Optimizing photosynthetic and respiratory parameters based on the seasonal variation pattern in regional net ecosystem productivity obtained from atmospheric inversion

Chen, Zhuoqi; Chen, Jing M.; Zheng, Xunhua; Jiang, Fei; Qin, Jun; Zhang, Shupeng; Yuan, Wenping; Ju, W.; Mo, Gang

doi:10.1007/s11434-015-0917-6

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…The GPP discrepancies of these models may stem from differently calibrated values of sensitive parameters, such as the maximum LUE in MTL-LUE [35,88], the slope between GPP and the combination of temperature and greenness in MTG [12,46,50], and the maximum carboxylation rate in BTL [89,90]. The sensitive parameters in MTL-LUE, MTG, and BTL are different, and their ecological definitions are also different.…”

Section: Comparisons Of Gpp Estimates From Mtl-lue Mtg and Btlmentioning

confidence: 99%

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

Xie

Jin

et al. 2021

Remote Sensing

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Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m−2yr−1. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models.

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Section: Comparisons Of Gpp Estimates From Mtl-lue Mtg and Btlmentioning

confidence: 99%

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

Xie

Jin

et al. 2021

Remote Sensing

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“…This study aims at exploring spatiotemporal variations of TBM parameters using atmospheric CO 2 concentration data through the newly developed Global Carbon Assimilation System (GCAS). GCAS is developed for optimizing global land surface net carbon fluxes and TBM parameters at 1° resolution at weekly time intervals (Chen et al, ; Zhang et al, , ; Zheng et al, , ).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO₂ Concentration Data With the Global Carbon Assimilation System (GCAS)

Chen

Zhang

et al. 2017

JGR Biogeosciences

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The Global Carbon Assimilation System that assimilates ground‐based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C ( Vmax25), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at middle‐high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

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Taking climate, land use, and social economy into estimation of carbon budget in the Guanzhong-Tianshui Economic Region of China

Zhou

et al. 2017

Environ Sci Pollut Res

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Carbon sequestration is an indispensable ecosystem service provided by soil and vegetation, so mapping and valuing the carbon budget by considering both ecological and social factors is an important trend in evaluating ecosystem services. In this work, we established multiple scenarios to evaluate the impacts of land use change, population growth, carbon emission per capita, and carbon markets on carbon budget. We quantified carbon sinks (aboveground and belowground) under different scenarios, using the Carnegie-Ames-Stanford Approach (CASA) model and an improved carbon cycle process model, and studied carbon sources caused by human activities by analyzing the spatial distribution of human population and carbon emission per capita. We also assessed the net present value (NPV) for carbon budgets under different carbon price and discount rate scenarios using NPV model. Our results indicate that the carbon budget of Guanzhong-Tianshui Economic Region is surplus: Carbon sinks range from 1.50 × 10 to 1.54 × 10 t, while carbon sources caused by human activities range from 2.76 × 10 to 7.60 × 10 t. And the NPV for carbon deficits range from 3.20 × 10 RMB to 1.52 × 10 RMB. From the perspective of ecological management, deforestation, urban sprawl, population growth, and excessive carbon consumption are considered as the main challenges in balancing carbon sources and sinks. Levying carbon tax would be a considerable option when decision maker develops carbon emission reduction policies. Our results provide a scientific and credible reference for harmonious and sustainable development in the Guanzhong-Tianshui Economic Region of China.

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Optimizing photosynthetic and respiratory parameters based on the seasonal variation pattern in regional net ecosystem productivity obtained from atmospheric inversion

Cited by 5 publications

References 49 publications

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO₂ Concentration Data With the Global Carbon Assimilation System (GCAS)

Taking climate, land use, and social economy into estimation of carbon budget in the Guanzhong-Tianshui Economic Region of China

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Optimizing photosynthetic and respiratory parameters based on the seasonal variation pattern in regional net ecosystem productivity obtained from atmospheric inversion

Cited by 5 publications

References 49 publications

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS)

Taking climate, land use, and social economy into estimation of carbon budget in the Guanzhong-Tianshui Economic Region of China

Contact Info

Product

Resources

About

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO₂ Concentration Data With the Global Carbon Assimilation System (GCAS)