scholarly journals Kilometer‐Scale Hydrologic Exchange Flows in a Gravel Bed River Corridor and Their Implications to Solute Migration

2020 ◽  
Vol 56 (2) ◽  
Author(s):  
John M. Zachara ◽  
Xingyuan Chen ◽  
Xuehang Song ◽  
Pin Shuai ◽  
Chris Murray ◽  
...  
Keyword(s):  
Exchange Flows ◽  
Gravel Bed ◽  
Gravel Bed River ◽  
Hydrologic Exchange ◽  
River Corridor

scholarly journals Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

2021 ◽  
Vol 4 ◽  
Author(s):  
Huiying Ren ◽  
Xuehang Song ◽  
Yilin Fang ◽  
Z. Jason Hou ◽  
Timothy D. Scheibe
Keyword(s):  
Machine Learning ◽  
Transit Time ◽  
Numerical Models ◽  
Data Availability ◽  
Regulated River ◽  
Exchange Flows ◽  
Extreme Gradient Boosting ◽  
Hydrologic Exchange ◽  
River Corridor ◽  
River Velocity

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

MONITORING UNSTEADY STREAMFLOW-STAGE HYSTERESIS BEHAVIOR OF A GRAVEL-BED RIVER

2018 ◽  
Vol 74 (4) ◽  
pp. I_313-I_318
Author(s):  
Mohamad Basel AL SAWAF ◽  
Kiyosi KAWANISI ◽  
Mochammad Meddy DANIAL
Keyword(s):  
Hysteresis Behavior ◽  
Gravel Bed ◽  
Gravel Bed River

scholarly journals Estimates of resource transfer via winged adult insects from the hyporheic zone in a gravel‐bed river

2021 ◽  
Author(s):  
Mirza A. T. M. Tanvir Rahman ◽  
Junjiro N. Negishi ◽  
Takumi Akasaka ◽  
Futoshi Nakamura
Keyword(s):  
Hyporheic Zone ◽  
Resource Transfer ◽  
Gravel Bed ◽  
Gravel Bed River

scholarly journals Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach

2018 ◽  
Author(s):  
Pin Shuai ◽  
Xingyuan Chen ◽  
Xuehang Song ◽  
Glenn Hammond ◽  
John Zachara ◽  
...  
Keyword(s):  
Regulated River ◽  
Exchange Flows ◽  
River Reach ◽  
Hydrologic Exchange

scholarly journals STUDY ON THE EXCESSIVE GROWTH OF REVERINE TREES IN GRAVEL-BED RIVER

1999 ◽  
Vol 43 ◽  
pp. 971-976 ◽  
Author(s):  
Yoshihiko SHIMIZU ◽  
Shigeki KOBATAKE ◽  
Takayuki ARAFUNE ◽  
Satoshi OKADA
Keyword(s):  
Gravel Bed ◽  
Gravel Bed River

Morphological and Stratigraphical Signature of Floods In A Braided Gravel-Bed River Revealed From Flume Experiments

2013 ◽  
Vol 83 (11) ◽  
pp. 1032-1045 ◽  
Author(s):  
W. I. Van De Lageweg ◽  
W. M. Van Dijk ◽  
M. G. Kleinhans
Keyword(s):  
Flume Experiments ◽  
Gravel Bed ◽  
Gravel Bed River
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