DOI: 10.57760/sciencedb.11323
title: A dataset of carbon and water fluxes of the extremely aird desert ecosystem in Guazhou, Gansu (2019-2022)
title: 2019-2022年甘肃瓜州极旱荒漠生态系统碳水通量观测数据集

description: Extreme arid desert ecosystems are typically located in arid regions characterized by low precipitation and high temperatures. These ecosystems host native vegetation that exhibits remarkable adaptability to extreme environmental conditions. They play essential roles in maintaining soil stability, preventing erosion, and preserving unique biodiversity. Understanding the characteristics of carbon and water fluxes in extreme arid desert ecosystems, particularly in northwestern China, is crucial for assessing their contributions to the global carbon and water cycles. This dataset presents daily-scale carbon and water flux observations collection at the Guazhou Station, a scientific observation site situated in the arid desert ecosystem of Gansu, China. The dataset covers the period from January 1, 2019, to December 31, 2022. Carbon and water fluxes were measured using eddy covariance flux observation techniques. The data underwent rigorous calibration, quality control, and interpolation processes following ChinaFLUX data processing standards to create this final daily-scale dataset. The dataset includes key environmental meteorological observations, such as net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (ER), water flux (FH2O), evapotranspiration (ET), sensible heat flux (H), latent heat flux (LE), as well as global radiation (Rg), air temperature (Tair), dew point temperature (Tdew), soil water content (SWC), photosynthetic photon flux density (PPFD), relative humidity (RH), vapor pressure deficit (VPD), precipitation (P), and more. This dataset offers valuable foundational information for in-depth research on the role and significance of extreme arid desert ecosystems in the global carbon and water cycles. It serves as a critical resource for scientific investigations and contributes to our understanding of these ecosystems’ contributions to the Earth’s environmental dynamics.
description: Extreme arid desert ecosystems are typically located in arid regions characterized by low precipitation and high temperatures. These ecosystems host native vegetation that exhibits remarkable adaptability to extreme environmental conditions. They play essential roles in maintaining soil stability, preventing erosion, and preserving unique biodiversity. Understanding the characteristics of carbon and water fluxes in extreme arid desert ecosystems, particularly in northwestern China, is crucial for assessing their contributions to the global carbon and water cycles. This dataset presents daily-scale carbon and water flux observations collection at the Guazhou Station, a scientific observation site situated in the arid desert ecosystem of Gansu, China. The dataset covers the period from January 1, 2019, to December 31, 2022. Carbon and water fluxes were measured using eddy covariance flux observation techniques. The data underwent rigorous calibration, quality control, and interpolation processes following ChinaFLUX data processing standards to create this final daily-scale dataset. The dataset includes key environmental meteorological observations, such as net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (ER), water flux (FH2O), evapotranspiration (ET), sensible heat flux (H), latent heat flux (LE), as well as global radiation (Rg), air temperature (Tair), dew point temperature (Tdew), soil water content (SWC), photosynthetic photon flux density (PPFD), relative humidity (RH), vapor pressure deficit (VPD), precipitation (P), and more. This dataset offers valuable foundational information for in-depth research on the role and significance of extreme arid desert ecosystems in the global carbon and water cycles. It serves as a critical resource for scientific investigations and contributes to our understanding of these ecosystems’ contributions to the Earth’s environmental dynamics.