Authors: Christy Attaway*, , Inci Guneralp, Texas A&M University, Cesar R Castillo, Texas A&M University, Anthony Filippi, Texas A&M University
Topics: Geomorphology, Hazards, Risks, and Disasters
Keywords: fluvial geomorphology, hurricane, wind disturbance, blowdown, riparian forest, flood,
Session Type: Poster
Start / End Time: 8:00 AM / 9:40 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
Interactions between vegetation and the form and function of meandering rivers has been widely studied in the literature. However, we lack knowledge regarding how environmental factors can be used to predict tree susceptibility to blowdowns due to extreme weather, such as tropical cyclones, with coastal floodplain forests. Climate models forecast an increase in exposure to severe storms, emphasizing the need for understanding their consequent impacts. Past studies have created prediction models pertaining to forest disturbances arising from wind and fire, but have generally not focused on tropical cyclone-induced disturbances within floodplains. We contribute to the knowledge base by spatially analyzing blowdown trees across the landscape surrounding Mission River on the Coastal Bend of Texas resulting from Hurricane Harvey. Preliminary post-disturbance analysis using remote sensing and fieldwork within the Mission River floodplain has shown extensive blowdowns, in addition to widespread snapping of trunks and branches. This study analyzes factors likely to make trees more susceptible to blowdowns, including elevation, land cover, soil, and distance to river, among others. We produce a predictive damage model based on these factors. Identifying the factors that have the greatest influence on tree-blowdown susceptibility is important for proper management of low-lying coastal riverine habitats and the biota that rely on these systems. The influence of these factors and their interaction with broader biogeomorphic processes will be determined for the riparian zone and the floodplain to elucidate spatially-varying differences.