Streamflow modeling in the Colombian Andes: insights from watersheds with diverse physical properties and climate patterns

Vista sencilla de ítem
dc.contributor.affiliationAlvarez M., Facultad de Ingeniería, Ingeniería Ambiental, Universidad de Medellín, Medellín, Colombia
dc.contributor.affiliationBarco J., Facultad de Ingeniería, Ingeniería Ambiental, Universidad de Medellín, Medellín, Colombia
dc.contributor.authorAlvarez M.; Barco J.
dc.date.accessioned2025-04-28T22:10:21Z
dc.date.available2025-04-28T22:10:21Z
dc.date.issued2024
dc.descriptionThe Andean region of Colombia, characterized by high climate variability and watershed complex topography, gives rise to the main rivers of the Colombia fluvial network, essential for agriculture, ecosystems, consumption, and hydropower generation. This study evaluates the spatially lumped Sacramento Soil Moisture Accounting (SAC-SMA) model for 12 Colombian watersheds located in the Andean region with different climate regimes and geomorphological features. SAC-SMA Model performance was evaluated with Nash-Sutcliffe, Kling–Gupta efficiency, and Percent Bias. The model shows good performance, exhibiting NSE values > 0.5, KGE > 0.5, and Bias −9.8% for the calibration period and NSE > 0.3, KGE > 0.4, and Bias −7.5% for the validation period. The model results demonstrate the ability of the SAC-SMA model to accurately represent mid-range flows, moist conditions, and dry conditions in nearly all the studied watersheds. However, the model encounters difficulties in capturing high flows. © 2024 IAHR and WCCE.
dc.identifier.doi10.1080/23249676.2024.2443743
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn23249676
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/8880
dc.language.isoeng
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.programIngeniería Ambientalspa
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85212844226&doi=10.1080%2f23249676.2024.2443743&partnerID=40&md5=bb5f5585394b85309de58112f1c219e3
dc.relation.referencesAmjad M., Yilmaz M.T., Yucel I., Yilmaz K.K., Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography, J Hydrol, 584, (2020)
dc.relation.referencesAngarita H., Wickel A.J., Sieber J., Chavarro J., Maldonado-Ocampo J.A., Herrera-R G.A., Delgado J., Purkey D., Basin-scale impacts of hydropower development on the Mompós depression wetlands, Colombia, Hydrol Earth Syst Sci, 22, 5, pp. 2839-2865, (2018)
dc.relation.referencesAnselm N., Rojas O., Brokamp G., Schutt B., Spatiotemporal variability of precipitation and its statistical relations to enso in the high Andean Rio Bogotá Watershed, Colombia, Earth Interact, 24, 3, pp. 1-17, (2020)
dc.relation.referencesArias P.A., Garreaud R., Poveda G., Espinoza J.C., Molina-Carpio J., Masiokas M., Viale M., Scaff L., van Oevelen P.J., Hydroclimate of the Andes part II: hydroclimate variability and sub-continental patterns, Front Earth Sci, 8 , (2021)
dc.relation.referencesBarreto-Martin C., Sierra-Parada R., Calderon-Rivera D., Jaramillo-Londono A., Mesa-Fernandez D., Spatio-temporal analysis of the hydrological response to land cover changes in the sub-basin of the Chicú river, Colombia, Heliyon, 7, 7, (2021)
dc.relation.referencesBeven K., Young P., A guide to good practice in modeling semantics for authors and referees, Water Resour Res, 49, 8, pp. 5092-5098, (2013)
dc.relation.referencesBirhanu D., Kim H., Jang C., Park S., Does the complexity of evapotranspiration and hydrological models enhance robustness?, Sustainability, 10, 8, (2018)
dc.relation.referencesBournas A., Baltas E., Comparative analysis of rain gauge and radar precipitation estimates towards rainfall-runoff modelling in a peri-urban basin in Attica, Greece, Hydrology, 8, 1, (2021)
dc.relation.referencesBrazil L.E., Hudlow M.D., Calibration procedures use with the national weather service river forecast system, IFAC Proc, 13, 3, pp. 457-466, (1981)
dc.relation.referencesBuchtele J., Elias V., Tesar M., Herrmann A., Runoff components simulated by rainfall runoff models, Hydrol Sci J, 41, 1, pp. 49-60, (1996)
dc.relation.referencesBurnash R.J.C., The NWS river forecast system-catchment modeling, Computer models of watershed hydrology, pp. 311-366, (1995)
dc.relation.referencesBurnash R.J.C., Ferral R.L., Mcguire R.A., A generalized streamflow simulation system: conceptual modeling for digital computers, (1973)
dc.relation.referencesChen F.W., Liu C.W., Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan, Paddy Water Environ, 10, 3, pp. 209-222, (2012)
dc.relation.referencesDemargne J., Wu L., Regonda S.K., Brown J.D., Lee H., He M., Seo D.J., Hartman R., Herr H.D., Fresch M., Schaake J., Zhu Y., The science of NOAA’s operational hydrologic ensemble forecast service, Bull Am Meteorol Soc, 95, 1, pp. 79-98, (2014)
dc.relation.referencesDuan Q., Sorooshian S., Gupta V., Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour Res, 28, 4, pp. 1015-1031, (1992)
dc.relation.referencesEckhardt K., How to construct recursive digital filters for baseflow separation, Hydrol Process, 19, 2, pp. 507-515, (2005)
dc.relation.referencesElgamal A., Reggiani P., Jonoski A., Impact analysis of satellite rainfall products on flow simulations in the Magdalena River Basin, Colombia, J Hydrol Reg Stud, 9, pp. 85-103, (2017)
dc.relation.referencesEspinoza J.C., Garreaud R., Poveda G., Arias P.A., Molina-Carpio J., Masiokas M., Viale M., Scaff L., Hydroclimate of the Andes part I: main climatic features, Front Earth Sci, 8, (2020)
dc.relation.referencesHogue T.S., Sorooshian S., Gupta H., Holz A., A multistep automatic calibration scheme for river forecasting models, J Hydrometeorol, 1, 6, pp. 524-542, (2000)
dc.relation.referencesJimenez M., Velasquez N., Jimenez J E., Barco J., Blessent D., Lopez-Sanchez J., Castrillon S C., Valenzuela C., Therrien R., Boico V F., Munera J.C., Sequential surface and subsurface flow modeling in a tropical aquifer under different rainfall scenarios, Environ Model Softw, 149, (2022)
dc.relation.referencesKnoben W.J.M., Freer J.E., Woods R.A., Technical note: inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores, Hydrol Earth Syst Sci, 23, 10, pp. 4323-4331, (2019)
dc.relation.referencesKratzert F., Klotz D., Brenner C., Schulz K., Herrnegger M., Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol Earth Syst Sci, 22, 11, pp. 6005-6022, (2018)
dc.relation.referencesKratzert F., Klotz D., Herrnegger M., Sampson A.K., Hochreiter S., Nearing G.S., Toward improved predictions in ungauged basins: exploiting the power of machine learning, Water Resour Res, 55, 12, pp. 11344-11354, (2019)
dc.relation.referencesLe M.H., Lakshmi V., Bolten J., Bui D.D., Adequacy of satellite-derived precipitation estimate for hydrological modeling in Vietnam Basins, J Hydrol, 586, (2020)
dc.relation.referencesLi Z., An enhanced dual IDW method for high-quality geospatial interpolation, Sci Rep, 11, 1, (2021)
dc.relation.referencesLopez-Bermeo C., Montoya R.D., Caro-Lopera F.J., Diaz-Garcia J.A., Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America, Phys Chem Earth, 127, (2022)
dc.relation.referencesLopez Lopez P., Immerzeel W.W., Rodriguez Sandoval E.A., Sterk G., Schellekens J., Spatial downscaling of satellite-based precipitation and its impact on discharge simulations in the Magdalena river basin in Colombia, Front Earth Sci, 6, (2018)
dc.relation.referencesMoradkhani H., Sorooshian S., General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis, Hydrological Modelling and the Water Cycle, (2009)
dc.relation.referencesMoriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harmel R.D., Veith T.L., Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Trans ASABE, 50, 3, pp. 885-900, (2007)
dc.relation.referencesPalomino-Angel S., Anaya-Acevedo J.A., Botero B.A., Evaluation of 3B42V7 and IMERG daily-precipitation products for a very high-precipitation region in northwestern South America, Atmos Res, 217, pp. 37-48, (2019)
dc.relation.referencesPaul P.K., Zhang Y., Ma N., Mishra A., Panigrahy N., Singh R., Selecting hydrological models for developing countries: perspective of global, continental, and country scale models over catchment scale models, J Hydrol, 600, (2021)
dc.relation.referencesPoveda G., Alvarez D.M., Rueda O.A., Hydro-climatic variability over the Andes of Colombia associated with ENSO: a review of climatic processes and their impact on one of the Earth’s most important biodiversity hotspots, Clim Dyn, 36, 11-12, pp. 2233-2249, (2011)
dc.relation.referencesPoveda G., Espinoza J.C., Zuluaga M.D., Solman S.A., Garreaud R., van Oevelen P.J., High impact weather events in the Andes, Front Earth Sci, 8, (2020)
dc.relation.referencesRaczynski K., Dyer J., Development of an objective low flow identification method using breakpoint analysis, Water, 14, 14, (2022)
dc.relation.referencesRanatunga T., Tong S.T., Yang Y.J., An approach to measure parameter sensitivity in watershed hydrological modelling, Hydrol Sci J, 62, 1, pp. 76-92, (2017)
dc.relation.referencesRodriguez E., Sanchez I., Duque N., Arboleda P., Vega C., Zamora D., Lopez P., Kaune A., Werner M., Garcia C., Burke S., Combined use of local and global hydro meteorological data with hydrological models for water resources management in the Magdalena–Cauca macro basin–Colombia, Water Resour Manag, 34, 7, pp. 2179-2199, (2020)
dc.relation.referencesRoodsari B.K., Chandler D.G., Kelleher C., Kroll C.N., A comparison of SAC-SMA and adaptive neuro-fuzzy inference system for real-time flood forecasting in small urban catchments, J Flood Risk Manag, 12, S1, (2019)
dc.relation.referencesRuelland D., Ardoin-Bardin S., Billen G., Servat E., Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa, J Hydrol, 361, 1-2, pp. 96-117, (2008)
dc.relation.referencesRunning S., Mu Q., Zhao M., MOD16A2 MODIS/Terra net evapotranspiration 8-day L4 global 500 m SIN grid V006, (2017)
dc.relation.referencesSiqueira V.A., Paiva R.C., Fleischmann A.S., Fan F.M., Ruhoff A.L., Pontes P.R., Paris A., Calmant S., Collischonn W., Toward continental hydrologic–hydrodynamic modeling in South America, Hydrol Earth Syst Sci, 22, 9, pp. 4815-4842, (2018)
dc.relation.referencesTodini E., History and perspectives of hydrological catchment modelling, Hydrol Res, 42, 2-3, pp. 73-85, (2011)
dc.relation.referencesUribe N., Corzo G., Quintero M., van Griensven A., Solomatine D., Impact of conservation tillage on nitrogen and phosphorus runoff losses in a potato crop system in Fuquene watershed, Colombia, Agric Water Manag, 209, pp. 62-72, (2018)
dc.relation.referencesUrrea V., Ochoa A., Mesa O., Seasonality of rainfall in Colombia, Water Res Res, 55, 5, pp. 4149-4162, (2019)
dc.relation.referencesAn approach for using load duration curves in the development of TMDLs. EPA 841-B-07- 006, (2007)
dc.relation.referencesVelasquez N., Hoyos C.D., Velez J.I., Zapata E., Reconstructing the 2015 Salgar flash flood using radar retrievals and a conceptual modeling framework in an ungauged basin, Hydrol Earth Syst Sci, 24, 3, pp. 1367-1392, (2020)
dc.relation.referencesVillamizar S.R., Pineda S.M., Carrillo G.A., The effects of land use and climate change on the water yield of a watershed in Colombia, Water, 11, 2, (2019)
dc.relation.referencesWagner P.D., Fiener P., Wilken F., Kumar S., Schneider K., Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions, J Hydrol, 464-465, pp. 388-400, (2012)
dc.relation.referencesWang B., Sun H., Guo S., Huang J., Wang Z., Bai X., Gong X., Jin X., Strategy for deriving Sacramento model parameters using soil properties to improve its runoff simulation performances, Agronomy, 13, 6, (2023)
dc.relation.referencesWijayarathne D.B., Coulibaly P., Identification of hydrological models for operational flood forecasting in St. John’s, Newfoundland, Canada, J Hydrol Reg Stud, 27, (2020)
dc.relation.referencesXie J., Liu X., Wang K., Yang T., Liang K., Liu C., Evaluation of typical methods for baseflow separation in the contiguous United States, J Hydrol, 583, (2020)
dc.relation.referencesXu R., Tian F., Yang L., Hu H., Lu H., Hou A., Ground validation of GPM IMERG and trmm 3B42V7 rainfall products over Southern Tibetan plateau based on a high-density rain gauge network, J Geophys Res Atmos, 122, 2, pp. 910-924, (2017)
dc.relation.referencesYapo P.O., Gupta H.V., Sorooshian S., Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data, J Hydrol, 181, 1-4, pp. 23-48, (1996)
dc.relation.referencesYaseen Z.M., Sulaiman S.O., Deo R.C., Chau K.W., An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction, J Hydrol, 569, pp. 387-408, (2019)
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceJournal of Applied Water Engineering and Research
dc.sourceJ. Appl. Water Eng. Res.
dc.sourceScopus
dc.subjectAndes mountains
dc.subjectClimate variability
dc.subjectHydrograph recession
dc.subjectHydrology model
dc.subjectSAC-SMA
dc.subjectSteeper watersheds
dc.subjectAndes mountains
dc.subjectClimate variability
dc.subjectHydrograph recession
dc.subjectHydrology model
dc.subjectSAC-SMA
dc.subjectSteeper watersheds
dc.titleStreamflow modeling in the Colombian Andes: insights from watersheds with diverse physical properties and climate patterns
dc.typeArticle
dc.type.localArtículo revisado por paresspa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

Archivos

Colecciones

Indexados Scopus