## bdrc - Bayesian Discharge Rating Curves

This software package 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. (2020). Four models are implemented:

plm0() - Power-law model with a constant variance. This is a Bayesian hierarchical implementation of the most commonly used discharge rating curve model in hydrological practice.

plm() - Power-law model with variance that varies with stage.

gplm0() - Generalized power-law model with a constant variance. The generalized power-law is introduced in Hrafnkelsson et al. (2020).

gplm() - Generalized power-law model with variance that varies with stage. The generalized power-law is introduced in Hrafnkelsson et al. (2020).

## Installation

# Install release version from CRAN
install.packages("bdrc")
# Install development version from GitHub
devtools::install_github("sor16/bdrc")

## Getting started

It is very simple to fit a discharge rating curve with the bdrc package. All you need are two mandatory input arguments, formula and data. The formula is of the form y~x where y is discharge in m3/s and x is stage in m (it is very important that the data is in the correct units). data is a data.frame which must include x and y as column names. As an example, we will use data from the Swedish gauging station Krokfors, which is one of the datasets that come with the package. In this table, the Q column denotes discharge while W denotes stage:

gplm.fit <- gplm(Q~W,krokfors)

To dig deeper into the functionality of the package and the different ways to visualize a discharge rating curve model for your data, we recommend taking a look at our two vignettes.

## References

Hrafnkelsson, B., Sigurdarson, H., and Gardarsson, S. M. (2020). Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling. arXiv preprint 2010.04769.