Water use by trees is linked to precipitation: A case study of a mixed forest in a hilly area in southern China

Abstract

• Adaptability of water use of mixed forest to precipitation was studied in China. • The trees showed opposite water use patterns under different precipitation. • Q. acutissima showed only sensitivity to heavy rainfall. • LWC/LWP and WS of P. massoniana synergistically adapt to precipitation changes. Global climate change has increased the frequency of extreme precipitation events, impacting water use and the physiological properties of plants. Therefore, we assessed whether there is a synergistic or coupling relationship between water source transformation and the regulation of physiological factors in a mixed forest based on stable isotope and thermal diffusion principles. The absorption ratios of Quercus acutissima Carruth. to shallow water (62.0 % to 30.5 %) gradually decreased with rainfall, and the utilization ratio of deep water (21.2 % to 30.6 %) gradually increased with rainfall, indicating a sensitivity to different rainfall amounts. In contrast, Pinus massoniana Lamb. only showed sensitivity to heavy rain, changing its water source to the 0–10 cm (23.3 %) and 10–30 cm (53.3 %) soil layers in response to heavy rainfall. Leaf water content (LWC)/leaf water potential (LWP) of Q. acutissima and P. massoniana increased with rainfall intensity (P < 0.05). The sap flow rate was 2.17 times higher after moderate rainfall than before the rainfall event, but there were no significant changes between moderate and heavy rainfall events. These results indicated that LWC/LWP and water sources of P. massoniana varied simultaneously with precipitation intensity. Nevertheless, the sap flow rate and water sources of Q. acutissima exhibited trade-off relationships that mediated adaptation to precipitation changes. In the future, the two tree species should be planted together since the water use by these tree species was complementary. The findings of this study provide scientific evidence for the selection of tree species, precision irrigation, and accurate prediction of water use pattern in forest vegetation in the face of global climate change.