{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/78013"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/78013","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Spatial Temporal Analysis Using Hierarchical Bayesian Approach: Effects of Climate Variability on Primary Production of Deciduous Forests of the Northeastern U.S.","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Gao, Fangfei; 0000-0002-9024-8995"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wilson, Adam","Geography"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-06-28T20:32:37Z","date_published":"2018-06-28T20:32:37Z","updated_at":"2026-07-27T19:05:05Z","subjects":["ecology","climate change","geographic information science and geodesy"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/78013","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wilson, Adam","Geography"]},{"key":"dc:creator","label":"Author","values":["Gao, Fangfei; 0000-0002-9024-8995"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-06-28T20:32:37Z","2018","2018-05-14 15:18:49"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ecology","climate change","geographic information science and geodesy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/78013"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","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, and human health. One potential impact is on the productivity of deciduous forest ecosystems, which cover 22% of the planet and are an important component of the global carbon cycle. Yet, the influence of extreme weather on temperate forest production is poorly understood. This research estimates the effects of extreme weather on Gross Primary Production (GPP, a measure of how much carbon the forest removes from the atmosphere) of temperate deciduous forests in the Northeastern United States using data from the FLUXNET monitoring network. I used Hierarchical Bayesian models to account for climate variability (anomalies) and short-term weather events (such as drought, heat) to quantify the effects of climatic variability on forest ecosystem productivity. I also explored different lagged effects of climate variability on GPP and found that GPP is sensitive to climatic variability at different temporal scales and that the effects vary seasonally. For example, heavy precipitation has a positive effect on NEP early in the growing season but switches to a negative effect in late summer. In addition, I found that incorporating a lagged effect into the model reveals improves model performance suggesting that some of the short term variability can affect GPP for several weeks. In summary, this research improves our understanding of how lagged effect influence the GPP prediction and how climatic variability (anomalies and events) drives changes in ecosystem productivity and, ultimately, the global carbon cycle."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Spatial Temporal Analysis Using Hierarchical Bayesian Approach: Effects of Climate Variability on Primary Production of Deciduous Forests of the Northeastern U.S."]}]}],"canonical_facts":{"dc:contributor":["Wilson, Adam","Geography"],"dc:creator":["Gao, Fangfei; 0000-0002-9024-8995"],"dc:date":["2018-06-28T20:32:37Z","2018","2018-05-14 15:18:49"],"dc:description":["M.S.","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, and human health. One potential impact is on the productivity of deciduous forest ecosystems, which cover 22% of the planet and are an important component of the global carbon cycle. Yet, the influence of extreme weather on temperate forest production is poorly understood. This research estimates the effects of extreme weather on Gross Primary Production (GPP, a measure of how much carbon the forest removes from the atmosphere) of temperate deciduous forests in the Northeastern United States using data from the FLUXNET monitoring network. I used Hierarchical Bayesian models to account for climate variability (anomalies) and short-term weather events (such as drought, heat) to quantify the effects of climatic variability on forest ecosystem productivity. I also explored different lagged effects of climate variability on GPP and found that GPP is sensitive to climatic variability at different temporal scales and that the effects vary seasonally. For example, heavy precipitation has a positive effect on NEP early in the growing season but switches to a negative effect in late summer. In addition, I found that incorporating a lagged effect into the model reveals improves model performance suggesting that some of the short term variability can affect GPP for several weeks. In summary, this research improves our understanding of how lagged effect influence the GPP prediction and how climatic variability (anomalies and events) drives changes in ecosystem productivity and, ultimately, the global carbon cycle."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/78013"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["ecology","climate change","geographic information science and geodesy"],"dc:title":["Spatial Temporal Analysis Using Hierarchical Bayesian Approach: Effects of Climate Variability on Primary Production of Deciduous Forests of the Northeastern U.S."],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:05Z"}