Background

Bayesian Generalized Power-Law Rating Curves

The power-law model is commonly assumed in hydraulic practice to fit rating curves, and as mention above, its form is

\[Q(h) = a(h-c)^{b}\] (1)

where \(Q\), \(h\), \(a\), \(b\) and \(c\) are as before, see e.g., Venetis (1970), Clarke (1999) and Clarke et al. (2000). The second type of rating curve is the generalized power-law rating curve. Its construction is based on the hydraulics of open channel flow given by the formulas of Chézy and Manning which are of the form

\[Q = KR^{x}AS^{1/2}\] (2)

where \(K\) and \(x\) are constants; \(A\) is the area of the cross section; \(S\) is the slope of the channel; and \(R\) is the hydraulic radius, given by \(R = A/P\), where \(P\) is the wetted perimeter. According to Chézy \(x = 1/2\), while Manning claimed that \(x = 2/3\) (Chow, 1959). The form of the generalized power-law rating curve is

\[Q = a(h − c)^{f(h)}\] (3)

where \(a\) and \(c\) are constants; and \(f(h)\) is a function of \(h\), referred to as the power-law exponent. The relationship between (2) and (3) can be found by equating these two equations. In particular, the form of \(f(h)\) can be derived, namely,

\[f(h) = \frac{(x+1)\{ \log A(h) - \log A(1) \} - x \{ \log P(h) - \log P(1) \}}{\log h}\] (4)

Thus, \(f(h)\) is a function of the constant \(x\), the cross-sectional area \(A\), and the wetted perimeter \(P\). A Bayesian approach is proposed for estimating the parameters of the power-law model and the generalized power-law model. Bayesian inference requires specification of prior densities for unknown parameters and unknown quantities, along with a full probabilistic specification of the observed data. The goal of Bayesian inference is to obtain the posterior density of the model parameters, and then interrogate the posterior by calculating summary statistics, such as the posterior mean and the 95% posterior intervals. Analytical formulas for these summary statistics are intractable in most cases and thus they are computed by generating samples from the posterior density using Markov chain Monte Carlo simulation. The Bayesian power-law model is presented on a logarithmic scale as,

\[\log(Q_i) = \log(a) + b \log(h_i -c) + \epsilon_i, \quad i = 1,...,n,\] (5)

where \(\epsilon_i\) follows a normal distribution with mean zero and variance \(\sigma_{\epsilon}^2\), \(n\) is the number of paired observations, and \(a\), \(b\) and \(c\) are as before. The Bayesian inference scheme implemented for the power-law model is standard, however, for efficient posterior simulation, first, samples are obtained from the joint marginal posterior density of \((c, \sigma_{\epsilon}^2)\), then samples are obtained from the conditional posterior density of \((\log(a), b)\) conditional on \((c, \sigma_{\epsilon}^2)\). The Bayesian generalized power-law model is presented as a Bayesian hierarchical model. The function \(f(h)\) is modeled at the latent level as \(b\) plus a mean zero continuous stochastic process \(\beta(h)\), which is assumed to be twice mean-square differentiable. The model is presented on a logarithmic scale as,

\[\log (Q_i) = \log (a) + (b + \beta(h_i)) \log(h_i -c) + \varepsilon_i, \quad i = 1,...,n,\] (6)

where \(\epsilon_i\) follows a normal distribution with mean zero and variance \(\sigma_{\epsilon}^2(h_i)\) that can vary with water elevation. Here the parameters \(a\), \(b\) and \(c\) play a similar role as in the Bayesian power-law model. The stochastic process \(\beta(h)\) is assumed a priori to be a Gaussian process governed by a Matérn covariance function with smoothness parameter \(\nu = 2.5\), see Matérn (1960). This model is constrained by setting \(b=1.835\). An efficient posterior simulation is achieved by sampling from the joint posterior density of the hyperparameters of the model, and then sampling from the conditional density of the latent parameters given the hyperparameters.

References

Chow, V. (1959). Open-Channel Hydraulics. McGraw-Hill. New York.

Clarke, R. (1999). Uncertainty in the estimation of mean annual flood due to rating-curve indefinition. Journal of Hydrology 222(1-4). 185–190.

Clarke, R., Mendiondo, E., Brusa L. (2000). Uncertainties in mean discharges from two large South American rivers due to rating curve variability. Hydrological Sciences 45(2). 221–236.

Hrafnkelsson, B., Sigurdarson, H., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711.

Matérn, B. (1960). Spatial variation. Stochastic models and their application to some problems in forest surveys and other sampling investigations. Meddelanden från statens Skogsforskningsinstitut. 49(5).

Venetis, C. (1970). A note on the estimation of the parameters in logarithmic stage-discharge relationships with estimates of their error. International Association of Scientific Hydrology. Bulletin XV 2(6). 105–111.