Quantification of Backwater Effect in The Jingjiang Reach Caused by the
Confluence of Dongting Lake Using a Machine Learning Model
Abstract
The backwater effect caused by tributary inflow can significantly
elevate the water level profile upstream of a confluence point. However,
it still remains unclear that how the backwater effect in a river reach
is influenced by the mainstream and confluence discharges. In the
current study, various hydrological data measured in the Jingjiang Reach
were collected. Using the statistical analysis method, the backwater
degree and range were then determined under three representative
mainstream discharges in the Jingjiang Reach. The results showed that
the backwater degree increased with a higher mainstream discharge, and
there was a positive relationship between runoff ratio and backwater
degree under a certain representative mainstream discharge. The
backwater effect in the Jingjiang Reach decreased after the Three Gorges
Project operation. For example, the mean values of backwater degree for
low, moderate, and high mainstream discharges were 0.83, 1.61, and 2.41
m during the period 1990-2002, whereas they reduced to 0.30, 0.95, and
2.08 m in 2009-2020. In terms of backwater range, it extended upstream
with the mainstream discharge increasing from 7000 to 30000 m
3/s. Moreover, a random forest based machine learning
model was proposed to quantify the backwater effect under different
mainstream and confluence discharges, which can consider the effects of
multiple influencing factors, and the impacts of mainstream discharge,
confluence discharge, and channel degradation on the backwater effect in
the Jingjiang Reach. Taking Jianli station as an example, a decrease in
the mainstream discharge during the flood season led to a 7%-15%
increase in monthly mean backwater degree, while an increase in
mainstream discharge during the dry season led to a 1%-15% decrease in
monthly mean backwater degree. The increase in confluence discharge from
the Dongting Lake during the periods June to July and September to
November resulted in an increase of 11%-42% in monthly backwater
degree. The continuous channel degradation led to a decrease of 6%-19%
in monthly mean backwater degree. Influenced by a combination of these
three factors, the monthly mean backwater degree varied from a decrease
of 53% to an increase of 37%.