The Groundwater level dataset of subtropical evergreen broadleaf forest and Pteridium shrubland in Ailao Mountain from 2006 to 2023

Groundwater is crucial for maintaining the health of forest ecosystems, especially during dry seasons or periods of insufficient precipitation, as it provides essential moisture for forest vegetation. Fluctuations in groundwater levels directly affect plant growth and survival. Long-term monitoring of groundwater levels helps to reveal the hydrological characteristics of forest ecosystems, as well as the influence of different vegetation types on water conservation and their ecosystem services. Subtropical forests, the most widespread forest type in China, play a vital role in regulating global climate change and maintaining biodiversity. Ailao Mountain in Yunnan Province, an important subtropical forest region in China, possesses rich biodiversity and significant water conservation and carbon sequestration functions. Continuous, long-term, and high-quality ecological monitoring is one of the primary tasks of the Chinese Ecosystem Research Network (CERN), and groundwater levels are a key indicator for long-term monitoring of the terrestrial ecosystem's water environment within CERN. This dataset compiles groundwater level monitoring data for two vegetation types: evergreen broadleaf forest and Pteridium shrubland, covering the period from 2006 to 2023. The data were collected through manual observation, with a monitoring frequency of once per day, and strict data quality assurance and control were performed in accordance with CERN's standardized protocols. This dataset is of great significance for understanding forest hydrological dynamics and cycles, evaluating the water conservation capacity of different forest types, and predicting ecosystem responses to climate change and human activities. It also provides scientific evidence for optimizing forest management strategies and maintaining ecosystem health.