TY - JOUR
T1 - Time-Variability of Flow Recession Dynamics
T2 - Application of Machine Learning and Learning From the Machine
AU - Kim, Minseok
AU - Bauser, Hannes H.
AU - Beven, Keith
AU - Troch, Peter A.
N1 - Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS‐2023‐00212526). We gratefully acknowledge the support of National Science Foundation Grants EAR‐2120113 and GCR‐2121155. We also acknowledge support from the Philecology Foundation of Fort Worth Texas. Additional funding support was provided by the Office of the Vice President of Research at the University of Arizona and by the Technology and Research Initiative Fund (TRIF) Water, Environmental, and Energy Solutions (WEES) initiative at the University of Arizona (Shared Equipment Enhancement Funds). This work was also supported by Pusan National University Grant, 2022. Publisher Copyright: © 2023. American Geophysical Union. All Rights Reserved.
PY - 2023/5
Y1 - 2023/5
N2 - Flow recession analysis, relating discharge Q and its time rate of change −dQ/dt, has been widely used to understand catchment scale flow dynamics. However, data points in the recession plot, the plot of −dQ/dt versus Q, typically form a wide point cloud due to noise and hysteresis in the storage-discharge relationship, and it is still unclear what information we can extract from the plot and how to understand the information. There seem to be two contrasting approaches to interpret the plot. One emphasizes the importance of the ensemble characteristics of many recessions (i.e., the lower envelope or a measure of central tendency), and the other highlights the importance of the event scale analysis and questions the meaning of the ensemble characteristics. We examine if those approaches can be reconciled. We utilize a machine learning tool to capture the point cloud using the past trajectory of daily discharge. Our model results for a catchment show that most of the data points can be captured using 5 days of past discharge. We show that we can learn the catchment scale flow recession dynamics from what the machine learned. We analyze patterns learned by the machine and explain and hypothesize why the machine learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage-discharge relationship can be estimated based on the attractor.
AB - Flow recession analysis, relating discharge Q and its time rate of change −dQ/dt, has been widely used to understand catchment scale flow dynamics. However, data points in the recession plot, the plot of −dQ/dt versus Q, typically form a wide point cloud due to noise and hysteresis in the storage-discharge relationship, and it is still unclear what information we can extract from the plot and how to understand the information. There seem to be two contrasting approaches to interpret the plot. One emphasizes the importance of the ensemble characteristics of many recessions (i.e., the lower envelope or a measure of central tendency), and the other highlights the importance of the event scale analysis and questions the meaning of the ensemble characteristics. We examine if those approaches can be reconciled. We utilize a machine learning tool to capture the point cloud using the past trajectory of daily discharge. Our model results for a catchment show that most of the data points can be captured using 5 days of past discharge. We show that we can learn the catchment scale flow recession dynamics from what the machine learned. We analyze patterns learned by the machine and explain and hypothesize why the machine learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage-discharge relationship can be estimated based on the attractor.
KW - catchment flow attractor
KW - catchment sensitivity function
KW - flow recession dynamics
KW - machine learning
KW - master recession curve
KW - storage-discharge relationship
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U2 - 10.1029/2022WR032690
DO - 10.1029/2022WR032690
M3 - Article
SN - 0043-1397
VL - 59
JO - Water Resources Research
JF - Water Resources Research
IS - 5
M1 - e2022WR032690
ER -