Estimating climatic influence on spatial and temporal variations of grassland gross primary production: A remote sensing approach
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The influence of climate change on the global carbon (C) cycle, as well as changes in ecosystem properties that affect climate, are two active areas of research in global change studies. Examining spatial and temporal climate variations and their relationships with grassland gross primary production (GPP) is important to quantify this biome’s role in the global carbon cycle and climate dynamics. Remote sensing is an important method to study these spatially explicit phenomena. The primary objective of this study is to couple variations in US grassland GPP to spatial and temporal variations in precipitation and temperature. The spatial and temporal variability in grassland gross primary production (GPP) was assessed for four years (2001 – 2004). At continental scale, GPP was positively correlated with annual (January to December), hydrological year (September to August) precipitation, and temperature. Multiple linear regressions between precipitation, temperature, and their interaction improved the correlation with GPP. Highest values of correlation were observed on the south and north of the study area. However, hydrological year precipitation showed better correlation with grassland GPP suggesting that a time lag of 3 months exist between hydrological year precipitation and annual precipitation when correlated with vegetation growth. Modeling using ECOSYS and climatic model precipitation data suggested that under high CO2 emission scenario, grassland GPP will increase especially in the northern parts of the study area.