Comparison of point forecast accuracy of model averaging methods in hydrologic applications

Stochastic Environmental Research and Risk Assessment

Volumn 24, Issue 6, 2010, Pages 809-820

  Diks, Cees G H ^a   Vrugt, Jasper A ^b,c,d  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b CENTER FOR NONLINEAR STUDIES   (United States)

^c UNIVERSITY OF AMSTERDAM   (Netherlands)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bates Granger weights; Bayesian model averaging; Granger Ramanathan weights; Mallows model averaging; Streamflow forecasting; Tensiometric pressure head

Indexed keywords

AKAIKE'S INFORMATION CRITERIONS; AVERAGING METHOD; BAYESIAN MODEL AVERAGING; CONCEPTUAL MODEL; DENSITY FORECAST; ENVIRONMENTAL SYSTEMS; FORECAST ACCURACY; FORECAST ERRORS; HYDROLOGIC APPLICATIONS; HYDROLOGIC SYSTEMS; INFORMATION CRITERION; MODEL AVERAGING; MODEL OUTPUTS; MULTI-MODEL; PREDICTIVE DISTRIBUTIONS; PRESSURE HEADS; STATISTICAL LITERATURE; STREAMFLOW FORECASTING; VADOSE ZONE; WATER FLOWS;

BAYESIAN NETWORKS; STREAM FLOW; UNCERTAINTY ANALYSIS;

WEATHER FORECASTING;

ACCURACY ASSESSMENT; BAYESIAN ANALYSIS; COMPARATIVE STUDY; COMPUTER SIMULATION; FLOOD FORECASTING; GRANGER CAUSALITY TEST; HYDRAULIC HEAD; HYDROLOGY; NUMERICAL MODEL; PREDICTION; STREAMFLOW; TENSIOMETER; UNCERTAINTY ANALYSIS; WATER FLOW; WATERSHED;

EID: 77954425477     PISSN: 14363240     EISSN: 14363259     Source Type: Journal    
DOI: 10.1007/s00477-010-0378-z     Document Type: Article

References (40)

1. Bates JM, Granger CWJ (1969) The combination of forecasts. Oper Res Q 20: 451-468.
2. Bergström S (1995) The HBV model. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, Colorado, pp 443-476.
3. Bishop CH, Shanley KT (2008) Bayesian modeling averaging's problematic treatment of extreme weather and a paradigm shift that fixes it. Mon Weather Rev 136: 4641-4652.
4. Boughton WC (1993) A hydrograph based model for estimating the water yield of ungauged catchments. J Irrigat Drain Eng 116: 83-98.
5. Boyle DP, Gupta HV, Sorooshian S, Koren V, Zhang Z, Smith M (2001) Toward improved streamflow forecast: value of semidistributed modeling. Water Resour Res 37(11): 2749-2759.
6. Buckland ST, Burnham KP, Augustin NH (1997) Model selection: an integral part of inference. Biometrics 53: 603-618.
7. Burnash RJ, Ferral RL, McQuir RA (1973) A generalized streamflow simulation system. Technical Report. Joint Federal-State River Forecast.
8. Burnham KP, Anderson DR (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach, 2nd edn. Springer, New York.
9. Claeskens G, Hjort NL (2003) The focused information criterion. J Am Stat Assoc 98: 900-916.
10. Fiering MB (1967) Streamflow synthetic. MacMillan, London.
11. Gneiting T, Raftery AE, Westveld AH, Goldman T (2005) Calibrated probabilistic forecasting using ensemble model output statistics and CRPS estimation. Mon Weather Rev 133: 1098-1118.
12. Granger CWJ, Ramanathan R (1984) Improved methods of combining forecasts. J Forecast 3: 197-204.
13. Hansen BE (2007) Least-squares model averaging. Econometrica 75: 1175-1189.
14. Hansen BE (2008) Least-squares forecast averaging. J Econom 146: 342-350.
15. Hjort NL, Claeskens G (2003) Frequentist model average estimators. J Am Stat Assoc 98: 879-899.
16. Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging: a tutorial. Stat Sci 14: 382-417.
17. Kuczera G, Parent E (1998) Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the metropolis algorithm. J Hydrol 211: 69-85.
18. Marshall L, Nott D, Sharma A (2005) Hydrological model selection: a bayesian alternative. Water Resour Res 41. doi: 10. 1029/2004WR003719.
19. Neuman SP (2003) Maximum likelihood Bayesian averaging of uncertain model predictions. Stoch Environ Res Risk Assess 17: 291-305.
20. Nielsen SA, Hansen E (1973) Numerical simulation of the rainfall runoff processes on a daily basis. Nordic Hydrol 4: 171-190.
21. Oudin L, Perrin C, Mathevet T, Andréassian V, Michel C (2005) Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models. J Hydrol 320: 62-83. doi: 10. 1016/j. jhydrol. 2005. 07. 016.
22. Perrin C, Michel C, Andréassian V (2003) Improvement of a parsimonious model for streamflow simulation. J Hydrol 279: 275-289.
23. Raftery AE, Madigan D, Hoeting JA (1997) Bayesian model averaging for linear regression models. J Am Stat Assoc 92: 179-191.
24. Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133: 1155-1174.
25. Rojas R, Feyen L, Dassargues A (2008) Conceptual model uncertainty in groundwater modeling: combining generalized likelihood uncertainty estimation and Bayesian model averaging. Water Resour Res 44: 1-16.
26. Stockdale TN (2000) An overview of techniques for seasonal forecasting. Stoch Environ Res Risk Assess 14: 305-318.
27. Vrugt JA, Robinson BA (2007) Treatment of uncertainty using ensemble methods: comparison of sequential data assimilation and Bayesian model averaging. Water Resour Res 43: 1-15.
28. Vrugt JA, Bouten W, Gupta HV, Sorooshian S (2002) Toward improved identifiability of hydrologic model parameters: the information content of experimental data. Water Resour Res 38(12). doi: 10. 1029/2001WR001118.
29. Vrugt JA, Diks CGH, Gupta HV, Bouten W, Verstraten JM (2005) Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation. Water Resour Res 41(1). doi: 10. 1029/2004WR003059.
30. Vrugt JA, Clark MP, Diks CGH, Duan Q, Robinson BA (2006a) Multi-objective calibration of forecast ensembles using bayesian model averaging. Geophys Res Lett 33. doi: 10. 1029/2006GL027126.
31. Vrugt JA, Gupta HV, Dekker SC, Sorooshian S, Wagener T, Bouten W (2006b) Application of stochastic parameter optimization to the Sacramento soil moisture accounting model. J Hydrol 325: 288-307. doi: 10. 1016/j. hydrol. 2005. 10. 041.
32. Vrugt JA, Diks CGH, Clark MP (2008) Ensemble bayesian model averaging using Markov chain Monte Carlo sampling. Environ Fluid Dyn 8: 579-595.
33. Vrugt JA, ter Braak CJF, Diks CGH, Robinson BA, Hyman JM, Higdon D (2009) Accelerating Markov chain Monte Carlo simulation by self-adaptive differential evolution with randomized subspace sampling. Int J Nonlinear Sci Numer Simul 10: 273-290.
34. Wagener T, Gupta HV (2005) Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Risk Assess 19: 378-387.
35. Wan ATK, Zhang X, Zou G (2010) Least squares model averaging by Mallows criterion. J Econom (forthcoming).
36. Wöhling T, Vrugt JA (2008) Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models. Water Resour Res 44: 1-18.
37. Wöhling T, Vrugt JA, Barkle GF (2008) Comparison of three multiobjective algorithms for inverse modeling of vadose zone hydrolic properties. Soil Sci Soc Am J 72: 305-319.
38. Yapo P, Gupta HV, Sorooshian S (1996) Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J Hydrol 181: 23-48.
39. Ye M, Neuman SP, Meyer PD (2004) Maximum likelihood bayesian averaging of spatial variability models in unsaturated fractured tuff. Water Resour Res 40. doi: 10. 1029/2003WR002557.
40. Ye M, Meyer PD, Neuman SP (2008) On model selection criteria in multimodel analysis. Water Resour Res 44: 1-12.