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1. Introduction To understand historical changes and predict future trends in ecosystems we must improve our understanding of individual ecosystem processes and their interactions with external environmental factors ( Fung et al. 2005 ; Friedlingstein et al. 2006 ; Thornton et al. 2009 ). Gross primary production (GPP) is the amount of carbon assimilated via photosynthesis and constitutes an important link in the terrestrial carbon cycle ( Ciais et al. 1997 ). Because of its importance to
1. Introduction To understand historical changes and predict future trends in ecosystems we must improve our understanding of individual ecosystem processes and their interactions with external environmental factors ( Fung et al. 2005 ; Friedlingstein et al. 2006 ; Thornton et al. 2009 ). Gross primary production (GPP) is the amount of carbon assimilated via photosynthesis and constitutes an important link in the terrestrial carbon cycle ( Ciais et al. 1997 ). Because of its importance to
nonvegetated areas were masked from further analysis to isolate relationships between PEM results and environmental parameters. 2.1. Production efficiency model calculations A biome-specific PEM was used to calculate gross primary productivity (GPP) and NPP for unmasked pixels within the 25-km resolution EASE-Grid domain. The PEM logic is described and verified in detail elsewhere (e.g., Running et al. 2000 ; Zhao et al. 2005 ; Heinsch et al. 2006 ) and summarized below. Gross primary production (g C m
nonvegetated areas were masked from further analysis to isolate relationships between PEM results and environmental parameters. 2.1. Production efficiency model calculations A biome-specific PEM was used to calculate gross primary productivity (GPP) and NPP for unmasked pixels within the 25-km resolution EASE-Grid domain. The PEM logic is described and verified in detail elsewhere (e.g., Running et al. 2000 ; Zhao et al. 2005 ; Heinsch et al. 2006 ) and summarized below. Gross primary production (g C m
vegetation growth, and alteration of land–atmosphere CO 2 exchange ( Randerson et al. 1997 ; Nemani et al. 2003 ; Angert et al. 2005 ). Net primary production (NPP) represents the sequestration of atmospheric CO 2 through plant photosynthesis or gross primary production (GPP), and carbon storage in vegetation biomass and soils. Net ecosystem production (NEP) is the residual difference between NPP and CO 2 losses from soil heterotrophic respiration and defines the net ecosystem–atmosphere exchange of
vegetation growth, and alteration of land–atmosphere CO 2 exchange ( Randerson et al. 1997 ; Nemani et al. 2003 ; Angert et al. 2005 ). Net primary production (NPP) represents the sequestration of atmospheric CO 2 through plant photosynthesis or gross primary production (GPP), and carbon storage in vegetation biomass and soils. Net ecosystem production (NEP) is the residual difference between NPP and CO 2 losses from soil heterotrophic respiration and defines the net ecosystem–atmosphere exchange of
the difference between gross primary production (GPP, the CO 2 fixed by vegetation in photosynthesis) and autotrophic respiration ( R A , the respiration of CO 2 by vegetation). Monthly GPP considers the effects of several factors and is calculated as follows: where C max is the maximum rate of C assimilation, PAR is photosynthetically active radiation, and f  (PHENOLOGY) is monthly leaf area relative to maximum monthly leaf area ( Raich et al. 1991 ). The function f  (FOLIAGE) is a scalar
the difference between gross primary production (GPP, the CO 2 fixed by vegetation in photosynthesis) and autotrophic respiration ( R A , the respiration of CO 2 by vegetation). Monthly GPP considers the effects of several factors and is calculated as follows: where C max is the maximum rate of C assimilation, PAR is photosynthetically active radiation, and f  (PHENOLOGY) is monthly leaf area relative to maximum monthly leaf area ( Raich et al. 1991 ). The function f  (FOLIAGE) is a scalar
1. Introduction Terrestrial gross primary productivity (GPP), the photosynthetic carbon sequestration by terrestrial ecosystems, plays an important role in the global carbon cycle ( Beer et al. 2010 ). Le Quéré et al. (2018) suggest that approximate 34% of anthropogenic CO 2 emissions are offset by the terrestrial GPP. As changes of such a CO 2 flux might disturb the carbon balance of the Earth system, alter atmospheric CO 2 concentration, and further cause profound climate anomalies
1. Introduction Terrestrial gross primary productivity (GPP), the photosynthetic carbon sequestration by terrestrial ecosystems, plays an important role in the global carbon cycle ( Beer et al. 2010 ). Le Quéré et al. (2018) suggest that approximate 34% of anthropogenic CO 2 emissions are offset by the terrestrial GPP. As changes of such a CO 2 flux might disturb the carbon balance of the Earth system, alter atmospheric CO 2 concentration, and further cause profound climate anomalies
SPEI ( Vicente-Serrano et al. 2013 ). Furthermore, SPI has been used to analyze the drought impacts on carbon dynamics ( Shi et al. 2013 ). For decades, drought indices merely serve as end products for drought monitoring and assessment, yet seldom have they been used in ecosystem modeling. Most studies focused on analyzing the correlations between drought indices and other variables such as gross primary production (GPP) and normalized difference vegetation index (NDVI). Those studies generally
SPEI ( Vicente-Serrano et al. 2013 ). Furthermore, SPI has been used to analyze the drought impacts on carbon dynamics ( Shi et al. 2013 ). For decades, drought indices merely serve as end products for drought monitoring and assessment, yet seldom have they been used in ecosystem modeling. Most studies focused on analyzing the correlations between drought indices and other variables such as gross primary production (GPP) and normalized difference vegetation index (NDVI). Those studies generally
those that do are limited by an inability to account for the decoupling of photosynthesis and transpiration (e.g., Felzer et al. 2009 ). While transpiration, runoff, and surface energy partitioning are expected to change in response to chronic O 3 exposure, the magnitude of these responses is not documented. The primary objective of this work is to predict global changes in transpiration, gross primary productivity (GPP), and runoff in response to chronic O 3 exposure. We use the parameterization
those that do are limited by an inability to account for the decoupling of photosynthesis and transpiration (e.g., Felzer et al. 2009 ). While transpiration, runoff, and surface energy partitioning are expected to change in response to chronic O 3 exposure, the magnitude of these responses is not documented. The primary objective of this work is to predict global changes in transpiration, gross primary productivity (GPP), and runoff in response to chronic O 3 exposure. We use the parameterization
). Especially in the arid region of northwestern China, the significant changes in climate may lead to the changes in vegetation growth ( Zhao et al. 2011 ). In southern China, the subtropical forests are threatened by their lack of resilience against long-term climate change ( Zhou et al. 2013 ). To evaluate the effects of climate change on terrestrial ecosystems, the gross primary production (GPP) and the net primary production (NPP) are widely used in literature ( Fang et al. 2013 ). For example, one
). Especially in the arid region of northwestern China, the significant changes in climate may lead to the changes in vegetation growth ( Zhao et al. 2011 ). In southern China, the subtropical forests are threatened by their lack of resilience against long-term climate change ( Zhou et al. 2013 ). To evaluate the effects of climate change on terrestrial ecosystems, the gross primary production (GPP) and the net primary production (NPP) are widely used in literature ( Fang et al. 2013 ). For example, one
wetland environmental conditions. The objectives in this paper are 1) to estimate CO 2 exchange rates [net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration rate ( R e )] of the introduced wetland in the CGEF and 2) to investigate the factors governing the CO 2 exchange rates. 2. Materials and methods a. Introduction of a wetland ecosystem into the CGEF The CGEF includes a geosphere module (GM) and a geosphere material circulation system (GMCS). The roof and
wetland environmental conditions. The objectives in this paper are 1) to estimate CO 2 exchange rates [net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration rate ( R e )] of the introduced wetland in the CGEF and 2) to investigate the factors governing the CO 2 exchange rates. 2. Materials and methods a. Introduction of a wetland ecosystem into the CGEF The CGEF includes a geosphere module (GM) and a geosphere material circulation system (GMCS). The roof and
[either as net ecosystem exchange (NEE) or gross primary productivity (GPP)], has been demonstrated to occur at multiple biomes, including deciduous forests, evergreen forests, maize fields, soy fields, grasslands, and tundra shrublands ( Cheng et al. 2015 ; Lee et al. 2018 ; Alton 2008 ). However, there is wide uncertainty around how strongly diffuse light and cloud conditions affect ecosystems, with estimates varying from NEE rates that increase up to 150% under cloudy conditions relative to clear
[either as net ecosystem exchange (NEE) or gross primary productivity (GPP)], has been demonstrated to occur at multiple biomes, including deciduous forests, evergreen forests, maize fields, soy fields, grasslands, and tundra shrublands ( Cheng et al. 2015 ; Lee et al. 2018 ; Alton 2008 ). However, there is wide uncertainty around how strongly diffuse light and cloud conditions affect ecosystems, with estimates varying from NEE rates that increase up to 150% under cloudy conditions relative to clear
