18 citations as recorded by crossref.

1. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. https://doi.org/10.1038/s41597-024-03594-5
2. Comparing Frequency-Matched and Natural Data Approaches for Estimating the Curve Number from Rainfall-Runoff Data A. Brandão et al. https://doi.org/10.1061/JHYEFF.HEENG-6400
3. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
4. Understanding changes in Iceland's streamflow dynamics in response to climate change H. Helgason et al. https://doi.org/10.5194/hess-30-3979-2026
5. EARLS: a runoff reconstruction dataset for Europe D. Klotz et al. https://doi.org/10.5194/essd-18-5485-2026
6. Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling A. Abbas et al. https://doi.org/10.5194/hess-30-3399-2026
7. 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
8. 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
9. 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
10. 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
11. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al. https://doi.org/10.5194/essd-17-5745-2025
12. A Global Benchmark of the Vector-Based Routing Model MizuRoute: Similarities and Divergent Patterns in Simulated River Discharge S. Xu et al. https://doi.org/10.3390/w18040485
13. Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data T. do Nascimento et al. https://doi.org/10.1038/s41597-025-05625-1
14. Enhancing the Representation of Glaciers and Ice Sheets in the ecLand Land-Surface Model: Impacts on Surface Energy Balance and Hydrology Across Scales G. Arduini et al. https://doi.org/10.5194/tc-20-1119-2026
15. How do geological map details influence the identification of geology-streamflow relationships in large-sample hydrology studies? T. do Nascimento et al. https://doi.org/10.5194/hess-29-7173-2025
16. AIFL: A global daily streamflow forecasting model using deterministic an LSTM pre-trained on ERA5-Land and fine-tuned on IFS M. Taccari et al. https://doi.org/10.1016/j.jhydrol.2026.136064
17. Evaluating E-OBS forcing data for large-sample hydrology using model performance diagnostics F. Clerc-Schwarzenbach & T. do Nascimento https://doi.org/10.5194/hess-30-119-2026
18. CAMELS-GB v2: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. https://doi.org/10.5194/essd-18-4345-2026