This software package fits a discharge rating curve based on the power-law and the generalized power-law from data on paired water elevation and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2022). Four models are implemented:

`plm0()`

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

`plm()`

- Power-law model with error variance that varies with water
elevation.

`gplm0()`

- Generalized power-law model with a constant error variance.
The generalized power-law is introduced in Hrafnkelsson et al. (2022).

`gplm()`

- Generalized power-law model with error variance that varies
with water elevation. The generalized power-law is introduced in
Hrafnkelsson et al. (2022).

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

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
ms
and x is water elevation 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
water elevation:

```
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.

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.

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