HESS Opinions: A few camels or a whole caravan?

Abstract. Large-sample datasets containing hydrometeorological time series and catchment attributes for hundreds of catchments in a country, many of them known as “Camels” (catchment attributes and meteorology for large-sample studies), have revolutionized hydrological modelling and enabled comparative analyses. The Caravan dataset is a compilation of several (“Camels” and other) large-sample datasets with uniform attribute names and data structure. This simplifies large-sample hydrology across regions, continents, or the globe. However, the use of the Caravan dataset instead of the original Camels or other large-sample datasets may affect model results and the conclusions derived thereof. For the Caravan dataset, the meteorological forcing data are based on ERA5-Land reanalysis data. Here, we describe the differences between the original precipitation, temperature, and potential evapotranspiration (E_pot) data for 1252 catchments in the CAMELS-US, CAMELS-BR, and CAMELS-GB datasets and the forcing data for these catchments in the Caravan dataset. The E_pot in the Caravan dataset is unrealistically high for many catchments but there are, not surprisingly, also considerable differences in the precipitation data. We show that the use of the forcing data from the Caravan dataset impairs hydrological model calibration for the vast majority of catchments, i.e., there is a drop in the calibration performance when using the forcing data from the Caravan dataset compared to the original Camels datasets. This drop is mainly due to the differences in the precipitation data. Therefore, we suggest extending the Caravan dataset with the forcing data included in the original Camels datasets wherever possible, so that users can choose which forcing data they want to use, or at least indicating clearly that the forcing data in Caravan come with a data quality loss and using the original datasets is recommended. Moreover, we suggest not using the E_pot data (and derived catchment attributes, such as the aridity index) from the Caravan dataset and replacing these with (or based on) alternative E_pot estimates.