Articles | Volume 24, issue 3
https://doi.org/10.5194/hess-24-1159-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-1159-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Mar 2020
Research article |
| 11 Mar 2020
| 11 Mar 2020
Recession analysis revisited: impacts of climate on parameter estimation
Department of Biological and Ecologic Engineering, Oregon State
University, Corvallis, OR 97330, USA
David E. Rupp
Oregon Climate Change Research Institute, College of Earth, Oceanic, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97330, USA
Clément Roques
Department of Earth Sciences, ETH Zurich, 8092 Zürich, Switzerland
John S. Selker
Department of Biological and Ecologic Engineering, Oregon State
University, Corvallis, OR 97330, USA
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Cited
35 citations as recorded by crossref.
- 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
- Inferring heavy tails of flood distributions through hydrograph recession analysis H. Wang et al. 10.5194/hess-27-4369-2023
- 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
- 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
- Characterizing hydrograph recessions from satellite-derived soil moisture S. Basso et al. 10.1016/j.scitotenv.2020.143469
- Event‐Based Recession Analysis for Estimation of Basin‐Wide Characteristic Drainage Timescale and Groundwater Storage Trends M. Hameed et al. 10.1029/2023WR035829
- Recession discharge from compartmentalized bedrock hillslopes C. Roques et al. 10.5194/hess-26-4391-2022
- A probabilistic framework for robust master recession curve parameterization M. Gao et al. 10.1016/j.jhydrol.2023.129922
- 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
- A Nonlinear Recession Model for Horizontal Aquifers H. Basha 10.1029/2022WR034297
- baseflow: a MATLAB and GNU Octave package for baseflow recession analysis M. Cooper & T. Zhou 10.21105/joss.05492
- 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
- A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli 10.1002/hyp.14718
- Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
- Evaluation of seasonal catchment dynamic storage components using an analytical streamflow duration curve model C. Huang & H. Yeh 10.1186/s42834-022-00161-8
- Revisiting the Origins of the Power‐Law Analysis for the Assessment of Concentration‐Discharge Relationships A. Wymore et al. 10.1029/2023WR034910
- Landscape structures regulate the contrasting response of recession along rainfall amounts J. Lee et al. 10.5194/hess-27-4279-2023
- Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
- Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession M. Cooper et al. 10.1029/2022WR033154
- Tracer-aided assessment of catchment groundwater dynamics and residence time R. Zhu et al. 10.1016/j.jhydrol.2021.126230
- Baseflow signature behaviour of mountainous catchments around the North China Plain S. Lyu et al. 10.1016/j.jhydrol.2022.127450
- Hydrograph recession extraction algorithm (HYDRA): Minimizing influence of stage uncertainty in identification of recession events B. Thomas 10.1016/j.advwatres.2021.103937
- 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
- Increasing non‐linearity of the storage‐discharge relationship in sub‐Arctic catchments A. Hinzman et al. 10.1002/hyp.13860
- 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
- 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
- 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
- Streamflow Recession Analysis Using Water Height E. Jachens et al. 10.1029/2020WR027091
- Evaluation of spring flows using recession flow analysis techniques R. Kale et al. 10.2166/ws.2024.114
- Recession curve power-law exponent estimation: is there a perfect approach? D. Sharma & B. Biswal 10.1080/02626667.2022.2070022
- 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
- An Empirical Reevaluation of Streamflow Recession Analysis at the Continental Scale A. Tashie et al. 10.1029/2019WR025448
- Investigating meteorological effect on river flow recession rate in an arid environment L. Gunawardhana & G. Al-Rawas 10.1080/02626667.2020.1798009
- Spatial and Temporal Patterns in Baseflow Recession in the Continental United States A. Tashie et al. 10.1029/2019WR026425
- 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
31 citations as recorded by crossref.
- 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
- Inferring heavy tails of flood distributions through hydrograph recession analysis H. Wang et al. 10.5194/hess-27-4369-2023
- 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
- 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
- Characterizing hydrograph recessions from satellite-derived soil moisture S. Basso et al. 10.1016/j.scitotenv.2020.143469
- Event‐Based Recession Analysis for Estimation of Basin‐Wide Characteristic Drainage Timescale and Groundwater Storage Trends M. Hameed et al. 10.1029/2023WR035829
- Recession discharge from compartmentalized bedrock hillslopes C. Roques et al. 10.5194/hess-26-4391-2022
- A probabilistic framework for robust master recession curve parameterization M. Gao et al. 10.1016/j.jhydrol.2023.129922
- 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
- A Nonlinear Recession Model for Horizontal Aquifers H. Basha 10.1029/2022WR034297
- baseflow: a MATLAB and GNU Octave package for baseflow recession analysis M. Cooper & T. Zhou 10.21105/joss.05492
- 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
- A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli 10.1002/hyp.14718
- Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine M. Kim et al. 10.1029/2022WR032690
- Evaluation of seasonal catchment dynamic storage components using an analytical streamflow duration curve model C. Huang & H. Yeh 10.1186/s42834-022-00161-8
- Revisiting the Origins of the Power‐Law Analysis for the Assessment of Concentration‐Discharge Relationships A. Wymore et al. 10.1029/2023WR034910
- Landscape structures regulate the contrasting response of recession along rainfall amounts J. Lee et al. 10.5194/hess-27-4279-2023
- Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
- Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession M. Cooper et al. 10.1029/2022WR033154
- Tracer-aided assessment of catchment groundwater dynamics and residence time R. Zhu et al. 10.1016/j.jhydrol.2021.126230
- Baseflow signature behaviour of mountainous catchments around the North China Plain S. Lyu et al. 10.1016/j.jhydrol.2022.127450
- Hydrograph recession extraction algorithm (HYDRA): Minimizing influence of stage uncertainty in identification of recession events B. Thomas 10.1016/j.advwatres.2021.103937
- 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
- Increasing non‐linearity of the storage‐discharge relationship in sub‐Arctic catchments A. Hinzman et al. 10.1002/hyp.13860
- 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
- 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
- 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
- Streamflow Recession Analysis Using Water Height E. Jachens et al. 10.1029/2020WR027091
- Evaluation of spring flows using recession flow analysis techniques R. Kale et al. 10.2166/ws.2024.114
- Recession curve power-law exponent estimation: is there a perfect approach? D. Sharma & B. Biswal 10.1080/02626667.2022.2070022
- 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
4 citations as recorded by crossref.
- An Empirical Reevaluation of Streamflow Recession Analysis at the Continental Scale A. Tashie et al. 10.1029/2019WR025448
- Investigating meteorological effect on river flow recession rate in an arid environment L. Gunawardhana & G. Al-Rawas 10.1080/02626667.2020.1798009
- Spatial and Temporal Patterns in Baseflow Recession in the Continental United States A. Tashie et al. 10.1029/2019WR026425
- 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
Latest update: 22 Nov 2024
Short summary
Recession analysis uses the receding streamflow following precipitation events to estimate watershed-average properties. Two methods for recession analysis use recession events individually or all events collectively. Using synthetic case studies, this paper shows that analyzing recessions collectively produces flawed interpretations. Moving forward, recession analysis using individual recessions should be used to describe the average and variability of watershed behavior.
Recession analysis uses the receding streamflow following precipitation events to estimate...
