10 citations as recorded by crossref.

1. Investigating hydrological modeling uncertainties in the Mediterranean region by combining precipitation and soil moisture products V. Sivelle et al. https://doi.org/10.1016/j.ejrh.2025.103015
2. Assessing the Performance of Two Hydrologic Models for Forecasting Daily Streamflows in the Cazones River Basin (Mexico) F. González-Leiva et al. https://doi.org/10.4236/ojmh.2016.63014
3. Quantifying the Added Values of a Merged Precipitation Product in Streamflow Prediction over the Central Himalayas S. Guragain et al. https://doi.org/10.3390/rs17132170
4. Assessing Hydrologic Uncertainty Processor Performance for Flood Forecasting in a Semiurban Watershed S. Han et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001828
5. Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models I. Yoosefdoost et al. https://doi.org/10.1007/s11269-022-03068-6
6. Using micro‐catchment experiments for multi‐local scale modelling of nature‐based solutions B. Hankin et al. https://doi.org/10.1002/hyp.14418
7. Identifying advantages and drawbacks of two hydrological models based on a sensitivity analysis: a study in two Chilean watersheds V. Parra et al. https://doi.org/10.1080/02626667.2018.1538593
8. Sensitivity and identifiability analysis of a conceptual-lumped model in the headwaters of the Benue River Basin, Cameroon: implications for uncertainty quantification and parameter optimization R. Nonki et al. https://doi.org/10.2166/nh.2023.243
9. Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting M. Ghaith et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001866
10. Effect of different likelihood measures on parameter conditioning and model uncertainty F. Baig et al. https://doi.org/10.1007/s00477-025-02909-1