16 citations as recorded by crossref.

1. CAMELS-FI: hydrometeorological time series and landscape properties for 320 catchments in Finland I. Seppä et al. https://doi.org/10.5194/essd-18-4745-2026
2. Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model A. Shokri et al. https://doi.org/10.5194/hess-30-757-2026
3. Historical trends of seasonal droughts in Australia M. Grant et al. https://doi.org/10.5194/hess-29-5555-2025
4. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al. https://doi.org/10.5194/essd-17-5745-2025
5. Caravan-Qual: A global scale integration of stream water quality observations into a large-sample hydrology dataset E. Jones et al. https://doi.org/10.1038/s41597-026-07352-7
6. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
7. Streamflow elasticity as a function of aridity V. Andréassian et al. https://doi.org/10.5194/hess-30-1865-2026
8. Uncertainty, temporal variability, and influencing factors of empirical streamflow sensitivities S. Gnann et al. https://doi.org/10.5194/hess-30-779-2026
9. A systematic evaluation of 15 actual evapotranspiration formulations within conceptual hydrological models G. Burns et al. https://doi.org/10.5194/hess-30-4509-2026
10. Estimation of root zone water storage capacity (Sr) in natural ecosystems subject to high interannual climate variability M. Khorami et al. https://doi.org/10.1016/j.jhydrol.2026.135354
11. Future changes in seasonal drought in Australia A. Ukkola et al. https://doi.org/10.5194/hess-30-1463-2026
12. Time shift between precipitation and evaporation has more impact on annual streamflow variability than the elasticity of potential evaporation V. Andréassian et al. https://doi.org/10.5194/hess-29-5477-2025
13. Discharge-based classifications of spatio-temporal patterns of potentially gaining and losing subcatchments in the Bode River catchment, Central Germany C. Lei et al. https://doi.org/10.1016/j.ejrh.2026.103161
14. Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America W. Knoben et al. https://doi.org/10.5194/hess-29-5791-2025
15. SAGE GUI: Fast and flexible differentiable hydrologic modeling with analytic gradients J. Vrugt & J. Frame https://doi.org/10.1016/j.envsoft.2026.107194
16. Setting the bar: benchmarks for model performances in large-sample hydrology J. Seibert et al. https://doi.org/10.5194/hess-30-5857-2026