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

Leng, Jiye; Chen, Jing M.; Li, Wenyu; Luo, Xiangzhong; Xu, Mingzhu; Liu, Jane; Wang, Rong; Rogers, Cheryl; Li, Bolun; Yan, Yulin

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 (inline-formulam) and the maximum carboxylation rate at 25 °C (inline-formula M2inlinescrollmathml V normal cmax normal 25 27pt16ptsvg-formulamathimg6843e2ad912be589c7d614809a188ea9 essd-16-1283-2024-ie00001.svg27pt16ptessd-16-1283-2024-ie00001.png )) 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 inline-formulam and inline-formula M4inlinescrollmathml V normal cmax normal 25 27pt16ptsvg-formulamathimg4bc77211af1a071fee3fcd80512b787a essd-16-1283-2024-ie00002.svg27pt16ptessd-16-1283-2024-ie00002.png 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 (inline-formulaR2) and slopes between hourly tower-measured and modeled fluxes are inline-formulaR2=0.83, regression slope inline-formula=0.92 for GPP, and inline-formulaR2=0.72, regression slope inline-formula=1.04 for ET. At the global scale, we estimated a global mean GPP of inline-formula137.78±3.22 Pg C yrinline-formula−1 (mean inline-formula± 1 SD) with a positive trend of 0.53 Pg C yrinline-formula−2 (inline-formulap<0.001), and an ET of inline-formula M15inlinescrollmathml normal 89.03 ± normal 0.82 × normal 10 normal 3 90pt14ptsvg-formulamathimg26af02e93deb17bd3ffd0e8d74550174 essd-16-1283-2024-ie00003.svg90pt14ptessd-16-1283-2024-ie00003.png  kminline-formula3 yrinline-formula−1 with a slight positive trend of inline-formula0.10×103 kminline-formula3 yrinline-formula−2 (inline-formulap<0.001) from 2001 to 2020. The spatial pattern of our estimates agrees well with other products, with inline-formulaR2=0.77–0.85 and inline-formulaR2=0.74–0.90 for GPP and ET, respectively. Overall, this new global hourly dataset serves as a “handshake” among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global GPP and ET with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles. The hourly GPP and ET estimates are available at https://doi.org/10.57760/sciencedb.ecodb.00163https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) and the accumulated daily datasets are available at https://doi.org/10.57760/sciencedb.ecodb.00165https://doi.org/10.57760/sciencedb.ecodb.00165 (Leng et al., 2023b).

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Leng, Jiye / Chen, Jing M. / Li, Wenyu / et al: Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations. 2024. Copernicus Publications.

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