Impact of deforestation on precipitation extremes in China based on land use model intercomparison project models

Deforestation has a significant influence on the hydrological cycle. Understanding the impact of deforestation on precipitation extremes is crucial for addressing global environmental challenges. This study investigates the impact of deforestation on precipitation extremes (R95p index, which represents the total amount of precipitation exceeding the 95th percentile of the reference period) in China, using outputs from three earth system models (CanESM5, IPSL-CM6A-LR, and MIROC-ES2L). All models, along with their multimodel mean, indicate a general decrease in R95p in Northeast China and southern China, and changes in Northwest China and the Tibetan Plateau are minimal. In contrast, the responses are model-dependent in the Huanghuai and Jianghuai regions. The overall nationwide multimodel mean suggests an annual R95p decrease of 10.7 mm, with individual model variations ranging from −28.0 to 2.0 mm. Further analysis using precipitation extremes scaling reveals a high spatial correlation with direct precipitation extremes changes on both annual and seasonal scales, albeit with slightly smaller magnitudes. Decomposing the response into dynamic and thermodynamic scaling, the authors find that dynamic contributions predominantly drive the changes in precipitation extremes on both annual and seasonal scales. The authors findings highlight the substantial role of dynamic processes in modulating the response of precipitation extremes to deforestation in China.

摘要

森林砍伐对水循环影响显著, 理解森林砍伐对极端降水的影响对于应对全球环境挑战至关重要.基 于CanESM5, IPSL-CM6A-LR和MIROC-ES2L三个地球系统模式, 本文探讨了森林砍伐对中国极端降水 (R95p指数, 即超过参考期第95百分位降水量总和) 的影响. 所有模式及其集合平均表明, 森林砍伐后我国东北和南方R95p普遍减少; 西北和青藏高原的变化较小; 而黄淮和江淮地区的响应则依赖于模式. 进一步, 在年和季节尺度上, 极端降水物理尺度诊断方法得到的极端降水响应与上述响应具有高空间相似性, 且动力作用主导了森林砍伐对极端降水的影响.