So many catchments, so many choices: challenges with large-sample hydrological datasets

The field of large-sample hydrology is developing at a rapid pace. The increasing availability of hydrometeorological data and information on catchment attributes is primarily thanks to the authors of the various CAMELS datasets and similar datasets, who have put tremendous efforts into creating these resources. This progress has enabled the use of datasets from various regions to conduct large-sample studies. For some regions, large-sample datasets are becoming available at sub-daily resolutions, which will further expand the possibilities for studies in large-sample hydrology. 

Many of these datasets offer multiple time series for a certain variable: for example, precipitation time series originating from different sources, or potential evapotranspiration time series calculated using different equations. Furthermore, there is a considerable number of catchments that are represented in multiple large-sample datasets, either because there is more than one dataset for a particular country (which is the case for Brazil) or because they are included in overarching datasets such as Caravan or EStreams. While this wealth of data is a real treasure, it also poses significant challenges to users of large-sample hydrological data because decisions need to be taken on what data to use (and for what reason). Furthermore, questions on the reliability of the different data are inevitable. Many users end up doing individual data checks on their own or taking more or less random decisions. This reduces the comparability between different studies. 

In this contribution, we present examples of the challenges that arise when working with large-sample hydrological data. We show the results of comparisons between different data sources that are meant to represent the same variable and how these affect model simulations. The presentation aims to stimulate discussion about a more uniform approach to making decisions on which data to use when working with large-sample hydrological datasets.