River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia

Numerical models are important tools for analyzing and solving water resources problems; however, a model’s reliability heavily depends on its calibration. This paper presents a method based on Design of Experiments theory for calibrating numerical models of rivers by considering the interaction bet...

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Autores:
J. Acuña, Guillermo
Ávila, Humberto
A. Canales, Fausto
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
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oai:repositorio.cuc.edu.co:11323/4947
Acceso en línea:
https://hdl.handle.net/11323/4947
https://repositorio.cuc.edu.co/
Palabra clave:
calibration
river modeling
design of experiments
MIKE-21C model
Meta River
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openAccess
License
CC0 1.0 Universal
id RCUC2_91c21498cab0e08e88e4f30f7373a76b
oai_identifier_str oai:repositorio.cuc.edu.co:11323/4947
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
title River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
spellingShingle River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
calibration
river modeling
design of experiments
MIKE-21C model
Meta River
title_short River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
title_full River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
title_fullStr River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
title_full_unstemmed River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
title_sort River Model Calibration Based on Design of Experiments Theory. A Case Study: Meta River, Colombia
dc.creator.fl_str_mv J. Acuña, Guillermo
Ávila, Humberto
A. Canales, Fausto
dc.contributor.author.spa.fl_str_mv J. Acuña, Guillermo
Ávila, Humberto
A. Canales, Fausto
dc.subject.spa.fl_str_mv calibration
river modeling
design of experiments
MIKE-21C model
Meta River
topic calibration
river modeling
design of experiments
MIKE-21C model
Meta River
description Numerical models are important tools for analyzing and solving water resources problems; however, a model’s reliability heavily depends on its calibration. This paper presents a method based on Design of Experiments theory for calibrating numerical models of rivers by considering the interaction between different calibration parameters, identifying the most sensitive parameters and finding a value or a range of values for which the calibration parameters produces an adequate performance of the model in terms of accuracy. The method consists of a systematic process for assessing the qualitative and quantitative performance of a hydromorphological numeric model. A 75 km reach of the Meta River, in Colombia, was used as case study for validating the method. The modeling was conducted by using the software package MIKE-21C, a two-dimensional flow model. The calibration is assessed by means of an Overall Weighted Indicator, based on the coefficient of determination of the calibration parameters and within a range from 0 to 1. For the case study, the most significant calibration parameters were the sediment transport equation, the riverbed load factor and the suspended load factor. The optimal calibration produced an Overall Weighted Indicator equal to 0.857. The method can be applied to any type of morphological models.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-07-12T13:46:52Z
dc.date.available.none.fl_str_mv 2019-07-12T13:46:52Z
dc.date.issued.none.fl_str_mv 2019-06-29
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 2073-4441
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/4947
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2073-4441
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/4947
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.3390/w11071382
dc.relation.references.spa.fl_str_mv 1. Church, M.; Ferguson, R.I. Morphodynamics: Rivers beyond steady state. Water Resour. Res. 2015, 51, 1883–1897. [CrossRef] 2. Yadav, B.; Eliza, K. A hybrid wavelet-support vector machine model for prediction of lake water level fluctuations using hydro-meteorological data. Measurement 2017, 103, 294–301. [CrossRef] 3. Zhu, Q.; Wang, Y.P.; Gao, S.; Zhang, J.; Li, M.; Yang, Y.; Gao, J. Modeling morphological change in anthropogenically controlled estuaries. Anthropocene 2017, 17, 70–83. [CrossRef] 4. Pascolo, S.; Petti, M.; Bosa, S. On the wave bottom shear stress in shallow depths: The role ofwave period and bed roughness. Water 2018, 10, 1348. [CrossRef] 5. Logan, B.L.; McDonald, R.R.; Nelson, J.M.; Kinzel, P.J.; Barton, G.J. Use of Multidimensional Modeling to Evaluate a Channel Restoration Design for the Kootenai River, Idaho; Scientific Investigations Report 2010–5213; U.S. Geological Survey: Reston, VA, USA, 2011. 6. Stewart, G.; Anderson, R.; Wohl, E. Two-dimensional modelling of habitat suitability as a function of discharge on two Colorado rivers. River Res. Appl. 2005, 21, 1061–1074. [CrossRef] 7. Ouédraogo, W.; Raude, J.; Gathenya, J. Continuous modeling of the Mkurumudzi River catchment in Kenya using the HEC-HMS conceptual model: Calibration, validation, model performance evaluation and sensitivity analysis. Hydrology 2018, 5, 44. [CrossRef] 8. Refsgaard, J.C.; Henriksen, H.J. Modelling guidelines—Terminology and guiding principles. Adv. Water Resour. 2004, 27, 71–82. [CrossRef] 9. Kannan, N.; Santhi, C.; White, M.J.; Mehan, S.; Arnold, J.G.; Gassman, P.W. Some Challenges in hydrologic model calibration for large-scale studies: A case study of SWAT model application to Mississippi–Atchafalaya River basin. Hydrology 2019, 6, 17. [CrossRef] 10. Arsenault, R.; Brissette, F.; Martel, J.L. The hazards of split-sample validation in hydrological model calibration. J. Hydrol. 2018, 566, 346–362. [CrossRef] 11. Hernandez-Suarez, J.S.; Nejadhashemi, A.P.; Kropp, I.M.; Abouali, M.; Zhang, Z.; Deb, K. Evaluation of the impacts of hydrologic model calibration methods on predictability of ecologically-relevant hydrologic indices. J. Hydrol. 2018, 564, 758–772. [CrossRef] 12. Kavetski, D.; Kuczera, G.; Franks, S.W. Calibration of conceptual hydrological models revisited: 1. Overcoming numerical artefacts. J. Hydrol. 2006, 320, 173–186. [CrossRef] 13. Guerrero, M.; Di Federico, V.; Lamberti, A. Calibration of a 2-D morphodynamic model using water–sediment flux maps derived from an ADCP recording. J. Hydroinforma. 2013, 15, 813–828. [CrossRef] 14. Troy, T.J.; Wood, E.F.; Sheffield, J. An efficient calibration method for continental-scale land surface modeling. Water Resour. Res. 2008, 44, 1–13. [CrossRef] 15. Getirana, A.C.V. Integrating spatial altimetry data into the automatic calibration of hydrological models. J. Hydrol. 2010, 387, 244–255. [CrossRef] 16. Francés, F.; Vélez, J.I.; Vélez, J.J. Split-parameter structure for the automatic calibration of distributed hydrological models. J. Hydrol. 2007, 332, 226–240. [CrossRef] 17. Singh, S.; Bárdossy, A. Hydrological model calibration by sequential replacement of weak parameter sets using depth function. Hydrology 2015, 2, 69–92. [CrossRef] 18. Beven, K. Prophecy, reality and uncertainty in distributed hydrological modelling. Adv. Water Resour. 1993, 16, 41–51. [CrossRef] 19. Wright, K.A.; Goodman, D.H.; Som, N.A.; Alvarez, J.; Martin, A.; Hardy, T.B. Improving hydrodynamic modelling: An analytical framework for assessment of two-dimensional hydrodynamic models. River Res. Appl. 2017, 33, 170–181. [CrossRef] 20. Paarlberg, A.J.; Guerrero, M.; Huthoff, F.; Re, M. Optimizing dredge-and-dump activities for river navigability using a hydro-morphodynamic model. Water 2015, 7, 3943–3962. [CrossRef] 21. Wu, K.; Yeh, K.C.; Lai, Y.G. A combined field and numerical modeling study to assess the longitudinal channel slope evolution in a mixed alluvial and soft bedrock stream. Water 2019, 11, 735. [CrossRef] 22. Guan, M.; Liang, Q. A two-dimensional hydro-morphological model for river hydraulics and morphology with vegetation. Environ. Model. Softw. 2017, 88, 10–21. [CrossRef] 23. Kang, T.; Kimura, I.; Shimizu, Y. Responses of bed morphology to vegetation growth and flood discharge at a sharp river bend. Water 2018, 10, 223. [CrossRef] 24. Castro-Bolinaga, C.F.; Fox, G.A. Streambank erosion: Advances in monitoring, modeling and management. Water 2018, 10, 1346. [CrossRef] 25. Bosa, S.; Petti, M.; Pascolo, S. Numerical modelling of cohesive bank migration. Water 2018, 10, 961. [CrossRef] 26. Klein, A. Verification of Morphodynamic Models on Channels, Trenches, and Pits. Master’s Thesis, TU Delft, Delft, The Netherlands, March 2004. 27. Van Waveren, R.H.; Groot, S.; Scholten, H.; van Geer, F.; Wösten, H.; Koeze, R.; Noort, J. Good Modelling Practice Handbook; RWS-RIZA: Lelystad, The Netherlands, 1999. 28. DHI. MIKE 21C Curvilinear model for river morphology—Scientific Documentation. Available online: http:// manuals.mikepoweredbydhi.help/2017/Water_Resources/MIKE21C_Scientific_documentation.pdf (accessed on 11 November 2018). 29. Papanicolaou, A.N.T.; Krallis, G.; Edinger, J. Sediment transport modeling review—Current and future developments. J. Hydraul. Eng. 2008, 134, 1–14. [CrossRef] 30. Mueller, E.R.; Pitlick, J. Sediment supply and channel morphology in mountain river systems: 1. Relative importance of lithology, topography, and climate. J. Geophys. Res. Earth Surf. 2013, 118, 2325–2342. [CrossRef] 31. Sear, D.A.; Newson, M.D.; Thorne, C.R. Guidebook of Applied Fluvial Geomorphology; Thomas Telford Ltd: London, UK, 2010. 32. Matte, P.; Secretan, Y.; Morin, J. Hydrodynamic modeling of the St. Lawrence fluvial estuary. I: Model setup, calibration, and validation. J. Waterw. Port Coast. Ocean Eng. 2017, 143, 04017010. [CrossRef] 33. Chaves, H.M.L.; Alipaz, S. An integrated indicator based on basin hydrology, environment, life, and policy: The watershed sustainability index. Water Resour. Manag. 2007, 21, 883–895. [CrossRef] 34. DHI Water & Environment. MIKE 21 Flow Model FM—User Guide: Sand Transport Module, incl. Shoreline Morphology; DHI Water & Environment: Hørsholm, Denmark, 2017. 35. De Villiers, J. 2D Modelling of Turbulent Transport of Cohesive Sediments in Shallow Reservoirs. Master’s Thesis, University of Stellenbosch, Stellenbosch, South Africa, 2006. 36. Beck, J.S.; Basson, G.R. Klein River estuary (South Africa): 2D numerical modelling of estuary breaching. Water SA 2008, 34, 33–38. 37. Jain, R. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling, 1st ed.; John Wiley & Sons: Hoboken, NJ, USA, 1991. 38. Montgomery, D.C. Design and Analysis of Experiments, 9th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2017. 39. Kleijnen, J.P.C. Experimental design for sensitivity analysis, optimization, and validation of simulation models. In Handbook of Simulation; Banks, J., Ed.; John Wiley & Sons: Hoboken, NJ, USA, 1998; pp. 173–223. 40. Lee, R. Statistical design of experiments for screening and optimization. Chemie-Ingenieur-Technik 2019, 91, 191–200. [CrossRef] 41. Dorfmann, C.; Knoblauch, H. ADCP measurements in a reservoir of a run-of-river Hydro Power Plant. In Proceedings of the 6th International Symposium on Ultrasonic Doppler Methods for Fluid Mechanics and Fluid Engineering, Prague, Czech Republic, 9–11 September 2008; pp. 45–48. 42. Universidad del Norte. Actualización de los Estudios y Diseños para la Navegabilidad del río Meta entre Cabuyaro (K804) y Puerto Carreño (K0); Universidad del Norte: Barranquilla, Colombia, 2013. 43. Plan Maestro Fluvial de Colombia 2015; Ministerio de Transporte: Bogotá, Colombia, 2015. 44. Caracterización del Transporte en Colombia—Diagnóstico y Proyectos de Transporte e Infraestructura; Ministerio de Transporte: Bogotá, Colombia, 2005. 45. Pasternack, G.B.; Gilbert, A.T.; Wheaton, J.M.; Buckland, E.M. Error propagation for velocity and shear stress prediction using 2D models for environmental management. J. Hydrol. 2006, 328, 227–241. [CrossRef] 46. Talmon, A.M. Bed Topography of River Bends with Suspended Sediment Transport. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands, 1992. 47. DHI Water & Environment. MIKE 21 Flow Model—User Guide: Hydrodynamic Module; DHI Water & Environment: Hørsholm, Denmark, 2017. 48. García, M.H. Sediment transport and morphodynamics. In Sedimentation Engineering; American Society of Civil Engineers: Reston, VA, USA, 2008; pp. 21–163. 49. Engelund, F.; Hansen, E. A monograph on sediment transport in alluvial streams; Technical University of Denmark: Copenhagen, Denmark, 1967. 50. Yang, C.T. Incipient motion and sediment transport. J. Hydraul. Div. 1973, 99, 1679–1704. 51. Van Rijn, L.C. Sediment transport, part II: Suspended load transport. J. Hydraul. Eng. 1984, 110, 1613–1641. [CrossRef] 52. Hidroconsultas LTDA. Estudios básicos en el río Meta para la línea base de ingeniería tendiente a definir el sistema más adecuado para el mantenimiento de un canal navegable, obras de encauzamiento y demás obras fluviales entre la desembocadura del río Casanare y Puerto Texas; Hidroconsultas LTDA: Bogota, Colombia, 2003.
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spelling J. Acuña, GuillermoÁvila, HumbertoA. Canales, Fausto2019-07-12T13:46:52Z2019-07-12T13:46:52Z2019-06-292073-4441https://hdl.handle.net/11323/4947Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Numerical models are important tools for analyzing and solving water resources problems; however, a model’s reliability heavily depends on its calibration. This paper presents a method based on Design of Experiments theory for calibrating numerical models of rivers by considering the interaction between different calibration parameters, identifying the most sensitive parameters and finding a value or a range of values for which the calibration parameters produces an adequate performance of the model in terms of accuracy. The method consists of a systematic process for assessing the qualitative and quantitative performance of a hydromorphological numeric model. A 75 km reach of the Meta River, in Colombia, was used as case study for validating the method. The modeling was conducted by using the software package MIKE-21C, a two-dimensional flow model. The calibration is assessed by means of an Overall Weighted Indicator, based on the coefficient of determination of the calibration parameters and within a range from 0 to 1. For the case study, the most significant calibration parameters were the sediment transport equation, the riverbed load factor and the suspended load factor. The optimal calibration produced an Overall Weighted Indicator equal to 0.857. The method can be applied to any type of morphological models.J. Acuña, Guillermo-0000-0002-7233-8161-600Ávila, Humberto-0000-0002-8064-9584-600A. Canales, Fausto-0000-0002-6858-1855-600engWaterhttps://doi.org/10.3390/w110713821. Church, M.; Ferguson, R.I. Morphodynamics: Rivers beyond steady state. Water Resour. Res. 2015, 51, 1883–1897. [CrossRef] 2. Yadav, B.; Eliza, K. A hybrid wavelet-support vector machine model for prediction of lake water level fluctuations using hydro-meteorological data. Measurement 2017, 103, 294–301. [CrossRef] 3. Zhu, Q.; Wang, Y.P.; Gao, S.; Zhang, J.; Li, M.; Yang, Y.; Gao, J. Modeling morphological change in anthropogenically controlled estuaries. Anthropocene 2017, 17, 70–83. [CrossRef] 4. Pascolo, S.; Petti, M.; Bosa, S. On the wave bottom shear stress in shallow depths: The role ofwave period and bed roughness. Water 2018, 10, 1348. [CrossRef] 5. Logan, B.L.; McDonald, R.R.; Nelson, J.M.; Kinzel, P.J.; Barton, G.J. Use of Multidimensional Modeling to Evaluate a Channel Restoration Design for the Kootenai River, Idaho; Scientific Investigations Report 2010–5213; U.S. Geological Survey: Reston, VA, USA, 2011. 6. Stewart, G.; Anderson, R.; Wohl, E. Two-dimensional modelling of habitat suitability as a function of discharge on two Colorado rivers. River Res. Appl. 2005, 21, 1061–1074. [CrossRef] 7. Ouédraogo, W.; Raude, J.; Gathenya, J. Continuous modeling of the Mkurumudzi River catchment in Kenya using the HEC-HMS conceptual model: Calibration, validation, model performance evaluation and sensitivity analysis. Hydrology 2018, 5, 44. [CrossRef] 8. Refsgaard, J.C.; Henriksen, H.J. Modelling guidelines—Terminology and guiding principles. Adv. Water Resour. 2004, 27, 71–82. [CrossRef] 9. Kannan, N.; Santhi, C.; White, M.J.; Mehan, S.; Arnold, J.G.; Gassman, P.W. Some Challenges in hydrologic model calibration for large-scale studies: A case study of SWAT model application to Mississippi–Atchafalaya River basin. Hydrology 2019, 6, 17. [CrossRef] 10. Arsenault, R.; Brissette, F.; Martel, J.L. The hazards of split-sample validation in hydrological model calibration. J. Hydrol. 2018, 566, 346–362. [CrossRef] 11. Hernandez-Suarez, J.S.; Nejadhashemi, A.P.; Kropp, I.M.; Abouali, M.; Zhang, Z.; Deb, K. Evaluation of the impacts of hydrologic model calibration methods on predictability of ecologically-relevant hydrologic indices. J. Hydrol. 2018, 564, 758–772. [CrossRef] 12. Kavetski, D.; Kuczera, G.; Franks, S.W. Calibration of conceptual hydrological models revisited: 1. Overcoming numerical artefacts. J. Hydrol. 2006, 320, 173–186. [CrossRef] 13. Guerrero, M.; Di Federico, V.; Lamberti, A. Calibration of a 2-D morphodynamic model using water–sediment flux maps derived from an ADCP recording. J. Hydroinforma. 2013, 15, 813–828. [CrossRef] 14. Troy, T.J.; Wood, E.F.; Sheffield, J. An efficient calibration method for continental-scale land surface modeling. Water Resour. Res. 2008, 44, 1–13. [CrossRef] 15. Getirana, A.C.V. Integrating spatial altimetry data into the automatic calibration of hydrological models. J. Hydrol. 2010, 387, 244–255. [CrossRef] 16. Francés, F.; Vélez, J.I.; Vélez, J.J. Split-parameter structure for the automatic calibration of distributed hydrological models. J. Hydrol. 2007, 332, 226–240. [CrossRef] 17. Singh, S.; Bárdossy, A. Hydrological model calibration by sequential replacement of weak parameter sets using depth function. Hydrology 2015, 2, 69–92. [CrossRef] 18. Beven, K. Prophecy, reality and uncertainty in distributed hydrological modelling. Adv. Water Resour. 1993, 16, 41–51. [CrossRef] 19. Wright, K.A.; Goodman, D.H.; Som, N.A.; Alvarez, J.; Martin, A.; Hardy, T.B. Improving hydrodynamic modelling: An analytical framework for assessment of two-dimensional hydrodynamic models. River Res. Appl. 2017, 33, 170–181. [CrossRef] 20. Paarlberg, A.J.; Guerrero, M.; Huthoff, F.; Re, M. Optimizing dredge-and-dump activities for river navigability using a hydro-morphodynamic model. Water 2015, 7, 3943–3962. [CrossRef] 21. Wu, K.; Yeh, K.C.; Lai, Y.G. A combined field and numerical modeling study to assess the longitudinal channel slope evolution in a mixed alluvial and soft bedrock stream. Water 2019, 11, 735. [CrossRef] 22. Guan, M.; Liang, Q. A two-dimensional hydro-morphological model for river hydraulics and morphology with vegetation. Environ. Model. Softw. 2017, 88, 10–21. [CrossRef] 23. Kang, T.; Kimura, I.; Shimizu, Y. Responses of bed morphology to vegetation growth and flood discharge at a sharp river bend. Water 2018, 10, 223. [CrossRef] 24. Castro-Bolinaga, C.F.; Fox, G.A. Streambank erosion: Advances in monitoring, modeling and management. Water 2018, 10, 1346. [CrossRef] 25. Bosa, S.; Petti, M.; Pascolo, S. Numerical modelling of cohesive bank migration. Water 2018, 10, 961. [CrossRef] 26. Klein, A. Verification of Morphodynamic Models on Channels, Trenches, and Pits. Master’s Thesis, TU Delft, Delft, The Netherlands, March 2004. 27. Van Waveren, R.H.; Groot, S.; Scholten, H.; van Geer, F.; Wösten, H.; Koeze, R.; Noort, J. Good Modelling Practice Handbook; RWS-RIZA: Lelystad, The Netherlands, 1999. 28. DHI. MIKE 21C Curvilinear model for river morphology—Scientific Documentation. Available online: http:// manuals.mikepoweredbydhi.help/2017/Water_Resources/MIKE21C_Scientific_documentation.pdf (accessed on 11 November 2018). 29. Papanicolaou, A.N.T.; Krallis, G.; Edinger, J. Sediment transport modeling review—Current and future developments. J. Hydraul. Eng. 2008, 134, 1–14. [CrossRef] 30. Mueller, E.R.; Pitlick, J. Sediment supply and channel morphology in mountain river systems: 1. Relative importance of lithology, topography, and climate. J. Geophys. Res. Earth Surf. 2013, 118, 2325–2342. [CrossRef] 31. Sear, D.A.; Newson, M.D.; Thorne, C.R. Guidebook of Applied Fluvial Geomorphology; Thomas Telford Ltd: London, UK, 2010. 32. Matte, P.; Secretan, Y.; Morin, J. Hydrodynamic modeling of the St. Lawrence fluvial estuary. I: Model setup, calibration, and validation. J. Waterw. Port Coast. Ocean Eng. 2017, 143, 04017010. [CrossRef] 33. Chaves, H.M.L.; Alipaz, S. An integrated indicator based on basin hydrology, environment, life, and policy: The watershed sustainability index. Water Resour. Manag. 2007, 21, 883–895. [CrossRef] 34. DHI Water & Environment. MIKE 21 Flow Model FM—User Guide: Sand Transport Module, incl. Shoreline Morphology; DHI Water & Environment: Hørsholm, Denmark, 2017. 35. De Villiers, J. 2D Modelling of Turbulent Transport of Cohesive Sediments in Shallow Reservoirs. Master’s Thesis, University of Stellenbosch, Stellenbosch, South Africa, 2006. 36. Beck, J.S.; Basson, G.R. Klein River estuary (South Africa): 2D numerical modelling of estuary breaching. Water SA 2008, 34, 33–38. 37. Jain, R. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling, 1st ed.; John Wiley & Sons: Hoboken, NJ, USA, 1991. 38. Montgomery, D.C. Design and Analysis of Experiments, 9th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2017. 39. Kleijnen, J.P.C. Experimental design for sensitivity analysis, optimization, and validation of simulation models. In Handbook of Simulation; Banks, J., Ed.; John Wiley & Sons: Hoboken, NJ, USA, 1998; pp. 173–223. 40. Lee, R. Statistical design of experiments for screening and optimization. Chemie-Ingenieur-Technik 2019, 91, 191–200. [CrossRef] 41. Dorfmann, C.; Knoblauch, H. ADCP measurements in a reservoir of a run-of-river Hydro Power Plant. In Proceedings of the 6th International Symposium on Ultrasonic Doppler Methods for Fluid Mechanics and Fluid Engineering, Prague, Czech Republic, 9–11 September 2008; pp. 45–48. 42. Universidad del Norte. Actualización de los Estudios y Diseños para la Navegabilidad del río Meta entre Cabuyaro (K804) y Puerto Carreño (K0); Universidad del Norte: Barranquilla, Colombia, 2013. 43. Plan Maestro Fluvial de Colombia 2015; Ministerio de Transporte: Bogotá, Colombia, 2015. 44. Caracterización del Transporte en Colombia—Diagnóstico y Proyectos de Transporte e Infraestructura; Ministerio de Transporte: Bogotá, Colombia, 2005. 45. Pasternack, G.B.; Gilbert, A.T.; Wheaton, J.M.; Buckland, E.M. Error propagation for velocity and shear stress prediction using 2D models for environmental management. J. Hydrol. 2006, 328, 227–241. [CrossRef] 46. Talmon, A.M. Bed Topography of River Bends with Suspended Sediment Transport. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands, 1992. 47. DHI Water & Environment. MIKE 21 Flow Model—User Guide: Hydrodynamic Module; DHI Water & Environment: Hørsholm, Denmark, 2017. 48. García, M.H. Sediment transport and morphodynamics. In Sedimentation Engineering; American Society of Civil Engineers: Reston, VA, USA, 2008; pp. 21–163. 49. Engelund, F.; Hansen, E. A monograph on sediment transport in alluvial streams; Technical University of Denmark: Copenhagen, Denmark, 1967. 50. Yang, C.T. Incipient motion and sediment transport. J. Hydraul. Div. 1973, 99, 1679–1704. 51. Van Rijn, L.C. Sediment transport, part II: Suspended load transport. J. Hydraul. Eng. 1984, 110, 1613–1641. [CrossRef] 52. Hidroconsultas LTDA. Estudios básicos en el río Meta para la línea base de ingeniería tendiente a definir el sistema más adecuado para el mantenimiento de un canal navegable, obras de encauzamiento y demás obras fluviales entre la desembocadura del río Casanare y Puerto Texas; Hidroconsultas LTDA: Bogota, Colombia, 2003.CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2calibrationriver modelingdesign of experimentsMIKE-21C modelMeta RiverRiver Model Calibration Based on Design of Experiments Theory. 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