Applicability of the NAM and DBM models to estimate flow discharges in high Andean sub-catchments of Ecuador

Authors

  • Andrés Quichimbo Grupo de Ciencias de la Tierra y del Ambiente, Dirección de Investigación (DIUC), Universidad deCuenca, Av. 12 de abril S/N, Cuenca, Ecuador.
  • Raúl Vázquez Grupo de Ciencias de la Tierra y del Ambiente, Dirección de Investigación (DIUC), Universidad deCuenca, Av. 12 de abril S/N, Cuenca, Ecuador. https://orcid.org/0000-0003-2581-5372
  • Esteban Samaniego Facultad de Ingeniería, Universidad de Cuenca

DOI:

https://doi.org/10.18537/mskn.04.02.07

Keywords:

hydrological modeling, DBM (Data-Based Mechanistic), Transfer Function (TF), State Dependant Parameters (SDP), Nedbor-Afstromnings Model (NAM), calibration

Abstract

A Data-Based Mechanistic (DBM) model and the Nedbor-Afstromnings Model (NAM) were applied to simulate the rainfall-runoff relationship of two Andean basins, different in size, located in southern Ecuador. This article provides a comparative analysis of both modeling approaches, with emphasis on the evaluation of the model performance. The study revealed that the DBM model better mimics the rainfall-runoff system than the NAM model representing the river basin by a structure composed of three linear reservoirs.

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References

ASCE, 2000a. Artificial neural networks in hydrology. 1: Preliminary concepts. J. Hydrol. Eng., 5(2), 115–123.

ASCE, 2000b. Artificial neural networks in hydrology. 2: Hydrology applications. J. Hydrol. Eng., 5(2), 124–137.

Beven, K.J., 2004. Rainfall - Runoff Modelling: The Primer. Wiley, 253 pp.

Célleri, R., L. Timbe, R. Vázquez, 2003. Assessment of the relation between the NAM rainfall-runoff model parameters and the physical catchment properties. HIP-VI UNESCO Technical Documents in Hydrology, 66, 9-16.

Chow, V.T., D.R. Maidment, L.W. Mays, 1988. Applied Hydrology. McGraw-Hill International Editions, Singapore, 572 pp.

CONELEC, 2009. Estadística del sector eléctrico ecuatoriano. Consejo Nacional de Electricidad, Quito, Ecuador, 388 pp.

CONELEC, 2011. Estadística del sector eléctrico ecuatoriano. Consejo Nacional de Electricidad, Quito, Ecuador, 189 pp.

DHI, 2000. MIKE 11 A Modelling System for Rivers and Channels. Danish Hydraulic Institute, Denmark, 292 pp.

Feyen, J., R.F. Vázquez, 2011. Modeling hydrological consequences of climate and land use change - Progress and Challenges. Maskana, 2(2), 83-100.

Govindaraju, R.S., A.R. Rao, 2000. Artificial Neural Networks in hydrology. Springer-Verlag, Boston, USA, 348 pp.

Hickey, J.T., G.E. Diaz, 1999. From flow to fish to dollars: An integrated approach to water allocation. J. Am. Water Resour. Assoc., 35(5), 1053-1067.

Hsu, K. L., H.V. Gupta, S. Sorooshian, 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res., 31(10), 2517-2531.

Kalman, R.E., 1960. A New Approach to Linear Filtering and Prediction Problems. Trans. ASME -J. Basic Engin., 82, 35-45.

Krause, P., D.P. Boyle, F. Bäse, 2005. Comparison of different efficiency criteria for hydrological model assessment. Adv. Geosci., 5, 89–97.

Lees, M.J., 2000a. Data-based mechanistic modelling and forecasting of hydrological systems. J. Hydroinformatics, 2(1), 15-34.

Lees, M.J., 2000b. Advances in Transfer Function Based Flood Forecasting. In: Flood forecasting: What does current research offer the practitioner? Lees, M.J., P. Walsh (eds.), BHS occasional paper No. 12, Centre for Ecology and Hydrology, British Hydrological Society, 7, 40-54.

Legates, D.R., G.J. McCabe, 1999. Evaluating the use of goodness-of-fit Measures in hydrologic and hydroclimatic model validation. Water Resour. Res., 35, 233-241.

Lin, Z., 2003. Modeling environmental systems under uncertainty: Towards a synthesis of data-based and theory-based models (Ph.D. Thesis). University of Georgia, Georgia, USA, 209 pp.

Loague, K., R.E. Green, 1991. Statistical and graphical methods for evaluating solute transport models: overview and applications. J. Contam. Hydrol., 7: 51-73.

Lorrai, M., G.M. Sechi, 1995. Neural nets for modeling rainfall runoff transformations. Water Resour. Manage., 9, 299-313.

Mwakalila, S., P. Campling, J. Feyen, G. Wyseure, K. Beven, 2001. Application of a data-based mechanistic modelling (DBM) approach for predicting runoff generation in semi-arid regions. Hydrol. Process., 15, 2281-2295.

Nash, I.E., I.V. Sutcliffe, 1970. River flow forecasting through conceptual models. J. Hydrol., 10, 282-290.

Nelder, J.A., R. Mead, 1965. A Simplex Method for Function Minimization. Comput. J., 7, 308-313.

Nielsen, S.A., E. Hansen, 1973. Numerical simulation of the rainfall-runoff process on a daily basis. Nordic Hydrol., 4, 171-190.

Palmer, M., E. Bernhardt, E. Chornesky, S. Collins, A. Dobson, C. Duke, B. Gold, R. Jacobson, S. Kingsland, R. Kranz, M. Mappin, M.L. Martinez, F. Micheli, J. Morse, M. Pace, M. Pascual, S. Palumbi, O.J. Reichman, A. Simons, A. Townsend, M. Turner, 2004. Ecology for a crowded planet. Science, 304, 1251-1252.

Pedregal, D.J., C.J. Taylor, P.C. Young, 2007. System identification, time-series analysis and forecasting. CAPTAIN handbook: The Captain Toolbox. Downloaded from http://captaintoolbox.co.uk/Captain _Toolbox.html, 273 pp.

Poff, N.L., J.D. Olden, D. Merritt, D. Pepin, 2007. Homogenization of regional river dynamics by dams and global biodiversity implications. Proc. Natl. Acad. Sci.USA, 104, 5732-5737.

Refsgaard, J.C., 1997. Parameterisation, calibration and validation of distributed hydrological models. J. Hydrol., 198, 69-97.

Refsgaard, J.C., Knudsen, J., 1996. Operational validation and intercomparison of different types of hydrological models. Water Resour. Res., 32, 2189-2202.

Rodell, M., I. Velicogna, J.S. Famiglietti, 2009. Satellite-based estimates of groundwater depletion in India. Nature, 460, 999-1002.

Romanowicz, R.J., P.C. Young, K.J. Beven, F. Pappenberger, 2008. A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting. Adv. Water Resour., 31, 1048-1056.

Sanderson, E.W., M. Jaitech, M.A. Levy, K.H. Redford, A.V. Wannebo, G. Woolmer, 2002. The human footprint and the last of the wild. BioScience, 52, 891-904.

Savenije, H.H.G., 2009. The art of hydrology. Hydrol. Earth Syst. Sci., 13, 157-161.

Singh, V.P., D.A. Woolhiser, 2002. Mathematical Modeling of Watershed Hydrology. J. Hydrol. Eng., 7(4), 270–292.

Scanlon, B.R., K.E. Keese, A.L. Flint, L.E. Flint, C.B. Gaye, M. Edmunds, I. Simmers, 2006. Global synthesis of groundwater recharge in semiarid and arid regions. Hydrol. Process., 20, 3335-3370.

Vázquez, R.F., 2003. Assessment of the performance of physically based distributed codes simulating medium size hydrologycal systems. PhD Thesis, Faculty of Engineering, ISBN 90-5682-416-3, KU Leuven, Leuven, Bélgica, 335 pp.

Vázquez, R.F., 2010. Modelación hidrológica de una microcuenca Altoandina ubicada en el Austro Ecuatoriano. Maskana, 1, 79-90.

Vázquez, R.F., L. Feyen, J. Feyen, J.C. Refsgaard, 2002. Effect of grid size on effective parameters and model performance of the MIKE-SHE code. Hydrol. Process., 16, 355-372.

Vázquez, R.F., P. Willems, J. Feyen, 2008. Improving the predictions of a MIKE SHE catchment-scale application by using a multi-criteria approach. Hydrol. Process., 22, 2159-2179.

Vázquez, R.F., K. Beven, J. Feyen, 2009. GLUE based assessment on the overall predictions of a MIKE SHE application. Water Resour. Manag., 23(7), 1325-1349.

Vázquez, R.F., R. Célleri, E. Samaniego, P. Vanegas, L.V. Campozano, J.M. Orellana, A.O. Vázquez, 2012. Proyecto Gestión de datos y Modelización Hidrológica para soporte al pronóstico de alerta temprana del Sistema Paute Integral (Informe Final). Corporación Eléctrica del Ecuador (CELEC E.P.), Cuenca, Ecuador.

Wagener, T., M. Sivapalan, B.L. McGlynn, 2008. Catchment classification and services. Toward a new paradigm for catchment hydrology driven by societal needs. In: Anderson, M.G. (ed.), Encyclopedia of Hydrological Sciences, John Wiley, Chichester, UK.

Wang, L., 2012. System identification, environmental modelling and control system design. Springer-Verlag, London, UK, 653 pp.

Wheater, H.S., A.J. Jakeman, K.J. Beven, 1993. Progress and directions in rainfall-runoff modelling. In: Jakeman, A.J., M.B. Beck, M.J. McAleer (eds.), Modelling change in environmental systems John Wiley, Chichester, UK, 101-132.

Willmont, J.C., 1981. On the validation of models. Physic. Geogr., 2, 184-194.

Young, P., Jakeman, A., 1979. Refined instrumental variable methods of recursive time-series analysis Part I. Single input, single output systems. Int. J. Control, 29, 1-30.

Young, P., Jakeman, A., 1980. Refined instrumental variable methods of recursive time-series analysis Part III. Extensions. Int. J. Control, 31, 741-764.

Young, P.C., 1973. Recursive approaches to time-series analysis. Bulletin Inst. Maths and its Applications, 10, 209-224.

Young, P.C., 1993. Time variable and state dependent modelling of non-stationary and non-linear time series. In: Rao, T.S. (ed.), Development in Time Series Analysis. Chapman & Hall, London, UK, 374-413.

Young, P.C., 1998. Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environ. Modell. Softw., 13, 1867-1874.

Young, P.C., 2001. Data-Based mechanistic modelling and validation of rainfall-flow processes. In: Anderson, M.G., P.D. Bates (eds.), Model Validation: Perspectives in Hydrological Science. John Wiley, Chichester, UK, 117-161.

Young, P.C., 2002a. Data-based mechanistic and top-down modelling. Proc. Int. Conf. on Integrated Assessment and Decision Support (iEMSs2002), 24-27.

Young, P.C., 2002b. Advances in real–time flood forecasting. Phil. Trans. R. Soc. Lond. A, 360, 1433-1450.

Young, P.C., 2011. Recursive estimation and time-series analysis: An introduction for the student and practitioner. (2nd ed.), Springer-Verlag, 505 pp.

Young, P.C., 2013. Hypothetico-inductive data-based mechanistic modeling of hydrological systems. Water Resour. Res., 1-67. Downloaded from http://captaintoolbox.co.uk/Captain _Toolbox.html.

Young, P.C., P. McKenna, J. Bruun, 2001. Identification of non-linear stochastic systems by state dependent parameter estimation. Int. J. Control, 74, 1837-1857.

Young, P.C., C.M. Tomlin, 2000. Data-Based mechanistic modelling and adaptive flow forecasting. In: Lees, M.J., P. Walsh (eds.), What does current research offer the practitioner? BHS occasional paper No 12, Centre for Ecology and Hydrology, British Hydrological Society, UK, 25-39.

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Published

2013-12-25

How to Cite

Quichimbo, A., Vázquez, R., & Samaniego, E. (2013). Applicability of the NAM and DBM models to estimate flow discharges in high Andean sub-catchments of Ecuador. Maskana, 4(2), 85–103. https://doi.org/10.18537/mskn.04.02.07

Issue

Vol. 4 No. 2 (2013)

Section

Research articles

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