bdrc
Bayesian Discharge Rating Curves
Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2022)
bdrc - Bayesian Discharge Rating Curves 
The bdrc package provides tools for fitting discharge rating curves
using Bayesian hierarchical models. It implements both the classical
power-law and the novel generalized power-law models, offering
flexibility in handling various hydrological scenarios.
This package implements four models as described in Hrafnkelsson et al. (2022):
-
plm0()- Power-law model with constant log-error variance. -
plm()- Power-law model with stage-dependent log-error variance. -
gplm0()- Generalized power-law model with constant log-error variance. -
gplm()- Generalized power-law model with stage-dependent log-error variance.
Installation
# Install release version from CRAN
install.packages("bdrc")
# Install development version from GitHub
devtools::install_github("sor16/bdrc")
Usage
Fitting a discharge rating curve with bdrc is straightforward:
library(bdrc)
data(krokfors)
gplm.fit <- gplm(Q ~ W, krokfors)
summary(gplm.fit)
plot(gplm.fit)
Key-features
- Easy-to-use interface for fitting Bayesian discharge rating curves
- Features the novel Generalized power-law rating curve model (Hrafnkelsson et al., 2022)
- Multiple model options to suit different hydrological scenarios
- Built-in visualization tools for model results and diagnostics
- Integrates R and C++ for efficient MCMC sampling with parallel processing
Getting started
For a deeper dive into the package’s functionality, visualization options, and the underlying theory of the models, please check out our vignettes:
References
Hrafnkelsson, B., Sigurdarson, H., Rögnvaldsson, S., Jansson, A. Ö., Vias, R. D., and Gardarsson, S. M. (2022). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling, Environmetrics, 33(2):e2711. doi: https://doi.org/10.1002/env.2711