Testing across vegetation types for common environmental dependencies of Gross Primary Production

2020 ◽  
Author(s):  
Keith Bloomfield ◽  
Benjamin Stocker ◽  
Colin Prentice
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Eddy Covariance ◽  
Empirical Analysis ◽  
Gross Primary Production ◽  
Incident Light ◽  
Vapour Pressure Deficit ◽  
Additive Models ◽  
Theoretical Modelling ◽  
Wide Range

<p>Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions.  The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established.  At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO<sub>2</sub>.  Models of GPP make a variety of different assumptions when ‘scaling-up’ the standard model of photosynthesis.  As a troublesome consequence, they make a variety of different predictions about how GPP responds to contemporary environmental change.</p><p>This problem can be tackled by theoretically based modelling, or by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements.  Theoretical modelling has provided an explanation for why ‘light-use efficiency’ (LUE) models work well at time scales of a week or longer.  The same logic provides a justification for the use of LUE as a key metric in an empirical analysis.  By focusing on LUE, we can isolate the controls of GPP that are distinct from its over-riding control by absorbed light.  We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time).  These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network.  We have combined the flux data with a satellite product (FPAR from MODIS) that provides spatial estimates of the fraction of incident light absorbed by green vegetation.  Soil moisture at flux sites was estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids.  LUE was then calculated as the amount of carbon fixed per unit of absorbed light.  We then considered additive models (incorporating multiple explanatory factors) that support non-linear responses, including a peaked response to temperature.  Recognising that our longitudinal data are not fully independent, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.</p><p>In arriving at a final parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content, are all salient predictors of LUE.  The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months.  Model performance was not significantly improved by inclusion of additional variables such as rainfall, site elevation or vegetation category (e.g. Plant Functional Type, PFT).  This empirical analysis supports the notion that GPP is predictable using a single model structure that is common to different PFTs.</p>

Predictions of Gross Primary Production from a suite of Terrestrial Biome Models display divergent relationships with key environmental variables

2021 ◽  
Author(s):  
Keith Bloomfield ◽  
Benjamin Stocker ◽  
Trevor Keenan ◽  
Colin Prentice
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Eddy Covariance ◽  
Empirical Analysis ◽  
Gross Primary Production ◽  
Additive Models ◽  
Theoretical Modelling ◽  
Environmental Drivers ◽  
Model Structure ◽  
Wide Range

<p>Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions.  The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established.  At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO<sub>2</sub>.  Models of GPP make a variety of different assumptions when ‘scaling-up’ the standard model of photosynthesis.  As a troublesome consequence, they make a variety of different predictions about how GPP responds to projected environmental change.</p><p>This problem can be tackled by theoretical modelling and by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements.  Theoretical modelling has provided an explanation for why ‘light-use efficiency’ (LUE) models work well at time scales of a week or longer.  The same logic provides a justification for the use of LUE as a key metric in an empirical analysis.  By focusing on LUE, we can isolate the drivers of GPP independent of its over-riding control by absorbed light.  We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time).  These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network.  We have combined the flux data with a satellite product (EVI from MODIS) that allows spatial estimates of the fraction of incident light absorbed by green vegetation.  Matching soil moisture data were estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids.  LUE was then calculated as the amount of carbon fixed per unit of absorbed light.  We then explored additive models (incorporating multiple explanatory factors) that support non-linear responses.  Recognising that our longitudinal data lack independence, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.</p><p>In arriving at a preferred parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content are all salient predictors of LUE.  The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months.  As a model-comparison exercise, we used that portion of our dataset which overlaps the North American Carbon Program to apply our empirical model structure to site-based estimates of GPP generated by 19 discrete Terrestrial Biosphere Models (TBMs).  The comparative analysis reveals wide variation between the TBMs in the shape, strength and even sign of the environmental effects on modelled GPP.</p><p>This empirical analysis suggests it is feasible to predict GPP using a single model structure, common across vegetation categories.  And the appeal of such universal approaches is highlighted by inconsistent relationships with key environmental drivers within extant terrestrial models.</p>

scholarly journals P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

2020 ◽  
Vol 13 (3) ◽  
pp. 1545-1581 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Quantum Yield ◽  
Primary Production ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals Identification and Modeling Carbon and Energy Fluxes from Eddy Covariance Time Series Measurements in Rice and Rainfed Crops

2021 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Víctor Cicuéndez ◽  
Javier Litago ◽  
Víctor Sánchez-Girón ◽  
Laura Recuero ◽  
César Sáenz ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Balance ◽  
Gross Primary Production ◽  
Meteorological Variables ◽  
Water Fluxes ◽  
Energy Fluxes ◽  
Global Carbon

Gross primary production (GPP) represents the carbon (C) uptake of ecosystems through photosynthesis and it is the largest flux of the global carbon balance. Our overall objective in this research is to identify and model GPP dynamics and its relationship with meteorological variables and energy fluxes based on time series analysis of eddy covariance (EC) data in two different agroecosystems, a Mediterranean rice crop in Spain and a rainfed cropland in Germany. Crops exerted an important influence on the energy and water fluxes dynamics existing a clear feedback between GPP, meteorological variables and energy fluxes in both type of crops.

scholarly journals Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e110407 ◽  
Author(s):  
Dan Liu ◽  
Wenwen Cai ◽  
Jiangzhou Xia ◽  
Wenjie Dong ◽  
Guangsheng Zhou ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Gross Primary Production

scholarly journals How drought severity constrains gross primary production(GPP) and its partitioning among carbon pools in a <i>Quercus ilex</i> coppice?

2014 ◽  
Vol 11 (23) ◽  
pp. 6855-6869 ◽  
Author(s):  
S. Rambal ◽  
M. Lempereur ◽  
J. M. Limousin ◽  
N. K. Martin-StPaul ◽  
J. M. Ourcival ◽  
...  
Keyword(s):  
Primary Production ◽  
Water Deficit ◽  
Eddy Covariance ◽  
Quercus Ilex ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Carbon Fluxes ◽  
Stem Growth ◽  
Drought Severity ◽  
Mediterranean Ecosystem

Abstract. The partitioning of photosynthates toward biomass compartments plays a crucial role in the carbon (C) sink function of forests. Few studies have examined how carbon is allocated toward plant compartments in drought-prone forests. We analyzed the fate of gross primary production (GPP) in relation to yearly water deficit in an old evergreen Mediterranean Quercus ilex coppice severely affected by water limitations. Carbon fluxes between the ecosystem and the atmosphere were measured with an eddy covariance flux tower running continuously since 2001. Discrete measurements of litterfall, stem growth and fAPAR allowed us to derive annual productions of leaves, wood, flowers and acorns, and an isometric relationship between stem and belowground biomass has been used to estimate perennial belowground growth. By combining eddy covariance fluxes with annual net primary productions (NPP), we managed to close a C budget and derive values of autotrophic, heterotrophic respirations and carbon-use efficiency (CUE; the ratio between NPP and GPP). Average values of yearly net ecosystem production (NEP), GPP and Reco were 282, 1259 and 977 g C m−2. The corresponding aboveground net primary production (ANPP) components were 142.5, 26.4 and 69.6 g C m−2 for leaves, reproductive effort (flowers and fruits) and stems, respectively. NEP, GPP and Reco were affected by annual water deficit. Partitioning to the different plant compartments was also impacted by drought, with a hierarchy of responses going from the most affected – the stem growth – to the least affected – the leaf production. The average CUE was 0.40, which is well in the range for Mediterranean-type forest ecosystems. CUE tended to decrease less drastically in response to drought than GPP and NPP did, probably due to drought acclimation of autotrophic respiration. Overall, our results provide a baseline for modeling the inter-annual variations of carbon fluxes and allocation in this widespread Mediterranean ecosystem, and they highlight the value of maintaining continuous experimental measurements over the long term.

Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China

2015 ◽  
Vol 17 (4) ◽  
pp. 753-762
Author(s):  
Mingquan Wu ◽  
Shakir Muhammad ◽  
Fang Chen ◽  
Zheng Niu ◽  
Changyao Wang
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Eddy Covariance ◽  
Gross Primary Production ◽  
Model Performance ◽  
Wetland Ecosystem ◽  
Wetland Ecosystems ◽  
New Model ◽  
Estuarine Wetland ◽  
Better Than

A new model performance better than the MODIS GPP product for wetland ecosystems was proposed and validated.

scholarly journals Changes in surface hydrology, soil moisture and gross primary production in the Amazon during the 2015/2016 El Niño

2018 ◽  
Vol 373 (1760) ◽  
pp. 20180084 ◽  
Author(s):  
Erik van Schaik ◽  
Lars Killaars ◽  
Naomi E. Smith ◽  
Gerbrand Koren ◽  
L. P. H. van Beek ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
El Niño ◽  
Amazon Basin ◽  
El Nino ◽  
Gross Primary Production ◽  
Surface Hydrology ◽  
The North ◽  
North Eastern ◽  
The Impact

The 2015/2016 El Niño event caused severe changes in precipitation across the tropics. This impacted surface hydrology, such as river run-off and soil moisture availability, thereby triggering reductions in gross primary production (GPP). Many biosphere models lack the detailed hydrological component required to accurately quantify anomalies in surface hydrology and GPP during droughts in tropical regions. Here, we take the novel approach of coupling the biosphere model SiBCASA with the advanced hydrological model PCR-GLOBWB to attempt such a quantification across the Amazon basin during the drought in 2015/2016. We calculate 30–40% reduced river discharge in the Amazon starting in October 2015, lagging behind the precipitation anomaly by approximately one month and in good agreement with river gauge observations. Soil moisture shows distinctly asymmetrical spatial anomalies with large reductions across the north-eastern part of the basin, which persisted into the following dry season. This added to drought stress in vegetation, already present owing to vapour pressure deficits at the leaf, resulting in a loss of GPP of 0.95 (0.69 to 1.20) PgC between October 2015 and March 2016 compared with the 2007–2014 average. Only 11% (10–12%) of the reduction in GPP was found in the (wetter) north-western part of the basin, whereas the north-eastern and southern regions were affected more strongly, with 56% (54–56%) and 33% (31–33%) of the total, respectively. Uncertainty on this anomaly mostly reflects the unknown rooting depths of vegetation. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Incorporating non-stomatal limitation improves the performance of leaf and canopy models at high vapour pressure deficit

2019 ◽  
Author(s):  
J Yang ◽  
R A Duursma ◽  
M G De Kauwe ◽  
D Kumarathunge ◽  
M Jiang ◽  
...  
Keyword(s):  
Gas Exchange ◽  
Primary Production ◽  
Vapour Pressure ◽  
Leaf Gas Exchange ◽  
Gross Primary Production ◽  
Vapour Pressure Deficit ◽  
Stomatal Limitation ◽  
Flow Measurements ◽  
Hydraulic Limitation ◽  
The Future

Abstract Vapour pressure deficit (D) is projected to increase in the future as temperatures rise. In response to increased D, stomatal conductance (gs) and photosynthesis (A) are reduced, which may result in significant reductions in terrestrial carbon, water, and energy fluxes. It is thus important for gas exchange models to capture the observed responses of gs and A with increasing D. We tested a series of coupled A-gs models against leaf gas exchange measurements from the Cumberland Plain Woodland (Australia), where D regularly exceeds 2 kPa and can reach 8 kPa in summer. Two commonly used A-gs models (Leuning 1995 and Medlyn et al. 2011) were not able to capture the observed decrease in A and gs with increasing D at the leaf scale. To explain this decrease in A and gs, two alternative hypotheses were tested: hydraulic limitation (i.e., plants reduce gs and/or A due to insufficient water supply) and non-stomatal limitation (i.e., downregulation of photosynthetic capacity). We found that the model that incorporated a non-stomatal limitation captured the observations with high fidelity and required the fewest number of parameters. While the model incorporating hydraulic limitation captured the observed A and gs, it did so via a physical mechanism that is incorrect. We then incorporated a non-stomatal limitation into the stand model, MAESPA, to examine its impact on canopy transpiration and gross primary production. Accounting for a non-stomatal limitation reduced the predicted transpiration by ~19%, improving the correspondence with sap flow measurements, and gross primary production by ~14%. Given the projected global increases in D associated with future warming, these findings suggest that models may need to incorporate non-stomatal limitation to accurately simulate A and gs in the future with high D. Further data on non-stomatal limitation at high D should be a priority, in order to determine the generality of our results and develop a widely applicable model.

scholarly journals Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1346 ◽  
Author(s):  
Joanna Joiner ◽  
Yasuko Yoshida ◽  
Yao Zhang ◽  
Gregory Duveiller ◽  
Martin Jung ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Satellite Observation ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Light Use Efficiency ◽  
High Productivity ◽  
Use Efficiency

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

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