Tree-ring-width based streamflow reconstruction based on the random forest algorithm for the source region of the Yangtze River, China - Li, Jun; Wang, Zhaoli; Lai, Chengguang; Zhang, Zhenxing (Catena (Giessen), 2019)
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. •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.