Improved Spring Peak-Flow Forecasting Using Ensemble Meteorological Predictions
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 2
Abstract
The potential of ensemble meteorological forecasts in improving ensemble spring peak flow prediction up to 14 days ahead is investigated for the Saguenay-Lac-Saint Jean watershed located in northeastern Canada. Large-scale ensemble meteorological forecasts (precipitation and temperature) generated by the National Center for Environmental Prediction’s (NCEP) Global Forecast System (GFS) are bias corrected for two meteorological stations in the watershed. The bias corrected NCEP ensemble meteorological forecast data are used as input in the hydrological model Hydrologiska Byråns Vattenbalan-avdelning (HBV) to simulate ensemble reservoir inflows and Serpent River flows up to 14 days ahead. The ensemble inflow and flow forecasts are compared with climatology as well as with the case in which only observed historical data are used for spring peak flow forecasting. The study results show that there is a significant improvement for the longer forecast range in the model forecast performance when bias-corrected NCEP forecast data are used. The improvement for forecasts for the spring season as well as for the entire year is revealed by Brier and rank probability skill score (BSS and RPSS, respectively) for reservoir inflow and Serpent River flow forecasts. Visual inspection of scatter plots between observed and simulated flows, and hydrographs of ensemble mean and ensemble members also reveal the potential of NCEP meteorological forecasts for improving spring flow forecasting.
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References
Aluminium Company of Canada (ALCAN). (2000). Donnees hydrologiques (CD-ROM), Jonquiere, QC.
Bartholmes, J., and Todini, E. (2005). “Coupling meteorological and hydrological models for flood forecasting.” Hydrol. Earth Syst. Sci., 9(4), 333–346.
Beck, M. B. (1987). “Water quality modeling—A review of the analysis of uncertainty.” Water Resour. Res., 23(8), 1393–1442.
Bergstrom, S. (1991). “Principles and confidence in hydrological modelling.” Nordic Hydrol., 22, 123–136.
Boucher, M., Perreault, L., and Anctil, F. (2009). “Tools for assessment of hydrological ensemble forecasts obtained by neural networks.” J. Hydroinf., 11(3–4), 297–307.
Brier, G. W. (1950). “Verification of forecasts expressed in terms of probabilities.” Mon. Weather Rev., 78(1), 1–3.
Clark, M. P., and Hay, L. E. (2004). “Use of medium-range numerical weather prediction model output to produce forecasts of streamflow.” J. Hydrometeorol., 5(1), 15–32.
Cloke, H. L., and Pappenberger, F. (2009). “Ensemble flood forecasting: A review.” J. Hydrol., 375(3–4), 613–626.
Coulibaly, P. (2003). “Impact of meteorological predictions on real-time spring flow forecasting.” Hydrol. Processes, 17(18), 3791–3801.
Coulibaly, P., Anctil, F., and Bobee, B. (2000). “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.” J. Hydrol., 230(3–4), 244–257.
Coulibaly, P., Anctil, F., and Bobee, B. (2001). “Multivariate reservoir inflow forecasting using temporal neural networks.” J. Hydrol. Eng., 367–376.
Franz, K. J., Hartmann, H. C., Soroshian, S., and Bales, R. (2003). “Verification of national weather service ensemble streamflow predictions for water supply forecasting in the Colorado River basin.” J. Hydrometeorol., 4(6), 1105–1118.
Gangopadhyay, S., Clark, M., Brandon, D., Wener, K., and Rajagopalan, B. (2004). “Effects of spatial and temporal aggregation on the accuracy of statistically downscaled precipitation estimates in the upper Colorado River basin.” J. Hydrometeorol., 5(6), 1192–1206.
Hagedorn, R. T., Hamill, T. M., and Whitaker, J. S. (2008). “Probabilistic forecast calibration using ECMWF and GFS ensemble forecasts. Part I: 2-meter temperature.” Mon. Weather Rev., 136(7), 2608–2619.
Hamill, T. M., and Whitaker, J. S. (2006). “Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application.” Mon. Weather Rev., 134(11), 3209–3229.
Hamill, T. M., and Whitaker, J. S. (2007). “Ensemble calibration of 500 hPa geopotential and 850 hPa and 2-meter temperatures using reforecasts.” Mon. Weather Rev., 135(9), 3273–3280.
Hamill, T. M., Whitaker, J. S., and Wei, X. (2004). “Ensemble re-forecasting: Improving medium-range forecast skill using retrospective forecasts.” Mon. Weather Rev., 132(6), 1434–1447.
Hartmann, H. C., Pagano, T. C., Bales, R., and Sorooshian, S. (2002). “Confidence builders: Evaluating seasonal climate forecasts from user perspectives.” Bull. Am. Meteorol. Soc., 83(5), 683–698.
Ines, A. V. M., and Hansen, J. W. (2006). “Bias correction of daily GCM rainfall for crop simulation studies.” Agric. For. Meteorol., 138(1–4), 44–53.
Khan, M. S., and Coulibaly, P. (2006). “Bayesian neural network for rainfall-runoff modeling.” Water Resour. Res., 42(7), W07409.
Lindstrom, G., Johansson, B., Magnus, P., Gardelin, M., and Bergstrom, S. (1997). “Development and test of the distributed HBV-96 hydrological model.” J. Hydrol., 201(1–4), 272–288.
Liu, X. (2007). “Downscaling meteorological predictions for short-term hydrologic forecasting.” M.A.Sc. thesis, Dept. of Civil Engineering, McMaster Univ., Hamilton, ON.
Liu, X., and Coulibaly, P. (2011). “Downscaling ensemble weather predictions for improved week-2 hydrologic forecasts.” J. Hydrometeorol., 12(6), 1564–1580.
Mullen, S. L., and Buizza, R. (2001). “Quantitative precipitation forecasts over the United States by the ECMWF ensemble prediction system.” Mon. Weather Rev., 129(4), 638–663.
Murphy, A. H., Brown, B. G., and Chen, Y. (1989). “Diagnostic verification of temperature forecasts.” Weather Forecasting, 4(4), 485–501.
Najafi, M. R., Moradkhani, H., and Piechota, T. C. (2012). “Ensemble streamflow prediction: Climate signal weighting methods vs. climate forecast system reanalyses.” J. Hydrol., 442–443, 105–116.
Nester, T., Komma, J., Viglione, A., and Bloschl, G. (2012). “Flood forecast errors and ensemble spreade—A case study.” Water Resour. Res., 48, W10502.
Renner, M., Werner, M. G. F., Rademacher, S., and Sprokkereef, E. (2009). “Verification of ensemble flow forecasts for the river Rhine.” J. Hydrol., 376(3–4), 463–475.
Roulin, E. (2007). “Skill and relative economic value of medium-range hydrological ensemble predictions.” Hydrol. Earth Syst. Sci., 11(2), 725–737.
Roulin, E., and Vannitsem, S. (2005). “Skill of medium-range hydrologic ensemble predictions.” J. Hydrometeorol., 6(5), 729–744.
Samuel, J., Coulibaly, P., and Metcalfe, R. A. (2012). “Evaluation of future flow variability in ungauged basins: Validation of combined methods.” Adv. Water Resour., 35, 121–140.
Sharma, M., Coulibaly, P., and Dibike, Y. B. (2011). “Assessing the need for downscaling RCM data for hydrologic impact study.” ASCE J. Hydrol. Eng., 534–539.
Sivakumar, B., Jawawardena, A. W., and Fernando, T. M. K. G. (2002). “River flow forecasting: Use of phase-space reconstruction and artificial neural networks approaches.” J. Hydrol., 265(1–4), 225–245.
Sorman, A. A., Sensoy, A., Tekeli, A. E., Sorman, A. U., and Akyurek, Z. (2009). “Modelling and forecasting snowmelt runoff process using the HBV model in the eastern part of Turkey.” Hydrol. Processes, 23(7), 1031–1040.
Swedish Meteorological and Hydrological Institute (SMHI). (2006). Integrated hydrological modelling system manual, version 5.10, Norrköping, Sweden.
Theis, S., Hense, A., and Damrath, U. (2005). “Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach.” Meteorol. Appl., 12(3), 257–268.
Toth, Z., and Kalnay, E. (1997). “Ensemble forecasting at NCEP and the breeding method.” Mon. Weather Rev., 125(12), 3297–3319.
Twedt, T. M., Schaake, J. C., and Peck, E. I. (1977). “National Weather Service extended streamflow prediction.” Proc., 45th Western Snow Conf., Western Snow Conference, Brush Prairie, WA, 52–57.
Velázquez, J. A., et al. (2009). “An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting.” Hydrol. Earth Syst. Sci., 13(11), 2221–2231.
Wang, Q., and Robertson, D. E. (2011). “Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences.” Water Resour. Res., 47(2), W02546.
Watson, J. S., and Colucci, S. (2002). “Evaluation of ensemble predictions of blocking in the NCEP global spectral model.” Mon. Weather Rev., 130(12), 3008–3021.
Weigel, A. P., Liniger, M. A., and Appenzeller, C. (2007). “Notes and correspondence: Generalization of the discrete brier and ranked probability skill scores for weighted multimodel ensemble forecasts.” Mon. Weather Rev., 135(7), 2778–2785.
Werner, K., Brndon, D., Clark, M., and Gangopandhyay, S. (2004). “Climate index weighting schemes for NWS ESP-based seasonal volume forecasts.” J. Hydrometeorol., 5(6), 1076–1090.
Whitaker, J. S., Vitart, F., and Wei, X. (2006). “Improving week two forecasts with multi-model re-forecast ensembles.” Mon. Weather Rev., 134(8), 2279–2284.
Wilks, D. S. (1995). “Statistical methods in the atmospheric sciences: An introduction.” International geophysics series, Vol. 59, Academic Press, San Diego.
Wilks, D. S. (2000). “Diagnostic verification of the climate prediction center long-lead outlooks, 1995–1998.” J. Clim., 13(13), 2389–2403.
Wilks, D. S., and Hamill, T. M. (2007). “Comparison of ensemble-MOS methods using GFS reforecasts.” Mon. Weather Rev., 135(6), 2379–2390.
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Published In
Journal of Hydrologic Engineering
Volume 20 • Issue 2 • February 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Oct 19, 2012
Accepted: Apr 28, 2014
Published online: Jul 29, 2014
Discussion open until: Dec 29, 2014
Published in print: Feb 1, 2015
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