High-Resolution Discharge Forecasting for Snowmelt and Rainfall Mixed Events - Publication - MOST Wiedzy


High-Resolution Discharge Forecasting for Snowmelt and Rainfall Mixed Events


Discharge events induced by mixture of snowmelt and rainfall are strongly nonlinear due to consequences of rain-on-snow phenomena and snowmelt dependence on energy balance. However, they received relatively little attention, especially in high-resolution discharge forecasting. In this study, we use Random Forests models for 24 h discharge forecasting in 1 h resolution in a 105.9 km 2 urbanized catchment in NE Poland: Biala River. The forcing data are delivered by Weather Research and Forecasting (WRF) model in 1 h temporal and 4 × 4 km spatial resolutions. The discharge forecasting models are set in two scenarios with snowmelt and rainfall and rainfall only predictors in order to highlight the effect of snowmelt on the results (both scenarios use also pre-forecast discharge based predictors). We show that inclusion of snowmelt decrease the forecast errors for longer forecasts’ lead times. Moreover, importance of discharge based predictors is higher in the rainfall only models then in the snowmelt and rainfall models. We conclude that the role of snowmelt for discharge forecasting in mixed snowmelt and rainfall environments is in accounting for nonlinear physical processes, such as initial wetting and rain on snow, which cannot be properly modelled by rainfall only


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artykuł w czasopiśmie wyróżnionym w JCR
Published in:
Water no. 10, edition 1, pages 1 - 18,
ISSN: 2073-4441
Publication year:
Bibliographic description:
Berezowski T., Chybicki A.: High-Resolution Discharge Forecasting for Snowmelt and Rainfall Mixed Events// Water. -Vol. 10, iss. 1 (2018), s.1-18
Digital Object Identifier (open in new tab) 10.3390/w10010056
Bibliography: test
  1. Buttle, J.M.; Xu, F. Snowmelt Runoff in Suburban Environments. Hydrol. Res. 1988, 19, 19-40. open in new tab
  2. Surfleet, C.G.; Tullos, D. Variability in effect of climate change on rain-on-snow peak flow events in a temperate climate. J. Hydrol. 2013, 479, 24-34. open in new tab
  3. Wever, N.; Jonas, T.; Fierz, C.; Lehning, M. Model simulations of the modulating effect of the snow cover in a rain-on-snow event. Hydrol. Earth Syst. Sci. 2014, 18, 4657-4669. open in new tab
  4. Cohen, J.; Ye, H.; Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 2015, 42, 7115-7122. open in new tab
  5. Langhammer, J.;Česák, J. Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series. Water 2016, 8, 560. open in new tab
  6. Rogelis, M.C.; Werner, M. Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas. Hydrol. Earth Syst. Sci. Discuss. 2017, 2017, 1-32. open in new tab
  7. Li, J.; Chen, Y.; Wang, H.; Qin, J.; Li, J.; Chiao, S. Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model. Hydrol. Earth Syst. Sci. 2017, 21, 1279-1294. open in new tab
  8. Tao, J.; Wu, D.; Gourley, J.; Zhang, S.Q.; Crow, W.; Peters-Lidard, C.; Barros, A.P. Operational hydrological forecasting during the IPHEx-IOP campaign-Meet the challenge. J. Hydrol. 2016, 541, 434-456. open in new tab
  9. Bauer-Gottwein, P.; Jensen, I.H.; Guzinski, R.; Bredtoft, G.K.T.; Hansen, S.; Michailovsky, C.I. Operational river discharge forecasting in poorly gauged basins: The Kavango River basin case study. Hydrol. Earth Syst. Sci. 2015, 19, 1469-1485. open in new tab
  10. De Lima, G.R.T.; Santos, L.B.L.; de Carvalho, T.J.; Carvalho, A.R.; Cortivo, F.D.; Scofield, G.B.; Negri, R.G. An operational dynamical neuro-forecasting model for hydrological disasters. Model. Earth Syst. Environ. 2016, 2, 1-9. open in new tab
  11. Oleson, K.W.; Lawrence, D.M.; Gordon, B.; Flanner, M.G.; Kluzek, E.; Peter, J.; Levis, S.; Swenson, S.C.; Thornton, E.; Feddema, J.; et al. Technical Description of Version 4.0 of the Community Land Model (CLM);
  12. National Center for Atmospheric Research: Boulder, CO, USA, 2010. open in new tab
  13. Lawrence, D.M.; Oleson, K.W.; Flanner, M.G.; Thornton, P.E.; Swenson, S.C.; Lawrence, P.J.; Zeng, X.; Yang, Z.L.; Levis, S.; Sakaguchi, K.; et al. Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model. J. Adv. Model. Earth Syst. 2011, 3, doi:10.1029/2011MS00045. open in new tab
  14. Benjamin, S.G.; Grell, G.A.; Brown, J.M.; Smirnova, T.G.; Bleck, R. Mesoscale Weather Prediction with the RUC Hybrid Isentropic-Terrain-Following Coordinate Model. Mon. Weather Rev. 2004, 132, 473-494. open in new tab
  15. Niu, G.Y.; Yang, Z.L.; Mitchell, K.E.; Chen, F.; Ek, M.B.; Barlage, M.; Kumar, A.; Manning, K.; Niyogi, D.; Rosero, E.; et al. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res. 2011, 116, doi:10.1029/2010JD015139. open in new tab
  16. Wang, Z.; Zeng, X.; Decker, M. Improving snow processes in the Noah land model. J. Geophys. Res. 2010, 115, D20108, doi:10.1029/2009JD013761. open in new tab
  17. Jin, J.; Wen, L. Evaluation of snowmelt simulation in the Weather Research and Forecasting model. J. Geophys. Res. Atmos. 2012, 117, doi:10.1029/2011JD016980. open in new tab
  18. Förster, K.; Meon, G.; Marke, T.; Strasser, U. Effect of meteorological forcing and snow model complexity on hydrological simulations in the Sieber catchment (Harz Mountains, Germany). Hydrol. Earth Syst. Sci. 2014, 18, 4703-4720. open in new tab
  19. Wu, X.; Shen, Y.; Wang, N.; Pan, X.; Zhang, W.; He, J.; Wang, G. Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, north-west China. Hydrol. Process. 2016, 30, 3967-3977. open in new tab
  20. Zhao, Q.; Liu, Z.; Ye, B.; Qin, Y.; Wei, Z.; Fang, S. A snowmelt runoff forecasting model coupling WRF and DHSVM. Hydrol. Earth Syst. Sci. 2009, 13, 1897-1906. open in new tab
  21. Buttle, J.M. Effects of suburbanization upon snowmelt runoff. Hydrol. Sci. J. 1990, 35, 285-302. open in new tab
  22. Valtanen, M.; Sillanpaa, N.; Setala, H. Effects of land use intensity on stormwater runoff and its temporal occurrence in cold climates. Hydrol. Process. 2013, 28, 2639-2650. open in new tab
  23. Valeo, C.; Ho, C. Modelling urban snowmelt runoff. J. Hydrol. 2004, 299, 237-251. open in new tab
  24. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32. open in new tab
  25. Instytut Meteorologii i Gospodarki Wodnej-Państwowy Instytut Badawczy (IMGW-PIB). 1 h Discharge Time Series for Zawady (ID: 153230060) Gauging Station at Biała River (ID: 26168) for the Period 2010-2016; IMGW-PIB: Warsaw, Poland, 2017. open in new tab
  26. Nakanishi, M.; Niino, H. Development of an Improved Turbulence Closure Model for the Atmospheric Boundary Layer. J. Meteorol. Soc. Jpn. 2009, 87, 895-912. open in new tab
  27. Mlawer, E.J.; Taubman, S.J.; Brown, P.D.; Iacono, M.J.; Clough, S.A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. Atmos. 1997, 102, 16663-16682. open in new tab
  28. Dudhia, J. Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. J. Atmos. Sci. 1989, 46, 3077-3107. open in new tab
  29. National Centers for Environmental Information-National Oceanic and Atmospheric Administration (NOAA-NCEI). Daily Precipitation, Temperature and Snow Depth Time Series for the Bialystok, PL Station (ID: PLM00012295) 2010-2014; NOAA-NCEI: Washington, DC, USA, 2017. open in new tab
  30. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2016.
  31. Liaw, A.; Wiener, M. Classification and Regression by randomForest. R News 2002, 2, 18-22. open in new tab
  32. Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 2006, 15, 259-263. open in new tab
  33. Berezowski, T.; Chormański, J.; Batelaan, O.; Canters, F.; Van de Voorde, T. Impact of remotely sensed land-cover proportions on urban runoff prediction. Int. J. Appl. Earth Obs. Geoinf. 2012, 16, 54-65. open in new tab
  34. Kioutsioukis, I.; de Meij, A.; Jakobs, H.; Katragkou, E.; Vinuesa, J.F.; Kazantzidis, A. High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation. Atmos. Res. 2016, 167, 156-174. open in new tab
  35. Yu, W.; Nakakita, E.; Kim, S.; Yamaguchi, K. Impact Assessment of Uncertainty Propagation of Ensemble NWP Rainfall to Flood Forecasting with Catchment Scale. Adv. Meteorol. 2016, 2016, 1384302. open in new tab
  36. García-Díez, M.; Fernández, J.; Fita, L.; Yagüe, C. Seasonal dependence of WRF model biases and sensitivity to PBL schemes over Europe. Q. J. R. Meteorol. Soc. 2012, 139, 501-514. open in new tab
  37. Chybicki, A.; Łubniewski, Z.; Kamiński, L.; Bruniecki, K.; Markiewicz, L. Numerical weather prediction-data fusion to GIS systems and potential applications. In The Future with GIS. Katedra Systemow Geoinformatycznych;
  38. Krvatski Informsticki Zbor-GIS Forum: Zagreb, Croatia, 2011; pp. 56-61.
  39. Barbetta, S.; Coccia, G.; Moramarco, T.; Todini, E. Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India. Water 2016, 8, 463. open in new tab
  40. Barge, J.; Sharif, H. An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow. Water 2016, 8, 247. open in new tab
  41. Peng, T.; Zhou, J.; Zhang, C.; Fu, W. Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks. Water 2017, 9, 406. open in new tab
  42. Sung, J.; Lee, J.; Chung, I.M.; Heo, J.H. Hourly Water Level Forecasting at Tributary Affected by Main River Condition. Water 2017, 9, 644. open in new tab
  43. Wang, J.; Shi, P.; Jiang, P.; Hu, J.; Qu, S.; Chen, X.; Chen, Y.; Dai, Y.; Xiao, Z. Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting. Water 2017, 9, 48. open in new tab
  44. Li, B.; Yang, G.; Wan, R.; Dai, X.; Zhang, Y. Comparison of random forests and other statistical methods for the prediction of lake water level: A case study of the Poyang Lake in China. Hydrol. Res. 2016, 47, 69-83. open in new tab
  45. Albers, S.J.; Déry, S.J.; Petticrew, E.L. Flooding in the Nechako River Basin of Canada: A random forest modeling approach to flood analysis in a regulated reservoir system. Can. Water Resour. J. Rev. Can. Ressour. Hydr. 2015, 41, 250-260. open in new tab
  46. Francke, T.; López-Tarazón, J.; Schröder, B. Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests. Hydrol. Process. 2008, 22, 4892-4904. open in new tab
  47. Buttle, J. Soil moisture and groundwater responses to snowmelt on a drumlin sideslope. J. Hydrol. 1989, 105, 335-355. open in new tab
  48. Bengtsson, L.; Westerström, G. Urban snowmelt and runoff in northern Sweden. Hydrol. Sci. J. 1992, 37, 263-275. open in new tab
  49. Tyszewski, S.; Kardel, I. Studium Hydrograficzne Doliny Rzeki Białej z Wytycznymi Do Zagospodarowania Rekreacyjnowypoczynkowego I Elementami Małej Retencji Oraz Prace Hydrologiczne Niezbdne Do Sporzdzenia Dokumentacji Hydrologicznej; Pro Woda: Warsaw, Poland, 2009. (In Polish)
  50. Givati, A.; Lynn, B.; Liu, Y.; Rimmer, A. Using the WRF Model in an Operational Streamflow Forecast System for the Jordan River. J. Appl. Meteorol. Climatol. 2012, 51, 285-299. open in new tab
  51. Harr, R. Some characteristics and consequences of snowmelt during rainfall in western Oregon. J. Hydrol. 1981, 53, 277-304. open in new tab
  52. Chu, H.; Wei, J.; Li, J.; Qiao, Z.; Cao, J. Improved Medium-and Long-Term Runoff Forecasting Using a Multimodel Approach in the Yellow River Headwaters Region Based on Large-Scale and Local-Scale Climate Information. Water 2017, 9, 608. open in new tab
  53. Fassnacht, S.R.; Records, R.M. Large snowmelt versus rainfall events in the mountains. J. Geophys. Res. Atmos. 2015, 120, 2375-2381. open in new tab
  54. Berezowski, T.; Chormański, J.; Batelaan, O. Skill of remote sensing snow products for distributed runoff prediction. J. Hydrol. 2015, 524, 718-732. open in new tab
  55. Berezowski, T.; Nossent, J.; Chormanski, J.; Batelaan, O. Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model. Hydrol. Earth Syst. Sci. 2015, 19, 1887-1904. c 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). open in new tab
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