Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction
Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their c...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-05-01
|
| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fmars.2020.00361/full |
| _version_ | 1818033199483191296 |
|---|---|
| author | Fred Scott Fred Scott Jose A. A. Antolinez Robert McCall Curt Storlazzi Ad Reniers Stuart Pearson Stuart Pearson |
| author_facet | Fred Scott Fred Scott Jose A. A. Antolinez Robert McCall Curt Storlazzi Ad Reniers Stuart Pearson Stuart Pearson |
| author_sort | Fred Scott |
| collection | DOAJ |
| description | Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system. |
| first_indexed | 2024-12-10T06:19:28Z |
| format | Article |
| id | doaj.art-516f005c56dc4958912af6b879ec9b5a |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-12-10T06:19:28Z |
| publishDate | 2020-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-516f005c56dc4958912af6b879ec9b5a2022-12-22T01:59:22ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452020-05-01710.3389/fmars.2020.00361531672Hydro-Morphological Characterization of Coral Reefs for Wave Runup PredictionFred Scott0Fred Scott1Jose A. A. Antolinez2Robert McCall3Curt Storlazzi4Ad Reniers5Stuart Pearson6Stuart Pearson7Unit Marine and Coastal Systems, Deltares, Delft, NetherlandsW.F. Baird & Associates, Oakville, ON, CanadaUnit Marine and Coastal Systems, Deltares, Delft, NetherlandsUnit Marine and Coastal Systems, Deltares, Delft, NetherlandsU.S. Geological Survey, Santa Cruz, CA, United StatesDepartment of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, NetherlandsDepartment of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, NetherlandsUnit Marine and Coastal Systems, Deltares, Delft, NetherlandsMany coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.https://www.frontiersin.org/article/10.3389/fmars.2020.00361/fulldata miningcluster analysisK-meanscoral reefswave runupXBeach |
| spellingShingle | Fred Scott Fred Scott Jose A. A. Antolinez Robert McCall Curt Storlazzi Ad Reniers Stuart Pearson Stuart Pearson Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction Frontiers in Marine Science data mining cluster analysis K-means coral reefs wave runup XBeach |
| title | Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction |
| title_full | Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction |
| title_fullStr | Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction |
| title_full_unstemmed | Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction |
| title_short | Hydro-Morphological Characterization of Coral Reefs for Wave Runup Prediction |
| title_sort | hydro morphological characterization of coral reefs for wave runup prediction |
| topic | data mining cluster analysis K-means coral reefs wave runup XBeach |
| url | https://www.frontiersin.org/article/10.3389/fmars.2020.00361/full |
| work_keys_str_mv | AT fredscott hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction AT fredscott hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction AT joseaaantolinez hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction AT robertmccall hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction AT curtstorlazzi hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction AT adreniers hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction AT stuartpearson hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction AT stuartpearson hydromorphologicalcharacterizationofcoralreefsforwaverunupprediction |