Functional relationships capture how variables co-vary across specific spatial or temporal
domains. However, these relationships often take complex forms beyond linear, and they may
only hold for sub-sets of the domain. More problematically, it is often a priori unknown how
such sub-domains are defined. Here we present a new method called SONAR (diScovery Of
fuNctionaAl Relationships) that enables the automated discovery of functional relationships in
large datasets. SONAR operates on existing unstructured data and is designed to be an
explorative tool for large datasets where manual search for functional relationships would be
impossible. We test the method on groundwater recharge outputs of several global hydrological
models to explore its usefulness and limitations. Further, we compare SONAR to the established
CART (Classification and Regression Trees) and CIT (Conditional Inference Trees) methods.
SONAR results in smaller trees with functional relationships in the leaf nodes instead of specific
classes or numbers. SONAR provides a robust and automated method for the exploration of
functional relationships.