Runoff Calculations For Ungauged River Basins Of The Russian Arctic Region

• Main Author: Ayzel Georgy
• Format: Conference Object
• Language: unknown
• Published: Zenodo 2016
• Subjects: arctic, runoff, modeling, ungauged basins, machine learning, HBV; Arctic; Arctic Ocean; Kay; Beck; Nash; Rho; Sutcliffe; Gusev; Lakshmi; Siberia
• Online Access: https://dx.doi.org/10.5281/zenodo.61066; https://zenodo.org/record/61066

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. : {"references": ["Ayzel G.V. Artificial neural network technique implementation for the hydrological model parameters search // Russian Scientific Journal. 2014. V. 40. \u2116 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. \u2116 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\u2010scale regionalization of hydrologic model parameters. Water Resources Research. (in press)", "Bergstr\u00f6m, S., 1976, Development and application of a conceptual runoff model for Scandinavian catchments. SMHI RHO 7. Norrk\u00f6ping. 134 pp.", "Breiman L. Random forests //Machine learning. \u2013 2001. \u2013 V. 45. \u2013 \u2116. 1. \u2013 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., Bl\u00f6schl 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)\u2014a review //Hydrological Sciences Journal. 2013. No 58(6). \u0421. 1198-1255.", "Nash J. E., Sutcliffe J. V. River flow forecasting through conceptual models part I\u2014A discussion of principles //Journal of hydrology. \u2013 1970. \u2013 \u0422. 10. \u2013 \u2116. 3. \u2013 \u0421. 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. \u2013 2015. \u2013 \u0422. 42. \u2013 \u2116. 2. \u2013 \u0421. 186-197.", "Oudin L., Kay A., Andr\u00e9assian V., Perrin C. Are seemingly physically similar catchments truly hydrologically similar? //Water Resources Research. 2010. \u0422. 46. No. 11.", "Razavi T., Coulibaly P. Streamflow prediction in ungauged basins: review of regionalization methods // Journal of hydrologic engineering. 2012. \u0422. 18. No 8. \u0421. 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\u2019Connell P.E., Oki T., Pomeroy J.W., Schertzer D., Uhlenbrook S., Zehe E. IAHS Decade on Predictions in Ungauged Basins (PUB), 2003\u20132012: Shaping an exciting future for the hydrological sciences // Hydrological Sciences Journal. 2003. \u0422. 48. No 6. \u0421. 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\u2010Interim reanalysis data. Water Resources Research, 50(9), 7505-7514."]}