Ecosystem Respiration and Environmental Driver Dataset for Glacierized and Non-glacierized Areas of Southeastern Tibet(2019)

Ecosystem Respiration and Environmental Driver Dataset for Glacierized and Non-glacierized Areas of Southeastern Tibet(2019) Data Year: 2019 Spatial Resolution: 0.1° × 0.1° Spatial Coverage: Southeastern part of the Tibetan Plateau (26°52′—33°44′N, 92°09′—103°04′E) 1. Dataset Overview This dataset is designed to provide high-precision data support for assessing the carbon budget of high-altitude mountainous areas, with a specific focus on the primary successional zones of glacier retreat and mature ecosystems. The dataset integrates dynamic climate variables and static soil/topographic environmental attributes. All multi-source data were strictly standardized spatially and resampled to a uniform 0.1° × 0.1° grid resolution. To reveal the binary heterogeneity of driving mechanisms, the data is explicitly divided into non-glacierized and glacierized regions. 2. File Inventory The dataset consists of two Excel files based on distinct ecological environments: Non-glacierized area2019.xls: Contains monthly panel data for non-glacierized areas (elevation < 4500 m), which are mainly mature mountain forest and alpine meadow ecosystems. (Sample size: N=7548) glacierized area2019.xls: Contains monthly panel data for glaciers and periglacial regions (elevation > 4500 m), which are continuously disturbed by the glacial freeze-thaw cycle and exhibit unique primary succession characteristics. (Sample size: N=6600) 3. Data Sources The dependent variable (Reco) uses a 2019 global carbon flux dataset with a spatial resolution of 0.1° × 0.1°, obtained from the National Tibetan Plateau Scientific Data Center (https://data.tpdc.ac.cn/home). Among the independent variables, soil organic carbon (SOC), soil pH (PH), total nitrogen (Total N), soil bulk density (BDOD), soil silt content (SILT), soil sand content (SAND), soil clay content (CLAY), soil cation exchange capacity (CEC), soil gravel volume fraction (CFVO), and normalized difference vegetation index (NDVI) were all obtained from the National Earth System Science Data Center(https://www.geodata.cn/main/). Glacier cover area(Glacier Area), average monthly temperature(Temperature), average monthly precipitation(Precipitation), and DEM data(including ASPECT and SLOPE) were obtained from the National Tibetan Plateau Science Data Center. 4. Variables Description (Note: Please verify the specific column headers and units in your Excel files) Target Variable: Reco: Ecosystem Respiration Flux (Unit: ) Dynamic Drivers (Monthly Data): TEMP: Average Monthly Temperature (Unit: K/10) PCPN: Precipitation (Unit: mm) NDVI: Normalized Difference Vegetation Index Static Drivers (Spatial Attributes): Glc AREA: Glacier Area (Unit: km²) DEM: Elevation (Unit: m) SLOPE: Topographic Slope ASPECT: Topographic Aspect SOC: Soil Organic Carbon (Unit: dg/kg) PH: Soil pH value TOTALN: Total Nitrogen (Unit: cg/kg) BDOD: Bulk Density (Unit: cg/cm³) SILT: Silt Content (Unit: g/kg) SAND: Sand Content(g/kg) CLAY: Clay Content(g/kg) CEC: Cation Exchange Capacity (Unit: mmol(c)/kg) CFVO: Volumetric Fraction of Coarse Fragments(cm3/dm3) 5. Usage Notes Non-linear Modeling Recommended: Due to the complex terrain and dramatic environmental gradients in southeastern Tibet, traditional linear regression models are often limited by assumptions of factor independence. It is highly recommended to use machine learning algorithms (e.g., XGBoost) to effectively capture complex non-linear characteristics and interactions. Binary Heterogeneity Consideration: Analysis of this dataset reveals significant binary heterogeneity in the driving mechanisms. In non-glacierized areas, Reco primarily follows a heat-driven energy-limited pattern. Conversely, in glacierized areas, it is fundamentally constrained by soil substrate (SOC). Researchers should analyze the two Excel files separately or account for this threshold effect when conducting global modeling.