Evaluation of a data-based hydrological model for simulating the runoff of medium sized Andean basins
DOI:
https://doi.org/10.18537/mskn.01.01.05Keywords:
data-mining, data-based hydrological model, split-sample, streamflow components, calibration, validation, multi-objective evaluation, step-wise analysisAbstract
Using timeseries of rainfall and streamflow of two basins in the Andean mountain range, South Ecuador, different in size (300 and 1260 Km2), a generalized lumped conceptual model (VHM), offering the possibility of using different levels of complexity with number of model parameters varying between 5 and 15, was tested. To increase the information timeseries of total streamflow were split in timeseries of quick, intermittent and baseflow, and the timeseries were discretized to select independent events of high and low flows. The paper outlines in detailed the procedure for the model structure identification, calibration and validation, as well as the multi-objective criteria approach used to evaluate the performance of the model and its components. It has been shown that the model structure, consisting of a module for soil storage and quick flow, was able to model for both basins the water balance and streamflow components with acceptable accuracy. A low value of the soil water storage facilitates the model calibration but it is not a guarantee that the model performs better. The study further reveals that the risk of over-parameterization and associated uncertainty reduces strongly the more simple the structure of the model.
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Aizen, V., E. Aizen, G. Glarizin, H.A. Loaiciga, 2000. Simulation of daily runoff in Central Asian alpine watersheds. J. Hydrol., 238, 15-34.
Atkinson, S.E., R.A. Woods, M. Sivapalan, 2002. Climate and landscape control son water balance model complexity over changing timescales. Water Resour. Res., 38, 1314, doi: 10.1029/2002WR001487.
Babovic, V., 2005. Data mining in hydrology. Hydrol. Process., 19, 1511-1515.
Bacuilima, F., J. Bacuilima, W. Bermeo, 1999. Caracterización del clima por microcuencas en el austro ecuatoriano. Tesis de grado. Facultad de Ingeniería, Universidad de Cuenca, Cuenca, Ecuador.
Beven, K.J., A.M. Binley, 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrol. Process., 6, 279-298.
Box, G.E.P., D.R. Cox, 1964. An analysis of transformations. J. Roy. Stat. Soc., 211-243, Discussion 244-252.
Burlando, P., F. Pellicciotti, U. Strasser, 2002. Modelling mountainous water systems between learning and speculating. Nord. Hydrol., 33, 47-74.
Célleri, R., 2007. Rainfall variability and rainfall-runoff dynamics in the Paute river basin – Southern Ecuadorian Andes. PhD dissertation. Faculty of Engineering, Katholieke Universiteit Leuven. Leuven, Belgium.
Célleri, R., P. Willems, W. Buytaert, J. Feyen, 2007. Space-time rainfall variability in the Paute Basin, Ecuadorian Andes. Hydrol. Process., 21(24), 3316-3327.
Chapman, T., 1991. Comment on ‘Evaluation of automated techniques for base flow and recession analyses’ by Nathan RJ and McMahon TA. Water Resour. Res., 27, 1783-1784.
Croke, B.F.W., R.A. Letcher, A.J. Jakeman, 2006. Development of a distributed flow model for underpinning assessment of water allocation options in the Namoi River Basin, Australia. J. of Hydrol., 319, 51-71.
Eder, G., M. Fuchs, H.P. Nachtnebel, W. Loibl, 2005. Semi-distributed modelling of he monthly water balance in an alpine catchment. Hydrol. Process., 19, 2339-2360.
Frawley, W., G. Piatetsky-Shapiro, C. Matheus, 1992. Knowledge Discovery in Databases: An Overview. AI Magazine, 213-228.
Jakeman, A.J., I.G. Littlewood, P.G. Whitehead, 1990. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. J. Hydrol., 117, 275-300.
Klemes, V., 1983. Conceptualization and scale in hydrology. J. Hydrol., 65, 1-23.
Klemes, V., 1986. Operational testing of hydrological simulation models. Hydrolog. Sci. J., 31, 13-24.
Kokkonen, T.S., A.J. Jakeman, 2001. A comparison of metric and conceptual approaches in rainfall-runoff modeling and its implications. Water Resour. Res., 37, 2345-2352.
Lees, M.J., L.A. Camacho, P. Whitehead, 1998. Extension of the QUASAR river quality model to incorporate dead-zone mixing. Hydrol. Earth Syst. Sc., 2, 353-365.
Lees, M.J., 2000. Data-based mechanistic modelling and forecasting of hydrological systems. J. Hydroinformatics, 2, 15-34.
Limbrick, K.J., 2002. Estimating daily recharge to the Chalk aquifer of southern England - a simple methodology. Hydrol. Earth Syst. Sc., 6, 485-495.
Littlewood, I.G., B.F.W. Croke, A.J. Jakeman, M. Sivapalan, 2003. The role of ‘top-down’ modelling for Prediction in Ungaged Basins (PUB). Hydrol. Process.,17, 1673-1679.
Nash, J.E., I.V. Sutcliffe, 1970. River flow forecasting through conceptual models. J. Hydrol., 273, 282-290.
Post, D.A., A.J. Jakeman, 1996. Relationships between catchment attributes and hydrological response characteristics in small australian mountain ash catchments. Hydrol. Process., 10, 877-892.
Refsgaard, J.C., J. Knudsen, 1996. Operational validation and intercomparison of different types of hydrological models. Water Resour. Res., 32, 2189-2202.
Schreider, S.Y., A.D. Jakeman, 2001.Streamflow modelling on a subdaily time step in the Upper Murray Basin. Math. Comput. Model., 33, 659-668.
Sefton, C.E.M., S.M. Howarth, 1998. Relationships between dynamic response characteristics and physical descriptors of catchments in England and Wales. J. Hydrol., 211, 1-16.
Singh, V.P., D.K. Frevert, 2002a. Mathematical models of large watershed hydrology. Water Resour. Publ., LLC.
Singh, V.P, D.K. Frevert, 2002b. Mathematical models of small watershed hydrology and applications. Water Resour. Publ., LLC.
Tabios, G., J. Salas, 1985. Comparative analysis of techniques for spatial interpolation of precipitation. Water Resour. Bull., 21, 365-380
Tote, C., B. De Bièvre, 2005. Generación de mapas regionales de evapotranspiracion media mensual para el Austro Ecuatoriano. Informe interna, Programa para el Manejo del Agua y del Suelo, Universidad de Cuenca, Cuenca, Ecuador.
Willems, P., 2000. Probabilistic immission modelling of receiving surface waters. Ph.D thesis, Faculty of Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
Willems, P., 2004. WETSPRO: Water Engineering Time Series PROcessing tool, Methodology and User’s Manual. Hydraulics Laboratory, Katholieke Universiteit Leuven, Leuven, Belgium.
Willems, P., 2005. Guidance document for calibration and verification of rainfall-runoff models. Internal report, Hydraulics Laboratory, Katholieke Universiteit Leuven, Leuven, Belgium, 25 pp.
Young, P., 1998. Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environ. Modell. Softw., 13, 105-122.
Young, P., 2001. Data-based mechanistic modelling and validation of rainfall-flow processes. In: Anderson, B., P.D. Bates (Eds.). Model Validation: Perspectives in Hydrological Science. John Wiley and Sons, Ltd., 117-161.
Young, P., 2003. Top -down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale. Hydrol. Process., 17, 2195-2217.
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