Preprints
https://doi.org/10.5194/essd-2023-328
https://doi.org/10.5194/essd-2023-328
24 Aug 2023
 | 24 Aug 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

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

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

Abstract. Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool to provide benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these models often estimate GPP and ET at coarse daily or monthly step, hindering analysis of ecosystems 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 estimation of global GPP and ET datasets at a 0.25° resolution from 2001 to 2020 simulated with a widely used diagnostic TBM – 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 observation, 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 ET of 89.03 ± 0.82 × 103 km3 yr-1 with a slight positive trend of 0.10 × 103 km3 yr-2 (p < 0.001) from 2001 to 2020. The spatial pattern of our estimates agrees well with other products, with R2 = 0.77–0.85 and R2 = 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 and accumulated daily GPP and ET estimates are available at https://doi.org/10.5281/zenodo.8240492 (Leng et al., 2023).

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

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-328', Anonymous Referee #1, 14 Sep 2023
    • AC2: 'Reply on RC1', Jiye Leng, 03 Jan 2024
  • RC2: 'Comment on essd-2023-328', Anonymous Referee #2, 26 Sep 2023
    • AC1: 'Reply on RC2', Jiye Leng, 03 Jan 2024
    • AC3: 'Reply on RC2', Jiye Leng, 03 Jan 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-328', Anonymous Referee #1, 14 Sep 2023
    • AC2: 'Reply on RC1', Jiye Leng, 03 Jan 2024
  • RC2: 'Comment on essd-2023-328', Anonymous Referee #2, 26 Sep 2023
    • AC1: 'Reply on RC2', Jiye Leng, 03 Jan 2024
    • AC3: 'Reply on RC2', Jiye Leng, 03 Jan 2024
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan

Data sets

Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, Yulin Yan https://doi.org/10.5281/zenodo.8240492

Model code and software

Biosphere-atmosphere Exchange Process Simulator (BEPS) Jiye Leng, Jing M. Chen, Xiangzhong Luo, Jane Liu https://github.com/JChen-UToronto/BEPS_hourly_site

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

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Short summary
We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
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