Dynamic rating curve assessment in unstable rivers using Ornstein-Uhlenbeck processes

Water Resources Research

Volumn 47, Issue 2, 2011, Pages

  Reitan, Trond ^a   Petersen Øverleir, Asgeir ^b  

^a UNIVERSITY OF OSLO   (Norway)

^b Statkraft Energy AS ^*  (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ANALYSIS; GAUGING STATIONS; IN-CHANNELS; INFORMATIVE PRIORS; MARKOV CHAIN MONTE CARLO SIMULATION; MORPHOLOGICAL PROCESS; ORNSTEIN-UHLENBECK PROCESS; PARAMETER DISTRIBUTIONS; PARTICLE FILTERING; PLAUSIBLE MODEL; POWER LAW MODEL; RATING CURVE; STAGE-DISCHARGE; STOCHASTIC PROCESS; TEMPORAL INSTABILITY; UNSOLVED PROBLEMS;

COMPUTER SIMULATION; CURVE FITTING; MARKOV PROCESSES; MONTE CARLO METHODS; RATING; RIVERS; SIGNAL FILTERING AND PREDICTION; STOCHASTIC MODELS;

STREAM FLOW;

BAYESIAN ANALYSIS; CHANNEL HYDRAULICS; DATA SET; DISCHARGE; FLUVIAL GEOMORPHOLOGY; HYDROMETRY; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; POWER LAW; RATING CURVE; RIVER FLOW; STEADY FLOW;

NORWAY;

EID: 79952018685     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2010WR009504     Document Type: Article

References (34)

1. Andrieu, C., A. Doucet, and R. Holenstein (2010), Particle Markov chain Monte Carlo methods, J. Royal Stat. Soc. B, 72(3), 269-342.
2. Árnason, S. (2005), Estimating nonlinear hydrological rating curves and discharge using the Bayesian approach, M.S. thesis, Fac. of Eng., Univ. of Iceland.
3. Arulampalam, S. M., S. Maskell, N. Gordon, and T. Clapp (2002), A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. Signal Process., 50, 174-188. (Pubitemid 34291500)
4. Berger, O. B. (1985), Statistical Decision Theory and Bayesian Analysis, 2nd ed., Springer-Verlag, New York.
5. Burkham, D. E., and D. R. Dawdy (1970), Error analysis of streamflow data for an alluvial stream, U.S. Geol. Prof. Pap., 655-C.
6. Carlin, B. P., N. G. Polson, and D. S. Stoffer (1992), A Monte Carlo approach to nonnormal and nonlinear state-space modeling, J. Am. Stat. Assoc., 87, 493-500.
7. Di Baldassarre, G., and A. Montanari (2009), Uncertainty in river discharge observations: A quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913-921, doi:10.5194/hess-13-913-2009.
8. Geyer, C. J. (1991), Markov chain Monte Carlo maximum likelihood, in Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, edited by E. M. Keramides, pp. 156-163, Interface Found., Fairfax Station, Va.
9. Gordon, N. J., D. J. Salmond, and A. F. M. Smith (1993), A novel approach to non-linear and non-Gaussian Bayesian state estimation, IEE Proc., Part F, 140, 107-113.
10. Herschy, R. W. (1995), Streamflow Measurement, 2nd ed., E & FN Spon, London.
11. International Standards Organization (1998), Determination of the stage-discharge relation, Stand. ISO 1100/2, Geneva, Switzerland.
12. Kitagawa, G. (1996), Monte Carlo filter and smoother for non-Gaussian nonlinear state space models, J. Comput. Graph. Stat., 5, 1-25.
13. Knighton, A. D. (1998), Fluvial Forms and Processes: A New Perspective, Edward Arnold, London.
14. Kuczera, G. (1996), Correlated measurement error in flood frequency inference, Water Resour. Res., 32, 2119-2128.
15. Lang, M., K. Probanz, B. Renard, E. Renouf, and E. Sauquet (2010), Extrapolation of rating curves by hydraulic modeling, with application to flood frequency analysis, Hydrol. Sci. J., 55, 883-898.
16. Leonard, J., M. Mietton, H. Najib, and P. Gourbesville (2000), Rating curve modeling with Manning's equation to manage instability and improve extrapolation, Hydrol. Sci. J., 45, 739-750.
17. Maskell, S., M. Rollason, N. Gordon, and D. Salmond (2003), Efficient particle filtering for multiple target tracking with application to tracking in structured images, Image Vision Comput., 21, 931-939.
18. McMillan, H., F. Pappenberger, J. Freer, T. Krueger, and M. Clark (2010), Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions, Hydrol. Processes, 24, 1270-1284.
19. Melcher, N. B., and J. F. Walker (1991), Evaluation of selected methods for determining streamflow during periods of ice effect, U.S. Geol. Surv. Water Supply Pap., 2378.
20. Moyeed, R. A., and R. T. Clarke (2005), The use of Bayesian methods for fitting rating curves, with case studies, Adv. Water Resour., 28, 807-818. (Pubitemid 40792492)
21. Petersen-Øverleir, A. (2004), Accounting for heteroscedasticity in rating curve estimates, J. Hydrol. Eng., 292, 173-181.
22. Petersen-Øverleir, A. (2008), The net effect of sample variability and rating curve imprecision in regional flood frequency analysis, in Northern hydrology and its global role: Proceedings of the XXV Nordic hydrological conference, edited by O. G. Sveinsson et al., pp. 266-274, Nordic Hydrologic Programme, Reykjavik, Iceland.
23. Petersen-Øverleir, A., and T. Reitan (2009), Accounting for rating curve imprecision in flood frequency analysis using likelihood-based methods, J. Hydrol., 366, 89-100.
24. Pitt, M. K. (2002), Smooth particle filters for likelihood evaluation and maximisation, Tech. Rep. no. 651, Dept. Econ., Univ. Warwick, Coventry.
25. Potter, K. W., and J. F. Walker (1985), An empirical study of flood measurement error, Water Resour. Res., 21, 403-406.
26. Rantz, S. E., et al., (1982), Measurement and computation of streamflow, vol. 2, Computation of discharge, U.S. Geol. Surv. Water Supply Pap., 2175.
27. Reitan, T., and A. Petersen-Øverleir (2008), Bayesian power-law regression with a location parameter, with applications for construction of discharge rating curves, Stochastic Environ. Res. Risk Assess., 22, 351-365, doi:10.1007/s00477-007-0119-0.
28. Reitan, T., and A. Petersen-Øverleir (2009), Bayesian methods for estimating multi-segment discharge rating curves, Stochastic Environ. Res. Risk Assess., 23, 627-642, doi:10.1007/s00477-008-0248-0.
29. 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.
30. Roberts, G. O., A. Gelman, and W. R. Gilks (1997), Weak convergence and optimal scaling of random walk Metropolis algorithms, Ann. Appl. Probab., 7, 110-120.
31. Schumm, S. A. (1977), The Fluvial System, John Wiley, New York.
32. Taylor H. M., and S. Karlin (1998), An Introduction to Stochastic Modeling, 3rd ed., Academic, San Diego, Calif.
33. Venetis, C. (1970), A note on the estimation of the parameters in logarithmic stage-discharge relationships with estimates of their error, Bull. Int. Assoc. Sci. Hydrol., 15, 105-111.
34. Whitfield, P. H., and M. Hendrata (2006), Assessing detectability of change in low flows in future climates from stage-discharge measurements, Can. Water Resour. J., 31, 1-12.