How NOT to identify streamflow events?

Examining catchment response to precipitation at event scale is useful for understanding how various hydrological systems store and release water. Many of such event scale characteristics, for example event runoff coefficient and event time scale are also important engineering metrics used for design. However, deriving these characteristics requires identification of discrete precipitation-streamflow events from continuous hydrometeorological time series.

Event identification is not at all a trivial task. It becomes even more challenging when working with very large datasets that encompass a wide range of spatial and temporal dynamics. Approaches range from visual expert judgement to baseflow-separation-based methods and objective methods based on the coupled dynamics of precipitation and streamflow. Here, we would like to present our experience in the quest to devise the “ideal” method for large datasets – and trust us, we tried, a lot. We demonstrate that expert-based methods can be seriously flawed simply by changing a few meta parameters, such as the length of displayed periods, baseflow-separation-based methods deliver completely opposite results when different underlying separation methods are selected, and objective methods suddenly fail when dynamics with different temporal scales are simultaneously present.

Ultimately, we realized that finding a one-size-fits-all method was not possible and that compromises had to be made to select sufficiently representative events across large datasets. Therefore, we advocate for pragmatic case-specific evaluation criteria and for transparency in event identification to make study results reproducible and fit for purpose, if not perfect.