Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system

Water Resources Research

Volumn 50, Issue 4, 2014, Pages 3108-3130

  Bourdin, Dominique R ^a   Nipen, Thomas N ^a   Stull, Roland B ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

distributed hydrological modeling; ensemble forecasting; inflow forecasting; probability; probability calibration; uncertainty

Indexed keywords

CALIBRATION; COMPUTER SIMULATION; DISTRIBUTION FUNCTIONS; ERRORS; MATHEMATICAL MODELS; PARAMETERIZATION; PROBABILITY; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS;

CUMULATIVE PROBABILITIES; DISTRIBUTED HYDROLOGIC MODEL; DISTRIBUTED HYDROLOGICAL MODEL; EMPIRICAL DISTRIBUTIONS; ENSEMBLE FORECASTING; INITIAL AND BOUNDARY CONDITIONS; METEOROLOGICAL OBSERVATION; UNCERTAINTY;

FORECASTING;

CALIBRATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; HYDROLOGICAL MODELING; INFLOW; PARAMETERIZATION; PROBABILITY; RESERVOIR; SEASONAL VARIATION; UNCERTAINTY ANALYSIS; WATERSHED;

EID: 84898057785     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2014WR015462     Document Type: Article

References (68)

1. Anderson, J. L., (1996), A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Clim., 9 (7), 1518-1530.
2. Andreadis, K. M., and, D. P. Lettenmaier, (2006), Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872-886.
3. Benoit, R., M. Desgagné, P. Pellerin, S. Pellerin, Y. Chartier, and, S. Desjardins, (1997), The Canadian MC2: A semi-Lagrangian, semi-implicit wideband atmospheric model suited for finescale process studies and simulation, Mon. Weather Rev., 125 (10), 2382-2415.
4. Blasone, R.-S., J. A. Vrugt, H. Madsen, D. Rosbjerg, B. A. Robinson, and, G. A. Zyvoloski, (2008), Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling, Adv. Water Resour., 31 (4), 630-648, doi: 10.1016/j.advwatres.2007.12.003.
5. Bourdin, D. R., and, R. B. Stull, (2013), Bias-corrected short-range Member-to-Member ensemble forecasts of reservoir inflow, J. Hydrol., 502, 77-88, doi: 10.1016/j.jhydrol.2013.08.028.
6. Bourdin, D. R., S. W. Fleming, and, R. B. Stull, (2012), Streamflow modelling: A primer on applications, approaches and challenges, Atmos. Ocean, 50 (4), 507-536, doi: 10.1080/07055900.2012.734276.
7. Box, G. E. P., and, D. R. Cox, (1964), An analysis of transformations, J. R. Stat. Soc., 26 (2), 211-252.
8. Brocker, J., and, L. A. Smith, (2007), Increasing the reliability of reliability diagrams, Wea. Forecasting, 22, 651-661, doi: 10.1175/WAF993.1.
9. Buizza, R., (1997), Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system, Mon. Weather Rev., 125 (1), 99-119.
10. Candille, G., S. Beauregard, and, N. Gagnon, (2010), Bias correction and multiensemble in the NAEFS context or how to get a "free calibration" through a multiensemble approach, Mon. Weather Rev., 138 (11), 4268-4281, doi: 10.1175/2010MWR3349.1.
11. Chow, V. T., (1954), The log-probability law and its engineering applications, Proc. Am. Soc. Civ. Eng., 80 (5), 536-1-536-25.
12. Clark, M. P., D. E. Rupp, R. A. Woods, X. Zheng, R. P. Ibbitt, A. G. Slater, J. Schmidt, and, M. J. Uddstrom, (2008), Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model, Adv. Water Resour., 31, 1309-1324.
13. DeChant, C. M., and, H. Moradkhani, (2011a), Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation, Hydrol. Earth Syst. Sci., 15, 3399-3410.
14. DeChant, C. M., and, H. Moradkhani, (2011b), Radiance data assimilation for operational snow and streamflow forecasting, Adv. Water Resour., 34, 351-364.
15. Duan, Q., N. K. Ajami, X. Gao, and, S. Sorooshian, (2007), Multi-model ensemble hydrologic prediction using Bayesian model averaging, Adv. Water Resour., 30 (5), 1371-1386, doi: 10.1016/j.advwatres.2006.11.014.
16. Eckel, F. A., and, M. K. Walters, (1998), Calibrated probabilistic quantitative precipitation forecasts based on the MRF ensemble, Wea. Forecasting, 13 (4), 1132-1147.
17. Gneiting, T., A. E. Raftery, A. H. Westveld, and, T. Goldman, (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev., 133 (5), 1098-1118, doi: 10.1175/MWR2904.1.
18. Gneiting, T., F. Balabdaoui, and, A. E. Raftery, (2007), Probabilistic forecasts, calibration and sharpness, J. R. Stat. Soc. Ser. B, 69, 243-268, doi:10.1111/j.1467-9868.2007.00587.x.
19. Graeff, T., E. Zehe, T. Blume, T. Francke, and, B. Schröder, (2012), Predicting event response in a nested catchment with generalized linear models and a distributed watershed model, Hydrol. Processes, 26 (24), 3749-3769, doi: 10.1002/hyp.8463.
20. Grell, G., J. Dudhia, and, D. R. Stauffer, (1994), A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), Tech. Rep. TN-398+STR, Natl. Cent. for Atmos. Res., Boulder, Colo.
21. Grimit, E. P., and, C. F. Mass, (2002), Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest, Wea. Forecasting, 17, 192-205.
22. Hamill, T. M., (2001), Interpretation of rank histograms for verifying ensemble forecasts, Mon. Weather Rev., 129 (3), 550-560.
23. Hamill, T. M., and, S. J. Colucci, (1997), Verification of Eta-RSM short-range ensemble forecasts, Mon. Weather Rev., 125 (6), 1312-1327.
24. Hamill, T. M., and, S. J. Colucci, (1998), Evaluation of Eta-RSM probabilistic precipitation forecasts, Mon. Weather Rev., 126 (3), 711-724.
25. Hashino, T., A. A. Bradley, and, S. S. Schwartz, (2007), Evaluation of bias-correction methods for ensemble streamflow volume forecasts, Hydrol. Earth Syst. Sci., 11, 939-950.
26. Hersbach, H., (2000), Decomposition of the continuous ranked probability score for ensemble prediction systems, Wea. Forecasting, 15, 559-570.
27. Johnson, C., and, R. Swinbank, (2009), Medium-range multimodel ensemble combination and calibration, Q. J. R. Meteorol. Soc., 135, 777-794, doi: 10.1002/qj.383.
28. Kouwen, N., (2010), WATFLOOD/WATROUTE Hydrological Model Routing and Flow Forecasting System, Univ. of Waterloo, Waterloo, Ont.
29. Krzysztofowicz, R., (2001), The case for probabilistic forecasting in hydrology, J. Hydrol., 249 (1-4), 2-9, doi:10.1016/S0022-1694(01)00420-6.
30. Kuczera, G., and, E. Parent, (1998), Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm, J. Hydrol., 211, 69-84.
31. Leisenring, M., and, H. Moradkhani, (2011), Snow water equivalent prediction using Bayesian data assimilation methods, Stochastic Environ. Res. Risk Assess., 25, 253-270.
32. Lewis, D., M. J. Singer, R. A. Dahlgren, and, K. W. Tate, (2000), Hydrology in a California oak woodland watershed: A 17-year study, J. Hydrol., 240 (1-2), 106-117, doi:10.1016/S0022-1694(00)00337-1.
33. Madadgar, S., H. Moradkhani, and, D. Garen, (2012), Towards improved post-processing of hydrologic forecast ensembles, Hydrol. Processes, 28, 104-122, doi: 10.1002/hyp.9562.
34. Mascaro, G., E. R. Vivoni, and, R. Deidda, (2011), Impact of basin scale and initial condition on ensemble streamflow forecast uncertainty, paper presented at 25th Conference on Hydrology, Am. Meteorol. Soc., Seattle, Wash.
35. McCollor, D., and, R. Stull, (2008), Hydrometeorological accuracy enhancement via postprocessing of numerical weather forecasts in complex terrain, Wea. Forecasting, 23, 131-144, doi: 10.1175/2007WAF2006107.1.
36. Moradkhani, H., K.-L. Hsu, H. V. Gupta, and, S. Sorooshian, (2005a), Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resour. Res., 41, W05012, doi: 10.1029/2004WR003604.
37. Moradkhani, H., S. Sorooshian, H. V. Gupta, and, P. R. Houser, (2005b), Dual state-parameter estimation of hydrological models using ensemble Kalman filter, Adv. Water Resour., 28, 135-147.
38. Murphy, A. H., (1973), A new vector partition of the probability score, J. Appl. Meteorol., 12, 595-600.
39. Nash, J. E., and, I. V. Sutcliffe, (1970), River flow forecasting through conceptual models. Part I: A discussion of principles, J. Hydrol., 10 (3), 282-290, doi:10.1016/0022-1694(70)90255-6.
40. Nipen, T., (2012), A component-based probabilistic weather forecasting system for operational usage, PhD thesis, Dep. of Earth, Ocean and Atmos. Sci., Univ. of B. C., Vancouver.
41. Nipen, T., and, R. Stull, (2011), Calibrating probabilistic forecasts from an NWP ensemble, Tellus, Ser. A, 63 (5), 858-875, doi: 10.1111/j.1600-0870. 2011.00535.x.
42. Olsson, J., and, G. Lindström, (2008), Evaluation and calibration of operational hydrological ensemble forecasts in Sweden, J. Hydrol., 350 (1-2), 14-24, doi: 10.1016/j.jhydrol.2007.11.010.
43. Palmer, T. N., G. J. Shutts, R. Hagedorn, F. J. Doblas-Reyes, T. Jung, and, M. Leutbecher, (2005), Representing model uncertainty in weather and climate prediction, Annu. Rev. Earth Planet. Sci., 33, 163-193, doi: 10.1146/annurev.earth.33.092203.122552.
44. Parrish, M. A., H. Moradkhani, and, C. M. DeChant, (2012), Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation, Water Resour. Res., 48, W03519, doi: 10.1029/2011WR011116.
45. Pauwels, V. R. N., and, G. J. M. De Lannoy, (2006), Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge, J. Hydrometeorol., 7, 458-477.
46. Pinson, P., P. McSharry, and, H. Madsen, (2010), Reliability diagrams for non-parametric density forecasts of continuous variables: Accounting for serial correlation, Q. J. R. Meteorol. Soc., 136 (646), 77-90, doi: 10.1002/qj.559.
47. Raftery, A. E., T. Gneiting, F. Balabdaoui, and, M. Polakowski, (2005), Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133 (5), 1155-1174, doi: 10.1175/MWR2906.1.
48. Reggiani, P., M. Renner, A. H. Weerts, and, P. A. H. J. M. van Gelder, (2009), Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational River Rhine forecasting system, Water Resour. Res., 45, W02428, doi: 10.1029/2007WR006758.
49. Roulin, E., (2007), Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrol. Earth Syst. Sci., 11, 725-737, doi:10.5194/hess-11-725-2007.
50. Roulston, M. S., and, L. A. Smith, (2002), Evaluating probabilistic forecasts using information theory, Mon. Weather Rev., 130 (6), 1653-1660.
51. Schoups, G., and, J. A. Vrugt, (2010), A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors, Water Resour. Res., 46, W10531, doi: 10.1029/2009WR008933.
52. Schulla, J., (2012), Model description WaSiM (Water balance Simulation Model), Hydrol. Software Consult. J. Schulla, Zürich, Switzerland.
53. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X. Y. Huang, W. Wang, and, J. G. Powers, (2008), A description of the Advanced Research WRF version 3, Tech. Rep. TN-475+STR, Natl. Cent. for Atmos. Res., Boulder, Colo.
54. Sloughter, J. M., A. E. Raftery, T. Gneiting, and, C. Fraley, (2007), Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Mon. Weather Rev., 135, 3209-3220.
55. Stedinger, J. R., (1980), Fitting log normal distributions to hydrologic data, Water Resour. Res., 16 (3), 481-490, doi: 10.1029/WR016i003p00481.
56. Steinschneider, S., and, C. Brown, (2011), Influences of North Atlantic climate variability on low-flows in the Connecticut River basin, J. Hydrol., 409 (1-2), 212-224, doi: 10.1016/j.jhydrol.2011.08.038.
57. Stensrud, D. J., H. E. Brooks, J. Du, M. S. Tracton, and, E. Rogers, (1999), Using ensembles for short-range forecasting, Mon. Weather Rev., 127, 433-446.
58. Talagrand, O., R. Vautard, and, B. Strauss, (1997), Evaluation of probabilistic prediction systems, in Proceedings of the Workshop on Predictability, pp. 1-25, European Centre for Medium-Range Weather Forecasts, Reading, U. K.
59. Thiemann, M., M. Trosset, H. Gupta, and, S. Sorooshian, (2001), Bayesian recursive parameter estimation for hydrologic models, Water Resour. Res., 37 (10), 2521-2535.
60. Tolson, B. A., and, C. A. Shoemaker, (2007), Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resour. Res., 43, W01413, doi: 10.1029/2005WR004723.
61. Vrugt, J. A., H. V. Gupta, L. A. Bastidas, W. Bouten, and, S. Sorooshian, (2003a), Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water Resour. Res., 39 (8), 1214, doi: 10.1029/2002WR001746.
62. Vrugt, J. A., H. V. Gupta, W. Bouten, and, S. Sorooshian, (2003b), A shuffled complex evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39 (8), 1201, doi: 10.1029/2002WR001642.
63. Vrugt, J. A., C. G. H. Diks, W. Bouten, and, M. P. Clark, (2008), Ensemble Bayesian model averaging using Markov Chain Monte Carlo sampling, Environ. Fluid Mech., 8, 579-595, doi: 10.1007/s10652-008-9106-3.
64. Wang, Q. J., D. E. Robertson, and, F. H. S. Chiew, (2009), A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites, Water Resour. Res., 45, W05407, doi: 10.1029/2008WR007355.
65. Wilks, D. S., (2006), Statistical Methods in the Atmospheric Sciences, 2nd ed., 627 pp., Academic Press, Boston, MA.
66. Wilson, L. J., S. Beauregard, A. E. Raftery, and, R. Verret, (2007), Calibrated surface temperature forecasts from the Canadian ensemble prediction system using Bayesian model averaging, Mon. Weather Rev., 135 (4), 1364-1385, doi: 10.1175/MWR3347.1.
67. Wood, A. W., and, J. C. Schaake, (2008), Correcting errors in streamflow forecast ensemble mean and spread, J. Hydrometeorol., 9 (1), 132-148, doi: 10.1175/2007JHM862.1.
68. Yuan, H., J. A. McGinley, P. J. Schultz, C. J. Anderson, and, C. Lu, (2008), Short-range precipitation forecasts from the time-lagged multimodel ensembles during the HMT-West-2006 Campaign, J. Hydrometeorol., 9, 477-491.