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- W3035620550 abstract "Recently, light use efficiency (LUE) models have been widely used for calculating gross primary productivity (GPP). Unfortunately, these models are only suitable for flat areas without considering the topographic effects on plant photosynthesis. In this study, considering the topographic controls on direct radiation, diffuse radiation, and sunlit canopy area, an adjusted model called mountainous two-leaf (MTL)-LUE is developed from the original two-leaf LUE model (TL-LUE) to improve the GPP simulations in mountainous areas. The MTL-LUE model was validated at an eddy covariance (EC) site with apparent topography in the carbon footprint area. Results showed that the daily GPP simulations from MTL-LUE (root mean square error = 1.29 gC m−2 d−1) matched better with EC GPP than those from the TL-LUE model (root mean square error = 1.84 gC m−2 d−1), confirming the improvement of the MTL-LUE model in mountainous areas. Moreover, results also indicated that the MTL-LUE-simulated GPP showed obvious spatial variations in the study area, suggesting that the MTL-LUE model could characterize the effects of topography on plant photosynthesis. More specifically, canopy GPP was found to decrease when slope increased in shaded terrains and has no obvious variations in different slope ranges when exposed to the sun. Furthermore, results also indicated that a moderate increase in canopy GPP would be expected when the sky clearness decreased, which depended on the gap between the GPP increase of shaded leaves and the GPP decrease of sunlit leaves. This study highlights the potential of the MTL-LUE model in improving GPP simulations in mountainous areas." @default.
- W3035620550 created "2020-06-19" @default.
- W3035620550 creator A5019197462 @default.
- W3035620550 creator A5082765830 @default.
- W3035620550 date "2020-07-03" @default.
- W3035620550 modified "2023-09-29" @default.
- W3035620550 title "An Adjusted Two‐Leaf Light Use Efficiency Model for Improving GPP Simulations Over Mountainous Areas" @default.
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- W3035620550 doi "https://doi.org/10.1029/2019jd031702" @default.
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