Statistical and process-based modeling analyses of tree growth response to climate in semi-arid area of north central China: A case study of Pinus tabulaeformis

Abstract

Statistical modeling techniques and the Vaganov-Shashkin (VS) forward model of tree ring formation were used to investigate tree growth response of Pinus tabulaeformis to climate variations in semi-arid north central China. Both statistical and process-based modeling techniques were shown to be capable of simulating and evaluating climate-tree growth relationships for the study area, but the process-based VS model produced results that were more physically interpretable. Statistical modeling results indicate that both moisture and temperature have significant effects on tree growth during the growing season, with the most important months being May-August. The VS modeled results validated the above statistical modeling results, and further clarified the effects on tree growth of the seasonal distribution of temperature and soil moisture, soil moisture status prior to the growing season, and the start and end dates of the growing season. Under current and projected climate scenarios, our modeling results suggest significant tree growth reduction in north central China, and the possibility that regional forests may reduce their capacity to sequester carbon. Copyright 2008 by the American Geophysical Union.