Potential Net Primary Productivity in South America: Application of a Global Model

Raich, James W.; Rastetter, Edward B.; Melillo, Jerry M.; Kicklighter, David W.; Steudler, Paul A.; Peterson, Bruce J.; Grace, Annette L.; Moore, Berrien; Vörösmarty, Charles J

doi:10.2307/1941899

Cited by 774 publications

(572 citation statements)

References 99 publications

(151 reference statements)

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“…Based on previous publications, we used the ε 0 value of 0.39 g C mol −1 PPFD at the US-Bo1 site (soybean), 0.42 g C mol −1 PPFD at the US-ARb site (tallgrass prairie), and 0.69 g C mol −1 PPFD at the US-Ne1 site (corn) (Kalfas et al, 2011;Wagle, Xiao, & Suyker, 2015;. T scalar is estimated at each time step as in the Terrestrial Ecosystem Model (Raich et al, 1991),…”

Section: The Vegetation Photosynthesis Model (Vpm)mentioning

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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Section: The Vegetation Photosynthesis Model (Vpm)mentioning

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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“…Soil carbon and nitrogen dynamics Friend et al (1997) investigated several approaches to predicting soil carbon and nitrogen dynamics in Hybrid v3.0, including those described by Thornley and Verberne (1989) and Raich et al (1991). They concluded that the CENTURY model (Parton et al, 1987(Parton et al, , 1993 was the best for simulating soil C and N dynamics.…”

Section: Tree Height Diameter 6olume and Mortalitymentioning

confidence: 99%

TRIPLEX: a generic hybrid model for predicting forest growth and carbon and nitrogen dynamics

Peng

Liu

Dang

et al. 2002

Ecological Modelling

189

119

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“…Despite the problems with the Q 10 model, it has been widely used in modeling the process of soil respiration (Heimann et al, 1989;Raich et al, 1991;Running and Hunt, 1993;Schimel et al, 2000). Unlike the Arrhenius equation and its modified form by Lloyd and Taylor (1994), the Q 10 model relates the rate of soil respiration to varying temperature through a parameter, Q 10 , which determines the rate of change of soil respiration with respect to temperature.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid confounding effects with other factors such as soil moisture and litter quality, Kirschbaum (1995) selected laboratory-based measurements only, but obtained an even greater range of Q 10 from 2.5 at 20°C to 8 at 0°C. Although it has been well documented that Q 10 varies with temperature, this parameter has commonly been treated as a constant in ecosystem models (Heimann et al, 1989;Raich et al, 1991;Running and Hunt, 1993;Potter et al, 1993;Schimel et al, 2000). Due to the nonlinear nature of the relationship between Q 10 and respiration, small changes in Q 10 may cause significant differences in estimated soil respiration fluxes (Townsend et al, 1992;Holland et al, 1995;Kutsch and Kappen, 1997).…”

Section: Introductionmentioning

confidence: 99%

Temperature sensitivity of soil respiration and its effects on ecosystem carbon budget: nonlinearity begets surprises

2002

Ecological Modelling

139

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Nonlinearity is a salient feature in all complex systems, and it certainly characterizes biogeochemical cycles in ecosystems across a wide range of scales. Soil carbon emission is a major source of uncertainty in estimating the terrestrial carbon budget at the ecosystem level and above. Due to the lack of consideration of the nonlinearity in temperature sensitivity of soil respiration, several commonly used ecosystem models produce substantially different estimates of soil respiration with the same or similar model input. In this paper we demonstrated that the response of soil respiration to changes in temperature sensitivity is nonlinear and, thus, that the oversimplified formulations may significantly reduce the accuracy of ecosystem models in predicting carbon fluxes. To alleviate this problem, we have developed a general model of temperature sensitivity of soil respiration that explicitly considers this nonlinearity. The model was supported by our field measurements from a forest ecosystem, and used to assess the uncertainty in estimating the soil CO 2 efflux with several commonly used ecosystem models. Our results indicated that the variations and nonlinearity of the soil respiration-temperature relationship and its dependence on moisture may have important implications for ecosystem carbon modeling at regional and global scales. In other words, 'small causes' may lead to 'large effects' in complex ecosystems in terms of carbon dynamics. In particular, when the variability in temperature sensitivity of soil respiration was incorporated in the several commonly used ecosystem models, the carbon source-sink relationship for terrestrial ecosystems under future global warming scenarios became dramatically different from those reported previously. Thus, we advocate that confidence limits are both necessary and feasible for simulated carbon budget from ecosystem models. Based on field measurements and model simulations, our study provides useful information for computing such confidence limits. In addition, our new model of temperature sensitivity of soil respiration seems more general and yet realistic, and can improve the accuracy of ecosystem models in predicting carbon fluxes at large scales.

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Potential Net Primary Productivity in South America: Application of a Global Model

Cited by 774 publications

References 99 publications

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

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

TRIPLEX: a generic hybrid model for predicting forest growth and carbon and nitrogen dynamics

Temperature sensitivity of soil respiration and its effects on ecosystem carbon budget: nonlinearity begets surprises

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