Advertisement

Water Resources

, Volume 45, Supplement 2, pp 1–7 | Cite as

Runoff Predictions in Ungauged Arctic Basins Using Conceptual Models Forced by Reanalysis Data

  • G. V. AyzelEmail author
Article
  • 50 Downloads

Abstract

Due to global warming, the problem of assessing water resources and their vulnerability to climate drivers in the Arctic region has become a focus in the recent years. This study is aimed at investigating three lumped hydrological models to predict daily runoff of large-scale Arctic basins in the case of substantial data scarcity. All models were driven only by meteorological forcing reanalysis dataset without any additional information about landscape, soil, or vegetation cover properties of the studied basins. Model parameter regionalization based on transferring the whole parameter set showed good efficiency for predictions in ungauged basins. We run a blind test of the proposed methodology for ensemble runoff predictions on five sub-basins, for which only monthly observations were available, and obtained promising results for current water resources assessment for a broad domain of ungauged basins in the Russian Arctic.

Keywords:

hydrologic modeling runoff ungauged basins reanalysis Arctic 

Notes

ACKNOWLEDGMENTS

This publication was supported by Geo.X, the Research Network for Geosciences in Berlin and Potsdam. The model development and evaluation part (Section 4.1–4.2) was supported by the Russian Science Foundation, project no. 16-17-10039. River runoff data were kindly provided by the Global Runoff Data Centre (GRDC), D-56068 Koblenz, Germany. Georgy Ayzel thanks James Bennett and Guillaume Thirel for their contribution to the publication’s idea distillation, useful recommendations, and positive criticism.

REFERENCES

  1. 1.
    Arsenault, R. and Brissette, F., 2016. Multi-model averaging for continuous streamflow prediction in ungauged basins, Hydrol. Sci. J., vol. 61, no. 13, pp. 2443–2454.CrossRefGoogle Scholar
  2. 2.
    Ayzel, G.V., Gusev, E.M., and Nasonova, O.N., River runoff evaluation for ungauged watersheds by SWAP model. 2. Application of methods of physiographic similarity and spatial geostatistics, Water Resour., 2017, vol. 44, no. 4, pp. 547–558.CrossRefGoogle Scholar
  3. 3.
    Ayzel, G. and Izhitskiy, A., Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea, Proc. Int. Assoc. Hydrol. Sci., 2018, vol. 379, pp. 151–158.Google Scholar
  4. 4.
    Beck, H.E., van Dijk, A.I., de Roo, A., Miralles, D.G., McVicar, T.R., Schellekens, J., and Bruijnzeel, L.A., Global-scale regionalization of hydrologic model parameters, Water Res. Res., 2016, vol. 52, no. 5, pp. 3599–3622.CrossRefGoogle Scholar
  5. 5.
    Blöschl, G. and Sivapalan, M., Scale issues in hydrological modelling: a review. Hydrol. Processes, 1995, vol. 9, nos. 3–4, pp. 251–290.CrossRefGoogle Scholar
  6. 6.
    Chiew, F.H.S., Peel, M.C., and Western, A.W., Application and testing of the simple rainfall-runoff model SIMHYD, Mathematical Models of Small Watershed Hydrology and Applications, 2002, pp. 335–367.Google Scholar
  7. 7.
    Gelfan, A., Semenov, V.A., Gusev, E., Motovilov, Y., Nasonova, O., Krylenko, I., and Kovalev, E., Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability, Hydrol. Earth Syst. Sci., 2015, vol. 19, no. 6, pp. 2737–2754.CrossRefGoogle Scholar
  8. 8.
    Gusev, E.M., Nasonova, O.N., Dzhogan, L.Y., and Ayzel, G.V., Simulating the formation of river runoff and snow cover in the northern West Siberia, Water Resour., 2015, vol. 42, no. 4, pp. 460–467.CrossRefGoogle Scholar
  9. 9.
    Hrachowitz, M., Savenije, H.H.G., Blöschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., and Fenicia, F., A decade of Predictions in Ungauged Basins (PUB)–a review, Hydrol. Sci. J., 2013, vol. 58, no. 6, pp. 1198–1255.CrossRefGoogle Scholar
  10. 10.
    Jain, S.K. and Sudheer, K.P., Fitting of hydrologic models: a close look at the Nash–Sutcliffe index, J. Hydrol. Eng., 2008, vol. 13, no. 10, pp. 981–986.CrossRefGoogle Scholar
  11. 11.
    Klemeš, V., Operational testing of hydrological simu-lation models. Hydrol. Sci. J., 1986, vol. 31, no. 1, pp. 13–24.CrossRefGoogle Scholar
  12. 12.
    Li, H., Beldring, S., and Xu, C.Y., Stability of model performance and parameter values on two catchments facing changes in climatic conditions, Hydrol. Sci. J., 2015, vol. 60, nos. 7–8, pp. 1317–1330.CrossRefGoogle Scholar
  13. 13.
    Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S., Development and test of the distributed HBV-96 hydrological model. J. Hydrol., 1997, vol. 201, nos. 1–4, pp. 272–288.CrossRefGoogle Scholar
  14. 14.
    Merz, R. and Blöschl, G., Regionalisation of catchment model parameters. J. Hydrol., 2004, vol. 287, nos. 1–4, pp. 95–123.CrossRefGoogle Scholar
  15. 15.
    Nasonova, O.N., Gusev, Y.M. and Kovalev, Y.E., Impact of uncertainties in meteorological forcing data and land surface parameters on global estimates of terrestrial water balance components, Hydrol. Processes, 2011, vol. 25, no. 7, pp. 1074–1090.CrossRefGoogle Scholar
  16. 16.
    Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Le Moine, N., Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments, Water Res. Res., 2008, vol. 44, no. 3.Google Scholar
  17. 17.
    Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil, F., and Loumagne, C., Which potential evapotranspiration input for a lumped rainfall–runoff model?: Part 2—Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling, J. Hydrol., 2005, vol. 303, nos. 1–4, pp. 290–306.CrossRefGoogle Scholar
  18. 18.
    Perrin, C., Michel, C., and Andréassian, V., Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 2003, vol. 279, nos. 1–4, pp. 275–289.CrossRefGoogle Scholar
  19. 19.
    Razavi, T. and Coulibaly, P., Streamflow prediction in ungauged basins: review of regionalization methods, J. Hydrol. Eng., 2012, vol. 18, no. 8, pp. 958–975.CrossRefGoogle Scholar
  20. 20.
    Reichl, J.P.C., Western, A.W., McIntyre, N.R., and Chiew, F.H.S., Optimization of a similarity measure for estimating ungauged streamflow, Water Res. Res., 2009, vol. 45, no. 10.Google Scholar
  21. 21.
    Shiklomanov, A.I., Lammers, R.B., and Vörösmar-ty, C.J., Widespread decline in hydrological monitoring threatens pan-Arctic research, Eos, Trans. Amer. Geophys. Union, 2002, vol. 83, no. 2, pp.13–17.CrossRefGoogle Scholar
  22. 22.
    Sivapalan, M., Takeuchi, K., Franks, S.W., Gupta, V.K., Karambiri, H., Lakshmi, V., Liang, X., McDonnell, J.J., Mendiondo, E.M., O’connell, P.E., and Oki, T., 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
  23. 23.
    Slater, A.G., Bohn, T.J., McCreight, J.L., Serreze, M.C., and Lettenmaier, D.P., A multimodel simulation of pan-Arctic hydrology, J. Geophys. Res.: Biogeosci., 2007, vol. 112, no. G4.Google Scholar
  24. 24.
    Storn, R. and Price, K., Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 1997, vol. 11, no. 4, pp. 341–359.CrossRefGoogle Scholar
  25. 25.
    Thirel, G., Andréassian, V., Perrin, C., Audouy, J.N., Berthet, L., Edwards, P., Folton, N., Furusho, C., Kuentz, A., Lerat, J., and Lindström, G., Hydrology under change: an evaluation protocol to investigate how hydrological models deal with changing catchments, Hydrol. Sci. J., 2015, vol. 60, nos. 7–8, pp. 1184–1199.CrossRefGoogle Scholar
  26. 26.
    Valéry, A., Andréassian, V. and Perrin, C., “As simple as possible but not simpler”: What is useful in a temperature-based snow-accounting routine? Part 1–Comparison of six snow accounting routines on 380 catchments, J. Hydrol., 2014, vol. 517, pp. 1166–1175.CrossRefGoogle Scholar
  27. 27.
    Weedon, G.P., Balsamo, G., Bellouin, N., Gomes, S., Best, M.J., and Viterbo, P., The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Res. Res., 2014, vol. 50, no. 9, pp. 7505–7514.CrossRefGoogle Scholar
  28. 28.
    Zakharova, E.A., Kouraev, A.V., Biancamaria, S., Kolmakova, M.V., Mognard, N.M., Zemtsov, V.A., Kirpotin, S.N., and Decharme, B., Snow cover and spring flood flow in the Northern Part of Western Siberia (the Poluy, Nadym, Pur, and Taz Rivers), J. Hydrometeor., 2011, vol. 12, no. 6, pp. 1498–1511.CrossRefGoogle Scholar
  29. 29.
    Zhang, Y. and Chiew, F.H., Relative merits of different methods for runoff predictions in ungauged catchments, Water Res. Res., 2009, vol. 45, no. 7.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Institute of Earth and Environmental Science, University of PotsdamPotsdamGermany
  2. 2.Water Problems Institute, Russian Academy of SciencesMoscowRussia

Personalised recommendations