EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

CAMELS-spat: catchment data for spatially distributed large-sample hydrology

Wouter Knoben1 and Martyn Clark1,2
Wouter Knoben and Martyn Clark
  • 1University of Saskatchewan, Coldwater Laboratory, Geography and Planning, Canmore, Canada
  • 2Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK, Canada

The recent publication of large-sample datasets for hydrologic modeling and analysis has led to a revival of comparative hydrology. The “CAMELS” branch of these datasets currently provide catchment attributes and meteorological time series for basins located in the United States, Chile, Brazil, Australia and Great-Britain, with a dataset for France under development. A key characteristic of these datasets is that information is provided as catchment-averaged data; i.e. each catchment is treated as a lumped entity with no spatial variability. Some progress is being made to extend large-sample hydrology to include spatially distributed data, most notably by the recent LamaH dataset which covers part of Central Europe.

Here we present progress on developing a continental domain dataset for large-sample hydrology intended for spatially distributed modeling and analysis. Our domain covers the United States and Canada, expanding both geographically and climatically on the region covered by the LamaH dataset. We focus mostly on relatively undisturbed headwater catchments, because accurate data on water management policies and infrastructure can be difficult to obtain. Our aim is to provide the necessary data for process-based modeling and analysis at a sub-daily temporal resolution. 

How to cite: Knoben, W. and Clark, M.: CAMELS-spat: catchment data for spatially distributed large-sample hydrology, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2650,, 2023.