For variety of reasons, we need hydrological models for our short- and long-term predictions and planning. However, it is no secret that these models always suffer from some degree of bias. This bias can stem from many different and often interacting sources. Some examples are biases in underlying model assumptions, missing processes, model parameters, calibration parameters, and imperfections in input data (

Beven and Binley, 1992).

The question of how to use models, given all these uncertainties, has been an active area of research for at least 50 years and will probably remain so for the foreseeable future, but going through that is not the focus of this blog post.

In this post, I explain a technique called

*bias correction* that is frequently used in an attempt to improve model predictions. I also introduce an R package for bias correction that I recently developed; the package is called “biascorrection.” Although most of the examples in this post are about hydrological models, the arguments and the R package might be useful in other disciplines, for example with atmospheric models that have been one of the hotspots of bias correction applications (for example,

here,

here and

here). The reason is that the algorithm follows a series of simple mathematical procedures that can be applied to other questions and research areas.

Link to the blog post:

https://waterprogramming.wordpress.com/2020/09/15/introducing-the-r-package-biascorrection/