An R package to partition observation data used for model development and evaluation to achieve model generalizability

Development of environmental models generally requires available data to be split into “development” and “evaluation” subsets. How this is done can significantly affect a model's outputs and performance. However, data splitting is generally done in a subjective, ad-hoc manner, with little justification, raising questions regarding the reliability of the findings of many modelling studies. To address this issue, we present and demonstrate the value of an R-package along with high-level guidelines for implementing many state-of-the-art data splitting methods in order to develop the model in a considered, defensible, consistent, repeatable and transparent fashion, thereby improving the generalizability of the resulting models. Results from two rainfall-runoff case studies show that models with high generalization ability can be achieved even when the available data contain rare, extreme events. Additionally, data splitting methods can be used to explicitly quantify the parameter uncertainty associated with data splitting and the resulting bounds on model predictions.