Articles | Volume 28, issue 17
https://doi.org/10.5194/hess-28-4219-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4219-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
12 Sep 2024
Research article | Highlight paper |
| 12 Sep 2024
| 12 Sep 2024
Large-sample hydrology – a few camels or a whole caravan?
Franziska Clerc-Schwarzenbach
CORRESPONDING AUTHOR
Department of Geography, University of Zurich, 8057 Zurich, Switzerland
Giovanni Selleri
Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, 40136 Bologna, Italy
Mattia Neri
Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, 40136 Bologna, Italy
Elena Toth
Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, 40136 Bologna, Italy
Ilja van Meerveld
Department of Geography, University of Zurich, 8057 Zurich, Switzerland
Jan Seibert
Department of Geography, University of Zurich, 8057 Zurich, Switzerland
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Cited
13 citations as recorded by crossref.
- CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. 10.5194/essd-16-5625-2024
- Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al. 10.1016/j.envsoft.2025.106527
- Evaluation of national and international gridded meteorological products for rainfall-runoff modelling in Northern Italy G. Sarigil et al. 10.1016/j.ejrh.2024.102031
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. 10.5194/essd-17-1551-2025
- Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al. 10.5194/hess-29-841-2025
- ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed J. Soares et al. 10.1016/j.jhydrol.2025.132674
- Testing a low-complexity spatially distributed model to simulate the intra-annual dynamics of soil erosion and sediment delivery F. Matthews et al. 10.1016/j.catena.2025.109054
- Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. 10.1098/rsta.2024.0287
- Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. 10.1016/j.envsoft.2025.106350
- ROBIN: Reference observatory of basins for international hydrological climate change detection S. Turner et al. 10.1038/s41597-025-04907-y
- Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
- EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al. 10.1038/s41597-024-03706-1
- Hierarchical Deep Learning for Consistent Multi‐Timescale Hydrological Forecasting M. Jahangir & J. Quilty 10.1029/2024WR038105
10 citations as recorded by crossref.
- CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. 10.5194/essd-16-5625-2024
- Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al. 10.1016/j.envsoft.2025.106527
- Evaluation of national and international gridded meteorological products for rainfall-runoff modelling in Northern Italy G. Sarigil et al. 10.1016/j.ejrh.2024.102031
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. 10.5194/essd-17-1551-2025
- Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al. 10.5194/hess-29-841-2025
- ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed J. Soares et al. 10.1016/j.jhydrol.2025.132674
- Testing a low-complexity spatially distributed model to simulate the intra-annual dynamics of soil erosion and sediment delivery F. Matthews et al. 10.1016/j.catena.2025.109054
- Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. 10.1098/rsta.2024.0287
- Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. 10.1016/j.envsoft.2025.106350
- ROBIN: Reference observatory of basins for international hydrological climate change detection S. Turner et al. 10.1038/s41597-025-04907-y
3 citations as recorded by crossref.
- Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
- EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al. 10.1038/s41597-024-03706-1
- Hierarchical Deep Learning for Consistent Multi‐Timescale Hydrological Forecasting M. Jahangir & J. Quilty 10.1029/2024WR038105
Latest update: 21 Aug 2025
Executive editor
Large sample hydrology datasets such as Caravan provides the community with hydrometeorological information and catchment attributes for many catchments in the world and offers the opportunity for hydrological research. However, there are considerable differences between the forcing data of Caravan compared to the CAMELS datasets, especially with potential evaporation. This can lead to wrong conclusions on catchment hydrological drivers and affect regionalization. This papers shows the important of robustness of large sample datasets and the need to keep assessing that.
Large sample hydrology datasets such as Caravan provides the community with hydrometeorological...
Short summary
We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
We show that the differences between the forcing data included in three CAMELS datasets (US, BR,...
