A dataset of groundwater level in the small watershed of Dinghushan Forest (2002 – 2020)

Abstract

Groundwater is one of the important components in the hydrological cycle of the basin. Its dynamic change is of great value for understanding the hydrological regulation mechanism, the cycle of material elements and ecosystem service function of terrestrial ecosystem. Covered with subtropical monsoon evergreen broad-leaved forest and its transitional vegetation types, Dinghushan National Nature Reserve has been well protected since the 1950s. Dinghushan Forest Ecosystem Research Station set up four groundwater level observation wells downstream of the complete catchment area of Dinghushan Nature Reserve according to the observation specifications and quality control requirements of terrestrial ecosystem water environment, so as to carry out long-term standardized positioning observation of the depth of groundwater level. This dataset collates the long-term monitoring data of four groundwater level observation wells from 2002 to 2020. It is aimed to provide basic data support for the study of groundwater response process, the evaluation of hydrological service function of forest ecosystem and the sustainable management of forest resources under the background of global climate change.

Validation of Two Newly Developed Albedo Schemes Based on the Observations Over the Region With Evergreen Broadleaved Forest in Southern China

“…The observation data for the validation of the newly developed albedo schemes is from the Dinghushan forest ecosystem research station in Guangdong Province, China (Figure 10). It is located at 23°10'25.4" N, 112°32'3.7"E, and the underlying surface is also a subtropical evergreen broad-leaved forest (Liu et al, 2020). We select the solar shortwave radiation and reflected radiation data in 2010 as the analysis data.…”

“…The observation data from the land-atmosphere interaction observation tower station in Zhuhai Phoenix Mountain (Wei, 2023) and Dinghushan forest ecosystem research station Guangdong Province (Liu et al, 2020) were used in this study. The driving data used in the model simulations is China Meteorological Forcing Dataset (Yang et al, 2019).…”