2006–2023年哀牢山亚热带常绿阔叶林和毛蕨菜灌草丛地下水位数据集

地下水对于维持森林生态系统的健康非常重要，尤其在旱季或降水不足的情况下，地下水为森林植被提供必要的水分，其变化直接影响植物的生长和存活。长期定位观测地下水位有助于揭示森林生态系统的水文特征、不同植被类型对水源涵养的影响及其生态服务功能。亚热带森林是我国分布最广的森林类型，对调控全球气候变化和维持生物多样性具有重要作用。云南哀牢山作为我国重要的亚热带森林分布区，拥有丰富的生物多样性，并具备显著的水源涵养和碳汇功能。连续、长期、高质量的生态监测是中国生态系统研究网络（CERN）的主要任务之一，其中，地下水位是CERN陆地生态系统水环境长期定位观测的关键指标。本数据集整理了常绿阔叶林和毛蕨菜灌草丛两种植被类型的地下水位监测数据，覆盖时间为2006年至2023年。数据通过人工观测法获取，监测频率为每日一次，数据质量的保证和控制严格按照CERN的统一规范进行。该数据集对于揭示森林水文动态和循环过程、评估不同森林类型的水源涵养能力，以及预测生态系统对气候变化和人类活动的响应具有重要意义，并为优化森林管理策略和维护生态系统健康提供了科学依据。

Groundwater plays a vital role in maintaining the health of forest ecosystems. During dry seasons or periods of inadequate precipitation in particular, groundwater supplies essential moisture to forest vegetation, and its variations directly impact plant growth and survival. Long-term in-situ monitoring of groundwater levels contributes to uncovering the hydrological features of forest ecosystems, the effects of distinct vegetation types on water conservation, and their associated ecosystem service functions. Subtropical forests represent the most extensively distributed forest type in China, exerting a critical role in regulating global climate change and preserving biodiversity. The Ailao Mountains in Yunnan Province, a key subtropical forest distribution region in China, harbor rich biodiversity and exhibit prominent water conservation and carbon sink functions. Continuous, long-term and high-quality ecological monitoring constitutes one of the primary missions of the Chinese Ecosystem Research Network (CERN), where groundwater level serves as a key indicator for long-term in-situ monitoring of the aquatic environment of terrestrial ecosystems under CERN's framework. This dataset collates groundwater level monitoring data for two vegetation types: evergreen broad-leaved forest and scrub-grassland dominated by Pteridium revolutum (common bracken fern), covering the period from 2006 to 2023. The data were acquired through manual observations, with a daily monitoring frequency. Strict data quality assurance and control were conducted in accordance with CERN's unified specifications. This dataset holds substantial importance for elucidating forest hydrological dynamics and cycling processes, assessing the water conservation capabilities of different forest types, and forecasting the responses of ecosystems to climate change and human activities. It further provides a scientific foundation for optimizing forest management strategies and sustaining ecosystem health.