the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A double-Manning approach to compute robust rating curves and hydraulic geometries
Abstract. Rating curves describe river discharge as a function of water-surface elevation ("stage"), and are applied globally for stream monitoring, flood-hazard prediction, and water-resources assessment. Because most rating curves are empirical, they typically require years of data collection and are easily affected by changes in channel hydraulic geometry. Here we present a straightforward strategy based on Manning's equation to address both of these issues. This "double-Manning" approach employs Manning's equation for flow in and above the channel and a Manning-inspired power-law relationship for flows across the floodplain. When applied to data from established stream gauges, we can solve for Manning's n, channel-bank height, and two floodplain-flow parameters. When applied to limited data from a field campaign, constraints from Manning's equation and the surveyed cross section permit a robust fit that matches ground truth. Using these double-Manning fits, we can dynamically adjust the rating curve to account for channel width, depth, and/or slope evolution. Such rating-curve flexibility, combined with a formulation based in flow mechanics, enables predictions amidst coupled hydrologic – geomorphic change, which increasingly occurs as climate warms and humans modify the land surface and subsurface. Open-source software with example implementations is available via GitHub, Zenodo, and PyPI.
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Status: open (until 02 May 2024)
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RC1: 'Comment on egusphere-2023-3118', Anonymous Referee #1, 21 Feb 2024
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The main purpose of this research paper is to introduce a novel approach, called double-Manning approach, for computing stage-discharge relations (rating curves) in compound channels, i.e. at river sites with a main channel and a floodplain. The method, based on the addition of two simplified Manning equations, is illustrated through three gaging stations with variable density of data, size and geometry.
Sadly, I have to say that I don’t think that this approach is really a valuable innovation compared to already published rating curve methods, an active field of research in the past decade (cf. the collective paper by Kiang et al. (2018) which compares 7 methods for rating curve determination and uncertainty analysis). Kiang’s paper and the main references they cite should be considered by the Authors. The third site included in their comparison (the Taf River at Clog-y-Fran, Wales, United Kingdom) actually has a compound channel, which several of the compared methods (the 3 Bayesian methods NVE, BaRatin, BayBi, at least) represent using an addition of power-law equations (one for each channel) with explicit parameters derived from the Manning equation. The wide-channel approximation is necessary to get a relation in the form of Q=a(h-h0)^b for each channel equation, as also mentioned in this article, however it does not make a large difference for most natural rivers.
Typically, the SPD model proposed by Manasanarez et al. (2019b) and cited in the article is based on such principles, which the Authors seem to ignore. It allows the management of shifts, flexible rating curves and hydrogeomorphic feedbacks, unlike what is claimed in Sections 6.3 and 6.4. Bayesian methods do provide additional information (through the prior knowledge) unlike what is stated page 2 line 55 and elsewhere throughout the paper. The parameters of the rating curve equation can be interpreted hydraulically and can be assumed to vary over time (including the channel width, roughness, etc.). Also, many hydrometric sites may not be represented by just a combination of two Manning equations, especially when natural or artificial section controls are active (cf. the Mahurangi site in Kiang’s paper). Then, the proposed model will not suit, whereas other software accommodate much more diverse rating curve models.
Therefore, rating curve methods based on the same (or very similar) ‘double-Manning’ approach already exist, especially Bayesian methods. They offer the same opportunity of hydraulic and geomorphic interpretation of the rating curve parameters, through a stochastic framework that accounts for the uncertainty of the stage-discharge data, of the rating curve parameters and of the discharge outputs (cf. eg. Petersen-Overleir and Reitan 2005, Le Coz et al. 2014). This is a huge advantage over the deterministic optimization procedure introduced in this paper, since hydrometric uncertainty quantification is recognized as absolutely necessary (McMillan et al., 2017). The NVE, BaRatin and SPD software are open-source and publicly available.
Therefore, I don’t clearly see what is the added value of the proposed method.
I quickly reproduced the rating curve analyses using the open-source software BaRatinAGE 2.2 (https://github.com/BaRatin-tools/BaRatinAGE), using a ‘double-Manning’ approach and similar prior information as in the paper, and assuming 10% uncertainty for all the discharge measurements. See the obtained rating curves with uncertainty results in the attached supplement. There seems to be some cleaning needed in the datasets, and there are reproducibility issues: there are several data files (.tsv) for the Cannon case. It is unclear which is used in the article. The Minnesota .tsv files is in ft and cfs instead of the SI units of the paper. The stage and discharge measurements at LaDormida look rather uncertain according to the comments.
References
Kiang, J. E., Gazoorian, C., McMillan, H., Coxon, G., Le Coz, J., Westerberg, I. K., et al. (2018). A comparison of methods for streamflow uncertainty estimation. Water Resources Research, 54, 7149–7176. https://doi.org/10.1029/2018WR022708
Le Coz, J., Renard, B., Bonnifait, L., Branger, F., & Le Boursicaud, R. (2014). Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: A Bayesian approach. Journal of Hydrology, 509, 573–587. https://doi.org/10.1016/j.jhydrol.2013.11.016
McMillan, H., Seibert, J., Petersen-Overleir, A., Lang, M., White, P., Snelder, T., et al. (2017). How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications. Water Resources Research, 53, 5220–5228. https://doi.org/10.1002/2016WR020328
Petersen-Overleir, A., & Reitan, T. (2005). Objective segmentation in compound rating curves. Journal of Hydrology, 311(1–4), 188–201. https://doi.org/10.1016/j.jhydrol.2005.01.016
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CC1: 'Comment on egusphere-2023-3118', Richard Koehler, 26 Mar 2024
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Authors have not completed a basic spell check or grammar check. Multiple misspelled words, grammar errors, and inconsistent phrasing detracts from this article. Article rating curve figures are inconsistent with USGS rating curves for same site. Authors have reversed the axes and have used linear, not log10, scaling. This makes it very difficult to compare figures from this article to official, quality controlled, USGS rating curves. This reviewer does not recommend this article for publication.
Citation: https://doi.org/10.5194/egusphere-2023-3118-CC1 -
RC2: 'Comment on egusphere-2023-3118', Anonymous Referee #2, 16 Apr 2024
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In this paper, the authors introduce a compound channel rating curve method/tool which they describe as a “double-Manning approach”. This approach relies on parameterization of two empirical flow equations (Qch and Qfp) to predict discharge at all depths, both channel-contained and at flood stage. While the tool can be used when discharge data are unavailable, the results presented in this manuscript at three test sites use an optimization routine (included in the tool) to select parameter sets that best fit this piecewise Manning’s power function.
While the approach using a piecewise power function to estimate compound channel flows is not necessarily novel, I see this tool as being a good additional resource for individuals that need a quick estimate of discharge, particularly in situations where flow data is limited. The tool could lend itself well to widespread use, however, end-users need a basic understanding of the CLI which may be prohibitive.
It does seem appropriate that the authors submit a manuscript that accompanies the software tool, but for this paper to be publishable, the authors may want to structure it in a way that 1) does a better job acknowledging the other piecewise power function regression tools described in the literature, and 2) improves on the method to account for error and uncertainty.
Citation: https://doi.org/10.5194/egusphere-2023-3118-RC2
Data sets
Minnesota River near Jordan: stream-gauge data and double-Manning fit J. Jones and A. D. Wickert https://doi.org/10.5281/zenodo.10334289
Cannon River at Welch: stream-gauge data (stage, discharge, and sediment grain size) and double-Manning fit J. Jones et al. https://doi.org/10.5281/zenodo.10334497
Stream-gauging Data: La Dormida Captación G. C. Ng et al. https://doi.org/10.5281/zenodo.10334038
Model code and software
doublemanning A. D. Wickert https://doi.org/10.5281/zenodo.7495274
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