Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates

Hydrology and Earth System Sciences

Volumn 17, Issue 9, 2013, Pages 3455-3472

  Spaaks, J H ^a,b   Bouten, W ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b NETHERLANDS ESCIENCE CENTER   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ASSIMILATION ALGORITHMS; DIAGNOSTIC METHODOLOGY; DISTRIBUTED HYDROLOGIC MODEL; ENSEMBLE KALMAN FILTER; PARAMETER ESTIMATION METHOD; PARAMETER OPTIMIZATION; SHUFFLED COMPLEX EVOLUTION; SPATIALLY DISTRIBUTED HYDROLOGIC MODELING;

EVOLUTIONARY ALGORITHMS; ITERATIVE METHODS; MODEL STRUCTURES; SPATIAL DISTRIBUTION;

THREE DIMENSIONAL COMPUTER GRAPHICS;

ALGORITHM; BEDROCK; CALIBRATION; DISCHARGE; ENSEMBLE FORECASTING; ERROR CORRECTION; HILLSLOPE; HYDROLOGICAL MODELING; KALMAN FILTER; PARAMETERIZATION; THREE-DIMENSIONAL MODELING; WATER BUDGET;

EID: 84883734589     PISSN: 10275606     EISSN: 16077938     Source Type: Journal    
DOI: 10.5194/hess-17-3455-2013     Document Type: Article

References (65)

1. Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, doi:10.1029/2005WR004745, 2007. (Pubitemid 46364281)
2. Bastidas, L. A., Hogue, T. S., Sorooshian, S., Gupta, H. V., and Shuttleworth, W. J.: Parameter sensitivity analysis for different complexity land surface models using multicriteria methods, J. Geophys. Res., 111, D20101, doi:10.1029/2005JD006377, 2006. (Pubitemid 44941261)
3. Beck, M. B.: Water quality modeling: a review of the analysis of uncertainty, Water Resour. Res., 23, 1393-1442, doi:10.1029/WR023i008p01393, 1987. (Pubitemid 17634286)
4. Beven, K. and Binley, A.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279-298, 1992.
5. Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling - a review, Hydrol. Process., 9, 251-290, 1995. (Pubitemid 26441703)
6. Box, G. E. P. and Tiao, G. C.: Bayesian Inference in Statistical Analysis, Addison-Wesley-Longman, Reading, Massachusetts, 1973.
7. Boyle, D. P., Gupta, H. V., and Sorooshian, S.: Toward improved calibration of hydrologic models: combining the strengths of manual and automatic methods, Water Resour. Res., 36, 3663-3674, 2000. (Pubitemid 32039916)
8. Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., and Smith, M.: Toward improved streamflow forecasts: value of semidistributed modeling, Water Resour. Res., 37, 2749-2759, doi:10.1029/2000WR000207, 2001. (Pubitemid 32996668)
9. Clark, M. P. and Vrugt, J. A.: Unraveling uncertainties in hydrologic model calibration: addressing the problem of compensatory parameters, Geophys. Res. Lett., 33, L06406, doi:10.1029/2005GL025604, 2006.
10. Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V.,Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): a modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, doi:10.1029/2007WR006735, 2008.
11. Doherty, J. and Welter, D.: A short exploration of structural noise, Water Resour. Res., 46, W05525, doi:10.1029/2009WR008377, 2010.
12. Duan, Q., Gupta, V. K., and Sorooshian, S.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015-1031, 1992.
13. Dunn, S. M.: Imposing constraints on parameter values of a conceptual hydrological model using baseflow response, Hydrol. Earth Syst. Sci., 3, 271-284, doi:10.5194/hess-3-271-1999, 1999. (Pubitemid 29483612)
14. Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective calibration approaches in hydrological modelling: a review, Hydrolog. Sci. J., 55, 58-78, doi:10.1080/02626660903526292, 2010.
15. Eigbe, U., Beck, M. B., Wheater, H. S., and Hirano, F.: Kalman filtering in groundwater flow modelling: problems and prospects, Stoch. Hydrol. Hydraul., 12, 15-32, 1doi:0.1007/s004770050007, 1998.
16. Evensen, G.: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143-10162, 1994.
17. Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343-367, doi:10.1007/s10236-003- 0036-9, 2003.
18. Franks, S.W., Gineste, P., Beven, K. J., and Merot, P.: On constraining the predictions of a distributed model: the incorporation of fuzzy estimates of saturated areas into the calibration process, Water Resour. Res., 34, 787-797, doi:10.1029/97WR03041, 1998. (Pubitemid 28159457)
19. Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, available at: http://www.jstor.org/stable/pdfplus/2246093. pdf, Stat. Sci., 7, 457-472, 1992.
20. Georgakakos, K. P., Seo, D., Gupta, H., Schaake, J., and Butts, M. B.: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles, J. Hydrol., 298, 222-241, doi:10.1016/j.jhydrol.2004.03. 037, 2004. (Pubitemid 39098775)
21. Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Boston, Massachusetts, 1989.
22. Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information,Water Resour. Res., 34, 751-763, 1998. (Pubitemid 28159454)
23. Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802-3813, doi:10.1002/hyp.6989, 2008.
24. Gupta, V. K. and Sorooshian, S.: The relationship between data and the precision of parameter estimates of hydrologic models, J. Hydrol., 81, 57-77, 1985.
25. Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T.: Bayesian model averaging: a tutorial, Stat. Sci., 14, 382-401, 1999.
26. Hopp, L. and McDonnell, J. J.: Connectivity at the hillslope scale: Identifying interactions between storm size, bedrock permeability, slope angle and soil depth, J. Hydrol., 376, 378-391, 2009.
27. Jazwinski, A. H.: Stochastic Processes and Filtering Theory, Academic Press, New York, 1970.
28. Kalman, R. E.: A new approach to linear filtering and prediction problems, available at: http://www.cs.unc.edu/-welch/kalman/ media/pdf/Kalman1960.pdf, T. ASME J. Basic Eng., 82, 35-45, 1960.
29. Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, doi:10.1029/2005WR004368, 2006a.
30. Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resour. Res., 42, W03408, doi:10.1029/2005WR004376, 2006b.
31. Kirchner, J. W.: Getting the right answers for the right reasons: linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, doi:10.1029/2005WR004362, 2006.
32. Klemeš, V.: Operational testing of hydrological simulation models, Hydrolog. Sci. J., 31, 13-24, 1986. (Pubitemid 16519390)
33. Kuczera, G. and Mroczkowski, M.: Assessment of hydrologic parameter uncertainty and the worth of multiresponse data, Water Resour. Res., 34, 1481-1489, doi:10.1029/98WR00496, 1998. (Pubitemid 28253521)
34. Kuczera, G. and Parent, E.: Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. Hydrol., 211, 69-85, 1998. (Pubitemid 29014557)
35. Lin, Z. and Beck, M. B.: On the identification of model structure in hydrological and environmental systems, Water Resour. Res., 43, W02402, doi:10.1029/2005WR004796, 2007. (Pubitemid 46502872)
36. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M. N., Teller, A. H., and Teller, E.: Equations of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1091, 1953.
37. Mroczkowski, M., Raper, G. P., and Kuczera, G.: The quest for more powerful validation of conceptual catchment models, Water Resour. Res., 33, 2325-2335, 1997. (Pubitemid 27426690)
38. Neuman, S. P.: Maximum likelihood Bayesian averaging of uncertain model predictions, Stoch. Env. Res. Risk A., 17, 291-305, doi:10.1007/s00477-003-0151- 7, 2003. (Pubitemid 38013163)
39. Nierop, K. G. J., Jansen, B., Vrugt, J. A., and Verstraten, J. M.: Copper complexation by dissolved organic matter and uncertainty assessment of their stability constants, Chemosphere, 49, 1191-1200, 2002. (Pubitemid 35386581)
40. Popper, K.: The Logic of Scientific Discovery, Routledge, first published as Logik der Forschung, 1935 by Verlag von Julius Springer, Vienna, Austria, 2009.
41. Raftery, A. E., Balabdaoui, F., Gneiting, T., and Polakowski, M.: Using Bayesian Model averaging to calibrate forecast ensembles, Tech. rep., Department of Statistics, University of Washington, Seattle, Washington, 2003.
42. Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian Model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155-1174, 2005. (Pubitemid 40829946)
43. Reusser, D. E. and Zehe, E.: Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity, Water Resour. Res., 47, W07550, doi:10.1029/2010WR009946, 2011.
44. Richards, L. A.: Capillary conduction of liquids through porous mediums, Physics, 1, 318-333, 1931.
45. Schoups, G. and Vrugt, J. A.: 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, 2010.
46. Seibert, J.: Multi-criteria calibration of a conceptual runoff model using a genetic algorithm, Hydrol. Earth Syst. Sci., 4, 215-224, doi:10.5194/hess-4-215-2000, 2000.
47. Sieber, A. and Uhlenbrook, S.: Sensitivity analyses of a distributed catchment model to verify the model structure, J. Hydrol., 310, 216-235, 2005. (Pubitemid 40837293)
48. Šimůnek, J.: SWMS 3D - numerical model of three-dimensional flow and solute transport in a variably saturated porous medium, software, avaiable at: http://www.pc-progress.com/Downloads/ Programs UCR/SWMS 3D.zip (last access: 2 February 2013), 1994.
49. Šimůnek, J., Huang, K., and van Genuchten, M. T.: The SWMS 3D Code for Simulating Water Flow and Solute Transport in Three- Dimensional Variably-Saturated Media, US Salinity Laboratory, Agricultural Research Service, Research Report No. 139, US Department of Agriculture, Riverside, California, 1995.
50. Sorooshian, S., Gupta, V. K., and Fulton, J. L.: Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runoff models: influence of calibration data variability and length on model credibility, Water Resour. Res., 19, 251-259, doi:10.1029/WR019i001p00251, 1983. (Pubitemid 14256109)
51. Spear, R. C. and Hornberger, G. M.: Eutrophication in peel inlet - II. Identification of critical uncertainties via generalized sensitivity analysis, Water Res., 14, 43-49, doi:10.1016/0043-1354(80)90040-8, 1980. (Pubitemid 10128472)
52. Tang, Y., Reed, P., and Wagener, T.: How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?, Hydrol. Earth Syst. Sci., 10, 289-307, doi:10.5194/hess-10-289-2006, 2006. (Pubitemid 44877445)
53. van Genuchten, M. T.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892-898, 1980.
54. von Bertalanffy, L.: The theory of open systems in physics and biology, Science, 111, 23-29, 1950.
55. Vrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., and Sorooshian, S.: Effective and efficient algorithm for multiobjective optimization of hydrologic models,Water Resour. Res., 39, 1214, doi:10.1029/2002WR001746, 2003a.
56. Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A shuffled complex evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters,Water Resour. Res., 39, 1201, doi:10.1029/2002WR001642, 2003b.
57. Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, doi:10.1029/2004WR003059, 2005. (Pubitemid 40359297)
58. Vrugt, J. A., van Belle, J., and Bouten, W.: Pareto front analysis of flight time and energy use in long-distance bird migration, J. Avian Biol., 38, 432-442, doi:10.1111/j.0908-8857.2007.03909.x, 2007. (Pubitemid 47013506)
59. Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13-26, doi:10.5194/hess-5-13-2001, 2001. (Pubitemid 32710655)
60. Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., and Gupta, H. V.: Towards reduced uncertainty in conceptual rainfallrunoff modelling: dynamic identifiability analysis, Hydrol. Process., 17, 455-476, doi:10.1002/hyp.1135, 2003. (Pubitemid 36234589)
61. Western, A. W. and Blöschl, G.: On the spatial scaling of soil moisture, J. Hydrol., 217, 203-224, 1999. (Pubitemid 29207814)
62. Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Multi-objective global optimization for hydrological models, J. Hydrol., 204, 83-97, 1998.
63. Young, P.: A general theory of modeling for badly defined dynamic systems, in: Modeling, Identification and Control in Environmental Systems - Proceedings of the IFIP Working Conference on Modeling and Simulation of Land, Air, and Water Resources Systems, edited by: Vansteenkiste, G. C., 103-135, North-Holland Pub. Co., Amsterdam, 1978.
64. Young, P.: Uncertainty and forecasting of water quality, in: The Validity and Credibility of Models for Badly Defined Systems, Springer Verlag, 69-98, 1983.
65. Young, P.: The identification and estimation of nonlinear stochastic systems, in: Nonlinear Dynamics and Statistics, Birkhäuser, Boston, 127-166, 2001.