Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations DOI Creative Commons
Jiye Leng, Jing M. Chen, Wenyu Li

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(3), P. 1283 - 1300

Published: March 13, 2024

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 often estimate GPP ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics the diurnal (hourly) scales, prescribe some essential parameters (i.e., Ball–Berry slope (m) maximum carboxylation rate 25 °C (Vcmax25)) as constant, inducing uncertainties in estimates ET. In this study, we present hourly estimations datasets 0.25° resolution from 2001 to 2020 simulated with widely used diagnostic TBM – Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance machine learning approaches derive upscale seasonally varied m Vcmax25 carbon water fluxes. The estimated are validated against flux observations, sensing, learning-based across multiple spatial temporal scales. correlation coefficients (R2) slopes between tower-measured modeled fluxes R2=0.83, regression =0.92 GPP, R2=0.72, =1.04 At scale, mean 137.78±3.22 Pg C yr−1 (mean ± 1 SD) positive trend 0.53 yr−2 (p<0.001), an 89.03±0.82×103 km3 slight 0.10×103 (p<0.001) 2020. pattern our agrees well other products, R2=0.77–0.85 R2=0.74–0.90 ET, respectively. Overall, new dataset serves “handshake” among process-based models, network, reliable long-term patterns facilitating studies related functional properties, carbon, cycles. available https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) accumulated https://doi.org/10.57760/sciencedb.ecodb.00165 2023b).

Language: Английский

Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations DOI Creative Commons
Jiye Leng, Jing M. Chen, Wenyu Li

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(3), P. 1283 - 1300

Published: March 13, 2024

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 often estimate GPP ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics the diurnal (hourly) scales, prescribe some essential parameters (i.e., Ball–Berry slope (m) maximum carboxylation rate 25 °C (Vcmax25)) as constant, inducing uncertainties in estimates ET. In this study, we present hourly estimations datasets 0.25° resolution from 2001 to 2020 simulated with widely used diagnostic TBM – Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance machine learning approaches derive upscale seasonally varied m Vcmax25 carbon water fluxes. The estimated are validated against flux observations, sensing, learning-based across multiple spatial temporal scales. correlation coefficients (R2) slopes between tower-measured modeled fluxes R2=0.83, regression =0.92 GPP, R2=0.72, =1.04 At scale, mean 137.78±3.22 Pg C yr−1 (mean ± 1 SD) positive trend 0.53 yr−2 (p<0.001), an 89.03±0.82×103 km3 slight 0.10×103 (p<0.001) 2020. pattern our agrees well other products, R2=0.77–0.85 R2=0.74–0.90 ET, respectively. Overall, new dataset serves “handshake” among process-based models, network, reliable long-term patterns facilitating studies related functional properties, carbon, cycles. available https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) accumulated https://doi.org/10.57760/sciencedb.ecodb.00165 2023b).

Language: Английский

Citations

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