摘要
研究自然降雨梯度下不同经营方式森林的径流和泥沙量有助于预测未来气候变化背景下水土流失模式。选择次生林以及由其转换而来的米槠人工幼林(人工幼林)和人工促进天然更新幼林(人促幼林)为研究对象,选取47场自然降雨事件构成一个降雨梯度,探讨不同森林类型水土流失对降雨增强的响应差异及其内在机制。结果发现:人促幼林和次生林因其植被垂直结构发育,降雨侵蚀力施加的侵蚀动能被植物体抵消,水土流失量低,其产量主要受到降雨量的调控,且随降雨的加强而缓慢增加;人工幼林覆盖度低,林下裸露,水土流失受到降雨侵蚀力的直接影响,产流和输沙量大,而且随着降雨强度的增加而快速增加.未来应多提倡人工促进更新的方式,人工林的经营要加强林下管理,增加覆盖。
A soil erosion experiment was set up in a secondary forest(SF) and two young forests converted from secondary forest which were young assisted naturally regenerated forest(NR) and young Castanopsis carlessi plantation(BC). 47 natural rainfall events were selected establishing a rainfall intensity gradient to investigate the response differences of soil erosion among forests to rainfall intensity increment and their underlying mechanisms. A very low soil erosion was found in both assisted naturally regenerated forest and secondary forest, which could be ascribed to the effects of dense vegetation on offsetting the rainfall kinetic energy. Soil erosions in assisted naturally regenerated forest and secondary forest were mainly regulated by rainfall amount and increased slowly with intensity enhancement. But in less covered Castanopsis carlessi plantations, the underground bare soil was largely affected by rainfall splashing and washing, resulting in intensive soil erosion. Thus, soil erosions in Castanopsis carlessi plantations were substantially controlled by rainfall erosivity and sensitive response to the rainfall increment. The results suggest that assisted natural regeneration should be widely advocated, and care should be taken to manage the underground vegetation and increase coverage of young forest.
作者
李志明
魏智华
LI Zhiming;WEI Zhihua(Gaoyang Forestry Workstation in Shunchang County,Shunchang 353200,Fujian,China;School of Geographical Sciences,Fujian Normal University,Fuzhou 350007,China;State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province,Fujian Normal University,Fuzhou 350007,China)
出处
《亚热带资源与环境学报》
2019年第3期15-22,共8页
Journal of Subtropical Resources and Environment
基金
国家自然科学基金资助项目(U1505233)