Setting the bar: benchmarks for model performances in large-sample hydrology

Seibert, Jan; Vis, Marc; Pool, Sandra

doi:10.5194/hess-30-5857-2026

Articles | Volume 30, issue 18

https://doi.org/10.5194/hess-30-5857-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-30-5857-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 30, issue 18

Research article

|

16 Sep 2026

Research article |

| 16 Sep 2026

Setting the bar: benchmarks for model performances in large-sample hydrology

Jan Seibert, Marc Vis, and Sandra Pool

Supplement

https://doi.org/10.5194/hess-30-5857-2026-supplement

Data sets

Upper and lower benchmarks for Large-Sample Hydrological datasets Marc Vis et al. https://doi.org/10.5281/zenodo.20039003

The CAMELS-CL dataset - links to files C. Alvarez-Garreton et al. https://doi.org/10.1594/PANGAEA.894885

CAMELS-DK: Hydrometeorological Time Series and Landscape Attributes for 3330 Catchments in Denmark J. Koch et al. https://doi.org/10.22008/FK2/AZXSYP

Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great Britain (CAMELS-GB) G. Coxon et al. https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9

Swedish Hydroclimatic Data 1961-2020 – Precipitation, Temperature and Streamflow Observations across 50 Catchments (CAMELS-SE) (Version 1) C. Teutschbein https://doi.org/10.57804/t3rm-v029

CAMELS-AUS v2: updated hydrometeorological timeseries and landscape attributes for an enlarged set of catchments in Australia (Version 2.03) K. Fowler et al. https://doi.org/10.5281/zenodo.14289037

CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil - link to files. (Version 1.2) V. B. P. Chagas et al. https://doi.org/10.5281/zenodo.15025488

LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files (Version 1.0) C. Klingler et al. https://doi.org/10.5281/zenodo.5153305

CAMELS-FR dataset O. Delaigue et al. https://doi.org/10.57745/WH7FJR

CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany (Version 1.1.0) A. Dolich et al. https://doi.org/10.5281/zenodo.16755906

CAMELS-LUX: Highly Resolved Hydro-Meteorological and Atmospheric Data for Physiographically Characterized Catchments around Luxembourg J. Nijzink et al. https://doi.org/10.5281/zenodo.13846619

CAMELS-ES: Catchment Attributes and Meteorology for Large-Sample Studies – Spain (Version 1.0.2) J. Casado Rodríguez https://doi.org/10.5281/zenodo.8428374

Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH) (Version 0.9) M. Höge et al. https://doi.org/10.5281/zenodo.15025258

CAMELS: Catchment Attributes and MEteorology for Large-sample Studies (Version 1.2) A. J. Newman et al. https://doi.org/10.5065/D6MW2F4D

Short summary

We studied how well simple bucket-type models can reproduce observed river flow using large data sets from many regions in the world. Model performance varies widely depending on local conditions, so fixed performance thresholds are misleading. To better judge model performances, we propose lower and upper benchmarks. These benchmarks help to better understand what level of model performance is achievable and, thus, enable us to compare models across different catchments.