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Bayesian methods for estimating multi-segment discharge rating curves
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“The optimization of those rating curves using Bayesian Markov Chain Monte Carlo have proven to enable estimating rating curves with the possibility of considering prior information, handling observational uncertainties, and allowing for estimating credible confidence intervals6,26,48,49.”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [49]
In the citing paper
“The parameters a, b, and z0 were estimated using a Bayesian Markov Chain Monte Carlo (MCMC) approach55, widely used for rating-curve estimation (e.g., refs. 6,26,49,66)”
From Global scale river discharge and mean depth from radar altimetry and model (2026) · cites this paper as [49]
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Research works printed but not linked: 15
- Bacon DW, Watts DG (1972) Estimating the transition between two intersecting straight lines. Biometrica 58(3):525–534 · DOI · Paper page
- Carlin BP, Gelfand AE, Smith AFM (1992) Hierarchical Bayesian analysis of changepoint problems. Appl Stat 41(2):389–405 · DOI · Paper page
- Denison DGT, Mallick BK, Smith AFM (1998) Automatic Bayesian curve fitting. J R Stat Soc 60(2):333–350 · DOI · Paper page
- Fenton JD (2001) Rating curves: part 2, representation and approximation. In: Proceedings of conference on hydraulic civil engineering, Hobart, 28–30 November. Inst Eng, Australia
- Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Biometrika 82(4):711–732 · DOI · Paper page
- Herschey RW (1995) Streamflow measurement, 2nd edn. Chapman & Hall, Great Britain
- Jain SC (2001) Open channel flow. Wiley, Chichester
- Liu JS (2001) Monte Carlo strategies in scientific computing. Springer, New York
- Moyeeda RA, Clarke RT (2005) The use of Bayesian methods for fitting rating curves, with case studies. Adv Water Res 28(8):807–818 · DOI · Paper page
- Petersen-Øverleir A, Reitan T (2005) Objective segmentation in compound rating curves. J Hydrol 311:188–201 · DOI
- Reitan T, Petersen-Øverleir A (2006) Existence of the frequentistic regression estimate of a power-law with a location parameter, with applications for making discharge rating curves. Stoch Environ Res Risk Asses 20(6):445–453 · doi:10.1007/s00477-006-0037-6
- Reitan T, Petersen-Øverleir A (2008) Bayesian power-law regression with a location parameter, with applications for construction of discharge rating curves. Stoch Environ Res Risk Asses 22:351–365 · doi:10.1007/s00477-007-0119-0
- Smith M, Kohn R (1996) Nonparametric regression using Bayesian variable selection. J Econ 75:317–343 · DOI · Paper page
- Stephens DA (1994) Bayesian Retrospective Multiple-Changepoint Identification. Appl Stat 43(1):159–178 · DOI · Paper page
- Venetis C (1970) A note on the estimation of the parameters in logarithmic stage–discharge relationships with estimation of their error. Bull Int Assoc Sci Hydrol 15:105–111 · doi:10.1080/02626667009493957
Cited by
Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality
McMillan, H., Krueger, T., & Freer, J. (2012) · Hydrological Processes · doi:10.1002/hyp.9384 · Cited by 522
Global scale river discharge and mean depth from radar altimetry and model
Paris, A., Garambois, P.-A., Gal, L., Cerbelaud, A., Larnier, K., David, C. H., Jucá Oliveira, R. A., Correa, S. W., Tourian, M. J., & Calmant, S. (2026) · Nature Communications · doi:10.1038/s41467-026-78275-5
Metadata
- Authors
- Reitan, T., & Petersen-Øverleir, A.
- Venue
- Stochastic Environmental Research and Risk Assessment
- Published
- 2008-06-18
- Volume
- 23
- Issue
- 5
- Pages
- 627-642
- ISSN
- 1436-3240
- 1436-3259
- Cited by
- 72
- References
- 0
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
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- CoChat. (2026). Citation check: Bayesian methods for estimating multi-segment discharge rating curves. https://app.cochat.ai/papers/10.1007/s00477-008-0248-0
- The paper
- Reitan, T., & Petersen-Øverleir, A. (2009). Bayesian methods for estimating multi-segment discharge rating curves. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-008-0248-0
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.