Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations

作者：Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, Yulin Yan · 发表于：Earth system science data · 年份：2024 · DOI：10.5194/essd-16-1283-2024 · 被引用次数：34 · 研究领域：Plant Water Relations and Carbon Dynamics、Atmospheric and Environmental Gas Dynamics、Meteorological Phenomena and Simulations

Abstract. Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool for providing benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these models often estimate GPP and ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics at the diurnal (hourly) scales, and prescribe some essential parameters (i.e., the Ball–Berry slope (m) and the maximum carboxylation rate at 25 °C (Vcmax25)) as constant, inducing uncertainties in the estimates of GPP and ET. In this study, we present hourly estimations of global GPP and ET datasets at a 0.25° resolution from 2001 to 2020 simulated with a widely used diagnostic TBM – the Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance observations and machine learning approaches to derive and upscale the seasonally varied m and Vcmax25 for carbon and water fluxes. The estimated hourly GPP and ET are validated against flux observations, remote sensing, and machine learning-based estimates across multiple spatial and temporal scales. The correlation coefficients (R2) and slopes between hourly tower-measured and modeled fluxes are R2=0.83, regression slope =0.92 for GPP, and R2=0.72, regression slope =1.04 for ET. At the global scale, we estimated a global mean GPP of 137.78±3.22 Pg C yr−1 (mean ± 1 SD) with a positive trend of 0.53 Pg C yr−2 (p<0.001), and an ET of 89.03±0.82×103 km3 yr−1 with a slight ...