Spatial temporal analysis using Hierarchical Bayesian approach: effects of climate variability on primary production of Deciduous Forests of the Northeastern US

Authors: Fangfei Gao*, University at Buffalo, Adam M. Wilson, University at Buffalo, Yingying Xie, University at Buffalo
Topics: Biogeography, Global Change, Environmental Science
Keywords: NEP, extreme weather, phenology, global carbon change, Bayesian model
Session Type: Paper
Day: 4/12/2018
Start / End Time: 5:20 PM / 7:00 PM
Room: Bayside B, Sheraton, 4th Floor
Presentation File: No File Uploaded

Extreme weather events including drought, heavy rain, and heat waves are occurring with higher frequency and can cause substantial impacts to human and natural systems, such as forestry, water resources, human health. One potential impact is on the primary productivity of deciduous forest ecosystems, which cover 22% of the planet. Thus it is important to understand the influence of extreme weather on global CO2 budgets.  In this project, I estimate the effects of extreme weather on Net Ecosystem Productivity (NEP) of temperate deciduous forests in the Northeastern United States using data from the FLUXNET monitoring network.  I use a Hierarchical Bayesian model that accounts for mean seasonal variability, intra-annual seasonal anomalies, and short-term weather events (such as drought) to quantify the effects of climatic variability on forest ecosystem productivity.  The model suggests that NEP is sensitive to climatic variability at these different temporal scales, but that the effects vary seasonally.  For example, heavy precipitation has a positive effect on NEP in the early growing season but switches to a negative effect in late summer. In summary, this approach improves our understanding of how climatic variability drives changes in ecosystem productivity and, ultimately, the global carbon cycle.

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