- a School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
- b State Key Lab of Subtropical Building Science, South China University of Technology, Guangzhou 510641, China
- c The Prairie Research Institute, University of Illinois at Urbana-Champaign, 2204 Griffith Drive, Champaign, IL 61820, USA
https://www.sciencedirect.com/science/article/pii/S0341816219303583
Abstract
Extending
series of river streamflow based on tree-ring reconstruction is of
scientific and practical importance for understanding hydrological or
meteorological change of past.
To achieve more accurate reconstructions,
the intelligent learning algorithm random forest (RF) was proposed in
this study to reconstruct the annual streamflow of the source region of
the Yangtze River (SRYR).
The method was developed using tree-ring
chronologies ranging from 1485 to 2000 (AD) and annual streamflow from
1956 to 2000 (AD).
The relationship between streamflow and the main
large-scale atmospheric circulation as well as solar activity has also
been discussed.
The results show that:
a) RF model could capture a more
realistic characteristic of streamflow and show higher predictive
ability for streamflow reconstruction than bagged regression trees
(BRT), support vector machine (SVM), and simple linear regression (SLM).
b) A period of lower streamflow occurred during the late 16th and
mid-18th centuries, and the early 19th and mid-20th centuries
experienced higher streamflow; an interesting temporal pattern indicated
that the instrumental period was representative of individual highest
(1979) and lowest (1989) streamflow years;
in addition, a 2–8-year
significant periodical oscillation (at 95% confidence level) was
observed over most of the reconstructed series, with dominant periods of
2.5- and 4.9-year.
c) The variability of streamflow in the study area
was strongly associated with Pacific Decadal Oscillation (PDO), El
Nino-Southern Oscillation (ENSO) and solar activity. This study provides
reference for streamflow reconstruction based on tree-ring data and
helps to understand the hydrological variation of past in SRYR.
Highlights
- • We reconstructed the streamflow in SRYR using tree-ring data by RF algorithm.
- • The RF model outperforms than BRT, SVM, and SLM.
- • The RF model could capture a more realistic characteristic of streamflow.
- • Streamflow variability was strongly related with PDO, ENSO and solar activity.
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