Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States

Abstract

Gross primary production (GPP), the photosynthetic uptake of carbon, is an important variable in the global carbon cycle. Although continuous measurements of GPP are being collected from a network of micrometeorological towers, each site represents a small area with records available for only a limited period. As a result, GPP is commonly modeled over forested landscapes as a function of climatic and soil variables, often supplemented with satellite-derived estimates of the vegetation's light-absorbing properties. Since the late 1990s, a number of models have been developed to provide seasonal and annual estimates of GPP across much of the Earth. Each model, however, contains different underlying assumptions and requires different amounts of data. As a result, predictions vary, sometimes significantly. In this paper we compare modeled estimates of GPP for forested areas across the U.S.A. derived from: NASA's MODIS Product (MOD17); the C-Fix model using SPOT-VGT satellite-derived vegetation data; and the Physiological Principles Predicting Growth from Satellites (3-PGS) model, a process-based model that requires information on both climate and soil properties. The models predicted average ecoregion values of forest GPP between 9.8 and 14.1 MgC ha − 1 y − 1 across the United States. 3-PGS predicted the lowest values while the C-Fix model, which included a CO 2 fertilization factor, produced the highest estimates. In the western part of the country, estimates of GPP within a given ecoregion varied by as much as 50%, whereas in the northeast, where topography and climate are less extreme, variation in GPP was less than 10%. Within ecoregions, 3PGS predicted the most variation, reflecting its sensitivity to variation in soil properties. We conclude that where model predictions disagree, an opportunity is presented to evaluate underlying assumptions through sensitivity analyses, additional data collection and where more detailed study is warranted.

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

“…Due to different physiological principles, underlying assumptions, and amounts of input data, GPP predictions vary, sometimes significantly (Coops et al, 2009 to evaluating model principles and uncertainties of driving forcing (Cramer et al, 1999;Ito and Sasai, 2006;Jung et al, 2007;Beer et al, 2010) a systematic inter-comparison project across available GPP models is warranted to achieve mechanistic interpretation of the disagreement of GPP predictions (Ryu et al, 2011). The evaluation of water stress factors at flux sites in this study shows that the definition of water stress factors could be the reason resulting in the significant difference of trend between different GPP products, which highlights the direction of GPP model improvements.…”

Comparison of four light use efficiency models for estimating terrestrial gross primary production

“…However, the performances of those models are rarely validated with field observations across large areas (e.g., Yuan et al, 2010), and little is known about whether the better-performed LUE models can be further improved by model structural optimization (Medlyn, 2011;Hashimoto et al, 2013;Yang et al, 2013). Specifically, most previous efforts focused on only one of the LUE models and compared the model with other kinds of models at quite a few flux sites (Zhang et al, 2007;Coops et al, 2009;Wu et al, 2010). Yuan et al (2014) might be the first to compare multiple LUE models via field observations over large areas, but they did not investigate the strength and weakness of the various model structures, as a result, the potential of model improvement by structural optimization.…”

Remote sensing estimation of gross primary productivity and its response to climate change in the upstream of Heihe River Basin

“…MODIS数据进行空间分辨率为1 km的全球陆地生 态系统GPP估算 (Running et al, 1999;Heinsch et al, 2003)。该模型产品已经过北美、欧洲和我国通量网 多个站点的验证 (Plummer, 2006;Yang et al, 2007;Coops et al, 2009) (1)…”