In sor16/RCmodels: Bayesian Discharge Rating Curves

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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:

• Background
• Introduction
• Power-law tournament (model selection)

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

sor16/RCmodels documentation built on Sept. 13, 2024, 4:51 a.m.