Water Resources

, Volume 44, Issue 2, pp 169–179 | Cite as

Runoff evaluation for ungauged watersheds by SWAP model. 1. Application of artificial neural networks

  • E. M. GusevEmail author
  • G. V. Ayzel
  • O. N. Nasonova
Water Resourсes and the Regime of Water Bodies


The potentialities of artificial neural networks are studied as applied to estimating key model parameters required for calculating river runoff by SWAP model in the case of ungauged watersheds. The examined geographic objects were 323 experimental watersheds of MOPEX project. The quality of model parameter estimates based on ANNs with different architecture was analyzed.


river runoff hydrograph land-surface model SWAP MOPEX-watersheds parameter optimization artificial neural networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Appolov, B.A., Kalinin, G.P., and Komarov, V.D., Kurs gidrologicheskikh prognozov (Course of Hydrological Forecasts), Leningrad: Gidrometeoizdat, 1974.Google Scholar
  2. 2.
    Gusev, E.M. and Nasonova, O.N., Studying the effect of different approaches to estimating model parameters on the accuracy of river runoff simulation by SWAP model, Water Resour., 2007, vol. 34, no. 3, pp. 277–286.CrossRefGoogle Scholar
  3. 3.
    Gusev, E.M. and Nasonova, O.N., Parametrization of heat and water exchange on land surface for coupling hydrologic and climate models, Water Resour., 1998, vol. 25, no. 4, pp. 383–393.Google Scholar
  4. 4.
    Gusev, E.M., Nasonova, O.N., and Dzhogan, L.Ya., River runoff simulation in the northwestern Russia by land surface model SWAP, Vest. RFFI, 2013, vol. 78, no. 2, pp. 19–24.Google Scholar
  5. 5.
    Gusev, E.M., Nasonova, O.N., Dzhogan, L.Ya., and Kovalev, E.E., Northern Dvina runoff simulation using land–surface model SWAP and global databases, Water Resour., 2011, vol. 38, no. 4, pp. 470–483.CrossRefGoogle Scholar
  6. 6.
    Kruglov, V.V. and Borisov, V.V., Iskusstvennye neironnye seti. Teoriya i praktika (Artificial Neural Networks: Theory and Practice), Moscow: Goryachaya liniya–Telekom, 2001.Google Scholar
  7. 7.
    McCulloch, W. and Pitts, W., A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys., 1943, no. 5, pp. 115–133.CrossRefGoogle Scholar
  8. 8.
    Nasonova, O.N. and Gusev, E.M., Investigating the ability of a land surface model to reproduce river runoff with the accuracy of hydrological models, Water. Resour., 2008, vol. 35, no. 5, pp. 493–501.CrossRefGoogle Scholar
  9. 9.
    Nasonova, O.N., Gusev, E.M., and Aizel’, G.V., Optimizing land surface parameters for simulating river runoff from 323 MOPEX-watersheds, Water Resour., 2015, vol. 42, no. 2, pp. 186–197.CrossRefGoogle Scholar
  10. 10.
    Yasnitskii, L.N., Vvedenie v iskusstvennyi intellekt (Introduction to Artificial Intelligence), Moscow: Akademiya, 2005.Google Scholar
  11. 11.
    Anderson, R.M., Koren, V.I., and Reed, S.M., Using SSURGO data to improve Sacramento Model a priori parameter estimates, J. Hydrol., 2006, vol. 320, no. 1, pp. 103–116.CrossRefGoogle Scholar
  12. 12.
    Bastidas, L.A., Gupta, H.V., and Sorooshian, S., Emerging paradigms in the calibration of hydrologic models, Mathematical models of large watershed hydrology, Colorado: Water Resour. Publications, 2002, pp. 25–66.Google Scholar
  13. 13.
    Bastola, S., Ishidaira, H., and Takeuchi, K., Regionalisation of hydrological model parameters under parameter uncertainty: a case study involving TOPMODEL and basins across the globe, J. Hydrology, 2008, vol. 357, no. 3, pp. 188–206.CrossRefGoogle Scholar
  14. 14.
    Bishop, C.M., Neural networks for pattern recognition, Oxford: Oxford university press, 1995.Google Scholar
  15. 15.
    Dirmeyer, P., Gao, X., and Oki, T., The second global soil wetness project (GSWP-2), Int. GEWEX Project Office Publication, 2002, vol. 37, p. 75.Google Scholar
  16. 16.
    Duan, Q., Schaake, J., Andreassian, V., et al., Model parameter estimation experiment (MOPEX): an overview of science strategy and major results from the second and third workshops, J. Hydrol., 2006, vol. 320, nos. 1-2, pp. 3–17.CrossRefGoogle Scholar
  17. 17.
    Duan, Q., Sorooshian, S., and Gupta, V., Effective and efficient global optimization for conceptual rainfallrunoff models, Water Resour. Res., 1992, vol. 28, no. 4, pp. 1015–1031.CrossRefGoogle Scholar
  18. 18.
    Egbuniwe, N. and Todd, D.K., Application of the Stanford watershed model to Nigerian watersheds, J. Am. Water Resour. Association, 1976, vol. 12, no. 3, pp. 449–460.CrossRefGoogle Scholar
  19. 19.
    Gibbs, M.S., Maier, H.R., and Dandy, G.C., A generic framework for regression regionalization in ungauged catchments, Environ. Modelling & Software, 2012, vol. 27, pp. 1–14.CrossRefGoogle Scholar
  20. 20.
    Gusev, Y.M. and Nasonova, O.N., The simulation of heat and water exchange in the boreal spruce forest by the land-surface model SWAP, J. Hydrol., 2003, vol. 280, no. 1, pp. 162–191.CrossRefGoogle Scholar
  21. 21.
    Gusev, E.M., Nasonova, O.N., and Kovalev, E.E., Modeling the components of heat and water balance for the land surface of the globe, Water. Resour., 2006, vol. 33, no. 6, pp. 616–627.CrossRefGoogle Scholar
  22. 22.
    Haykin, S., Neural networks: a comprehensive foundation, Singapore: Prentice Hall PTR, 1994.Google Scholar
  23. 23.
    Heuvelmans, G., Muys, B., and Feyen, J., Regionalisation of the parameters of a hydrological model: comparison of linear regression models with artificial neural nets, J. Hydrol., 2006, vol. 319, no. 1, pp. 245–265.CrossRefGoogle Scholar
  24. 24.
    Hrachowitz, M., Savenije, H.H.G., Bloschl, G., et al., A decade of predictions in ungauged basins (pub)-a review, Hydrol. Sci. J., 2013, no. 58(6), pp. 1198–1255.CrossRefGoogle Scholar
  25. 25.
    Hundecha, Y. and Bardossy, A., Modelling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model, J. Hydrol., 2004, vol. 292, no. 1, pp. 281–295.CrossRefGoogle Scholar
  26. 26.
    James, L.D., Hydrologic modelling, parameter estimation, and watershed characteristics, J. Hydrol., 1972, vol. 17, no. 4, pp. 283–307.Google Scholar
  27. 27.
    Kay, A.L., Jones, D.A., Crooks, S.M., et al., A comparison of three approaches to spatial generalization of rainfall–runoff models, Hydrol. Processes, 2006, vol. 20, no. 18, pp. 3953–3973.CrossRefGoogle Scholar
  28. 28.
    Levenberg, K., A method for the solution of certain nonlinear problems in least squares, Quarterly of Appl. Math., 1944, vol. 2, pp. 164–168.CrossRefGoogle Scholar
  29. 29.
    Magette, W.L., Shanholtz, V.O., and Carr, J.C., Estimating selected parameters for the Kentucky watershed model from watershed characteristics, Water Resour. Res., 1976, vol. 12, no. 3, pp. 472–476.CrossRefGoogle Scholar
  30. 30.
    Marquardt, D.W., An algorithm for least-squares estimation of nonlinear parameters, J. Soc. for Industrial & Appl. Math., 1963, vol. 11, no. 2, pp. 431–441.CrossRefGoogle Scholar
  31. 31.
    McIntyre, N., Lee, H., Wheater, H., et al., Ensemble predictions of runoff in ungauged catchments, Water Resour. Res., 2005, vol. 41, no. 12, pp. 1–14.CrossRefGoogle Scholar
  32. 32.
    Merz, R. and Blöschl, G., Regionalisation of catchment model parameters, J. Hydrol., 2004, vol. 287, no. 1, pp. 95–123.CrossRefGoogle Scholar
  33. 33.
    Moriasi, D.N., Arnold, J.G., Van Liew, M.W., et al., Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Transactions of the ASABE, 2007, vol. 50, no. 3, pp. 885–900.CrossRefGoogle Scholar
  34. 34.
    Nash, J.E. and Sutcliffe, J.V., River flow forecasting through conceptual models part I–A discussion of principles, J. Hydrol., 1970, vol. 10, no. 3, pp. 282–290.CrossRefGoogle Scholar
  35. 35.
    Nasonova, O.N., Gusev, Ye.M., and Kovalev, Ye.E., Investigating the ability of a land surface model to simulate streamflow with the accuracy of hydrological models: a case study using MOPEX materials, J. Hydrometeorol., 2009, vol. 10, no. 5, pp. 1128–1150.CrossRefGoogle Scholar
  36. 36.
    Oja, E., A simplified neuron model as a principal component analyzer, J. Math. Biol., 1982, vol. 15, pp. 267–273.CrossRefGoogle Scholar
  37. 37.
    Oudin, L. and Andréassian, V., Perrin C., et al., Spatial proximity, physical similarity, regression and ungauged catchments: a comparison of regionalization approaches based on 913 French catchments, Water Resour. Res., 2008, vol. 44, no. 3, pp. 1–15.Google Scholar
  38. 38.
    Parajka, J., Merz, R., and Blöschl, G., A comparison of regionalisation methods for catchment model parameters, Hydrol. and Earth System Sci. Discussions, 2005, vol. 2, no. 2, pp. 509–542.CrossRefGoogle Scholar
  39. 39.
    Sivapalan, M., Takeuchi, K., Franks, S.W., et al., IAHS decade on predictions in ungauged basins (PUB), 2003-2012: Shaping an exciting future for the hydrological sciences, Hydrol. Sci. J., 2003, vol. 48, no. 6, pp. 857–880.CrossRefGoogle Scholar
  40. 40.
    Sorooshian, S. and Gupta, V., Model calibration, Computer Models of Watershed Hydrology, Colorado: Water Resour. Publications, 1995, pp. 23–68.Google Scholar
  41. 41.
    Vandewiele, G., Xu, C., and Huybrechts, W., Regionalisation of physically-based water balance models in Belgium. Application to ungauged catchments, Water Resour. Manage. Ser., 1991, vol. 5, nos. 3–4, pp. 199–208.CrossRefGoogle Scholar
  42. 42.
    Young, A., Stream flow simulation within UK ungauged catchments using a daily rainfall-runoff model, J. Hydrol., 2006, vol. 320, no. 1, pp. 155–172.CrossRefGoogle Scholar
  43. 43.
    Zhao, M. and Dirmeyer, P., Production and analysis of GSWP-2 near-surface meteorology data sets, COLA Technical Rep, 2003, no. 159.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  1. 1.Water Problems InstituteRussian Academy of SciencesMoscowRussia

Personalised recommendations