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

[1] The procedure of fitting rating curves in channels where the stage-discharge relationship is subject to changes driven by morphological processes remains one of the major unsolved problems in hydrometry. This paper addresses this issue by formulating the stage-discharge relationship as a steady flow, one-segmented power law model with parameters that are viewed as stochastic processes with characteristics associated with the temporal instabilities of the channel elements governing the stage-discharge relationship. A Bayesian analysis with informative priors and time-stage-discharge measurements as forcing data is used to determine the most plausible model and its posterior parameter distributions using Markov chain Monte Carlo simulation techniques and particle filtering. The proposed framework is applied to data from gauging stations in two unstable rivers and one stable river in Norway.

[1]  George Kuczera,et al.  Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors , 2010 .

[2]  D. Knighton Fluvial Forms and Processes: A New Perspective , 1998 .

[3]  D. Dawdy,et al.  Error analysis of streamflow data for an alluvial stream , 1970 .

[4]  Asgeir Petersen-Øverleir,et al.  Bayesian power-law regression with a location parameter, with applications for construction of discharge rating curves , 2008 .

[5]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[6]  C. Venetis,et al.  A NOTE ON THE ESTIMATION OF THE PARAMETERS IN LOGARITHMIC STAGE-DISCHARGE RELATIONSHIPS WITH ESTIMATES OF THEIR ERROR , 1970 .

[7]  Nicholas G. Polson,et al.  A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling , 1992 .

[8]  H. M. Taylor,et al.  An introduction to stochastic modeling , 1985 .

[9]  Norwood B. Melcher,et al.  Evaluation of selected methods for determining streamflow during periods of ice effect , 1990 .

[10]  S. Schumm The Fluvial System , 1977 .

[11]  A. Petersen-Øverleir,et al.  Accounting for heteroscedasticity in rating curve estimates , 2004 .

[12]  G. Kitagawa Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[13]  M. Pitt Smooth Particle Filters for Likelihood Evaluation and Maximisation , 2002 .

[14]  Florian Pappenberger,et al.  Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions , 2010 .

[15]  A. Gelman,et al.  Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .

[16]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[17]  Asgeir Petersen-Øverleir,et al.  Accounting for rating curve imprecision in flood frequency analysis using likelihood-based methods , 2009 .

[18]  Philippe Gourbesville,et al.  Rating curve modelling with Manning's equation to manage instability and improve extrapolation , 2000 .

[19]  S. E. Rantz,et al.  Measurement and computation of streamflow , 1982 .

[20]  Michel Lang,et al.  Extrapolation of rating curves by hydraulic modelling, with application to flood frequency analysis , 2010 .

[21]  C. Geyer Markov Chain Monte Carlo Maximum Likelihood , 1991 .

[22]  Asgeir Petersen-Øverleir,et al.  Bayesian methods for estimating multi-segment discharge rating curves , 2009 .

[23]  Neil J. Gordon,et al.  Efficient particle filtering for multiple target tracking with application to tracking in structured images , 2003, Image Vis. Comput..

[24]  George Kuczera,et al.  Correlated Rating Curve Error in Flood Frequency Inference , 1996 .

[25]  R. T. Clarke,et al.  The use of Bayesian methods for fitting rating curves, with case studies , 2005 .

[26]  Kenneth W. Potter,et al.  An empirical study of flood measurement error , 1985 .

[27]  A. Doucet,et al.  Particle Markov chain Monte Carlo methods , 2010 .

[28]  Paul H. Whitfield,et al.  Assessing Detectability of Change in Low Flows in Future Climates from Stage Discharge Measurements , 2006 .

[29]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .