Vegetation-specific model parameters are not required for estimating gross primary production

2014 ◽  
Vol 292 ◽  
pp. 1-10 ◽  
Author(s):  
Wenping Yuan ◽  
Wenwen Cai ◽  
Shuguang Liu ◽  
Wenjie Dong ◽  
Jiquan Chen ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Specific Model ◽  
Model Parameters

scholarly journals Are vegetation-specific model parameters required for estimating gross primary production?

2013 ◽  
Vol 6 (4) ◽  
pp. 5475-5488 ◽  
Author(s):  
W. Yuan ◽  
S. Liu ◽  
W. Cai ◽  
W. Dong ◽  
J. Chen ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Specific Model ◽  
Accurate Information ◽  
Model Parameters ◽  
Vegetation Types ◽  
Predictive Capacity ◽  
Ecosystem Carbon ◽  
Use Efficiency ◽  
Globally Distributed

Abstract. Models of gross primary production (GPP) are currently parameterized with vegetation-specific parameter sets and therefore require accurate information on the distribution of vegetation to drive them. Can this parameterization scheme be replaced with a vegetation-invariant set of parameter that can maintain or increase model applicability by reducing errors introduced from the uncertainty of land cover classification? Based on the measurements of ecosystem carbon fluxes from 150 globally distributed sites in a range of vegetation types, we examined the predictive capacity of seven light use efficiency (LUE) models. Two model experiments were conducted: (i) a constant set of parameters for various vegetation types and (ii) vegetation-specific parameters. The results showed no significant differences in model performances to simulate GPP while using both sets of parameters. These results indicate that a universal set of parameters, which is independent of vegetation cover type and characteristics can be adopted in prevalent LUE models. Availability of this well tested and universal set of parameters would help to improve the accuracy and applicability of LUE models in various biomes and geographic regions.

scholarly journals Reduction of Global Plant Production due to Droughts from 2001 to 2010: An Analysis with a Process-Based Global Terrestrial Ecosystem Model

2015 ◽  
Vol 19 (16) ◽  
pp. 1-21 ◽  
Author(s):  
Chang Liao ◽  
Qianlai Zhuang
Keyword(s):  
Primary Production ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Ecosystem Model ◽  
Plant Production ◽  
Model Parameters ◽  
Drought Impact ◽  
Terrestrial Ecosystem Model

Abstract Droughts dramatically affect plant production of global terrestrial ecosystems. To date, quantification of this impact remains a challenge because of the complex plant physiological and biochemical processes associated with drought. Here, this study incorporates a drought index into an existing process-based terrestrial ecosystem model to estimate the drought impact on global plant production for the period 2001–10. Global Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) data products are used to constrain model parameters and verify the model algorithms. The verified model is then applied to evaluate the drought impact. The study indicates that droughts will reduce GPP by 9.8 g C m−2 month−1 during the study period. On average, drought reduces GPP by 10% globally. As a result, the global GPP decreased from 106.4 to 95.9 Pg C yr−1 while the global net primary production (NPP) decreased from 54.9 to 49.9 Pg C yr−1. This study revises the estimation of the global NPP and suggests that the future quantification of the global carbon budget of terrestrial ecosystems should take the drought impact into account.

Improved estimation of gross primary production of paddy rice cropland with changing model parameters over phenological transitions

2021 ◽  
Vol 445 ◽  
pp. 109492
Author(s):  
Duan Huang ◽  
Hong Chi ◽  
Fengfei Xin ◽  
Akira Miyata ◽  
Minseok Kang ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Paddy Rice ◽  
Model Parameters

scholarly journals A simplified gross primary production and evapotranspiration model for boreal coniferous forests – is a generic calibration sufficient?

2015 ◽  
Vol 8 (7) ◽  
pp. 5089-5137 ◽  
Author(s):  
F. Minunno ◽  
M. Peltoniemi ◽  
S. Launiainen ◽  
M. Aurela ◽  
A. Lindroth ◽  
...  
Keyword(s):  
Primary Production ◽  
Process Model ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Model Complexity ◽  
Model Parameters ◽  
Water Fluxes ◽  
Site Specific ◽  
Bayesian Model Comparison ◽  
Wide Range

Abstract. The problem of model complexity has been lively debated in environmental sciences as well as in the forest modelling community. Simple models are less input demanding and their calibration involves a lower number of parameters, but they might be suitable only at local scale. In this work we calibrated a simplified ecosystem process model (PRELES) to data from multiple sites and we tested if PRELES can be used at regional scale to estimate the carbon and water fluxes of Boreal conifer forests. We compared a multi-site (M-S) with site-specific (S-S) calibrations. Model calibrations and evaluations were carried out by the means of the Bayesian method; Bayesian calibration (BC) and Bayesian model comparison (BMC) were used to quantify the uncertainty in model parameters and model structure. To evaluate model performances BMC results were combined with more classical analysis of model-data mismatch (M-DM). Evapotranspiration (ET) and gross primary production (GPP) measurements collected in 10 sites of Finland and Sweden were used in the study. Calibration results showed that similar estimates were obtained for the parameters at which model outputs are most sensitive. No significant differences were encountered in the predictions of the multi-site and site-specific versions of PRELES with exception of a site with agricultural history (Alkkia). Although PRELES predicted GPP better than evapotranspiration, we concluded that the model can be reliably used at regional scale to simulate carbon and water fluxes of Boreal forests. Our analyses underlined also the importance of using long and carefully collected flux datasets in model calibration. In fact, even a single site can provide model calibrations that can be applied at a wider spatial scale, since it covers a wide range of variability in climatic conditions.

scholarly journals Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 288 ◽  
Author(s):  
Qing Gu ◽  
Hui Zheng ◽  
Li Yao ◽  
Min Wang ◽  
Mingguo Ma ◽  
...  
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Carbon Fluxes ◽  
Arid Ecosystems ◽  
Model Parameters ◽  
Net Ecosystem Carbon Exchange ◽  
Dryland Ecosystems ◽  
Semi Arid

As an important component to quantify the carbon budget, accurate evaluation of terrestrial gross primary production (GPP) is crucial for large-scale applications, especially in dryland ecosystems. Based on the in situ data from six flux sites in northwestern China from 2014 to 2016, this study compares seasonal and interannual dynamics of carbon fluxes between these arid and semi-arid ecosystems and the atmosphere. Meanwhile, the reliability of multiple remotely-derived GPP products in representative drylands was examined, including the Breathing Earth System Simulator (BESS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and data derived from the OCO-2 solar-induced chlorophyll fluorescence (GOSIF). The results indicated that the carbon fluxes had clear seasonal patterns, with all ecosystems functioning as carbon sinks. The maize cropland had the highest GPP with 1183 g C m−2 y−1. Although the net ecosystem carbon exchange (NEE) in the Tamarix spp. ecosystem was the smallest among these flux sites, it reached 208 g C m−2 y−1. Furthermore, distinct advantages of GOSIF GPP (with R2 = 0.85–0.98, and RMSE = 0.87–2.66 g C m−2 d−1) were found with good performance. However, large underestimations in three GPP products existed during the growing seasons, except in grassland ecosystems. The main reasons can be ascribed to the uncertainties in the key model parameters, including the underestimated light use efficiency of the MODIS GPP, the same coarse land cover product for the BESS and MODIS GPP, the coarse gridded meteorological data, and distribution of C3 and C4 plants. Therefore, it still requires more work to accurately quantify the GPP across these dryland ecosystems.

scholarly journals Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers

2021 ◽  
Vol 13 (4) ◽  
pp. 794
Author(s):  
Haibo Wang ◽  
Jingfeng Xiao
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Model Parameters ◽  
Flux Tower ◽  
Plant Photosynthesis ◽  
Model Understanding ◽  
Using Data ◽  
Sensitive Parameters ◽  
Shed Light ◽  
Scope Model

Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (Vcmax), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIIF and GPP by incorporating satellite SIF products and flux tower data.

scholarly journals Estimation of crop gross primary production (GPP): fAPARchl versus MOD15A2 FPAR

2014 ◽  
Vol 153 ◽  
pp. 1-6 ◽  
Author(s):  
Qingyuan Zhang ◽  
Yen-Ben Cheng ◽  
Alexei I. Lyapustin ◽  
Yujie Wang ◽  
Feng Gao ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production

scholarly journals Soil drying weakens the positive effect of climate factors on global gross primary production

2021 ◽  
Vol 129 ◽  
pp. 107953
Author(s):  
Huan Chen ◽  
Xiaoyong Bai ◽  
Yangbing Li ◽  
Qin Li ◽  
Luhua Wu ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Soil Drying ◽  
Climate Factors ◽  
Positive Effect
Sign in / Sign up

Export Citation Format

Share Document