A model‐data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Schaefer, Kevin; Schwalm, Christopher R.; Williams, C. A.; Arain, M. Altaf; Barr, Alan G.; Chen, Jing M.; Davis, Kenneth J.; Dimitrov, Dimitre D.; Hilton, T. W.; Hollinger, David Y.; Humphreys, Elyn; Poulter, Benjamin; Raczka, Brett; Richardson, Andrew D.; Sahoo, Alok Kumar; Thornton, P. E.; Vargas, Rodrigo; Verbeeck, Hans; Anderson, Ryan S.; Baker, Ian; Black, T. A.; Bolstad, Paul V.; Chen, Jiquan; Curtis, Peter S.; Desai, Ankur R.; Dietze, Michael C.; Dragoni, D.; Gough, Christopher M.; Grant, R. F.; Gu, Lianhong; Jain, Atul K.; Kucharik, Christopher J.; Law, B. E.; Liu, Shuguang; Lokipitiya, Erandathie; Margolis, Hank A.; Matamala, Roser; McCaughey, J. H.; Monson, R. K.; Munger, J. William; Oechel, W. C.; Peng, Changhui; Price, David T.; Ricciuto, D. M.; Riley, William J.; Roulet, Nigel T.; Tian, Hanqin; Tonitto, Christina; Torn, M. S.; Weng, Ensheng; Zhou, Xiaolu

doi:10.1029/2012jg001960

Cited by 302 publications

(351 citation statements)

References 147 publications

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“…Global estimates of GPP exist, but are not solely based on measurements and, therefore, large uncertainties exist in these estimates (Anav et al, 2015). In LSMs, the correct simulation of GPP is important since errors in its calculation can propagate through the model and affect biomass and other flux calculations, such as net ecosystem exchange (NEE; Schaefer et al, 2012). The correct representation of leaf-level stomatal conductance influences GPP and transpiration, and errors in GPP can also introduce errors into simulated latent and sensible heat fluxes.…”

Section: Introductionmentioning

confidence: 99%

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

et al. 2017

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Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010]. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0.5 • × 0.5 • spatial resolution), the annual average global GPP simulated by JULES for 2001-2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CAR-DAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0.5 • × 0.5 • , 1 • × 1 • and 2 • × 2 • ) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year −1 . Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year −1 . On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5 • N-5 • S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30-60 • N).

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Section: Introductionmentioning

confidence: 99%

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

et al. 2017

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“…Previous studies have shown that EVI-based VPM, TG, GR, and VI models perform well in forest, grassland and cropland ecosystems under non-drought condition (Gitelson et al, 2006;Kalfas, Xiao, Vanegas, Verma, & Suyker, 2011;Sims et al, 2008;Wu, Gonsamo, Zhang, & Chen, 2014;Wu, Munger, Niu, & Kuang, 2010;Wu et al, 2011;Xiao et al, 2005). The performances of these models in agricultural and grassland ecosystems under drought conditions are still unclear (Mu et al, 2007;Schaefer et al, 2012). Drought affects (1) light absorption through changes in leaf chlorophyll content and leaf area index, and (2) LUE through increased water and temperature stresses, which may result in a decrease of GPP.…”

Section: Introductionmentioning

confidence: 99%

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Dong

Xiao

Wagle

et al. 2015

Remote Sensing of Environment

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Berrien III, "Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought" (2015). Accurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPAR canopy ) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPAR chl ) or green leaves (FPAR green ) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency (LUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m −2 day −1 (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 2.32, 3.16 and 3.90 g C m −2 day −1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPM uses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP, while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions.

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The increasing importance of atmospheric demand for ecosystem water and carbon fluxes

et al. 2016

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Soil moisture supply and atmospheric demand for water independently limit-and profoundly a ect-vegetation productivity and water use during periods of hydrologic stress [1][2][3][4] . Disentangling the impact of these two drivers on ecosystem carbon and water cycling is di cult because they are often correlated, and experimental tools for manipulating atmospheric demand in the field are lacking. Consequently, the role of atmospheric demand is often not adequately factored into experiments or represented in models 5-7 . Here we show that atmospheric demand limits surface conductance and evapotranspiration to a greater extent than soil moisture in many biomes, including mesic forests that are of particular importance to the terrestrial carbon sink 8,9 . Further, using projections from ten general circulation models, we show that climate change will increase the importance of atmospheric constraints to carbon and water fluxes in all ecosystems. Consequently, atmospheric demand will become increasingly important for vegetation function, accounting for >70% of growing season limitation to surface conductance in mesic temperate forests. Our results suggest that failure to consider the limiting role of atmospheric demand in experimental designs, simulation models and land management strategies will lead to incorrect projections of ecosystem responses to future climate conditions. Ecosystem moisture stress is often characterized by changes in soil water availability 10,11 . Declining soil moisture impedes the movement of water to evaporating sites at the soil or leaf surface 12 , reducing the surface conductance to water vapour (G S )-a key determinant of carbon and water cycling-and thereby evapotranspiration (ET). However, atmospheric demand for water, which is directly related to the atmospheric vapour pressure deficit (VPD), also affects G S and ET. Plants close their stomata to prevent excessive water loss when VPD is high [13][14][15][16] and thus, increases in VPD during periods of hydrologic stress represent an independent constraint on plant carbon uptake and water use in ecosystems.While the plant physiological community has long recognized the critical role of VPD in determining plant functioning, VPD is often overlooked in many fields of hydrologic and climate science. For example, precipitation manipulation experiments are frequently used to draw conclusions about ecosystem response to drought stress, even though VPD is unaffected by precipitation manipulation 10 . Some terrestrial ecosystem and ecohydrological models do not permit stomatal conductance to vary with atmospheric demand 5,11 . Many models designed to capture these impacts rely on empirical parameterizations for soil moisture and VPD stress that promote compensating effects and model equifinality 5 , and/or use relative humidity instead of VPD as the primary driver, with significant consequences for projections of

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A model‐data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Cited by 302 publications

References 147 publications

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

The increasing importance of atmospheric demand for ecosystem water and carbon fluxes

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