17 citations as recorded by crossref.

1. Integrating major ion geochemistry, stable isotopes (18O, 2H) and radioactive isotopes (222Rn, 14C, 36Cl, 3H) to understand the interaction between catchment waters and an intermittent river Z. Zhou et al. https://doi.org/10.1016/j.scitotenv.2023.167998
2. Quantifying baseflow-driven non-point source nutrient losses using a novel self-adaptive algorithm in a rain-fed agricultural watershed of eastern China S. He et al. https://doi.org/10.1016/j.iswcr.2026.100739
3. How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow S. Chen et al. https://doi.org/10.1016/j.jconhyd.2024.104441
4. The significance of groundwater contributions to New Zealand rivers C. Moore et al. https://doi.org/10.3389/frwa.2025.1584947
5. Symbolic regression-based regionalization of baseflow separation parameter using catchment-scale characteristics Y. Lin et al. https://doi.org/10.5194/hess-30-3425-2026
6. Geochemical footprints of climate-driven increases in groundwater contributions in a Canadian Rocky mountain watershed S. Kheirkhah et al. https://doi.org/10.1016/j.jhydrol.2026.135852
7. Baseflow-related phosphorus transport in an agriculture-dominated, human-influenced watershed: Quantification using baseflow separation and LOADEST X. Chen et al. https://doi.org/10.1016/j.jconhyd.2026.105121
8. Temporal evolution of hydrochemical changes in an acid mine drainage–impacted river using SWAT modeling and concentration–discharge relationships R. Moreno-González et al. https://doi.org/10.1016/j.wroa.2026.100563
9. Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm A. Nasiri Khiavi https://doi.org/10.1016/j.jhydrol.2024.131220
10. Simultaneous Separation of Runoff Pathways and Storage Times via Coupled Electrical Conductivity Mass Balance and Nonlinear Storage‐Discharge Relationship: Theory and Application Testing W. Yang et al. https://doi.org/10.1029/2024WR039052
11. High-Precision baseflow separation in data-scarce regions with machine learning models R. Zhong et al. https://doi.org/10.1016/j.jhydrol.2026.135571
12. Optimal baseflow separation scheme considering both high precision and low cost - take major watersheds in the United States as an example R. Zhong et al. https://doi.org/10.1016/j.jhydrol.2022.128133
13. Evaluation of baseflow separation techniques in North-Western Himalayas using tracer-based and non-tracer methods M. Ghezta et al. https://doi.org/10.1080/09715010.2025.2575306
14. Application of radon (222Rn) as an environmental tracer in hydrogeological and geological investigations: An overview S. Sukanya et al. https://doi.org/10.1016/j.chemosphere.2022.135141
15. Hydrological dynamics of the shire river: Insights from baseflow and recession analysis P. Kumambala et al. https://doi.org/10.1016/j.pce.2024.103758
16. Revisiting the Hydrograph Separation Issue Using High-Frequency Chemical Information J. Tunqui Neira et al. https://doi.org/10.1007/s10666-024-09963-z
17. Assessing the applicability of hydrogeochemical tracers to baseflow separation in the hilly and mountainous region of central Japan K. Mishra & S. Yoshimoto https://doi.org/10.1007/s40899-026-01372-2