40 citations as recorded by crossref.

1. Exploring hydrological recession dynamics through reference hydrological networks in the UK R. Hao et al. 10.2166/wcc.2025.381
2. A new method for effective parameterization of catchment-scale aquifer through event-scale recession analysis M. Hong & B. Mohanty 10.1016/j.advwatres.2023.104408
3. Inferring heavy tails of flood distributions through hydrograph recession analysis H. Wang et al. 10.5194/hess-27-4369-2023
4. Decorrelation is not dissociation: There is no means to entirely decouple the Brutsaert-Nieber parameters in streamflow recession analysis B. Biswal 10.1016/j.advwatres.2020.103822
5. Representing Bidirectional Hydraulic Continuum Between the Stream and Hillslope in the National Water Model for Improved Streamflow Prediction M. Hong & B. Mohanty 10.1029/2022MS003325
6. Characterizing hydrograph recessions from satellite-derived soil moisture S. Basso et al. 10.1016/j.scitotenv.2020.143469
7. Event‐Based Recession Analysis for Estimation of Basin‐Wide Characteristic Drainage Timescale and Groundwater Storage Trends M. Hameed et al. 10.1029/2023WR035829
8. Recession discharge from compartmentalized bedrock hillslopes C. Roques et al. 10.5194/hess-26-4391-2022
9. A probabilistic framework for robust master recession curve parameterization M. Gao et al. 10.1016/j.jhydrol.2023.129922
10. Analysis of the Behavior of Groundwater Storage Systems at Different Time Scales in Basins of South Central Chile: A Study Based on Flow Recession Records V. Parra et al. 10.3390/w15142503
11. A Nonlinear Recession Model for Horizontal Aquifers H. Basha 10.1029/2022WR034297
12. baseflow: a MATLAB and GNU Octave package for baseflow recession analysis M. Cooper & T. Zhou 10.21105/joss.05492
13. An improved method to estimate the rate of change of streamflow recession and basin synthetic recession parameters from hydrographs M. Gao et al. 10.1016/j.jhydrol.2021.127254
14. A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli 10.1002/hyp.14718
15. Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
16. Revegetation impacts on the flow characteristics in the Loess Plateau S. Ye et al. 10.1016/j.ejrh.2025.102654
17. Evaluation of seasonal catchment dynamic storage components using an analytical streamflow duration curve model C. Huang & H. Yeh 10.1186/s42834-022-00161-8
18. Revisiting the Origins of the Power‐Law Analysis for the Assessment of Concentration‐Discharge Relationships A. Wymore et al. 10.1029/2023WR034910
19. Landscape structures regulate the contrasting response of recession along rainfall amounts J. Lee et al. 10.5194/hess-27-4279-2023
20. Constructing a geography of heavy-tailed flood distributions: insights from common streamflow dynamics H. Wang et al. 10.5194/hess-29-1525-2025
21. Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession M. Cooper et al. 10.1029/2022WR033154
22. LM4-SHARC v1.0: resolving the catchment-scale soil–hillslope aquifer–river continuum for the GFDL Earth system modeling framework M. Hong et al. 10.5194/gmd-18-2275-2025
23. Tracer-aided assessment of catchment groundwater dynamics and residence time R. Zhu et al. 10.1016/j.jhydrol.2021.126230
24. Baseflow signature behaviour of mountainous catchments around the North China Plain S. Lyu et al. 10.1016/j.jhydrol.2022.127450
25. Hydrograph recession extraction algorithm (HYDRA): Minimizing influence of stage uncertainty in identification of recession events B. Thomas 10.1016/j.advwatres.2021.103937
26. Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies A. Gonzalez-Nicolas et al. 10.3390/w13131723
27. Increasing non‐linearity of the storage‐discharge relationship in sub‐Arctic catchments A. Hinzman et al. 10.1002/hyp.13860
28. Long-term decline in rainfall causing depletion in groundwater aquifer storage sustaining the springflow in the middle-Himalayan headwaters S. Tarafdar & S. Dutta 10.1007/s12040-023-02136-8
29. Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components T. Olarinoye et al. 10.5194/hess-26-5431-2022
30. Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach K. Eng et al. 10.1016/j.advwatres.2023.104440
31. Streamflow Recession Analysis Using Water Height E. Jachens et al. 10.1029/2020WR027091
32. Evaluation of spring flows using recession flow analysis techniques R. Kale et al. 10.2166/ws.2024.114
33. Estimating hydraulic properties and residence times of unconfined aquifers T. McMahon & R. Nathan 10.1016/j.jhydrol.2025.132861
34. Recession curve power-law exponent estimation: is there a perfect approach? D. Sharma & B. Biswal 10.1080/02626667.2022.2070022
35. Hillslope-scale exploration of the relative contribution of base flow, seepage flow and overland flow to streamflow dynamics N. Cornette et al. 10.1016/j.jhydrol.2022.127992
36. Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv 10.1016/j.jhydrol.2025.133033
37. An Empirical Reevaluation of Streamflow Recession Analysis at the Continental Scale A. Tashie et al. 10.1029/2019WR025448
38. Investigating meteorological effect on river flow recession rate in an arid environment L. Gunawardhana & G. Al-Rawas 10.1080/02626667.2020.1798009
39. Spatial and Temporal Patterns in Baseflow Recession in the Continental United States A. Tashie et al. 10.1029/2019WR026425
40. A comparative evaluation of automated recession extraction methods to determine the late-time recession characteristics of karst spring hydrographs K. ÖZDEMİR ÇALLI & A. HARTMANN 10.31807/tjwsm.930269