., & Reed, P. (2009). A top-down framework for watershed model evaluation and selection under uncertainty. Environmental Modelling & Software, 24(8), 901–916. https://doi.org/10.1016/j.envsoft.2008.12.012
Bergström, S. (1991). Principles and Confidence in Hydrological Modelling. Hydrology Research, 22(2), 123–136. https://doi.org/10.2166/nh.1991.0009
Beven, K., & Binley, A. (1992). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6(3), 279–298. https://doi.org/10.1002/hyp.3360060305
Biondi, D., Freni, G., Iacobellis, V., Mascaro, G., & Montanari, A. (2012). Validation of hydrological models: Conceptual basis, methodological approaches and a proposal for a code of practice. Physics and Chemistry of the Earth, Parts A/B/C, 42–44, 70–76. https://doi.org/10.1016/j.pce.2011.07.037
Boughton, W. (2004). The Australian water balance model. Environmental Modelling & Software, 19(10), 943–956. https://doi.org/https://doi.org/10.1016/j.envsoft.2003.10.007
Bras, R. L. (2015). Complexity and organization in hydrology: A personal view. Water Resources Research, 51(8), 6532–6548. https://doi.org/10.1002/2015WR016958
Brirhet, H., & Benaabidate, L. (2016). Comparison Of Two Hydrological Models (Lumped And Distributed) Over A Pilot Area Of The Issen Watershed In The Souss Basin, Morocco. European Scientific Journal, ESJ, 12(18), 347. https://doi.org/10.19044/esj.2016.v12n18p347
Budyko, M. . (1958). The heat balance of the earth’s surface (U. . D. of Commerce (ed.)). Translate from russian by N.A Stepanova.
Calder, I. R. (1998). Water use by forests, limits and controls. Tree Physiology, 18(8_9), 625–631. https://doi.org/10.1093/treephys/18.8-9.625
Camino, M., Bó, J., Cionchi, J., Del Río, J., López de Armentia, A., & De Marco, S. (2018). Estudio morfométrico de las cuencas de drenaje de la vertiente sur del sudeste de la provincia de Buenos Aires. Revista Universitaria de Geografía, 27(1). https://www.redalyc.org/articulo.oa?id=383257036005
Carvajal, L., & Roldán, E. (2007). Calibración del modelo lluvia-escorrentía agregado gr4j aplicación: cuenca del río Aburrá. Dyna, 74(152), 73–97. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=s0012-73532007000200007&lng=en&tlng=es
Casper, M. C., Grigoryan, G., Gronz, O., Gutjahr, O., Heinemann, G., & Ley, R. (2011). Analysis of projected hydrological behavior of catchments based on signature indices. Hydrology and Earth System Sciences Discussions, 8(2), 3571–3597. https://doi.org/10.5194/hessd-8-3571-2011
Cháves-Jiménez, A. (2009). APLICACIÓN DEL MÉTODO DE REGIONALIZACIÓN PARA LA DETERMINACIÓN DE CAUDALES EN EL PUENTE CARRASQUILLO. Universidad de Piura.
Chiew, F. H. S., Peel, M. C., Western, A. W., Singh, V. P., & Frevert, D. K. (2002). Application and testing of the simple rainfall-runoff model SIMHYD. In L. Water Resources Publications (Ed.), Mathematical models of small watershed hydrology and applications (pp. 335–367).
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., & Rasmussen, R. M. (2015). A unified approach for process‐based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51(4), 2498–2514. https://doi.org/10.1002/2015WR017198
Comber, A., & Zeng, W. (2019). Spatial interpolation using areal features: A review of methods and opportunities using new forms of data with coded illustrations. Geography Compass, 13(10). https://doi.org/10.1111/gec3.12465
Coxon, G., Freer, J., Lane, R., Dunne, T., Knoben, W. J. M., Howden, N. J. K., Quinn, N., Wagener, T., & Woods, R. (2019). DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology. Geoscientific Model Development, 12(6), 2285–2306. https://doi.org/10.5194/gmd-12-2285-2019
Crochemore, L., Perrin, C., Andréassian, V., Ehret, U., Seibert, S. P., Grimaldi, S., Gupta, H., & Paturel, J.-E. (2015). Comparing expert judgement and numerical criteria for hydrograph evaluation. Hydrological Sciences Journal, 60(3), 402–423. https://doi.org/10.1080/02626667.2014.903331
DANE. (2006). Censo General 2005. https://www.dane.gov.co/files/censo2005/boletin.pdf
DANE. (2019). Censo General 2018.
Díaz, L., & Alarcon, J. (2018). Estudio hidrológico y balance hídrico para determinar la oferta y la demanda de agua de la cuenca de la quebrada niscota para un acueducto interveredal en Nunchía, Casanare [Universidad Católica de Colombia]. https://repository.ucatolica.edu.co/bitstream/10983/15989/1/Proyecto Final.pdf
Didan, K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://lpdaac.usgs.gov/products/mod13q1v006/
Diskin, M. H., & Simon, E. (1977). A procedure for the selection of objective functions for hydrologic simulation models. Journal of Hydrology, 34(1–2), 129–149. https://doi.org/10.1016/0022-1694(77)90066-X
Duque, N., & Fuentes, C. (2019). Usando R para una fácil y eficiente predicción de la incertidumbre de simulaciones de modelos ambientales. Revista Hidrolatinoamericana de Jóvenes Investigadores y Profesionales, 3, 17–20. https://eb482193-b6aa-4d33-b48d-fc5ae67cfe31.filesusr.com/ugd/d728aa_0d0f1d9354bb4aa885fb9ff2cf37f562.pdf?index=true
Duque, N., Vega, C., Arboleda, P., & Zamora, D. (2020). DWBmodelUN: Dynamic Water Balance a Hydrological Model. https://cran.r-project.org/package=DWBmodelUN
Edijatno, N., Yang, X., Makhlouf, Z., & Michel, C. (1999). GR3J: a daily watershed model with three free parameters. Hydrological Sciences Journal, 44(2), 263–277. https://doi.org/10.1080/02626669909492221
Efstratiadis, A., & Koutsoyiannis, D. (2010). One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrological Sciences Journal, 55(1), 58–78. https://doi.org/10.1080/02626660903526292
ESRI Inc. (2020). ArcGIS Pro (Version 2.7). https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview
Fu, B. (1981). On the calculation of the evaporation from land surface. Chinese Journal of Atmospheric Sciences, 23–31.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(1), 150066. https://doi.org/10.1038/sdata.2015.66
Garay, D., & Agüero, J. (2018). Delimitación Hidrográfica y Caracterización Morfométrica de la Cuenca del Río Anzulón (INTA (ed.)).
García-Echeverri, C., Duque-Gardeazabal Nicolás, Carolina, V.-V., Arboleda-Obando, P., & Zamora, D. (2020). DWBmodelUN: an R-package for the hydrological model Dynamic Water Balance. 1. https://doi.org/https://doi.org/10.5194/egusphere-egu2020-4222
García-Echeverri, C., Mancipe, N., Rodríguez, C., González, C., & Zamora, D. (2021). Evaluación multitemporal del cambio de cobertura vegetal en cuencas colombianas a partir de índices de vegetación. III Congreso Internacional de Ríos y Humedales.
Garcia, F., Folton, N., & Oudin, L. (2017). Which objective function to calibrate rainfall–runoff models for low-flow index simulations? Hydrological Sciences Journal, 62(7), 1149–1166. https://doi.org/10.1080/02626667.2017.1308511
Graham, J. W. (2009). Missing Data Analysis: Making It Work in the Real World. Annual Review of Psychology, 60(1), 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530
Gui, H., Wu, Z., & Zhang, C. (2021). Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation. CLEAN – Soil, Air, Water, 49(8), 2000381. https://doi.org/10.1002/clen.202000381
Guilarte, R. (1978). Hidrología básica (F. de Ingeniería (ed.)).
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Hamel, P., Guswa, A. J., Sahl, J., & Zhang, L. (2017). Predicting dry-season flows with a monthly rainfall–runoff model: Performance for gauged and ungauged catchments. Hydrological Processes, 31(22), 3844–3858. https://doi.org/10.1002/hyp.11298
Hargreaves, G., & Samani, Z. (1985). Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1(2), 96–99. https://doi.org/10.13031/2013.26773
Hernández, S., Morales, L., & Sallis, P. (2011). Estimation of Reference Evapotranspiration Using Limited Climatic Data and Bayesian Model Averaging. 2011 UKSim 5th European Symposium on Computer Modeling and Simulation, 59–63. https://doi.org/10.1109/EMS.2011.81
Huard, D., & Mailhot, A. (2006). A Bayesian perspective on input uncertainty in model calibration: Application to hydrological model “abc.” Water Resources Research, 42(7). https://doi.org/10.1029/2005WR004661
Huete, A., Didan, K., Miura, T., Rodriguez, E. ., Gao, X., & Ferreira, L. . (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
Huo, J., & Liu, L. (2020). Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation. Computational Intelligence and Neuroscience, 2020, 1–23. https://doi.org/10.1155/2020/8594727
IDEAM. (2013). Zonificación y codificación de cuencas hidrográficas. http://documentacion.ideam.gov.co/openbiblio/bvirtual/022655/MEMORIASMAPAZONIFICACIONHIDROGRAFICA.pdf
IDEAM. (2018). Protocolo De Modelación Hidrológica e Hidráulica. http://documentacion.ideam.gov.co/openbiblio/bvirtual/023833/Protocolo_Modelacion_HH.pdf
IDEAM. (2019). Estudio Nacional del Agua 2018. Bogotá: Ideam 452 pp.
Jabloun, M., & Sahli, A. (2008). Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data. Agricultural Water Management, 95(6), 707–715. https://doi.org/10.1016/j.agwat.2008.01.009
K. Ajami, N., Gupta, H., Wagener, T., & Sorooshian, S. (2004). Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology, 298(1–4), 112–135. https://doi.org/10.1016/j.jhydrol.2004.03.033
Kallio, M., Guillaume, J. H. A., Virkki, V., Kummu, M., & Virrantaus, K. (2021). Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products. Geoscientific Model Development, 14(8), 5155–5181. https://doi.org/10.5194/gmd-14-5155-2021
Kim, H. J. (2001). Development of two-parametric hyperbolic model for daily streamflow simulation. PH. D. dissertation, Seoul National University, Seoul.
Kim, Kwon, & Han. (2018). Exploration of warm-up period in conceptual hydrological modelling. In Journal of Hydrology (Vol. 556). https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.11.015
Knoben, W. J. M., Freer, J. E., & Woods, R. A. (2019). Technical note: Inherent benchmark or not? Comparing NashSutcliffe and Kling-Gupta efficiency scores. Hydrology and Earth System Sciences. https://doi.org/https://doi.org/10.5194/hess-2019-327
Koutsoyiannis, D. (2016). Generic and parsimonious stochastic modelling for hydrology and beyond. Hydrological Sciences Journal, 61(2), 225–244. https://doi.org/10.1080/02626667.2015.1016950
Krause, P., Boyle, D. P., & Bäse, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89–97. https://doi.org/10.5194/adgeo-5-89-2005
Kunnath-Poovakka, A., & Eldho, T. I. (2019). A comparative study of conceptual rainfall-runoff models GR4J, AWBM and Sacramento at catchments in the upper Godavari river basin, India. Journal of Earth System Science, 128(2), 33. https://doi.org/10.1007/s12040-018-1055-8
Lamontagne, J. R., Barber, C. A., & Vogel, R. M. (2020). Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data. Water Resources Research, 56(9). https://doi.org/10.1029/2020WR027101
Lang, D., Zheng, J., Shi, J., Liao, F., Ma, X., Wang, W., Chen, X., & Zhang, M. (2017). A Comparative Study of Potential Evapotranspiration Estimation by Eight Methods with FAO Penman–Monteith Method in Southwestern China. Water, 9(10), 734. https://doi.org/10.3390/w9100734
Limbrunner, J., Vogel, R. M., & Chapram, S. (2006). The stream network storage volume is envisioned as a store which has groundwater outflow and direct runoff as inputs and streamflow as outputs. Here direct runoff is summed over all land-uses. https://sites.tufts.edu/richardvogel/files/2019/04/parsimonious-watershed-chap22.pdf
Liu, H. Q., & Huete, A. R. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33, 457–465.
Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. https://doi.org/10.1109/TIT.1982.1056489
MacQueen, J., & others. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281–297.
MADS, V. y D. T. (2010). Política Nacional para la Gestión Integral del Recurso Hídrico - PNGIRH.
Michel, C. (1983). Que peut-on faire en hydrologie avec un modèle conceptuel a un seul paramètre ? La Houille Blanche, 1, 39–44.
Mittal, M., Sharma, R. K., & Singh, V. P. (2014). Validation of k-means and threshold based clustering method. International Journal of Advancements in Technology, 5(2), 153–160.
Moriasi, D., Gitau, M., Pai, N., & Daggupati, P. (2015). Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria. Transactions of the ASABE, 58(6), 1763–1785. https://doi.org/10.13031/trans.58.10715
Muñoz, P. (2013). Apuntes de teledetección: Índices de vegetación. http://bibliotecadigital.ciren.cl/bitstream/handle/123456789/26389/Tema Indices de vegetación%2C Pedro Muñoz A.pdf?sequence=1&isAllowed=y
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
NOAA. (2021). Cold and warm episodes by season. https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php
Osman, M. S., Abu-Mahfouz, A. M., & Page, P. R. (2018). A Survey on Data Imputation Techniques: Water Distribution System as a Use Case. IEEE Access, 6, 63279–63291. https://doi.org/10.1109/ACCESS.2018.2877269
Peel, M. ., Chiew, F. H. ., Western, A. ., & McMahon, T. . (2000). Extension of Unimpaired Monthly Streamflow Data and Regionalisation of Parameter Values to Estimate Streamflow in Ungauged Catchments. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.726.9527&rep=rep1&type=pdf
Perrin, C., Michel, C., & Andréassian, V. (2003). Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology, 279(1–4), 275–289. https://doi.org/10.1016/S0022-1694(03)00225-7
Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., Osuch, M., & Kundzewicz, Z. W. (2017). Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models? Hydrological Sciences Journal, 62(4), 606–625. https://doi.org/10.1080/02626667.2016.1234712
QGIS.org. (2022). QGIS Geographic Information System (3.18.3). QGIS Association. https://www.qgis.org/
R Core Team. (2021). R: A Language and Environment for Statistical Computing. https://www.r-project.org/
Restrepo, B., & González, J. (2007). De Pearson a Spearman. Revista Colombiana de Ciencias Pecuarias, 20(2), 11. https://www.redalyc.org/articulo.oa?id=295023034010
Reyes, A., Barroso, F., & Carvajal, Y. (2010). Guía básica para la caracterización morfométrica de cuencas hidrográficas (P. Editorial (ed.)). Universidad del Valle.
Rodríguez-Sandoval, E., García-Echeverri, C., Sandoval-Barrera, J., Patarrollo-González, M., González-Ramírez, A., Agudelo-Duque, D., & Roldán-Novoa, M. (n.d.). Evaluating the IMERG precipitation satellite product to derive intensity-duration-frequency curves in colombia. Redin, En revisión.
Rodríguez, C. (2021). Evaluación del desempeño del modelo DWB para estimar curvas de duración de caudales en la cuenca del río Sogamoso. Universidad Nacional de Colombia.
Rodriguez, C., García-Echeverri, C., González, C., & Zamora, D. (2021). Efectos de las condiciones climáticas cambiantes en la simulación de la escorrentía en la cuenca del río Sogamoso. XXIV Seminario Nacional de Hidráulica e Hidrología.
Rodríguez, E., Sánchez, I., Duque, N., Arboleda, P., Vega, C., Zamora, D., López, P., Kaune, A., Werner, M., García, C., & Burke, S. (2019). Combined Use of Local and Global Hydro Meteorological Data with Hydrological Models for Water Resources Management in the Magdalena - Cauca Macro Basin – Colombia. Water Resources Management. https://doi.org/doi.org/10.1007/s11269-019-02236-5
Rouse J., Haas, R., Schell, J., & Deering, D. (1974). Monitoring Vegetation Systems in the Great Plains with Erts. In NASA Special Publication (Vol. 351, p. 309).
Rousseeuw, P. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
RStudio Team. (2020). RStudio: Integrated Development Environment for R. http://www.rstudio.com/
Silberstein, R. P. (2006). Hydrological models are so good, do we still need data? Environmental Modelling and Software, 21(9), 1340–1352. https://doi.org/10.1016/j.envsoft.2005.04.019
Simonneaux, V., Hanich, L., Boulet, G., & Thomas, S. (2008). Modelling runoff in the Rheraya Catchment (High Atlas, Morocco) using the simple daily model GR4J. Trends over the Last Decades.
Steinley, D., & Brusco, M. J. (2007). Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques. Journal of Classification, 24(1), 99–121. https://doi.org/10.1007/s00357-007-0003-0
Takeuchi, K., AO, T., & Ishidaira, H. (1999). Introduction of block-wise use of TOPMODEL and Muskingum-Cunge method for the hydroenvironmental simulation of a large ungauged basin. Hydrological Sciences Journal, 44(4), 633–646. https://doi.org/10.1080/02626669909492258
Tegegne, G., Park, D. K., & Kim, Y.-O. (2017). Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin. Journal of Hydrology: Regional Studies, 14, 49–66. https://doi.org/10.1016/j.ejrh.2017.10.002
Tekleab, S., Uhlenbrook, S., Mohamed, Y., Savenije, H. H. G., Temesgen, M., & Wenninger, J. (2011). Water balance modeling of Upper Blue Nile catchments using a top-down approach. Hydrology and Earth System Sciences, 15(7), 2179–2193. https://doi.org/10.5194/hess-15-2179-2011
Tian, Y., Xu, Y.-P., & Zhang, X.-J. (2013). Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models. Water Resources Management, 27(8), 2871–2888. https://doi.org/10.1007/s11269-013-0321-4
Todini, E., & Biondi, D. (2017). Calibration, parameter estimation, uncertainty, data assimilation, sensitivity analysis, and validation. In V. P. Singh (Ed.), Handbook of Applied Hydrology. McGraw Hill.
Tolson, B. A., & Shoemaker, C. A. (2007). Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 43(1). https://doi.org/10.1029/2005WR004723
Tolson, B. A., & Shoemaker, C. A. (2008). Efficient prediction uncertainty approximation in the calibration of environmental simulation models. Water Resources Research, 44(4), 1–19. https://doi.org/10.1029/2007WR005869
Tramblay, Y., Rouché, N., Paturel, J.-E., Mahé, G., Boyer, J.-F., Amoussou, E., Bodian, A., Dacosta, H., Dakhlaoui, H., Dezetter, A., Hughes, D., Hanich, L., Peugeot, C., Tshimanga, R., & Lachassagne, P. (2021). ADHI: the African Database of Hydrometric Indices (1950–2018). Earth System Science Data, 13(4), 1547–1560. https://doi.org/10.5194/essd-13-1547-2021
Traore, V. B., Sambou, S., Tamba, S., Fall, S., Diaw, A., & Cissé, M. (2014). Calibrating the Rainfall-Runoff Model GR4J and GR2M on the Koulountou River Basin, a Tributary of the Gambia River. American Journal of Environmental Protection.
Umaña, I. (2007). Más vale prevenir que lamentar - Las cuencas y la gestión del riesgo a los desastres naturales en Guatemala (FAO (ed.)).
Vásconez, M., Manchecho, A., Álvarez, C., Prehn, C., Cevallos, C., & Ortiz, L. (2019). Cuencas hidrográficas. https://dspace.ups.edu.ec/bitstream/123456789/19038/1/Cuencas hidrográficas.pdf
Wang, Q. J., Pagano, T. C., Zhou, S. L., Hapuarachchi, H. A. P., Zhang, L., & Robertson, D. E. (2011). Monthly versus daily water balance models in simulating monthly runoff. Journal of Hydrology, 404(3–4), 166–175. https://doi.org/10.1016/j.jhydrol.2011.04.027
Wardlow, B. D., & Egbert, S. L. (2010). A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. International Journal of Remote Sensing, 31(3), 805–830. https://doi.org/10.1080/01431160902897858
Zhang, L., Potter, N., Hickel, K., Zhang, Y., & Shao, Q. (2008). Water balance modeling over variable time scales based on the Budyko framework - Model development and testing. Journal of Hydrology, 360(1–4), 117–131. https://doi.org/10.1016/j.jhydrol.2008.07.021
Zhou, Y., Zhang, Y., Vaze, J., Lane, P., & Xu, S. (2015). Impact of bushfire and climate variability on streamflow from forested catchments in southeast Australia. Hydrological Sciences Journal, 60(7–8), 1340–1360. https://doi.org/10.1080/02626667.2014.961923
Zuo, D., Xu, Z., Wu, W., Zhao, J., & Zhao, F. (2014). Identification of streamflow response to climate change and human activities in the wei river Basin, China. Water Resources Management, 28(3), 833–851. https://doi.org/10.1007/s11269-014-0519-0
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 xii, 95 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
application/x-compressed
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ingeniería - Maestría en Ingeniería - Recursos Hidráulicos
dc.publisher.department.spa.fl_str_mv Departamento de Ingeniería Civil y Agrícola
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/81573/5/1116501914.2022.pdf
https://repositorio.unal.edu.co/bitstream/unal/81573/4/ANEXOS_TESIS_DWB_DIARIO_CGE.zip
https://repositorio.unal.edu.co/bitstream/unal/81573/6/license.txt
https://repositorio.unal.edu.co/bitstream/unal/81573/7/1116501914.2022.pdf.jpg
bitstream.checksum.fl_str_mv 1fed5759d23c9a99e9447d2df5bcc88e
0c0385868b62383a22c4e95fe967f837
8153f7789df02f0a4c9e079953658ab2
ed65d40cfc7ca0dd527e6278af4a3c0f
bitstream.checksumAlgorithm.fl_str_mv MD5
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_ 1814089836174245888
spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mancipe Munoz, Nestor Alonso1ec21831856a251e487e482eccfdde47600Zamora Ávila, David Andrés7383268024fea38a3b438b6df6ab2c44García Echeverri, Camila Andreae6efa4dc0dea26e99e848f5cc167e761Grupo de Investigación en Ingeniería de Recursos Hidrícos Gireh2022-06-13T19:47:02Z2022-06-13T19:47:02Z2022-03-17https://repositorio.unal.edu.co/handle/unal/81573Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasEste trabajo final de maestría presenta los resultados de la evaluación del modelo Dynamic Water Balance (DWB) a escala diaria para representar escorrentía en cuencas tropicales. El presente proyecto utilizó como insumo diferentes conjuntos de datos generados en el marco del Estudio Nacional del Agua del año 2018 (ENA2018) desarrollado por el IDEAM. La información suministrada corresponde a un total de 497 cuencas, de las cuales seleccionaron 30 para este estudio. La selección de las cuencas se realizó a través del algoritmo de clasificación no supervisada k-means que consideró variables morfométricas, hidroclimatológicas, demográficas y geográficas y su relación con una variable que cuantificó el cambio de las coberturas. De esta forma, se identificaron 8 clusters o grupos de cuencas con comportamiento similar y de características heterogéneas. A las 30 cuencas seleccionadas se les realizó una modelación hidrológica con DWB siguiendo el protocolo de modelación hidrológica, así que se hizo la evaluación de la incertidumbre dada por los parámetros del modelo. Dados los resultados encontrados en la revisión bibliográfica, se adoptó una función multiobjetivo que combinó el KGE y RVE con el fin de mejorar el desempeño de la escorrentía diaria, haciendo énfasis en la representación de la curva de duración de caudales siendo esta la mayor ventaja identificada por los desarrolladores del modelo en esta escala. El proceso de evaluación de los resultados arrojó que el modelo DWB logra reproducir las curvas de duración de caudales con excepción de los caudales bajos. Al evaluar la representación temporal a través de análisis de la función objetivo, el modelo arrojó resultados entre muy buenos y satisfactorios en más del 70% de las cuencas de los clusters 1, 5, 7 y 8, estas cuencas con buenos resultados se ubican principalmente sobre las cordilleras. Como resultado general, este trabajo condujo a la identificación de oportunidades de adaptación del modelo DWB con miras a mejorar la representación de la escorrentía diaria. Se identificó que se debe incluir el proceso de tránsito hidrológico y para este fin se presentaron dos estrategias que podrían ser aplicadas, la primera basada en la inclusión de este proceso dentro del modelo como es el caso de la convolución del hidrograma unitario que ha sido implementado en otros modelos hidrológicos parsimoniosos y la segunda basada en el acople con algoritmos externos que han sido diseñados para realizar este proceso. (Texto tomado de la fuente).This final master work presents the results of the evaluation of the model Dynamic Water Balance (DWB) model at daily scale to simulate runoff in tropical watersheds. The present project used as input dataset information generated in the framework of the 2018 National Water Study developed by IDEAM. Therefore, 30 out of 497 watersheds area selected to be assessed in this study. The selection of the watersheds is made through the unsupervised k-means classification algorithm that considered morphometric, hydroclimatological, demographic, and geographic variables and their relationship with a variable that quantified the change in land cover. Thus, 8 clusters or groups of watersheds with similar behavior and heterogeneous characteristics are identified. The 30 selected watersheds were subjected to hydrological modeling with DWB following the hydrological modeling protocol. Then, the uncertainty given by the model parameters is evaluated. Given the results found in the literature review, a multi-objective function combining the KGE and RVE is adopted to improve the daily runoff performance, emphasizing the representation of the flow duration curve which was the major advantage identified by the model developers at the daily scale. The process of evaluating the results showed that the DWB model can reproduce the flow duration curves except for low flows. When evaluating the temporal representation through analysis of the objective function, the model yields very good to satisfactory results in more than 70% of the watersheds in clusters 1, 5, 7 and 8, these basins with good results are located mainly in the mountains. As a general result, this work led to the identification of opportunities for adapting the DWB model to a daily simulation of runoff. It is identified that the hydrologic routing process should be included and for this purpose two strategies are suggested: [1] the inclusion of a hydrologic routing based on the convolution of the unit hydrograph that has been implemented in other parsimonious hydrologic models and [2] the coupling with external algorithms that have been designed to perform this process.Incluye anexosMaestríaMagíster en Ingeniería - Recursos HidráulicosModelación hidrológicaxii, 95 páginasapplication/pdfapplication/x-compressedspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Recursos HidráulicosDepartamento de Ingeniería Civil y AgrícolaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaHydrology - Mathematical modelsRunoffEscorrentíaCuencas hidrográficasHidrología - Modelos matemáticosWatershedsModelación matemáticaDWBClasificación no supervisadaK-meansCDCDDS-AUMathematical modelingDWBUnsupervised classificationK-meansFDCDDS-AUEvaluación del modelo hidrológico Dynamic Water Balance a escala diaria en cuencas tropicalesEvaluation of the Dynamic Water Balance hydrological model at daily scale in tropical watershedsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAnshuman, A., Kunnath-Poovakka, A., & Eldho, T. I. (2021). Performance evaluation of conceptual rainfall-runoff models GR4J and AWBM. ISH Journal of Hydraulic Engineering, 27(4), 365–374. https://doi.org/10.1080/09715010.2018.1556124Arboleda-Obando, P. (2018). Determinando los efectos del cambio climático y del cambio en usos del suelo en la Macro Cuenca Magdalena Cauca utilizando el modelo de suelo-superficie e hidrológico MESH. 253.Bai, P., Liu, W., & Guo, M. (2014). Impacts of climate variability and human activities on decrease in streamflow in the Qinhe River, China. Theoretical and Applied Climatology, 117(1–2), 293–301. https://doi.org/10.1007/s00704-013-1009-7Bai, Y., Wagener, T., & Reed, P. (2009). A top-down framework for watershed model evaluation and selection under uncertainty. Environmental Modelling & Software, 24(8), 901–916. https://doi.org/10.1016/j.envsoft.2008.12.012Bergström, S. (1991). Principles and Confidence in Hydrological Modelling. Hydrology Research, 22(2), 123–136. https://doi.org/10.2166/nh.1991.0009Beven, K., & Binley, A. (1992). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6(3), 279–298. https://doi.org/10.1002/hyp.3360060305Biondi, D., Freni, G., Iacobellis, V., Mascaro, G., & Montanari, A. (2012). Validation of hydrological models: Conceptual basis, methodological approaches and a proposal for a code of practice. Physics and Chemistry of the Earth, Parts A/B/C, 42–44, 70–76. https://doi.org/10.1016/j.pce.2011.07.037Boughton, W. (2004). The Australian water balance model. Environmental Modelling & Software, 19(10), 943–956. https://doi.org/https://doi.org/10.1016/j.envsoft.2003.10.007Bras, R. L. (2015). Complexity and organization in hydrology: A personal view. Water Resources Research, 51(8), 6532–6548. https://doi.org/10.1002/2015WR016958Brirhet, H., & Benaabidate, L. (2016). Comparison Of Two Hydrological Models (Lumped And Distributed) Over A Pilot Area Of The Issen Watershed In The Souss Basin, Morocco. European Scientific Journal, ESJ, 12(18), 347. https://doi.org/10.19044/esj.2016.v12n18p347Budyko, M. . (1958). The heat balance of the earth’s surface (U. . D. of Commerce (ed.)). Translate from russian by N.A Stepanova.Calder, I. R. (1998). Water use by forests, limits and controls. Tree Physiology, 18(8_9), 625–631. https://doi.org/10.1093/treephys/18.8-9.625Camino, M., Bó, J., Cionchi, J., Del Río, J., López de Armentia, A., & De Marco, S. (2018). Estudio morfométrico de las cuencas de drenaje de la vertiente sur del sudeste de la provincia de Buenos Aires. Revista Universitaria de Geografía, 27(1). https://www.redalyc.org/articulo.oa?id=383257036005Carvajal, L., & Roldán, E. (2007). Calibración del modelo lluvia-escorrentía agregado gr4j aplicación: cuenca del río Aburrá. Dyna, 74(152), 73–97. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=s0012-73532007000200007&lng=en&tlng=esCasper, M. C., Grigoryan, G., Gronz, O., Gutjahr, O., Heinemann, G., & Ley, R. (2011). Analysis of projected hydrological behavior of catchments based on signature indices. Hydrology and Earth System Sciences Discussions, 8(2), 3571–3597. https://doi.org/10.5194/hessd-8-3571-2011Cháves-Jiménez, A. (2009). APLICACIÓN DEL MÉTODO DE REGIONALIZACIÓN PARA LA DETERMINACIÓN DE CAUDALES EN EL PUENTE CARRASQUILLO. Universidad de Piura.Chiew, F. H. S., Peel, M. C., Western, A. W., Singh, V. P., & Frevert, D. K. (2002). Application and testing of the simple rainfall-runoff model SIMHYD. In L. Water Resources Publications (Ed.), Mathematical models of small watershed hydrology and applications (pp. 335–367).Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., & Rasmussen, R. M. (2015). A unified approach for process‐based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51(4), 2498–2514. https://doi.org/10.1002/2015WR017198Comber, A., & Zeng, W. (2019). Spatial interpolation using areal features: A review of methods and opportunities using new forms of data with coded illustrations. Geography Compass, 13(10). https://doi.org/10.1111/gec3.12465Coxon, G., Freer, J., Lane, R., Dunne, T., Knoben, W. J. M., Howden, N. J. K., Quinn, N., Wagener, T., & Woods, R. (2019). DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology. Geoscientific Model Development, 12(6), 2285–2306. https://doi.org/10.5194/gmd-12-2285-2019Crochemore, L., Perrin, C., Andréassian, V., Ehret, U., Seibert, S. P., Grimaldi, S., Gupta, H., & Paturel, J.-E. (2015). Comparing expert judgement and numerical criteria for hydrograph evaluation. Hydrological Sciences Journal, 60(3), 402–423. https://doi.org/10.1080/02626667.2014.903331DANE. (2006). Censo General 2005. https://www.dane.gov.co/files/censo2005/boletin.pdfDANE. (2019). Censo General 2018.Díaz, L., & Alarcon, J. (2018). Estudio hidrológico y balance hídrico para determinar la oferta y la demanda de agua de la cuenca de la quebrada niscota para un acueducto interveredal en Nunchía, Casanare [Universidad Católica de Colombia]. https://repository.ucatolica.edu.co/bitstream/10983/15989/1/Proyecto Final.pdfDidan, K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://lpdaac.usgs.gov/products/mod13q1v006/Diskin, M. H., & Simon, E. (1977). A procedure for the selection of objective functions for hydrologic simulation models. Journal of Hydrology, 34(1–2), 129–149. https://doi.org/10.1016/0022-1694(77)90066-XDuque, N., & Fuentes, C. (2019). Usando R para una fácil y eficiente predicción de la incertidumbre de simulaciones de modelos ambientales. Revista Hidrolatinoamericana de Jóvenes Investigadores y Profesionales, 3, 17–20. https://eb482193-b6aa-4d33-b48d-fc5ae67cfe31.filesusr.com/ugd/d728aa_0d0f1d9354bb4aa885fb9ff2cf37f562.pdf?index=trueDuque, N., Vega, C., Arboleda, P., & Zamora, D. (2020). DWBmodelUN: Dynamic Water Balance a Hydrological Model. https://cran.r-project.org/package=DWBmodelUNEdijatno, N., Yang, X., Makhlouf, Z., & Michel, C. (1999). GR3J: a daily watershed model with three free parameters. Hydrological Sciences Journal, 44(2), 263–277. https://doi.org/10.1080/02626669909492221Efstratiadis, A., & Koutsoyiannis, D. (2010). One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrological Sciences Journal, 55(1), 58–78. https://doi.org/10.1080/02626660903526292ESRI Inc. (2020). ArcGIS Pro (Version 2.7). https://www.esri.com/en-us/arcgis/products/arcgis-pro/overviewFu, B. (1981). On the calculation of the evaporation from land surface. Chinese Journal of Atmospheric Sciences, 23–31.Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(1), 150066. https://doi.org/10.1038/sdata.2015.66Garay, D., & Agüero, J. (2018). Delimitación Hidrográfica y Caracterización Morfométrica de la Cuenca del Río Anzulón (INTA (ed.)).García-Echeverri, C., Duque-Gardeazabal Nicolás, Carolina, V.-V., Arboleda-Obando, P., & Zamora, D. (2020). DWBmodelUN: an R-package for the hydrological model Dynamic Water Balance. 1. https://doi.org/https://doi.org/10.5194/egusphere-egu2020-4222García-Echeverri, C., Mancipe, N., Rodríguez, C., González, C., & Zamora, D. (2021). Evaluación multitemporal del cambio de cobertura vegetal en cuencas colombianas a partir de índices de vegetación. III Congreso Internacional de Ríos y Humedales.Garcia, F., Folton, N., & Oudin, L. (2017). Which objective function to calibrate rainfall–runoff models for low-flow index simulations? Hydrological Sciences Journal, 62(7), 1149–1166. https://doi.org/10.1080/02626667.2017.1308511Graham, J. W. (2009). Missing Data Analysis: Making It Work in the Real World. Annual Review of Psychology, 60(1), 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530Gui, H., Wu, Z., & Zhang, C. (2021). Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation. CLEAN – Soil, Air, Water, 49(8), 2000381. https://doi.org/10.1002/clen.202000381Guilarte, R. (1978). Hidrología básica (F. de Ingeniería (ed.)).Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003Hamel, P., Guswa, A. J., Sahl, J., & Zhang, L. (2017). Predicting dry-season flows with a monthly rainfall–runoff model: Performance for gauged and ungauged catchments. Hydrological Processes, 31(22), 3844–3858. https://doi.org/10.1002/hyp.11298Hargreaves, G., & Samani, Z. (1985). Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1(2), 96–99. https://doi.org/10.13031/2013.26773Hernández, S., Morales, L., & Sallis, P. (2011). Estimation of Reference Evapotranspiration Using Limited Climatic Data and Bayesian Model Averaging. 2011 UKSim 5th European Symposium on Computer Modeling and Simulation, 59–63. https://doi.org/10.1109/EMS.2011.81Huard, D., & Mailhot, A. (2006). A Bayesian perspective on input uncertainty in model calibration: Application to hydrological model “abc.” Water Resources Research, 42(7). https://doi.org/10.1029/2005WR004661Huete, A., Didan, K., Miura, T., Rodriguez, E. ., Gao, X., & Ferreira, L. . (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2Huo, J., & Liu, L. (2020). Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation. Computational Intelligence and Neuroscience, 2020, 1–23. https://doi.org/10.1155/2020/8594727IDEAM. (2013). Zonificación y codificación de cuencas hidrográficas. http://documentacion.ideam.gov.co/openbiblio/bvirtual/022655/MEMORIASMAPAZONIFICACIONHIDROGRAFICA.pdfIDEAM. (2018). Protocolo De Modelación Hidrológica e Hidráulica. http://documentacion.ideam.gov.co/openbiblio/bvirtual/023833/Protocolo_Modelacion_HH.pdfIDEAM. (2019). Estudio Nacional del Agua 2018. Bogotá: Ideam 452 pp.Jabloun, M., & Sahli, A. (2008). Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data. Agricultural Water Management, 95(6), 707–715. https://doi.org/10.1016/j.agwat.2008.01.009K. Ajami, N., Gupta, H., Wagener, T., & Sorooshian, S. (2004). Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology, 298(1–4), 112–135. https://doi.org/10.1016/j.jhydrol.2004.03.033Kallio, M., Guillaume, J. H. A., Virkki, V., Kummu, M., & Virrantaus, K. (2021). Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products. Geoscientific Model Development, 14(8), 5155–5181. https://doi.org/10.5194/gmd-14-5155-2021Kim, H. J. (2001). Development of two-parametric hyperbolic model for daily streamflow simulation. PH. D. dissertation, Seoul National University, Seoul.Kim, Kwon, & Han. (2018). Exploration of warm-up period in conceptual hydrological modelling. In Journal of Hydrology (Vol. 556). https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.11.015Knoben, W. J. M., Freer, J. E., & Woods, R. A. (2019). Technical note: Inherent benchmark or not? Comparing NashSutcliffe and Kling-Gupta efficiency scores. Hydrology and Earth System Sciences. https://doi.org/https://doi.org/10.5194/hess-2019-327Koutsoyiannis, D. (2016). Generic and parsimonious stochastic modelling for hydrology and beyond. Hydrological Sciences Journal, 61(2), 225–244. https://doi.org/10.1080/02626667.2015.1016950Krause, P., Boyle, D. P., & Bäse, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89–97. https://doi.org/10.5194/adgeo-5-89-2005Kunnath-Poovakka, A., & Eldho, T. I. (2019). A comparative study of conceptual rainfall-runoff models GR4J, AWBM and Sacramento at catchments in the upper Godavari river basin, India. Journal of Earth System Science, 128(2), 33. https://doi.org/10.1007/s12040-018-1055-8Lamontagne, J. R., Barber, C. A., & Vogel, R. M. (2020). Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data. Water Resources Research, 56(9). https://doi.org/10.1029/2020WR027101Lang, D., Zheng, J., Shi, J., Liao, F., Ma, X., Wang, W., Chen, X., & Zhang, M. (2017). A Comparative Study of Potential Evapotranspiration Estimation by Eight Methods with FAO Penman–Monteith Method in Southwestern China. Water, 9(10), 734. https://doi.org/10.3390/w9100734Limbrunner, J., Vogel, R. M., & Chapram, S. (2006). The stream network storage volume is envisioned as a store which has groundwater outflow and direct runoff as inputs and streamflow as outputs. Here direct runoff is summed over all land-uses. https://sites.tufts.edu/richardvogel/files/2019/04/parsimonious-watershed-chap22.pdfLiu, H. Q., & Huete, A. R. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33, 457–465.Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. https://doi.org/10.1109/TIT.1982.1056489MacQueen, J., & others. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281–297.MADS, V. y D. T. (2010). Política Nacional para la Gestión Integral del Recurso Hídrico - PNGIRH.Michel, C. (1983). Que peut-on faire en hydrologie avec un modèle conceptuel a un seul paramètre ? La Houille Blanche, 1, 39–44.Mittal, M., Sharma, R. K., & Singh, V. P. (2014). Validation of k-means and threshold based clustering method. International Journal of Advancements in Technology, 5(2), 153–160.Moriasi, D., Gitau, M., Pai, N., & Daggupati, P. (2015). Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria. Transactions of the ASABE, 58(6), 1763–1785. https://doi.org/10.13031/trans.58.10715Muñoz, P. (2013). Apuntes de teledetección: Índices de vegetación. http://bibliotecadigital.ciren.cl/bitstream/handle/123456789/26389/Tema Indices de vegetación%2C Pedro Muñoz A.pdf?sequence=1&isAllowed=yNash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6NOAA. (2021). Cold and warm episodes by season. https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.phpOsman, M. S., Abu-Mahfouz, A. M., & Page, P. R. (2018). A Survey on Data Imputation Techniques: Water Distribution System as a Use Case. IEEE Access, 6, 63279–63291. https://doi.org/10.1109/ACCESS.2018.2877269Peel, M. ., Chiew, F. H. ., Western, A. ., & McMahon, T. . (2000). Extension of Unimpaired Monthly Streamflow Data and Regionalisation of Parameter Values to Estimate Streamflow in Ungauged Catchments. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.726.9527&rep=rep1&type=pdfPerrin, C., Michel, C., & Andréassian, V. (2003). Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology, 279(1–4), 275–289. https://doi.org/10.1016/S0022-1694(03)00225-7Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., Osuch, M., & Kundzewicz, Z. W. (2017). Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models? Hydrological Sciences Journal, 62(4), 606–625. https://doi.org/10.1080/02626667.2016.1234712QGIS.org. (2022). QGIS Geographic Information System (3.18.3). QGIS Association. https://www.qgis.org/R Core Team. (2021). R: A Language and Environment for Statistical Computing. https://www.r-project.org/Restrepo, B., & González, J. (2007). De Pearson a Spearman. Revista Colombiana de Ciencias Pecuarias, 20(2), 11. https://www.redalyc.org/articulo.oa?id=295023034010Reyes, A., Barroso, F., & Carvajal, Y. (2010). Guía básica para la caracterización morfométrica de cuencas hidrográficas (P. Editorial (ed.)). Universidad del Valle.Rodríguez-Sandoval, E., García-Echeverri, C., Sandoval-Barrera, J., Patarrollo-González, M., González-Ramírez, A., Agudelo-Duque, D., & Roldán-Novoa, M. (n.d.). Evaluating the IMERG precipitation satellite product to derive intensity-duration-frequency curves in colombia. Redin, En revisión.Rodríguez, C. (2021). Evaluación del desempeño del modelo DWB para estimar curvas de duración de caudales en la cuenca del río Sogamoso. Universidad Nacional de Colombia.Rodriguez, C., García-Echeverri, C., González, C., & Zamora, D. (2021). Efectos de las condiciones climáticas cambiantes en la simulación de la escorrentía en la cuenca del río Sogamoso. XXIV Seminario Nacional de Hidráulica e Hidrología.Rodríguez, E., Sánchez, I., Duque, N., Arboleda, P., Vega, C., Zamora, D., López, P., Kaune, A., Werner, M., García, C., & Burke, S. (2019). Combined Use of Local and Global Hydro Meteorological Data with Hydrological Models for Water Resources Management in the Magdalena - Cauca Macro Basin – Colombia. Water Resources Management. https://doi.org/doi.org/10.1007/s11269-019-02236-5Rouse J., Haas, R., Schell, J., & Deering, D. (1974). Monitoring Vegetation Systems in the Great Plains with Erts. In NASA Special Publication (Vol. 351, p. 309).Rousseeuw, P. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.RStudio Team. (2020). RStudio: Integrated Development Environment for R. http://www.rstudio.com/Silberstein, R. P. (2006). Hydrological models are so good, do we still need data? Environmental Modelling and Software, 21(9), 1340–1352. https://doi.org/10.1016/j.envsoft.2005.04.019Simonneaux, V., Hanich, L., Boulet, G., & Thomas, S. (2008). Modelling runoff in the Rheraya Catchment (High Atlas, Morocco) using the simple daily model GR4J. Trends over the Last Decades.Steinley, D., & Brusco, M. J. (2007). Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques. Journal of Classification, 24(1), 99–121. https://doi.org/10.1007/s00357-007-0003-0Takeuchi, K., AO, T., & Ishidaira, H. (1999). Introduction of block-wise use of TOPMODEL and Muskingum-Cunge method for the hydroenvironmental simulation of a large ungauged basin. Hydrological Sciences Journal, 44(4), 633–646. https://doi.org/10.1080/02626669909492258Tegegne, G., Park, D. K., & Kim, Y.-O. (2017). Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin. Journal of Hydrology: Regional Studies, 14, 49–66. https://doi.org/10.1016/j.ejrh.2017.10.002Tekleab, S., Uhlenbrook, S., Mohamed, Y., Savenije, H. H. G., Temesgen, M., & Wenninger, J. (2011). Water balance modeling of Upper Blue Nile catchments using a top-down approach. Hydrology and Earth System Sciences, 15(7), 2179–2193. https://doi.org/10.5194/hess-15-2179-2011Tian, Y., Xu, Y.-P., & Zhang, X.-J. (2013). Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models. Water Resources Management, 27(8), 2871–2888. https://doi.org/10.1007/s11269-013-0321-4Todini, E., & Biondi, D. (2017). Calibration, parameter estimation, uncertainty, data assimilation, sensitivity analysis, and validation. In V. P. Singh (Ed.), Handbook of Applied Hydrology. McGraw Hill.Tolson, B. A., & Shoemaker, C. A. (2007). Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 43(1). https://doi.org/10.1029/2005WR004723Tolson, B. A., & Shoemaker, C. A. (2008). Efficient prediction uncertainty approximation in the calibration of environmental simulation models. Water Resources Research, 44(4), 1–19. https://doi.org/10.1029/2007WR005869Tramblay, Y., Rouché, N., Paturel, J.-E., Mahé, G., Boyer, J.-F., Amoussou, E., Bodian, A., Dacosta, H., Dakhlaoui, H., Dezetter, A., Hughes, D., Hanich, L., Peugeot, C., Tshimanga, R., & Lachassagne, P. (2021). ADHI: the African Database of Hydrometric Indices (1950–2018). Earth System Science Data, 13(4), 1547–1560. https://doi.org/10.5194/essd-13-1547-2021Traore, V. B., Sambou, S., Tamba, S., Fall, S., Diaw, A., & Cissé, M. (2014). Calibrating the Rainfall-Runoff Model GR4J and GR2M on the Koulountou River Basin, a Tributary of the Gambia River. American Journal of Environmental Protection.Umaña, I. (2007). Más vale prevenir que lamentar - Las cuencas y la gestión del riesgo a los desastres naturales en Guatemala (FAO (ed.)).Vásconez, M., Manchecho, A., Álvarez, C., Prehn, C., Cevallos, C., & Ortiz, L. (2019). Cuencas hidrográficas. https://dspace.ups.edu.ec/bitstream/123456789/19038/1/Cuencas hidrográficas.pdfWang, Q. J., Pagano, T. C., Zhou, S. L., Hapuarachchi, H. A. P., Zhang, L., & Robertson, D. E. (2011). Monthly versus daily water balance models in simulating monthly runoff. Journal of Hydrology, 404(3–4), 166–175. https://doi.org/10.1016/j.jhydrol.2011.04.027Wardlow, B. D., & Egbert, S. L. (2010). A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. International Journal of Remote Sensing, 31(3), 805–830. https://doi.org/10.1080/01431160902897858Zhang, L., Potter, N., Hickel, K., Zhang, Y., & Shao, Q. (2008). Water balance modeling over variable time scales based on the Budyko framework - Model development and testing. Journal of Hydrology, 360(1–4), 117–131. https://doi.org/10.1016/j.jhydrol.2008.07.021Zhou, Y., Zhang, Y., Vaze, J., Lane, P., & Xu, S. (2015). Impact of bushfire and climate variability on streamflow from forested catchments in southeast Australia. Hydrological Sciences Journal, 60(7–8), 1340–1360. https://doi.org/10.1080/02626667.2014.961923Zuo, D., Xu, Z., Wu, W., Zhao, J., & Zhao, F. (2014). Identification of streamflow response to climate change and human activities in the wei river Basin, China. Water Resources Management, 28(3), 833–851. https://doi.org/10.1007/s11269-014-0519-0Universidad Nacional de ColombiaEstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1116501914.2022.pdf1116501914.2022.pdfTesis de Maestría en Ingeniería - Recursos Hidráulicosapplication/pdf4777305https://repositorio.unal.edu.co/bitstream/unal/81573/5/1116501914.2022.pdf1fed5759d23c9a99e9447d2df5bcc88eMD55ANEXOS_TESIS_DWB_DIARIO_CGE.zipANEXOS_TESIS_DWB_DIARIO_CGE.zipAnexoapplication/zip1029153042https://repositorio.unal.edu.co/bitstream/unal/81573/4/ANEXOS_TESIS_DWB_DIARIO_CGE.zip0c0385868b62383a22c4e95fe967f837MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81573/6/license.txt8153f7789df02f0a4c9e079953658ab2MD56THUMBNAIL1116501914.2022.pdf.jpg1116501914.2022.pdf.jpgGenerated Thumbnailimage/jpeg4297https://repositorio.unal.edu.co/bitstream/unal/81573/7/1116501914.2022.pdf.jpged65d40cfc7ca0dd527e6278af4a3c0fMD57unal/81573oai:repositorio.unal.edu.co:unal/815732024-08-05 23:10:54.874Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Publicaciones similares