30 citations as recorded by crossref.

1. CAMELS-NZ: hydrometeorological time series and landscape attributes for New Zealand S. Bushra et al.
2. A dataset of land surface characteristics and time-series hydrometeorological data for typical catchments in China (2003–2020) H. MA et al.
3. Time shift between precipitation and evaporation has more impact on annual streamflow variability than the elasticity of potential evaporation V. Andréassian et al.
4. A heterogeneous weighting strategy for leveraging Cross-Basin data enhances the Usability of deep learning hydrological models S. Yoon et al.
5. CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al.
6. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al.
7. Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al.
8. GRDC-Caravan: extending Caravan with data from the Global Runoff Data Centre C. Färber et al.
9. QUADICA v2: extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany P. Ebeling et al.
10. Assessing temporal and spatial generalization of LSTMs for streamflow modeling in French watersheds with and without European training data M. Puche et al.
11. CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking O. Delaigue et al.
12. How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al.
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.
14. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al.
15. Streamflow elasticity as a function of aridity V. Andréassian et al.
16. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al.
17. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
18. Can discharge be used to inversely correct precipitation? A. Manoj J et al.
19. Future Streamflow and Hydrological Drought Under CMIP6 Climate Projections T. Liu et al.
20. Meteorological and hydrological dry-to-wet transition events are only weakly related over European catchments M. Brunner et al.
21. Modeling runoff with incomplete data: a comparison of hydrological, deep learning, and hybrid approaches J. Wu et al.
22. Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model T. Duong et al.
23. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al.
24. 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.
25. Uncertainty, temporal variability, and influencing factors of empirical streamflow sensitivities S. Gnann et al.
26. Climate change effects on river droughts in Bavaria using a hydrological large ensemble B. Poschlod et al.
27. Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model T. Duong et al.
28. Lowland transboundary river in a cold, semi-arid steppe: review of the Yesil River basin N. Ongdas et al.
29. Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al.
30. Evaluating E-OBS forcing data for large-sample hydrology using model performance diagnostics F. Clerc-Schwarzenbach & T. do Nascimento