Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

Water Resources Research

Volumn 52, Issue 10, 2016, Pages 8238-8259

  Bennett, James C ^a   Wang, Q J ^a   Li, Ming ^a   Robertson, David E ^a   Schepen, Andrew ^a  

^a CSIRO   (Australia)

Author keywords

CGCM; ensemble prediction; error modeling; hydrological uncertainty; seasonal streamflow forecasting

Indexed keywords

CATCHMENTS; CLIMATOLOGY; ERRORS; RAIN; RUNOFF; STOCHASTIC SYSTEMS; STREAM FLOW; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

CGCM; ENSEMBLE PREDICTION; ERROR MODEL; HYDROLOGICAL UNCERTAINTY; STREAMFLOW FORECASTING;

CLIMATE MODELS;

ACCURACY ASSESSMENT; ATMOSPHERE-OCEAN COUPLING; CALIBRATION; CATCHMENT; CLIMATE PREDICTION; CONCEPTUAL FRAMEWORK; ENSEMBLE FORECASTING; ERROR ANALYSIS; GENERAL CIRCULATION MODEL; HYDROLOGICAL MODELING; LONG-TERM CHANGE; PRECIPITATION (CLIMATOLOGY); RUNOFF; SEASONAL VARIATION; STREAMFLOW; UNCERTAINTY ANALYSIS;

EID: 84995902809     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2016WR019193     Document Type: Article

References (73)

1. Arnal, L., M. H. Ramos, E. Coughlan de Perez, H. L. Cloke, E. Stephens, F. Wetterhall, S. J. van Andel, and F. Pappenberger (2016), Willingness-to-pay for a probabilistic flood forecast: A risk-based decision-making game, Hydrol. Earth Syst. Sci., 20(8), 3109–3128, doi:10.5194/hess-20-3109-2016.
2. Ashok, K., H. Nakamura, and T. Yamagata (2007), Impacts of ENSO and Indian Ocean dipole events on the southern hemisphere storm-track activity during austral winter, J. Clim., 20(13), 3147–3163, doi:10.1175/jcli4155.1.
3. Barnston, A. G., M. K. Tippett, M. L. L'Heureux, S. Li, and D. G. DeWitt (2012), Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing?, Bull. Am. Meteorol. Soc., 93(5), ES48–ES50, doi:10.1175/bams-d-11-00111.2.
4. Bates, B. C., and E. P. Campbell (2001), A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall-runoff modeling, Water Resour. Res., 37(4), 937–947, doi:10.1029/2000WR900363.
5. Bennett, J. C., Q. J. Wang, P. Pokhrel, and D. E. Robertson (2014), The challenge of forecasting high streamflows 1-3 months in advance with lagged climate indices in southeast Australia, Nat. Hazards Earth Syst. Sci., 14(2), 219–233, doi:10.5194/nhess-14-219-2014.
6. Bracken, C., B. Rajagopalan, and J. Prairie (2010), A multisite seasonal ensemble streamflow forecasting technique, Water Resour. Res., 46, W03532, doi:10.1029/2009WR007965.
7. Bradley, A. A., M. Habib, and S. S. Schwartz (2015), Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach, Water Resour. Res., 51, 7382–7400, doi:10.1002/2014WR016811.
8. Charles, A., B. Timbal, E. Fernandez, and H. Hendon (2013), Analog downscaling of seasonal rainfall forecasts in the Murray Darling Basin, Mon. Weather Rev., 141(3), 1099–1117, doi:10.1175/mwr-d-12-00098.1.
9. Clark, M. P., S. Gangopadhyay, L. Hay, B. Rajagopalan, and R. Wilby (2004), The Schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields, J. Hydrometeorol., 5, 243–262, doi:10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2.
10. Cox, D. R., and N. Reid (2004), A note on pseudolikelihood constructed from marginal densities, Biometrika, 91(3), 729–737, doi: 10.1093/biomet/91.3.729.
11. Crochemore, L., M.-H. Ramos, F. Pappenberger, S. J. van Andel, and A. W. Wood (2015), An experiment on risk-based decision-making in water management using monthly probabilistic forecasts, Bull. Am. Meteorol. Soc., 97, 541–551, doi:10.1175/bams-d-14-00270.1.
12. Crochemore, L., M. H. Ramos, and F. Pappenberger (2016), Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, doi:10.5194/hess-2016-78.
13. Day, G. N. (1985), Extended streamflow forecasting using NWSRFS, J. Water Resour. Plann. Manage., 111(2), 157–170, doi:10.1061/(ASCE)0733-9496(1985)111:2(157).
14. Del Giudice, D., M. Honti, A. Scheidegger, C. Albert, P. Reichert, and J. Rieckermann (2013), Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, doi:10.5194/hess-17-4209-2013.
15. Duan, Q., S. Sorooshian, and V. K. Gupta (1994), Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol., 158(3–4), 265–284, doi:10.1016/0022-1694(94)90057-4.
16. Evin, G., D. Kavetski, M. Thyer, and G. Kuczera (2013), Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration, Water Resour. Res., 49, 4518–4524, doi:10.1002/wrcr.20284.
17. Evin, G., M. Thyer, D. Kavetski, D. McInerney, and G. Kuczera (2014), Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity, Water Resour. Res., 50, 2350–2375, doi:10.1002/2013WR014185.
18. Gneiting, T., and M. Katzfuss (2014), Probabilistic forecasting, Annu. Rev. Stat. Appl., 1(1), 125–151, doi:10.1146/annurev-statistics-062713-085831.
19. Grantz, K., B. Rajagopalan, M. Clark, and E. Zagona (2005), A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts, Water Resour. Res., 41, W10410, doi:10.1029/2004WR003467.
20. Guerrero, V. M. (1993), Time-series analysis supported by power transformations, J. Forecasting, 12(1), 37–48, doi: 10.1002/for.3980120104.
21. Hawthorne, S., Q. J. Wang, A. Schepen, and D. E. Robertson (2013), Effective use of GCM outputs for forecasting monthly rainfalls to long lead times, Water Resour. Res., 49, 5427–5436, doi:10.1002/wrcr.20453.
22. Hudson, D., A. G. Marshall, Y. Yin, O. Alves, and H. H. Hendon (2013), Improving intraseasonal prediction with a new ensemble generation strategy, Mon. Weather Rev., 141(12), 4429–4449, doi:10.1175/mwr-d-13-00059.1.
23. Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, doi:10.1029/2005WR004368.
24. Koster, R. D., S. P. P., Mahanama, B. Livneh, D. P. Lettenmaier, and R. H. Reichle (2010), Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow, Nat. Geosci., 3, 613–616, doi:10.1038/NGEO944.
25. Kuczera, G., D. Kavetski, S. Franks, and M. Thyer (2006), Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters, J. Hydrol., 331(1–2), 161–177, doi:10.1016/j.jhydrol.2006.05.010.
26. Lerat, J., et al. (2015), Dynamic streamflow forecasts within an uncertainty framework for 100 catchments in Australia, paper presented at HWRS 2015 Hydrology and Water Resources Symposium, Engineers Australia, Hobart, Australia.
27. Li, M., Q. J. Wang, and J. Bennett (2013), Accounting for seasonal dependence in hydrological model errors and prediction uncertainty, Water Resour. Res., 49, 5913–5929, doi:10.1002/wrcr.20445.
28. Li, M., Q. J. Wang, J. C. Bennett, and D. E. Robertson (2015), A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts, Hydrol. Earth Syst. Sci., 19(1), 1–15, doi:10.5194/hess-19-1-2015.
29. Li, M., Q. J. Wang, J. C. Bennett, and D. E. Robertson (2016), Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting, Hydrol. Earth Syst. Sci., 20(9), 3561–3579, doi:10.5194/hess-20-3561-2016.
30. Mahanama, S., B. Livneh, R. Koster, D. Lettenmaier, and R. Reichle (2012), Soil moisture, snow, and seasonal streamflow forecasts in the United States, J. Hydrometeorol., 13(1), 189–203, doi:10.1175/jhm-d-11-046.1.
31. Marshall, A. G., D. Hudson, M. C. Wheeler, O. Alves, H. H. Hendon, M. J. Pook, and J. S. Risbey (2014), Intra-seasonal drivers of extreme heat over Australia in observations and POAMA-2, Clim. Dyn., 43(7), 1915–1937, doi:10.1007/s00382-013-2016-1.
32. Murphy, A. H. (1977), The value of climatological, categorical and probabilistic forecasts in the cost-loss ratio situation, Mon. Weather Rev., 105(7), 803–816, doi:10.1175/1520-0493(1977)105<0803:tvocca>2.0.co;2.
33. Najafi, M. R., H. Moradkhani, and T. C. Piechota (2012), Ensemble streamflow prediction: Climate signal weighting methods vs. climate forecast system reanalysis, J. Hydrol., 442–443, 105–116, doi:10.1016/j.jhydrol.2012.04.003.
34. Olsson, J., C. B. Uvo, K. Foster, and W. Yang (2016), Technical Note: Initial assessment of a multi-method approach to spring-flood forecasting in Sweden, Hydrol. Earth Syst. Sci., 20(2), 659–667, doi:10.5194/hess-20-659-2016.
35. Peng, Z., Q. J. Wang, J. C. Bennett, A. Schepen, F. Pappenberger, P. Pokhrel, and Z. Wang (2014), Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China, J. Geophys. Res. Atmos., 119, 7116–7135, doi:10.1002/2013JD021162.
36. Priestley, C. H. B., and R. J. Taylor (1972), On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100(2), 81–92, doi:10.1175/1520-0493(1972)100<0081:otaosh>2.3.co;2.
37. Ramos, M. H., S. J. van Andel, and F. Pappenberger (2013), Do probabilistic forecasts lead to better decisions?, Hydrol. Earth Syst. Sci., 17(6), 2219–2232, doi:10.5194/hess-17-2219-2013.
38. Raupach, M. R., P. R. Briggs, V. Haverd, E. A. King, M. Paget, and C. M. Trudinger (2008), Australian Water Availability Project (AWAP), final report for Phase 3, report, 67 pp., CSIRO Mar. and Atmos. Res., Canberra.
39. Regonda, S. K., B. Rajagopalan, and M. Clark (2006), A new method to produce categorical streamflow forecasts, Water Resour. Res., 42, W09501, doi:10.1029/2006WR004984.
40. Renard, B., D. Kavetski, G. Kuczera, M. Thyer, and S. W. Franks (2010), Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, doi:10.1029/2009WR008328.
41. Robertson, D. E., P. Pokhrel, and Q. J. Wang (2013), Improving statistical forecasts of seasonal streamflows using hydrological model output, Hydrol. Earth Syst. Sci., 17(2), 579–593, doi:10.5194/hess-17-579-2013.
42. Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata (1999), A dipole mode in the tropical Indian Ocean, Nature, 401(6751), 360–363, doi:10.1038/43854.
43. Schaefli, B., D. B. Talamba, and A. Musy (2007), Quantifying hydrological modeling errors through a mixture of normal distributions, J. Hydrol., 332, 303–315, doi:10.1016/j.jhydrol.2006.07.005.
44. Schepen, A., and Q. J. Wang (2013), Toward accurate and reliable forecasts of australian seasonal rainfall by calibrating and merging multiple coupled GCMS, Mon. Weather Rev., 141(12), 4554–4563, doi:10.1175/MWR-D-12-00253.1.
45. Schepen, A., and Q. J. Wang (2014), Ensemble forecasts of monthly catchment rainfall out to long lead times by post-processing coupled general circulation model output, J. Hydrol., 519, 2920–2931, doi:10.1016/j.jhydrol.2014.03.017.
46. Schepen, A., and Q. J. Wang (2015), Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia, Water Resour. Res., 51, 1797–1812, doi:10.1002/2014WR016163.
47. Schepen, A., Q. J. Wang, and D. E. Robertson (2012), Combining the strengths of statistical and dynamical modeling approaches for forecasting Australian seasonal rainfall, J. Geophys. Res., 117, D20107, doi:10.1029/2012JD018011.
48. Schepen, A., Q. J. Wang, and D. E. Robertson (2014), Seasonal forecasts of australian rainfall through calibration and bridging of coupled GCM outputs, Mon. Weather Rev., 142(5), 1758–1770, doi:10.1175/MWR-D-13-00248.1.
49. Schepen, A., Q. J. Wang, and Y. Everingham (2016a), Calibration, bridging and merging to improve GCM seasonal temperature forecasts in Australia, Mon. Weather Rev., 144(6), 2421–2441, doi:10.1175/MWR-D-15-0384.1.
50. Schepen, A., Q. J. Wang, and D. E. Robertson (2016b), Application to post-processing of meteorological seasonal forecasting, in Handbook of Hydrometeorological Ensemble Forecasting, edited by Q. Duan et al., pp. 1–29, Springer-Verlag Berlin Heidelberg,, doi:10.1007/978-3-642-40457-3_18-1.
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. Seo, D. J., H. D. Herr, and J. C. Schaake (2006), A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction, Hydrol. Earth Syst. Sci. Discuss., 3, 1987–2035, doi:10.5194/hessd-3-1987-2006.
53. Shin, D., A. Schepen, T. Peatey, S. Zhou, A. MacDonald, T. Chia, J. Perkins, and N. Plummer (2011), WAFARi: A new modelling system for Seasonal Streamflow Forecasting service of the Bureau of Meteorology, Australia, paper presented at 19th International Congress on Modelling and Simulation, MSSANZ, Perth, Australia, 12-16 Dec.
54. Shukla, S., and D. P. Lettenmaier (2011), Seasonal hydrologic prediction in the United States: Understanding the role of initial hydrologic conditions and seasonal climate forecast skill, Hydrol. Earth Syst. Sci., 15, 3529–3538, doi:10.5194/hess-15-3529-2011.
55. Smith, T., L. Marshall, and A. Sharma (2015), Modeling residual hydrologic errors with Bayesian inference, J. Hydrol., 528, 29–37, doi:10.1016/j.jhydrol.2015.05.051.
56. Svensson, C., A. Brookshaw, A. A. Scaife, V. A. Bell, J. D. Mackay, C. R. Jackson, J. Hannaford, H. N. Davies, A. Arribas, and S. Stanley (2015), Long-range forecasts of UK winter hydrology, Environ. Res. Lett., 10(6), 064006, doi:10.1088/1748-9326/10/6/064006.
57. van Dijk, A. I. J. M., H. E. Beck, R. S. Crosbie, R. A. M. de Jeu, Y. Y. Liu, G. M. Podger, B. Timbal, and N. R. Viney (2013), The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49, 1040–1057, doi:10.1002/wrcr.20123.
58. Verdon, D. C., and S. W. Franks (2005), Indian Ocean sea surface temperature variability and winter rainfall: Eastern Australia, Water Resour. Res., 41, W09413, doi:10.1029/2004WR003845.
59. Wang, Q. J., and D. E. Robertson (2011), Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences, Water Resour. Res., 47, W02546, doi:10.1029/2010WR009333.
60. 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.
61. Wang, Q. J., T. C. Pagano, S. L. Zhou, H. A. P. Hapuarachchi, L. Zhang, and D. E. Robertson (2011), Monthly versus daily water balance models in simulating monthly runoff, J. Hydrol., 404(3–4), 166–175, doi:10.1016/j.jhydrol.2011.04.027.
62. Wang, Q. J., A. Schepen, and D. E. Robertson (2012a), Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging, J. Clim., 25, 5524–5537, doi:10.1175/JCLI-D-11-00386.1.
63. Wang, Q. J., D. L. Shrestha, D. E. Robertson, and P. Pokhrel (2012b), A log-sinh transformation for data normalization and variance stabilization, Water Resour. Res., 48, W05514, doi:10.1029/2011WR010973.
64. Wang, Q. J., M. Li, J. C. Bennett, and D. E. Robertson (2016), Stochastic updating for ensemble hydrological forecasting, in Water Infrastructure and the Environment: 56th New Zealand hydrological society and the 37th Australian hydrological and water resources symposium including the 7th IPENZ rivers group, edited by C. Pearson and M. Babister, pp. 1–2, Engineers Australia, Queenstown, New Zealand.
65. Werner, K., D. Brandon, M. Clark, and S. Gangopadhyay (2004), Climate index weighting schemes for NWS ESP-based seasonal volume forecasts, J. Hydrometeorol., 5(6), 1076–1090, doi:10.1175/jhm-381.1.
66. Wood, A. W., and D. P. Lettenmaier (2008), An ensemble approach for attribution of hydrologic prediction uncertainty, Geophys. Res. Lett., 35, L14401, doi:10.1029/2008GL034648.
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. Yeo, I. K., and R. A. Johnson (2000), A new family of power transformations to improve normality or symmetry, Biometrika, 87(4), 954–959, doi:10.1093/biomet/87.4.954.
69. Yuan, X. (2016), An experimental seasonal hydrological forecasting system over the Yellow River basin––Part 2: The added value from climate forecast models, Hydrol. Earth Syst. Sci., 20(6), 2453–2466, doi:10.5194/hess-20-2453-2016.
70. Yuan, X., E. F. Wood, and Z. Ma (2015a), A review on climate-model-based seasonal hydrologic forecasting: Physical understanding and system development, Wiley Interdisciplinary Rev. Water, 2(5), 523–536, doi:10.1002/wat2.1088.
71. Yuan, X., J. K. Roundy, E. F. Wood, and J. Sheffield (2015b), Seasonal forecasting of global hydrologic extremes: System Development and evaluation over GEWEX basins, Bull. Am. Meteorol. Soc., 96(11), 1895–1912, doi:10.1175/bams-d-14-00003.1.
72. Zappa, M., S. Jaun, U. Germann, A. Walser, and F. Fundel (2011), Superposition of three sources of uncertainties in operational flood forecasting chains, Atmos. Res., 100(2–3), 246–262, doi:10.1016/j.atmosres.2010.12.005.
73. Zhao, T., A. Schepen, and Q. J. Wang (2016), Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach, J. Hydrol., 541, Part B, 839–849, doi:10.1016/j.jhydrol.2016.07.040.