https://doi.org/10.5194/hess-30-2337-2026-supplement

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

Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil - link to files (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 (1.0) C. Klingler et al. https://doi.org/10.5281/zenodo.5153305

The CAMELS-CL dataset - links to files, data set 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 (6.0) J. Koch et al. https://doi.org/10.22008/FK2/AZXSYP

CAMELS-FR dataset (3.2) O. Delaigue et al. https://doi.org/10.57745/WH7FJR

CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany (1.0.0) A. Dolich et al. https://doi.org/10.5281/zenodo.13837553

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

LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland H. Helgason and B Nijssen https://doi.org/10.4211/HS.86117A5F36CC4B7C90A5D54E18161C91

CAMELS-IND: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India (2.2) N. K. Mangukiya et al. https://doi.org/10.5281/zenodo.14999580

Highly Resolved Hydro-Meteorological and Atmospheric Data for Physiographically Characterized Catchments around Luxembourg: Vol. preprint (v1.1) J. Nijzink et al. https://doi.org/10.5281/zenodo.14910359

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

HYSETS - A 14425 watershed Hydrometeorological Sandbox over North America R. Arsenault et al. https://doi.org/10.17605/OSF.IO/RPC3W

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

Caravan extension Israel - Israel dataset for large-sample hydrology (v4) M. Efrat https://doi.org/10.5281/zenodo.15181680

Caravan extension Germany - German dataset for large-sample hydrology (v1.1.1) A. Dolich et al. https://doi.org/10.5281/zenodo.14755229

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

Global prediction of extreme floods in ungauged watersheds (v3) G. Nearing https://doi.org/10.5281/zenodo.10397664

Global Daily Discharge Estimation Based on Grid Long Short-Term Memory (LSTM) Model and River Routing Y. Yang and M. Pan https://doi.org/10.5281/zenodo.15644728

LSTM regionalization datasets and codes R. Arsenault et al. https://doi.org/10.17605/OSF.IO/3S2PQ

Never train an LSTM on a single basin F. Kratzert https://doi.org/10.5281/zenodo.11247607

Streamflow datasets from the high-resolution, multiscale, differentiable HBV hydrologic models (v6) Y. Song et al. https://doi.org/10.5281/zenodo.15784945

Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS),1980-2016, Daymet Version 3 calibration L. E. Hay and J. H. LaFontaine https://doi.org/10.5066/P9PGZE0S

CAMELS benchmark models F. Kratzert https://doi.org/10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1

sruzzante/NSE-and-Variance-Components: v1.1 S. Ruzzante https://doi.org/10.5281/zenodo.18705708