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Geoinformatika 2017; 1(61) : 63-71 (in Russian) 

AUTO-CALIBRATION OF STREAMFLOW IN A SMALL RIVER CATCHMENT WITHIN SWAT-CUP

V.V. Osypov, N.N. Osadcha

Ukrainian Hydrometeorological Institute, 37, Nauki Ave., Kyiv, 03028, Ukraine, e-mail: valery_osipov@ukr.net

Purpose. The implementation of process-based SWAT model (Soil and Water Assessment Tool) to simulate streamflow in the territory of Ukraine. Comparison of different auto-calibration procedures of SWAT-CUP software for SWAT input parameters calibration.
Design/methodology/approach. The model was applied in a small Holovesnya catchment on the territory of the Desna water-balance station. 18 parameters were used for runoff calibration after the analysis of sensitivity. This parameter set was calibrated using four auto-calibration procedures available in SWAT-CUP: SUFI-2 (Sequential Uncertainty Fitting), PSO (Particle Swarm Optimization), GLUE (generalized likelihood uncertainty estimation), ParaSol (Parameter Solution). The Nash–Sutcliffe coefficient (NS), coefficient of determination (R2) and percentage of bias (PBIAS) were used to assess the model performance.
Findings. The model was calibrated against measured daily runoff of Holovesnya in 2007 and 2009. According to the common performance ratings of calibration efficiency, all SWAT-CUP procedures showed good close results. More detailed comparative analysis of the SWAT parameter values showed that the best results were obtained using SUFI-2 (NS = 0.68, R2 = 0.68, PBIAS = -1.6).
Practical value/implications. The successful implementation of SWAT was achieved for streamflow calibration in a small river catchment. The detailed analysis of the auto-calibration procedures of SWAT-CUP was carried out. In general, all methods of calibration of process-based models, including SWAT, use inverse modeling approach, which is associated with the inability to directly measure the majority of input parameters used in the model. The main disadvantage of this approach is the non-uniqueness of solutions, i.e. the existence of different parameter sets that satisfy the specified value of the objective function. In order to minimize this problem, there is a necessity for additional measurements, such as snow melt, flow above the gauge, etc.

Keywords: SWAT, SWAT-CUP, Inverse modeling, streamflow.

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  • References:

    1. The State Service of Ukraine for Geodesy, Cartography and Cadaster. The public cadastral map. URL: http://map.land.gov.ua/
      kadastrova-karta.
    2. Osypov V.V., Osadchaya N.N. Choosing a computer simulation model to analyze the nitrogen and phosphorus emission and its testing on a small river catchment. Nauk. Praci UkrHMI, 2016, vol. 268, pp. 66-72.
    3. Khrissanov N.I., Osipov G.K. The management of waterbody eutrophication. Sankt-Peterburg, Ghydrometeoyzdat, 1993, 280 p.
    4. Abbaspour K.C., Vejdani M., Haghighat S. SWAT-CUP calibration and uncertainty programs for SWAT. In Proc. Intl. Congress on Modelling and Simulation (MODSIM’07), 2007, pp. 1603-1609.
    5. ASCE. Criteria for evaluation of watershed models. J. Irrigation Drainage Eng., 1993. vol. 119, no. 3, pp. 429-442.
    6. Beck J.V., Arnold K.J. Parameter estimation in engineering and science. New York: John Wiley and Sons, 1977, 501 p.
    7. Beven, K., Binley A. The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Processes, 1992, no 6, pp. 279-298.
    8. Duan Q., Sorooshian S., Gupta H. V. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 1992, vol. 28, pp. 1015-1031.
    9. Gupta H.V., Sorooshian S., Yapo P.O. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J. Hydrol. Eng., 1999, vol. 4, no. 2, pp. 135-143.
    10. Holland J.H. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press, 1975, 183 p.
    11. Kennedy J., Eberhart R.C. Particle swarm optimization. Proc. IEEE. int. conf. on neural networks. New York, Piscataway, 1995, vol. 4, pp. 1942-1948.
    12. Konikow L.F. Predictive accuracy of a groundwater model – Lessons from a post audit. Ground Water, 1986, vol. 24, pp. 173-184.
    13. Kool J.B., Parker J.C., van Genuchten M.T. Parameter estimation for unsaturated flow and transport models – A review. J. Hydrol. (Amsterdam), 1987, vol. 91, pp. 255-293.
    14. Marquardt D.W. An algorithm for least-squares estimation of non-linear parameters. J. Soc. Indust. App. Math., 1963, vol. 11, pp. 431-441.
    15. McKay M.D., Beckman R.J., Conover W.J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 1979, vol. 21, pp. 239-245.
    16. Moriasi 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. Amer. Soc. Agric. Biol. Eng., 2007, vol. 50, pp. 885-900.
    17. Neitsch S.L., Arnold J.G., Kiniry J.R., Williams J.R. Soil and Water Assessment Tool. Theoretical Documentation. Texas Water Resources Institute Technical Report, 2011, no. 406, 618 p. URL: http://swat.tamu.edu.
    18. Nelder J.A., Mead R.A. Simplex method for function minimization. Computer Journal, 1965, vol. 7, pp. 308-313.
    19. Van Griensven A., Meixner T., Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Science and Technology, 2006, vol. 53, pp. 51-59.
    20. Van Genuchten M.Th. Non-equilibrium transport parameters from miscible displacement experiments. Res. Rep., 1981, vol. 119.
    21. Vrugt J.A., Bouten W., Gupta H.V., Hopmans J.W. Toward improved identifiability of soil hydraulic parameters: On the selection of a suitable parametric model. Vadose Zone J., 2003, vol. 2, pp. 98-113.
    22. Vrugt J.A., Gupta H.V., Bouten W., Sorooshian S. A shuffled complex evolution metropolis algorithm for optimiza­tion and uncertainty assessment of hydrologic model parameters. Water Resour. Res., 2003, vol. 39, p. 1201. URL: DOI:10.1029/2002WR001642.
    23. Wang Q.J. Using genetic algorithms to optimize model parameters. Environ. Model. Software, 1997, vol. 12, pp. 27-34.
    24. Wellen С., Kamran-Disfani A.-R., Arhonditsis G.B. Evaluation of the Current State of Distributed Watershed Nutrient Water Quality Modeling. Environ. Sci. Technol., 2015, no 49, pp. 3278-3290.
    25. Yapo P.O., Gupta H.V., Sorooshian S. Multi-objective global optimization for hydrologic models. J. Hydrol., 1998, vol. 204, pp. 83-97.
    26. Yeh W.W.-G. Review of the parameter identification procedures in groundwater hydrology: The inverse problem. Water Resour. Res., 1986, vol. 22, pp. 95-108.
    27. Zhang X., Srinivasan R., Zhao K., Van Liew M. Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrol. Processes, 2008, vol. 23, no. 3, pp. 430-441.