Towards a Multi-scale Montecarlo Climate Emulator for Coastal Flooding and Long-Term Coastal Change Modeling: The Beautiful Problem
Ana Rueda1, Jose A.A. Antolinez1, Christie Hegermiller2, Katherine Serafin3, Dylan Lawrence Anderson4, Peter Ruggiero5, Patrick Barnard6, Li H Erikson7, Sean Vitousek8, Paula Camus9, Antonio Tomas9, Mauricio Gonzalez9 and Fernando J. Mendez10, (1)University of Cantabria, Ciencias y Tecnicas del Agua y del Medio Ambiente, Santander, Spain, (2)University of California Santa Cruz, Santa Cruz, CA, United States, (3)University of Florida, Department of Geography, Ft Walton Beach, FL, United States, (4)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Coastal and Hydraulics Laboratory, Corvallis, United States, (5)Oregon State University, Corvallis, United States, (6)USGS California Water Science Center San Diego, Santa Cruz, United States, (7)USGS Pacific Science Ctr, Santa Cruz, CA, United States, (8)Stanford University, Stanford, CA, United States, (9)Environmental Hydraulics Institute, Universidad de Cantabria, Santander, Spain, (10)Universidad de Cantabria, GeoOcean, Santander, Spain
Abstract:
Long-term coastal evolution and coastal flooding hazards are the result of the non-linear interaction of multiple oceanographic, hydrological, geological and meteorological forcings (e.g., astronomical tide, monthly mean sea level, large-scale storm surge, dynamic wave set-up, shoreline evolution, backshore erosion). Additionally, interannual variability and trends in storminess and sea level rise are climate drivers that must be considered. Moreover, the chronology of the hydraulic boundary conditions plays an important role since a collection of consecutive minor storm events can have more impact than the 100-yr return level event. Therefore, proper modeling of shoreline erosion, beach recovery and coastal flooding should consider the sequence of storms, the multivariate nature of the hydrodynamic forcings, and the different time scales of interest (seasonality, interannual and decadal variability).
To address this ‘beautiful problem’, we propose a hybrid approach that combines: (a) numerical hydrodynamic and morphodynamic models (SWAN for wave transformation, a shoreline change model, X-Beach for modeling infragravity waves and erosion of the backshore during extreme events and RFSM-EDA (Jamieson et al, 2012) for high resolution flooding of the coastal hinterland); (b) long-term data bases (observational and hindcast) of sea state parameters, astronomical tides and non-tidal residuals; and (c) statistical downscaling techniques, non-linear data mining, and extreme value models.
The statistical downscaling approaches for multivariate variables are based on circulation patterns (Espejo et al., 2014), the chronology of the circulation patterns (Guanche et al, 2013) and the event hydrographs of multivariate extremes, resulting in a time-dependent climate emulator of hydraulic boundary conditions for coupled simulations of the coastal change and flooding models.
References
Espejo et al (2014) Spectral ocean wave climate variability based on circulation patterns, J Phys Oc, doi: 10.1175/JPO-D-13-0276.1
Guanche et al (2013) Autoregressive logistic regression applied to atmospheric circulation patterns, Clim Dyn, doi: 10.1007/s00382-013-1690-3
Jamieson et al (2012) A highly efficient 2D flood model with sub-element topography, Proc. Of the Inst Civil Eng., 165(10), 581-595
