Extended global terrestrial evapotranspiration and gross primary production dataset from 1982 to near present
- 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- 2Institute of Geographic Sciences and Natural Resources Research (Beijing, China)
- 3Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China
Abstract
The Penman–Monteith–Leuning (PML) model is a widely recognized diagnostic framework for estimating coupled terrestrial evapotranspiration (ET) and gross primary production (GPP). To address the critical need for high-fidelity, long-term, and near-present eco-hydrological records, we developed the PML-V2.2 dataset, spanning from 1982 to 2024. Driven by observation-constrained Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) meteorological variables, the dataset comprises three complementary products: (1) PML-V2.2a, an 8-day 500 m MODIS-based product (2000–2024) optimized for near-present monitoring; (2) PML-V2.2b, a half-month 0.1° AVHRR-based product (1982–2020) anchoring long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° record integrating the former two for seamless 43-year continuity (1982–2024). Our methodological framework features an expanded bottom-up calibration using 208 flux sites (~1400 site-years) across various plant functional types (PFT) and a refined parameterization that explicitly distinguishes between irrigated and rainfed croplands. This distinction effectively mitigated systematic biases in agricultural regions, reducing ET and GPP estimation errors by 8.7 % and 16.2 %, respectively. Performance evaluation reveals high accuracy across PFTs (cross-validation Nash-Sutcliffe Efficiency, NSE > 0.60, absolute bias < 5 %), while top-down water-balance validation across 56 large river basins during 1982–2016 and 152 basins during 2003–2020 confirms exceptional reliability (NSE: 0.89–0.91). The MODIS-based (V2.2a) and AVHRR-based (V2.2b) products exhibit high statistical and spatial agreement during their overlapping period (NSE = 0.90 and 0.79 for annual ET and GPP anomalies), ensuring a seamless transition across satellite epochs. Based on the consolidated PML-V2.2c dataset, global terrestrial annual ET and GPP during 1982–2024 are estimated at 65.8 × 103 km3 yr⁻1 (with 58.0 % from transpiration) and 143.0 PgC yr⁻1, respectively. Long-term analysis reveals significant (p < 0.01) increasing trends in GPP (0.315 PgC yr⁻2) and ET (0.019 × 103 km3 yr⁻2) during 1982–2024, where rapid growth in GPP and water use efficiency is partially offset by CO2-induced physiological water savings. By bridging the gap between satellite epochs, PML-V2.2 provides an internally consistent long-term global dataset for hydrology, ecology, and other Earth science studies. The dataset is freely accessible, with the 500 m resolution PML-V2.2a product hosted on Google Earth Engine, and all 0.1° PML-V2.2a/b/c versions archived at the National Tibetan Plateau Data Center under https://doi.org/10.11888/Terre.tpdc.303314 (Xu et al., 2026).
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BibTeX
@article{xu2026extended,
title = {{Extended global terrestrial evapotranspiration and gross primary production dataset from 1982 to near present}},
author = {Zhenwu Xu and Yongqiang Zhang and Dongdong Kong and Ning Ma and Xuanze Zhang},
journal = {Earth system science data},
year = {2026},
month = jul,
doi = {10.5194/essd-18-5663-2026},
url = {https://doi.org/10.5194/essd-18-5663-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - Extended global terrestrial evapotranspiration and gross primary production dataset from 1982 to near present
AU - Zhenwu Xu
AU - Yongqiang Zhang
AU - Dongdong Kong
AU - Ning Ma
AU - Xuanze Zhang
PY - 2026
DA - 2026/07/31
JO - Earth system science data
DO - 10.5194/essd-18-5663-2026
UR - https://doi.org/10.5194/essd-18-5663-2026
AB - The Penman–Monteith–Leuning (PML) model is a widely recognized diagnostic framework for estimating coupled terrestrial evapotranspiration (ET) and gross primary production (GPP). To address the critical need for high-fidelity, long-term, and near-present eco-hydrological records, we developed the PML-V2.2 dataset, spanning from 1982 to 2024. Driven by observation-constrained Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) meteorological variables, the dataset comprises three complementary products: (1) PML-V2.2a, an 8-day 500 m MODIS-based product (2000–2024) optimized for near-present monitoring; (2) PML-V2.2b, a half-month 0.1° AVHRR-based product (1982–2020) anchoring long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° record integrating the former two for seamless 43-year continuity (1982–2024). Our methodological framework features an expanded bottom-up calibration using 208 flux sites (~1400 site-years) across various plant functional types (PFT) and a refined parameterization that explicitly distinguishes between irrigated and rainfed croplands. This distinction effectively mitigated systematic biases in agricultural regions, reducing ET and GPP estimation errors by 8.7 % and 16.2 %, respectively. Performance evaluation reveals high accuracy across PFTs (cross-validation Nash-Sutcliffe Efficiency, NSE > 0.60, absolute bias < 5 %), while top-down water-balance validation across 56 large river basins during 1982–2016 and 152 basins during 2003–2020 confirms exceptional reliability (NSE: 0.89–0.91). The MODIS-based (V2.2a) and AVHRR-based (V2.2b) products exhibit high statistical and spatial agreement during their overlapping period (NSE = 0.90 and 0.79 for annual ET and GPP anomalies), ensuring a seamless transition across satellite epochs. Based on the consolidated PML-V2.2c dataset, global terrestrial annual ET and GPP during 1982–2024 are estimated at 65.8 × 103 km3 yr⁻1 (with 58.0 % from transpiration) and 143.0 PgC yr⁻1, respectively. Long-term analysis reveals significant (p < 0.01) increasing trends in GPP (0.315 PgC yr⁻2) and ET (0.019 × 103 km3 yr⁻2) during 1982–2024, where rapid growth in GPP and water use efficiency is partially offset by CO2-induced physiological water savings. By bridging the gap between satellite epochs, PML-V2.2 provides an internally consistent long-term global dataset for hydrology, ecology, and other Earth science studies. The dataset is freely accessible, with the 500 m resolution PML-V2.2a product hosted on Google Earth Engine, and all 0.1° PML-V2.2a/b/c versions archived at the National Tibetan Plateau Data Center under https://doi.org/10.11888/Terre.tpdc.303314 (Xu et al., 2026).
SN - 1866-3508
SN - 1866-3516
ER -