U016-02
Extreme Total Water Level Forecasts in Alternative Futures Modeling to Assess Coastal Community Adaptation Pathways
Monday, 14 December 2020: 11:38
Meredith Leung1, Peter Ruggiero2, Fernando J. Mendez3, John Bolte4, Dylan Anderson5, Ana Rueda3, Laura Cagigal6 and John J Marra7, (1)Oregon State University, College of Earth, Ocean, and Atmospheric Sciences, Corvallis, OR, United States, (2)Oregon State University, Corvallis, OR, United States, (3)University of Cantabria, Ciencias y Tecnicas del Agua y del Medio Ambiente, Santander, Spain, (4)Oregon State University, Biological and Ecological Engineering, Corvallis, OR, United States, (5)Oak Ridge Institute for Science and Education, United States Army Corps of Engineers, Kitty Hawk, NC, United States, (6)University of Auckland, School of Environment, Auckland, New Zealand, (7)NOAA Honolulu, Honolulu, HI, United States
Abstract:
Total water levels (TWLs) represent the sum of the processes that culminate in the maximum elevation that water reaches on backshore coastal features. With rising sea levels and changing patterns of storminess, chronic flooding and erosion events that result from extreme TWLs are evolving in frequency and severity. Probabilistic forecasts of extreme TWLs are critical for informed coastal planning as they can highlight areas of vulnerability and allow for a quantitative assessment of risk before hazardous events occur. When combined with an alternative futures land use change model, probabilistic TWL forecasts enable the exploration of different policy scenarios (e.g.
, coastal hardening vs managed retreat) to help determine which policies best support community resilience goals against chronic coastal hazards. The integration of probabilistic TWLs and alternative futures modeling is tested at the statewide scale in Oregon using the statistical framework, TESLA, and the agent-based, land use change model, Envision, which models coastal hazards on event to multi-decadal timescales across a series of climate and policy scenarios.
TESLA (Time varying Emulator for Short and Long-term Analysis of coastal flooding and erosion), is a stochastic climate emulator that produces probabilistic forecasts of the drivers of TWLs using statistical and machine learning techniques to analyze sea surface temperature, sea level pressure, and sea level pressure gradient data (Anderson et al., 2019). When combined with forecasts of tides and sea level rise projections, TESLA enables the exploration of the major drivers of the most damaging TWL events and the evaluation of how climate change may impact the severity of future chronic coastal hazards.
Probabilistic forecasts of TWLs feed into the chronic flooding and erosion sub-models of Envision. The integration of the TESLA and Envision frameworks allows for a probabilistic understanding of the impact both climate scenarios and policy decisions have on economic, structural, and social systems on the Oregon coast during multi-decadal simulations. This ‘envisioning alternative coastal futures’ framework incorporates stakeholder input throughout the modeling process to ensure useable, co-developed research that support informed coastal management.
