Elsevier

Coastal Engineering

Volume 157, April 2020, 103652
Coastal Engineering

Near-reef and nearshore tropical cyclone wave climate in the Great Barrier Reef with and without reef structure

https://doi.org/10.1016/j.coastaleng.2020.103652Get rights and content

Highlights

  • Tropical Cyclone wave climate has been estimated for Great Barrier Reef.

  • Uncertainty testing indicates prediction accuracies are approximately 20%.

  • Great Barrier Reef reduces nearshore cyclonic waves by factor 1.5 to 2.

Abstract

The Great Barrier Reef (GBR) coral coverage is in rapid decline from severe and sustained pressures from lagoon water quality, crown-of-thorns starfish (COTS), coral bleaching, tropical cyclones, pollution and diseases. The two recent GBR coral bleaching events (2016–2017) lead to Great Barrier Reef Marine Park Authority (GBRMPA) shifting their focus from passive management to active intervention (Great Barrier Reef Blueprint for resilience by GBRMPA). These active interventions, potentially able to increase GBR resilience, as there are reefs that, due to their physical location relative to all other reefs, river and estuary entrances, ocean currents, have favourable coral growth conditions. To undertake such interventions, various information is required including tropical cyclone wave climates. This paper develops tropical cyclone wave climates for the entire GBR. These wave climates were developed by simulating several thousand synthetic cyclones derived from the “HadGEM” general circulation model with RCP8.5 climate change scenario. The synthetic cyclones adopted herein include the following climate changes assessed by comparing averages of key forcing parameters between 1950 to 1999 and 2050 to 2099. Their average arrival rate increases from 2.25 to 2.41 cyclones/year and their average maximum wind speed increases from 24 to 28 m/s. Their average radius to maximum winds remains constant at 51 km. Two key challenges were resolved, namely, long runtimes and large files (600 m grid increment covering 1800 km by 280 km). Runtimes were reduced by excluding cyclones where their wind speeds over the entire event never exceeded 10 m/s within GBR itself or within 100 km of the GBR over water. Maximum wave heights were compared with an extended fetch empirical expression, which was based on satellite data of tropical cyclones in open waters, when cyclones were outside the GBR lagoon. These comparisons indicate that predicted wave heights have a lower bias using default wave generation parameters when compared with the extended fetch empirical expression. Prediction uncertainty was estimated at no more than 10% from various cyclonic wind-field models. The existing GBR reefs reduce nearshore wave or runup height by between 1.5 and 2 times compared to the no reef case. The reduction in wave or runup height was found to be minimal for 1 m sea level rise. These two findings indicate that there is more flooding potential from coral removal than SLR within the GBR lagoon.

Introduction

As the name suggests, the Great Barrier Reef (GBR) offers protection from Pacific Ocean waves (Gallop et al., 2014; Jaffrés and Heron, 2011) where lagoon waves measured by satellite altimeter data are largely dependent on local wind speed. Similar conclusions were drawn in (Callaghan et al., 2015) when estimating non-cyclonic wave climates. Waves would break on the seaward reefs initially and then further dissipate from bed friction, coral drag or additional breaking across reefs from irregular bathymetry. Combining this barrier to Pacific Ocean waves with recent measurements of fringing reefs providing coastal protection (Beck et al., 2018; Ferrario et al., 2014), has led to suggestions that the GBR is providing ecosystem functions of reduced wave height to Queensland coastal regions through reducing the nearshore wave climate. If cyclonic wave attenuation from the GBR is significant at reducing the nearshore wave height within the GBR lagoon, then shoreline management needs to adjust if this attenuation reduces with sea level rise. One way wave attenuation may remain unchanged is if vertical growth of coral keeps up with sea level rise. However, coral coverage is in rapid decline from severe and sustained pressures in the GBR. This decline is attributed to lagoon water quality, crown-of-thorns starfish (COTS), coral bleaching, tropical cyclones, pollution and diseases (De'ath et al., 2012; Hughes et al., 2017; Hughes et al., 2019; Ortiz et al., 2018). It is expected that reductions in coral coverage may lead to water depths over reefs increasing, which would lead to a reduction in wave attenuation. The question is then, how these attenuated waves grow while propagating through the GBR lagoon (i.e., between offshore reefs forming this barrier and the coastline). The GBR lagoon varies in width from tens of kilometres to one hundred kilometres across. During cyclonic wave generation, the largest waves occur within a few radius of maximum winds (Young and Burchell, 1996; Young and Vinoth, 2013; Young, 2017) of the tropical cyclone. Hence, tropical cyclones that are smaller than the GBR lagoon width and slow moving can reach their maximum wave height. This would offset barrier reef wave attenuation.

The GBR, the largest and most diverse coral reef on earth, is a challenging region for wave climate analysis. It is made up of around three thousand reefs of various morphologies and sizes, located between a few kilometres to 280 km offshore of the Queensland coast while extending over 1960 km alongshore (Fig. 1). Previous studies have done a compelling analysis of the impacts of historical cyclones on reefs (Puotinen et al., 2016). Here, we consider the impacts of the reefs on attenuating wave height and wave runup across the entire Great Barrier Reef Marine Park. Of the identified reefs, ca two third have surface areas that are greater than 0.36 km2 (Fig. 2). To model these reefs, grid increments of the order a few hundred meters are required. This grid resolution would lead to very large computer memory usage and file storage requirements. Previous numerical modelling using a 2.5 km grid increment of the GBR by Young and Hardy (1993) obtained reasonable cyclonic wave predictions where wave height measurements were available (away from shallow areas). A similar numerical modelling outcome was achieved by Hardy et al. (2000) who used ca 1.5 km grid increment. These modelling efforts, combined with increases in computing capacity, indicate modelling cyclonic wave climate under climate change is potentially feasible by simulating several thousand synthetic cyclones derived from a general circulation model forced by particular climate change scenario. Emanuel et al. (2008) has, for example, developed several thousand synthetic cyclones for the GBR, which was successfully applied by Wolff et al. (2016) to characterize tropical cyclone clustering and its impact on coral cover within the GBR.

This article uses Emanuel et al. (2008) synthetic tropical cyclones for estimating wave climate across the entire GBR. Wave climates will be estimated for three geometrical states of the reef; 1) existing state (Fig. 1), 2) with 1 m uniform sea level rise throughout the GBR and 3) with the entire reef removed back to the continental shelf. The third case, while not remotely possible, provides the wave climate in the absence of wave height reductions from the GBR. These three cyclonic wave climates will provide a first order estimate of the shoreline protection provided by the reef under cyclonic conditions.

The article is arranged into two main sections. The first of these two major sections is section 2 which presents the modelling approach, its limitations and sensitivity testing. The second of these two major sections is section 3 which presents wave climates for three scenarios obtained using the modelling approach of section 2. These two sections are arranged in a self-contained manner for easy of reading. A summary, including a brief discussion is communicated in section 4. The particular contents of Section 2 are: model type, cyclonic winds, bathymetry, reef locations and sizes, methods to handle lengthy simulation periods and large storage requirements. Winds sensitivity testing is included by comparing wave climate predictions between different parametric wind models and atmospheric boundary layer approaches. Wave height generation is tested by comparing predictions with satellite-derived maximum cyclonic wave height. These tests providing a measure of prediction accuracy and uncertainty. The particular contents of Section 3 are: three cyclonic wave climates are presented and discussed for existing reef geometries for with and without sea level rise, and a bare sloping continental shelf (e.g., as if the reef never existed).

The three wave climates described above are available for download at https://doi.org/10.14264/uql.2019.169, in self describing netCDF and geographical information system shapefile format.

Section snippets

Wave-modelling approach

Sections 2.1 through to 2.5 presents the modelling approach, with sensitives of several modelling choices tested herein. The wave climate information being sort are near to but offshore of reefs through the GBR and for along the Queensland Coastline but within the GBR Lagoon. The first objective provides reef managers with the cyclonic wave climate for each reef. The second objective facilitate estimation of shoreline wave climate. In this article, we use changes in wave runup and Gourlay

Tropical cyclone wave climates

The statistical simulations are used to estimate exceedance statistics of all cyclones modelled and for particular return periods, assuming quasi-stationary cyclone arrival rate (that is, the averaged arrive rate over the period simulated). Exceedance statistics are determined by ranking maximum wave heights as x1...xi...xN, assigning percentiles of 100N(i12), and linear interpolating percentiles of interest. Exceedance levels are 100% less its percentile. A review of literature around

Summary

The tropical cyclonic wave climates have been estimated (presented herein and available online) using Emanuel et al. (2008) estimated synthetic tropical cyclones, Emanuel (2004) gradient winds and Kepert (2001) atmospheric boundary layer, the simulating waves nearshore wave model (Booij et al., 1999; Holthuijsen, 2007; Ris et al., 1999) with a 600 m grid increment. Seven statistical simulations where used to estimate prediction uncertainties, the ecosystem functions related to reducing wave or

CRediT authorship contribution statement

David P. Callaghan: Methodology, Software, Validation, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization, Data curation. Peter J. Mumby: Conceptualization, Formal analysis, Resources, Data curation, Writing - review & editing, Project administration, Funding acquisition. Matthew S. Mason: Methodology, Software, Validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Shoreline data was provided by Queensland Government, reef outlines was provided by the Great Barrier Reef Part Authority, Commonwealth of Australia. The high performance computing was supported by Queensland Cyber Infrastructure Foundation and The University of Queensland.

References (59)

  • H.F. Stockdon et al.

    Empirical parameterization of setup, swash, and runup

    Coast. Eng.

    (2006)
  • A. Tănase Zanopol et al.

    Coastal impact assessment of a generic wave farm operating in the Romanian nearshore

    Energy

    (2014)
  • L.D. Wright et al.

    Morphodynamic variability of surf zones and beaches: a synthesis

    Mar. Geol.

    (1984)
  • I.R. Young et al.

    Hurricane generated waves as observed by satellite

    Ocean Eng.

    (1996)
  • I.R. Young et al.

    An “extended fetch” model for the spatial distribution of tropical cyclone wind–waves as observed by altimeter

    Ocean Eng.

    (2013)
  • M. Zijlema et al.

    Bottom friction and wind drag for wave models

    Coast. Eng.

    (2012)
  • C. Amante et al.

    Etopo1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis

    (2009)
  • Anonymous

    Great Barrier Reef Features. Great Barrier Reef Marine Park Authority, Spatial Data Information Services

    (2019)
  • K.K. Arkema et al.

    Embedding ecosystem services in coastal planning leads to better outcomes for people and nature

    Proc. Natl. Acad. Sci. Unit. States Am.

    (2015)
  • R.J. Beaman

    Project 3dgbr: A High-Resolution Depth Model for the Great Barrier Reef and Coral Sea

    (2010)
  • R.J. Beaman

    100/30 M-Resolution Bathymetry Grids for the Great Barrier Reef, SSSI Hydrography Commission Seminar

    (2018)
  • R.J. Beaman

    Great barrier reef bathymetry

  • M.W. Beck et al.

    The global flood protection savings provided by coral reefs

    Nat. Commun.

    (2018)
  • N. Booij et al.

    A third-generation wave model for coastal regions 1. Model description and validation

    J. Geophys. Res.

    (1999)
  • D.P. Callaghan et al.

    Modelling queensland tides from the gold coast to cooktown

  • S. Coles

    An Introduction to Statistical Modelling of Extreme Values. Springer Series in Statistics

    (2001)
  • G. De’ath et al.

    The 27–year decline of coral cover on the great barrier reef and its causes

    Proc. Natl. Acad. Sci. Unit. States Am.

    (2012)
  • K. Emanuel et al.

    Self-stratification of tropical cyclone outflow. Part i: implications for storm structure

    J. Atmos. Sci.

    (2011)
  • K. Emanuel et al.

    Hurricanes and global warming: results from downscaling ipcc ar4 simulations

    Bull. Am. Meteorol. Soc.

    (2008)
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