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Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production

Division of Science and Environmental Policy, California State University–Monterey Bay, Seaside, CA 93955, USA
NASA Ames Research Center, Moffett Field, CA 94035, USA
Bay Area Environmental Research Institute, Sonoma, CA 95476, USA
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
College of Resources Science & Technology, State Key Laboratory of Earth Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(3), 1258-1273;
Received: 19 January 2013 / Revised: 28 January 2013 / Accepted: 5 March 2013 / Published: 12 March 2013
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Projected changes in the frequency and severity of droughts as a result of increase in greenhouse gases have a significant impact on the role of vegetation in regulating the global carbon cycle. Drought effect on vegetation Gross Primary Production (GPP) is usually modeled as a function of Vapor Pressure Deficit (VPD) and/or soil moisture. Climate projections suggest a strong likelihood of increasing trend in VPD, while regional changes in precipitation are less certain. This difference in projections between VPD and precipitation can cause considerable discrepancies in the predictions of vegetation behavior depending on how ecosystem models represent the drought effect. In this study, we scrutinized the model responses to drought using the 30-year record of Global Inventory Modeling and Mapping Studies (GIMMS) 3g Normalized Difference Vegetation Index (NDVI) dataset. A diagnostic ecosystem model, Terrestrial Observation and Prediction System (TOPS), was used to estimate global GPP from 1982 to 2009 under nine different experimental simulations. The control run of global GPP increased until 2000, but stayed constant after 2000. Among the simulations with single climate constraint (temperature, VPD, rainfall and solar radiation), only the VPD-driven simulation showed a decrease in 2000s, while the other scenarios simulated an increase in GPP. The diverging responses in 2000s can be attributed to the difference in the representation of the impact of water stress on vegetation in models, i.e., using VPD and/or precipitation. Spatial map of trend in simulated GPP using GIMMS 3g data is consistent with the GPP driven by soil moisture than the GPP driven by VPD, confirming the need for a soil moisture constraint in modeling global GPP. View Full-Text
Keywords: GPP; VPD; precipitation; GIMMS 3g; TOPS GPP; VPD; precipitation; GIMMS 3g; TOPS
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MDPI and ACS Style

Hashimoto, H.; Wang, W.; Milesi, C.; Xiong, J.; Ganguly, S.; Zhu, Z.; Nemani, R.R. Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production. Remote Sens. 2013, 5, 1258-1273.

AMA Style

Hashimoto H, Wang W, Milesi C, Xiong J, Ganguly S, Zhu Z, Nemani RR. Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production. Remote Sensing. 2013; 5(3):1258-1273.

Chicago/Turabian Style

Hashimoto, Hirofumi, Weile Wang, Cristina Milesi, Jun Xiong, Sangram Ganguly, Zaichun Zhu, and Ramakrishna R. Nemani. 2013. "Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production" Remote Sensing 5, no. 3: 1258-1273.

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