Advertisement

Seasonal Ensemble Forecast Post-processing

  • Andy WoodEmail author
  • A. Sankarasubramanian
  • Pablo Mendoza
Reference work entry

Abstract

In many parts of the world, water resources systems manage sub-seasonal to seasonal (S2S) variability in climate and runoff in part through the use of operational streamflow forecasts, supplemented by predictions of climate and other hydrologic variables. S2S hydrologic forecasts are produced through both statistical and dynamical (model-based) approaches, and separate S2S forecasts may be combined in multi-model frameworks to increase their skill. Statistical post-processing can be used to enhance the skill and reliability of model-based S2S predictions, and to reduce bias, as well as to merge forecasts from multiple approaches. This chapter describes seasonal hydrologic forecast approaches and products, and presents common techniques used in both the post-processing of single ensemble forecast series as well as the combination of multiple forecasts. Also discussed are the sources of S2S hydrological predictability and particular challenges and opportunities related to post-processing seasonal hydrologic predictions, for which the sample sizes of past simulations, observations and predictions are relatively more limited than in the context of short to medium range prediction.

Keywords

Post-processing Seasonal forecast Multi-model combination Bias-correction Ensemble forecast Watershed model Statistical forecasting Predictability Hydrologic variability Climate 

References

  1. J. Beckers, A. Weerts, E. Tijdeman, E. Welles, ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction. Hydrol. Earth Syst. Sci. 20, 3277–3287 (2016).  https://doi.org/10.5194/hess-20-3277-2016CrossRefGoogle Scholar
  2. A.A. Berg, K.A. Mulroy, Streamflow predictability in the Saskatchewan/Nelson River basin given macroscale estimates of the initial soil moisture status. Hydrol. Sci. J. 51(4), 642–654 (2006).  https://doi.org/10.1623/hysj.51.4.6422006CrossRefGoogle Scholar
  3. K.J. Beven, Prophecy, reality and uncertainty in distributed hydrological modelling. Adv. Wat. Resour. 16, 41–51 (1993)CrossRefGoogle Scholar
  4. T.J. Bohn, M.Y. Sonessa, D.P. Lettenmaier, Seasonal hydrologic forecasting: do multimodel ensemble averages always yield improvements in forecast skill? J. Hydrometeorol. 11(6), 1358–1372 (2010)CrossRefGoogle Scholar
  5. C. Bracken, B. Rajagopalan, J. Prairie, A multisite seasonal ensemble streamflow forecasting technique. Water Resour. Res. 46, W03532 (2010).  https://doi.org/10.1029/2009WR007965CrossRefGoogle Scholar
  6. A.A. Bradley, M. Habib, S.S. Schwartz, Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach. Water Resour. Res. 51, 7382–7400 (2015).  https://doi.org/10.1002/2014WR016811CrossRefGoogle Scholar
  7. R.J.C. Burnash, R.L. Ferral, R.A. McGuire, A Generalized Streamflow Simulation System – Conceptual Modeling for Digital Computers (U.S. Department of Commerce National Weather Service and State of California Department of Water Resources, Sacramento, 1973)Google Scholar
  8. X. Chen, Z. Hao, N. Devineni, U. Lall, Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modeling. Hydrol. Earth Syst. Sci. 18, 1539–1548 (2014).  https://doi.org/10.5194/hess-18-1539-2014CrossRefGoogle Scholar
  9. M. Clark, S. Gangopadhyay, L. Hay, B. Rajagopalan, R. Wilby, The schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. J. Hydrometeor. 5, 243–262 (2004).  https://doi.org/10.1175/1525-7541(2004)005CrossRefGoogle Scholar
  10. L. Crochemore, M.-H. Ramos, F. Pappenberger, Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts. Hydrol. Earth Syst. Sci. 20, 3601–3618 (2016).  https://doi.org/10.5194/hess-20-3601-2016CrossRefGoogle Scholar
  11. G. Day, Extended streamflow forecasting using NWSRFS. J. Water. Res. Plan. Manag. 111(2), 157–170 (1985).  https://doi.org/10.1061/(ASCE)0733-9496(1985)CrossRefGoogle Scholar
  12. A. Dempster, N. Laird, D. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39, 1–38 (1977)Google Scholar
  13. N. Devineni, A. Sankarasubramanian, Improving the prediction of winter precipitation and temperature over the continental United States: role of the ENSO state in developing multimodel combinations. Mon. Weather Rev. 138(6), 2447–2468 (2010a).  https://doi.org/10.1175/2009MWR3112.1CrossRefGoogle Scholar
  14. N. Devineni, A. Sankarasubramanian, Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs. Geophys. Res. Lett. 37, L24704 (2010b).  https://doi.org/10.1029/2010GL044989CrossRefGoogle Scholar
  15. N. Devineni, A. Sankarasubramanian, S. Ghosh, Multimodel ensembles of streamflow forecasts: role of predictor state in developing optimal combinations. Water Resour. Res. 44, W09404 (2008).  https://doi.org/10.1029/2006WR005855CrossRefGoogle Scholar
  16. N. Devineni, U. Lall, N. Pederson, E. Cook, A tree ring based reconstruction of Delaware River basin streamflow using hierarchical Bayesian regression. J. Clim. 26, 4357–4374 (2013).  https://doi.org/10.1175/JCLI-D-11-00675.1CrossRefGoogle Scholar
  17. Q. Duan, N.K. Ajami, X. Gao, S. Sorooshian, Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv. Water Resour. 30(5), 1371–1386 (2007).  https://doi.org/10.1016/J.ADVWATRES.2006.11.014CrossRefGoogle Scholar
  18. D.C. Garen, Improved techniques in regression-based streamflow volume forecasting. J. Water Resour. Plan. Manag. 118, 654–670 (1992).  https://doi.org/10.1061/(ASCE)0733-9496CrossRefGoogle Scholar
  19. K.P. Georgakakos, D.-J. Seo, H. Gupta, J. Schaake, M.B. Butts, Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. J. Hydrol. 298(1–4), 222–241 (2004).  https://doi.org/10.1016/j.jhydrol.2004.03.037CrossRefGoogle Scholar
  20. K. Grantz, B. Rajagopalan, M. Clark, E. Zagona, A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts. Water Resour. Res. 41, W10410 (2005).  https://doi.org/10.1029/2004WR003467CrossRefGoogle Scholar
  21. W. Greuell, W.H.P. Franssen, R.W.A. Hutjes, Seasonal streamflow forecasts for Europe – II. Explanation of the skill. Hydrol. Earth Syst. Sci. Discuss. (2016).  https://doi.org/10.5194/hess-2016–604. in review
  22. R. Hagedorn, F. Doblas-Reyes, T. Palmer, The rationale behind the success of multimodel ensembles in seasonal forecasting I. Basic concept. Tellus. Ser.A. 57, 219–233 (2005)Google Scholar
  23. A.F. Hamlet, D.P. Lettenmaier, Columbia River streamflow forecasting based on ENSO and PDO climate signals. J. Water Resour. Plan. Manag. 125(6), 333–341 (1999)CrossRefGoogle Scholar
  24. S. Harrigan, C. Prudhomme, S. Parry, K. Smith, M. Tanguy, Benchmarking ensemble streamflow prediction skill in the UK. Hydrol. Earth Syst. Sci. Discuss. (2017).  https://doi.org/10.5194/hess-2017-449. in review
  25. T. Hashino, A.A. Bradley, S.S. Schwartz, Evaluation of bias-correction methods for ensemble streamflow volume forecasts. Hydrol. Earth Syst. Sci. 11, 939–950 (2007)CrossRefGoogle Scholar
  26. D. Helms, S.E. Phillips, P.F. Reich, The History of Snow Survey and Water Supply Forecasting. Natl. Bull. 290-9-6 (Natural Resources Conservation Service, U.S. Department of Agriculture, Washington, DC, 2008)Google Scholar
  27. J.M. Hidalgo-Muñoz, S.R. Gámiz-Fortis, Y. Castro-Díez, D. Argüeso, M.J. Esteban-Parra, Long-range seasonal streamflow forecasting over the Iberian Peninsula using large-scale atmospheric and oceanic information. Water Resour. Res. 51(5), 3543–3567 (2015).  https://doi.org/10.1002/2014WR016826CrossRefGoogle Scholar
  28. F. Hoss, P.S. Fischbeck, Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables. Hydrol. Earth Syst. Sci. 19, 3969–3990 (2015).  https://doi.org/10.5194/hess-19-3969-2015CrossRefGoogle Scholar
  29. B.P. Kirtman, D. Min, J.M. Infanti, J.L. Kinter, D.A. Paolino, Q. Zhang, H. van den Dool, S. Saha, M.P. Mendez, E. Becker, P. Peng, P. Tripp, J. Huang, D.G. DeWitt, M.K. Tippett, A.G. Barnston, S. Li, A. Rosati, S.D. Schubert, M. Rienecker, M. Suarez, Z.E. Li, J. Marshak, Y. Lim, J. Tribbia, K. Pegion, W.J. Merryfield, B. Denis, E.F. Wood, The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; Phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc. 95, 585–601 (2014).  https://doi.org/10.1175/BAMS-D-12-00050.1CrossRefGoogle Scholar
  30. R.D. Koster, S. Mahanama, Land surface controls on hydroclimatic means and variability. J. Hydrometeorol. 13, 1604–1620 (2012)CrossRefGoogle Scholar
  31. T. Krishnamurti, C. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, E. Williford, Multimodel ensemble forecasts for weather and seasonal climate. J. Clim. 13, 4196–4216 (2000)CrossRefGoogle Scholar
  32. F. Lehner, A.W. Wood, D. Llewellyn, D.B. Blatchford, A.G. Goodbody, F. Pappenberger, Mitigating the impacts of climate nonstationarity on seasonal streamflow predictability in the U.S. southwest. Geophys. Res. Lett. 44, 12,208 (2017).  https://doi.org/10.1002/2017GL076043CrossRefGoogle Scholar
  33. C.H. Lima, U. Lall, Spatial scaling in a changing climate: a hierarchical bayesian model for non-stationary multi-site annual maximum and monthly streamflow. J. Hydrol. 383(3), 307–318 (2010)CrossRefGoogle Scholar
  34. R. Linsley, N. Crawford, Continuous simulation models in urban hydrology. Geophys. Res. Lett. 1, 59–62 (1974).  https://doi.org/10.1029/GL001i001p00059CrossRefGoogle Scholar
  35. D. Lucatero, H. Madsen, J.C. Refsgaard, J. Kidmose, K.H. Jensen, Seasonal streamflow forecasts in the Ahlergaarde catchment Denmark: effect of preprocessing and postprocessing on skill and statistical consistency. Hydrol. Earth Syst. Sci. Discuss. (2017).  https://doi.org/10.5194/hess-2017-379. in review
  36. P.A. Mendoza, B. Rajagopalan, M.P. Clark, G. Cortes, J. McPhee, A robust multimodel framework for ensemble seasonal hydroclimatic forecasts. Water Resour. Res. 50, 6030 (2014).  https://doi.org/10.1002/2014WR015426CrossRefGoogle Scholar
  37. P.A. Mendoza, A.W. Wood, E.A. Clark, E. Rothwell, M.P. Clark, B. Nijssen, L.D. Brekke, J.R. Arnold, An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting. Hydrol. Earth Syst. Sci. 21, 3915–3935 (2017)CrossRefGoogle Scholar
  38. H. Moradkhani, M. Meier, Long-lead water supply forecast using large-scale climate predictors and independent component analysis. J. Hydrol. Eng. 15(10), 744–762 (2010).  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000246CrossRefGoogle Scholar
  39. M. Najafi, H. Moradkhani, Ensemble combination of seasonal streamflow forecasts. J. Hydrol. Eng. 21(1), 04015043 (2015).  https://doi.org/10.1061/(ASCE)HE.1943-5584.0001250CrossRefGoogle Scholar
  40. S. Opitz-Stapleton, S. Gangopadhyay, B. Rajagopalan, Generating streamflow forecasts for the Yakima River Basin using large-scale climate predictors. J. Hydrol. 341(3–4), 131–143 (2007).  https://doi.org/10.1016/j.jhydrol.2007.03.024CrossRefGoogle Scholar
  41. T.C. Pagano, D.C. Garen, T.R. Perkins, P.A. Pasteris, Daily updating of operational statistical seasonal water supply forecasts for the Western U.S. J. Am. Water Resour. Assoc. 45(3), 767–778 (2009).  https://doi.org/10.1111/j.1752-1688.2009.00321.xCrossRefGoogle Scholar
  42. T. Pagano, A.W. Wood, K. Werner, R. Tama-Sweet, Western U.S. water supply forecasting: a tradition evolves. Eos. Trans. AGU 95(3), 28 (2014)CrossRefGoogle Scholar
  43. T. Piechota, F. Chiew, Seasonal streamflow forecasting in eastern Australia and the El Niño–southern oscillation. Water Resour. Res. 34(11), 3035–3044 (1998)CrossRefGoogle Scholar
  44. T.C. Piechota, F.H.S. Chiew, J.A. Dracup, T.A. McMahon, Development of exceedance probability streamflow forecast. J. Hydrol. Eng. 6(1), 20–28 (2001)CrossRefGoogle Scholar
  45. D. Raff, L. Brekke, K.V. Werner, A. Wood, K. White, Short-Term Water Management Decisions: User Needs for Improved Climate, Weather, and Hydrologic Information. Technical Report CWTS-2013-1 (Bureau of Reclamation U.S. Army Corps of Engineers and National Oceanic and Atmospheric Administration, Denver, USA, 2013)Google Scholar
  46. A.E. Raftery, T. Gneiting, F. Balabdaoui, M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles. Mon. Weather Rev. 133, 1155–1174 (2005)CrossRefGoogle Scholar
  47. B. Rajagopalan, U. Lall, A k-nearest-neighbor simulator for daily precipitation and other weather variables. Water Resour. Res. 35(10), 3089–3101 (1999).  https://doi.org/10.1029/1999WR900028CrossRefGoogle Scholar
  48. B. Rajagopalan, U. Lall, S. Zebiak, Optimal categorical climate forecasts through multiple GCM ensemble combination and regularization. Mon. Weather Rev. 130(7), 1792–1811 (2002)CrossRefGoogle Scholar
  49. S.K. Regonda, B. Rajagopalan, M. Clark, E. Zagona, A multi-model ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin. Water Resour. Res. 42, W09404 (2006).  https://doi.org/10.1029/2005WR004653CrossRefGoogle Scholar
  50. B. Renard, A Bayesian hierarchical approach to regional frequency analysis. Water Resour. Res. 47, W11513 (2011).  https://doi.org/10.1029/2010WR010089CrossRefGoogle Scholar
  51. B. Renard, X. Sun, M. Lang, Bayesian methods for non-stationary extreme value analysis, in Extremes in a Changing Climate (Springer Netherlands, 2013), pp. 39–95Google Scholar
  52. D.E. Robertson, P. Pokhrel, Q.J. Wang, Improving statistical forecasts of seasonal streamflows using hydrological model output. Hydrol. Earth Syst. Sci. 17, 579–593 (2013).  https://doi.org/10.5194/hess-17-579-2013CrossRefGoogle Scholar
  53. E.A. Rosenberg, A.W. Wood, A.C. Steinemann, Statistical applications of physically based hydrologic models to seasonal streamflow forecasts. Water Resour. Res. 47, W00H14 (2011).  https://doi.org/10.1029/2010WR010101CrossRefGoogle Scholar
  54. E.A. Rosenberg, A.W. Wood, A.C. Steinemann, Informing hydrometric network design for statistical seasonal streamflow forecasts. J. Hydrometeorol. 14, 1587–1604 (2013).  https://doi.org/10.1175/JHM-D-12-0136.1CrossRefGoogle Scholar
  55. J.D. Salas, C. Fu, B. Rajagopalan, Long-range forecasting of Colorado streamflows based on hydrologic atmospheric and oceanic data. J. Hydrol. Eng. 16(6), 508–520 (2011).  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000343CrossRefGoogle Scholar
  56. A. Sankarasubramanian, U. Lall, Flood quantiles in a changing climate: seasonal forecasts and causal relations. Water Resour. Res. 39(5), 1134 (2003).  https://doi.org/10.1029/2002WR001593CrossRefGoogle Scholar
  57. R. Schefzik, A similarity-based implementation of the Schaake shuffle. Mon. Weather Rev. 144, 1909–1921 (2016).  https://doi.org/10.1175/MWR-D-15-0227.1CrossRefGoogle Scholar
  58. A. Schepen, Q.J. Wang, Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia. Water Resour. Res. 51, 1797 (2015).  https://doi.org/10.1002/2014WR016163CrossRefGoogle Scholar
  59. A. Schepen, Q.J. Wang, Y. Everingham, Calibration, bridging, and merging to improve GCM seasonal temperature forecasts in Australia. Mon. Wea. Rev. 144, 2421–2441 (2016).  https://doi.org/10.1175/MWR-D-15-0384.1CrossRefGoogle Scholar
  60. A. Schepen, T. Zhao, Q.J. Wang, D.E. Robertson, A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments. Hydrol. Earth Syst. Sci. 22, 1615–1628 (2018).  https://doi.org/10.5194/hess-22-1615-2018CrossRefGoogle Scholar
  61. D.-J. Seo, H. Herr, J. Schaake, A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction. Hydrol. Earth Syst. Sci. Discuss. 3, 1987–2035 (2006)CrossRefGoogle Scholar
  62. L.J. Slater, G. Villarini, A.A. Bradley, et al., Clim. Dyn. (2017).  https://doi.org/10.1007/s00382-017-3794-7
  63. S. Sorooshian, Q. Duan, V.K. Gupta, Calibration of rainfall-runoff models: Application of global optimization to the Sacramento Soil Moisture accounting model. Water Resour. Res. 29, 1185–1194 (1993)CrossRefGoogle Scholar
  64. F.A. Souza Filho, U. Lall, Seasonal to interannual ensemble streamflow forecasts for Ceara Brazil: applications of a multivariate semiparametric algorithm. Water Resour. Res. 39(11), 1307 (2003).  https://doi.org/10.1029/2002WR001373CrossRefGoogle Scholar
  65. G.A. Tootle, A.K. Singh, T.C. Piechota, I. Farnham, Long lead-time forecasting of U.S. streamflow using partial least squares regression. J. Hydrol. Eng. 12, 442–451 (2007)CrossRefGoogle Scholar
  66. R.D. Valencia, J.C. Schakke Jr., Disaggregation processes in stochastic hydrology. Water Resour. Res. 9(3), 580–585 (1973).  https://doi.org/10.1029/WR009i003p00580CrossRefGoogle Scholar
  67. A. Verdin, B. Rajagopalan, W. Kleiber, G. Podestá, F. Bert, A conditional stochastic weather generator for seasonal to multi-decadal simulations. J. Hydrol. (2015).  https://doi.org/10.1016/j.jhydrol.2015.12.036CrossRefGoogle Scholar
  68. T. Wagener, N. McIntyre, M.J. Lees, H.S. Wheater, H.V. Gupta, Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis, Hydrol. Processes. 17(2), 455–476 (2003)Google Scholar
  69. Q.J. Wang, D.E. Robertson, F.H.S. Chiew, A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res. 45(5), 1–18 (2009).  https://doi.org/10.1029/2008WR007355CrossRefGoogle Scholar
  70. H. Wang, A. Sankarasubramanian, R.S. Ranjithan, Integration of climate and weather information for improving 15-day-ahead accumulated precipitation forecasts. J. Hydrometeorol. 14(1), 186–202 (2013)CrossRefGoogle Scholar
  71. A.P. Weigel, M.A. Liniger, C. Appenzeller, Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q. J. R. Meteorol. Soc. 134(630), 241–260 (2008)CrossRefGoogle Scholar
  72. K. Werner, D. Brandon, M. Clark, S. Gangopadhyay, Climate index weighting schemes for NWS ESP-based seasonal volume forecasts. J. Hydrometeor, 5, 1076–1090 (2004).  https://doi.org/10.1175/JHM-381.1CrossRefGoogle Scholar
  73. S. Westra, A. Sharma, C. Brown, U. Lall, Multivariate streamflow forecasting using independent component analysis. Water Resour. Res. 44(2), 1–11 (2008).  https://doi.org/10.1029/2007WR006104CrossRefGoogle Scholar
  74. A.W. Wood, D.P. Lettenmaier, A new approach for seasonal hydrologic forecasting in the western U.S. Bull. Amer. Met. Soc. 87(12), 1699–1712 (2006).  https://doi.org/10.1175/BAMS-87-12-1699CrossRefGoogle Scholar
  75. A.W. Wood, D.P. Lettenmaier, An ensemble approach for attribution of hydrologic prediction uncertainty. Geophys. Res. Lett. 35, L14401 (2008).  https://doi.org/10.1029/2008GL034648
  76. A.W. Wood, J.C. Schaake, Correcting errors in stream ow forecast ensemble mean and spread. J. Hydrometeorol. 9, 132–148 (2008)CrossRefGoogle Scholar
  77. A.W. Wood, T. Hopson, A. Newman, L. Brekke, J. Arnold, M. Clark, Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. J. Hydrometeorol. 17, 651–668 (2016a).  https://doi.org/10.1175/JHM-D-14-0213.1CrossRefGoogle Scholar
  78. A.W. Wood, T. Pagano, M. Roos, Tracing the origins of ESP HEPEX historical hydrology series edition 1 (online at: https://hepex.irstea.fr/tracing-the-origins-of-esp/) (2016b)
  79. T. Zhao, J.C. Bennett, Q.J. Wang, A. Schepen, A.W. Wood, D.E. Robertson, M. Ramos, How suitable is quantile mapping for post processing GCM precipitation forecasts? J. Clim. 30, 3185–3196 (2017).  https://doi.org/10.1175/JCLI-D-16-0652.1CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Andy Wood
    • 1
    Email author
  • A. Sankarasubramanian
    • 2
  • Pablo Mendoza
    • 3
  1. 1.National Center for Atmospheric ResearchBoulderUSA
  2. 2.Department of Civil Construction and Environmental EngineeringNorth Carolina State UniversityRaleighUSA
  3. 3.Advanced Mining Technology Center (AMTC)Universidad de ChileSantiago de ChileChile

Section editors and affiliations

  • Andy Wood
    • 1
  • Thomas Hopson
    • 2
  1. 1.National Center for Atmospheric ResearchBoulderUSA
  2. 2.Research Applications Laboratory, National Center for Atmospheric ResearchColoradoUSA

Personalised recommendations