The litter moisture content dataset for typical vegetation types in the Ailao Mountains (2008–2024)

Litter moisture content is a key indicator of surface hydrological processes in forest ecosystems. Long-term monitoring of litter moisture content is essential for understanding temporal changes in forest water-retention capacity and for assessing hydrological and water conservation functions. Subtropical forests, one of the dominant forest types in China, are ecologically significant for global climate regulation and biodiversity conservation. The Ailao Mountains in Yunnan, a representative subtropical forest region, are characterized by high biodiversity and strong water conservation capacity. Continuous, high-quality ecological monitoring is one of the core missions of the Chinese Ecosystem Research Network (CERN), within which litter moisture content is a key variable in sustained terrestrial water monitoring. This study compiles monthly litter moisture content data from 2008 to 2024 across three typical vegetation types: summit mossy dwarf forest, Populus forest, and evergreen broad-leaved forest. Collected through manual monitoring in strict accordance with CERN protocols, this dataset reveals the dynamic responses of litter moisture content to seasonal variations and extreme weather events, providing critical data support for hydrological model improvement, watershed conservation evaluation, and predictions of ecosystem responses to climate change.