Hydrology
Published August 28, 2016 | Version v1
Conference paper Open

Runoff calculations for ungauged river basins of the Russian Arctic region

Creators

  • 1. Institute of Water Problems of Russian Academy of Sciences

Description

Arctic coastal systems are very sensitive to the freshwater budget mainly formed by river runoff. Great biases in estimation of total river runoff load to the Arctic Ocean proposed by the number of various scientific groups and insufficiency of physically-based, short-term, spatially diverse runoff predictions lead to strong necessity of state-of-art hydrological techniques implementation. At the moment the most powerful tools for the land hydrological cycle modeling are physically-based, conceptual or data-driven models. Better model – wider sources of hydrometeorological and landscape-related information we need to use to perform robust calculations. Severe climatic conditions of Arctic coastal region have led to weak river runoff monitoring net and a high level of uncertainties related to difficulties of direct measurements. There is the reason we need to develop modern techniques that allow providing effective runoff predictions by state-of-art models in the case of strong research data scarcity (for ungauged basins). Early stage of research aimed to coupling of conceptual hydrological model, cutting edge machine learning techniques and various sources of geographical data will be proposed with the call for intensification of cross-disciplinary research activities for the Arctic region sustainable development and safety.

Files

Ayzel_Georgy.pdf

Files (1.4 MB)

Name Size Download all
md5:638bc3a129457045e9b79b4c7590e77a
1.4 MB Preview Download

Additional details

References

  • Ayzel G.V. Artificial neural network technique implementation for the hydrological model parameters search // Russian Scientific Journal. 2014. V. 40. № 2. p. 282-287 (In Russian).
  • Ayzel G.V. River runoff calculations for ungauged basins: the potential of using hydrological model and artificial neural network technique // Engineering Surveys. 2014. № 7. p. 60-66 (In Russian).
  • Beck, H. E., van Dijk, A. I., de Roo, A., Miralles, D. G., McVicar, T. R., Schellekens, J., & Bruijnzeel, L. A. (2016). Global‐scale regionalization of hydrologic model parameters. Water Resources Research. (in press)
  • Bergström, S., 1976, Development and application of a conceptual runoff model for Scandinavian catchments. SMHI RHO 7. Norrköping. 134 pp.
  • Breiman L. Random forests //Machine learning. – 2001. – V. 45. – №. 1. – pp. 5-32.
  • Gudmundsson, L. and Seneviratne, S. I.: Observational gridded runoff estimates for Europe (E-RUN version 1.0), Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2015-38, in review, 2016.
  • Gusev, E. M., Nasonova, O. N., Dzhogan, L. Y., & Ayzel, G. V. (2015). Simulating the formation of river runoff and snow cover in the northern West Siberia. Water Resources, 42(4), 460-467.
  • Hrachowitz M., Savenije H.H.G., Blöschl G., McDonnelld J.J., Sivapalan M., Pomeroy J.W., Arheimer B., Blume T., Clark M.P., Ehret U., Fenicia F., Freer J.E., Gelfan A., Gupta, D.A. Hughes, R.W. Hut, A. Montanari, S. Pande, D. Tetzlaff, P.A. Troch, S. Uhlenbrook H.V., Wagener T., Winsemius H.C., Woods R.A., Zehe E., Cudennec C. A decade of Predictions in Ungauged Basins (PUB)—a review //Hydrological Sciences Journal. 2013. No 58(6). С. 1198-1255.
  • Nash J. E., Sutcliffe J. V. River flow forecasting through conceptual models part I—A discussion of principles //Journal of hydrology. – 1970. – Т. 10. – №. 3. – С. 282-290.
  • Nasonova O. N., Gusev E. M., Ayzel G. V. Optimizing land surface parameters for simulating river runoff from 323 MOPEX-watersheds //Water Resources. – 2015. – Т. 42. – №. 2. – С. 186-197.
  • Oudin L., Kay A., Andréassian V., Perrin C. Are seemingly physically similar catchments truly hydrologically similar? //Water Resources Research. 2010. Т. 46. No. 11.
  • Razavi T., Coulibaly P. Streamflow prediction in ungauged basins: review of regionalization methods // Journal of hydrologic engineering. 2012. Т. 18. No 8. С. 958-975.
  • 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., Oki T., Pomeroy J.W., Schertzer D., Uhlenbrook S., Zehe E. IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences // Hydrological Sciences Journal. 2003. Т. 48. No 6. С. 857-880.
  • The Global Runoff Data Center, 56068 Koblenz, Germany. www.bafg.de
  • Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., & Viterbo, P. (2014). The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data. Water Resources Research, 50(9), 7505-7514.