Wave Run-Up Forecast : West Maui

RegionsAboutDetailsAcknowledgements

Purpose of the Forecast

The PacIOOS Wave Run-Up Forecast is a tool to predict the potential occurrence of high sea levels and wave inundation impacting the shorelines of West Maui. The forecast was developed to increase community resilience and enhance preparedness by providing decision-makers, agency representatives, property owners, and community members with time to plan and respond in advance of potential flooding events. West Maui has experienced an increase in wave plus tide-driven flooding in recent years, leading to significant coastal erosion, damage to infrastructure and properties, and sediment pollution. The forecast is updated hourly (about 15 minutes after the hour). For a video introduction to the tool and its purpose, please check out the recording of an informational presentation.

West Maui wave run-up impacts

Left to right: Overwash impacts traffic at Honoapiʻilani Highway, July 2019 (Credit: Asa Ellison); Waves overtop hardened shoreline structures in a Wahikuli neighborhood, July 2020 (Credit: Don McLeish); Wave run-up causes erosion and sedimentation in Kahana, October 2018 (Credit: Don McLeish).

What is Wave Run-up and How to Read the Forecast?

Video tutorial on how to use the Wave Run-Up Forecast for West Maui.

Run-up is defined as the vertical reach of seawater onto the land at any given time (see red arrow in illustration below). When run-up levels are high, the risk for flooding and inundation increases. Due to West Maui's complex nearshore environment, a combination of factors can lead to impacts from wave run-up and inundation. Water level changes (including tides and other long-term variations), high winds, high swell waves, and infragravity waves (also commonly known as "surf beat") are all components that influence wave run-up. Additionally, highly variable bathymetry influences the magnitude of the wave run-up. The model that generates the PacIOOS Wave Run-Up Forecast takes all these factors into account and can therefore predict the variability along the shoreline.

While the forecast plot provides run-up heights as a vertical measure, the vertical values are not easily relatable to site conditions. What matters more is how run-up heights translate into horizontal overtopping and flooding (i.e., how far inland does the water travel). For that reason, photos of observed conditions are provided for each forecast under the "Run-up Examples" tab to make a connection between the vertical run-up heights and horizontal impacts. Users are asked to pay attention when the hatched cyan curve reaches into the red shaded area. Run-up thresholds are categorized into (a) Light Impacts, (b) Hazardous Impacts, and (c) Critical Impacts—potential impacts and damage associated with these categories can be viewed in the photo documentation from previous run-up events.

Wave run-up diagram

Schematic diagram illustrating the components contributing to the total wave run-up height. Run-up is the vertical reach of seawater onto the land at any given time.

Geographic Extent and Regions

The geographic scope of the West Maui Wave Run-up Forecast extends from Pāpalaua Wayside Park in the South to Lipoa Point in the North. To account for the variability along the shoreline, West Maui is divided into 12 regions based on similarities in wave dynamics, bathymetry, beach features and shapes, and infrastructure. Individual wave run-up forecasts were established for all 12 regions, informed by field observations and photographs.

Get Involved!

We continue to collect photo documentation to fine-tune the model (if necessary) and to identify the impacts of these run-up events along the West Maui shoreline. We are specifically interested in photos around peak tides or swells, or anytime waves overtop beach features. If possible, capture the maximum extent of water running up the shoreline. The contributions from you, our citizen scientists, greatly contribute to establishing accurate thresholds for the model. Mahalo for your support!

Please submit your photos at:

To view photo submissions, please check out:

Project Team

The PacIOOS Wave Run-Up Forecast was developed by co-investigator Professor Douglas Luther, Department of Oceanography at the University of Hawaiʻi School of Ocean and Earth Science and Technology, and his team: Dr. Martin Guiles, Dr. Assaf Azouri, and Camilla Tognacchini. Dr. Volker Roeber, Université de Pau et des Pays de l'Adour, is the developer of the BOSZ model and provided the model set-up for this project. Co-investigator Tara Owens, University of Hawaiʻi Sea Grant College Program (UH Sea Grant), serves as the local point of contact and engages with partners, stakeholders, and community members. Melissa Iwamoto, Director of PacIOOS, is the Principal Investigator of this project and responsible for the overall coordination and implementation. PacIOOS staff Fiona Langenberger, John Maurer, Ning Li, and Chip Young provide project support for outreach, data management, modeling, and field operations.

West Maui project team members

Left to right: Project team members visit West Maui to assess different sites along the shoreline; Divers deploy pressure sensors to help improve and validate the wave run-up model; Community workshops were held in Lahaina and Kīheī to provide a project overview and recruit citizen scientists.

Funding

The development of the West Maui Wave Run-Up Forecast was funded by NOAA's Regional Coastal Resilience Grants Program. Additional funding was provided by the Joint Institute for Marine and Atmospheric Research (JIMAR), UH Sea Grant, and PacIOOS to collect field data for model validation. UH Sea Grant also assisted with funding for our graduate student.

NOAA logo   PacIOOS logo    JIMAR logo    UH Sea Grant logo

Disclaimer

The PacIOOS Wave Run-Up Forecast does not serve as a warning system. In case of a possible inundation event, please consult with local authorities and emergency responders to seek further information and direction. The forecast is not accurate when a tsunami, tropical storm or cyclone watch/warning is in effect. For these events, please seek information for either tsunamis or tropical storms/hurricanes.

Contact

Please contact us at info@pacioos.org if you have any questions.

Disclaimer
A. Overview
B. What Influences Wave Run-up (Overwash, Flooding) Along West Maui?
B.1. What is Wave Run-up?
B.2. Sea and Swell Products: Swash, Setup, and Infragravity Waves
B.3. Model Validation
B.4. Tides, Long-Period Water Level Anomalies, and Secular Sea Level Change
C. The Run-up Metric
References

Disclaimer

Validated numerical models are used to provide a scientific prediction of existing and future conditions. As with any forecast, however, accuracy cannot be guaranteed and caution is advised. While considerable effort has been made to implement all forecast components in a thorough and accurate manner, various sources of error are possible at different times. The forecasts are provided free of charge without warranty of any kind.

A. Overview

Despite the shelter provided by Molokaʻi and Lānaʻi Islands, the coastline of West Maui is exposed year-round to ocean surface waves that are increasingly reshaping beaches, eroding vulnerable shores, inundating and damaging nearshore property and infrastructure, and endangering the safety of residents and tourists alike. The distinct bathymetry in the channels west of Maui is responsible for redirecting a significant portion of the wave energy coming from the north in winter and the south in summer directly towards West Maui's shores (see Figure 1). The negative impacts of these waves are also being exacerbated by the inexorable rise of sea level (SL). Wave run-up, the height up the shore that waves can reach, has been found to be a good indicator of the hazardous impacts of wave events in general. The specific relationship between run-up and both flooding and erosion potential will depend on the natural and man-made structural features of each stretch of coast. The physical basis for forecasting wave run-up along West Maui is described here.

Figure 1

Figure 1. (Click on image for larger view.) Gravity wave swells from both the north and south refract and focus energy into the entire West Maui region. These plots show significant gravity wave heights computed by the Boussinesq Ocean & Surf Zone (BOSZ) computer model described later in the text. The left panel shows waves propagating into the Maui region from the south, and the right panel shows them coming from the north. The irregular depths (bathymetry) of the seafloor cause wave energy from either direction to turn and focus onto West Maui's shores in a strongly spatially-dependent manner.

Wave Run-up Forecasts up to six days in advance are provided for guidance to increase community capacity to prepare for, and recover from, wave-driven impacts such as coastal flooding and erosion. The Wave Run-up Forecasts are presented graphically for twelve distinct shoreline regions along West Maui from Honolua Bay in the north to Pāpalaua Wayside Park. The geomorphological and infrastructure affinities of each region are described in each region's "Map" tab. The manner in which these run-up forecasts are created is novel and distinguishes them from other run-up forecasting efforts. Three significant aspects of the methodology are as follows:

  1. Surface gravity waves (usually called sea and swell) approaching West Maui are directly simulated in real-time through a sequence of computer models that begin with forecasts of global surface winds.
  2. All the significant ocean phenomena that contribute to run-up, and that are generated by the interaction of the sea and swell waves with each other, with the seafloor and with the shoreline, are explicitly simulated by a computer model. The model incorporates the spatial variability of the coast and seafloor, as well as the alongshore and offshore complexity of these phenomena. These additional phenomena include the following:
    1. a "setup", or rise, of SL at the shore due to the continual pushing of water towards the shore by the sea and swell;
    2. gravity waves at periods of a half minute to tens of minutes, frequently interpreted as "surges" of water at the shoreline, and often called infragravity waves; and,
    3. the reduced amplitudes at the shoreline of the sea and swell waves, producing what is called "swash", that result after the waves shed much of their energy to setup, infragravity waves, and turbulent dissipation (for example, due to overturning and bore formation) as they propagate towards the shore.
  3. Variations of SL that are not generated by sea and swell are explicitly estimated, including the following:
    1. an accurate model of the local tides; and,
    2. a mathematical projection of longer-period (half day and longer) SL variability up to six days into the future, such as is produced by sea breezes, open ocean currents, El Niño, etc.

The hydrodynamic computer model at the heart of the run-up forecasts was incorporated into this project with a grant from NOAA's Coastal Resilience Grants Program, with additional support from the Pacific Islands Ocean Observing System (PacIOOS) and the Joint Institute for Marine and Atmospheric Research (JIMAR), both in the University of Hawaiʻi School of Ocean and Earth Science and Technology (SOEST). Acquisition of SL and water current observations along West Maui, required for validating the numerical models, was funded by the University of Hawaiʻi Sea Grant College Program (Hawaiʻi Sea Grant), PacIOOS and JIMAR. Visualization and presentation of the forecasts was developed under PacIOOS support. Maintenance of the forecasts, including ongoing calibration of the run-up forecast with citizen scientist observations, will be accomplished under PacIOOS funding.

Back to Top

B. What Influences Wave Run-up (Overwash, Flooding) Along West Maui?

B.1. What is Wave Run-up?

Run-up is defined as the vertical reach of seawater onto the land at any given time. As illustrated in Figure 2, half a dozen different phenomena contribute to the total run-up height at a given shore. Long period SL anomalies, such as the annual cycle and anomalies associated with El Niño that last for months, are produced by slowly-varying oceanic processes that can raise (or lower) the water level. Tidal variability is also relatively slow compared with the wave-driven processes that produce changes in SL on much shorter time scales. As the sea and swell (hereafter, simply SS) waves shoal, they may or may not overturn or break, becoming bores, but will always continue toward the shore, producing a swash of water up the beach every few to tens of seconds. Both unbroken waves and bores push water toward the shore such that right at the shore the mean water level rises (measured over periods of hours), a process known as setup. As long as the wave trains persist, the nearshore setup is maintained. Infragravity waves (hereafter, simply IG waves) are long period waves with periods ranging from half a minute to tens of minutes and even hours (another common term of IG waves is "surf beat"). They can be thought of as producing surges of water onto the shore, as will be shown below. The most energetic IG waves are generated near the shore from a variety of complex interactions among the SS waves themselves and between the SS waves and the variable topography of the seafloor and geometry of the coast. Added to the total of all of these phenomena is the monotonic SL rise caused by global climate change.

Figure 2

Figure 2. (Click on image for larger view.) Schematic diagram illustrating the components contributing to the total wave run-up height, including their not uncommon amplitudes (bottom panel). Run-up is the vertical reach of seawater onto the land at any given time. Numerical values of run-up must be referenced to a particular calibrated height to make sense. In this document, the reference level is Mean Lower Low Water (MLLW, a typical NOAA datum).

Back to Top

B.2. Sea and Swell Products: Swash, Setup, and Infragravity Waves

It all starts with SS waves approaching the Hawaiian Islands from all directions, as is shown in Figure 3, although usually not all at once. As the waves travel from deep to shallow water, at some point they will start to interact with the ocean's bottom and be altered by the variations in water depth. This will change the direction and amplitude of the waves, a process called refraction that orients them so they propagate increasingly towards shallower depths. For a given stretch of reef, for instance, refraction results in focusing wave energy towards the shallower parts of the reef, often resulting in the best locations for surf riding. For West Maui, refraction means that high amplitude waves arriving from dramatically different directions can reach the shore, which explains the filaments of wave energy directed towards West Maui's shores in Figure 1 where SS waves come from either the north or south. Such strong modification of incoming wave trains, such as seen in Figure 1, demands computer simulations with very good horizontal resolution in order to produce accurate forecasts of SS waves and their products that contribute to run-up by differing amounts all along West Maui.

Figure 3

Figure 3. (Click on image for larger view.) Sea and Swell wave direction windows for West Maui (located at the center of the map).

Daily simulations from a series of numerical computer models, starting with global- and regional-scale simulations of surface winds and waves, followed by simulations of local-scale wave propagation and transformation, are being used to determine the wave field that reaches the West Maui coastline. These models are as follows:

  1. Global Forecast System (GFS; Environmental Modeling Center, 2003),
  2. Hawaiʻi Regional Weather Research & Forecasting (WRF; Skamarock et al., 2008),
  3. Global WaveWatch III (WW3; Tolman, 2014),
  4. Hawaiʻi Regional WaveWatch III (WW3; Tolman, 2014),
  5. Maui Nui Regional Simulating WAves Nearshore (SWAN; Booij et al., 1999),
  6. Inter-island Channels SWAN, and,
  7. West Maui Coastal Boussinesq Ocean & Surf Zone (BOSZ; Roeber and Cheung, 2012).

GFS and WRF are atmospheric models from which surface winds are output to provide forcing for the remaining three wave models. The modeling hierarchy, direction of information exchange, and grid resolutions, are shown in Figure 4.

Figure 4

Figure 4. (Click on image for larger view.) Hierarchy of the wind and wave numerical models that are used for creating the West Maui Run-up Forecasts. The direction of data flow among the models is indicated with the arrows. The model names, scale of computational domains, spatial grid resolutions, and the environmental variable plotted in color are shown next to each panel. These particular examples show wind or wave amplitudes from each model for January 9, 2019, 17:00 HST. Note that the color scales differ from model to model.

The swash and setup contributions to the total run-up are simulated in BOSZ, the last numerical model in the modeling sequence. BOSZ also tracks individual waves and properly resolves nonlinear processes such as interactions among pairs of waves. These kinds of interactions are an important source of the IG waves.

In addition to their role in wave-driven run-up, IG waves play significant roles in other coastal processes, such as rip currents, sediment transport, sand-bar formation, and harbor seiches. A good visual example of SS and IG waves combining to generate run-up is provided in the video in Figure 5 from Kaiaka Bay, Oʻahu, during a large northwest swell event on January 13, 2018. The short period SS waves can be seen breaking offshore, as well as many bores of different amplitudes that propagate and dissipate over the shallow part of the reef. Paying attention to the time stamp on the left (which advances by 10 sec steps), note the water surging over the beach at time 2:20 (min:sec) and then again at time 7:40. This is caused by an IG wave with a period of ~5.5 min. The surge episodes correspond to successive crests of that IG wave.

Figure 5. Time-lapse video during a large northwest swell at Kaiaka Bay, Oʻahu, on January 13, 2018. The short period SS waves can be seen breaking offshore, as well as various bores of different amplitudes that propagate and dissipate over the shallow part of the reef. Referring to the time stamp to the left, the water surging over the beach rocks at 2:20 (min:sec) and then again at 7:40 is caused by an IG wave with a period of ~5.5 min. The surge episodes correspond to crests of that IG wave.

Figure 6 provides a more quantitative example of swash and surge amplitudes. Shown are time series (that is, time evolution) plots of observed SL at a site from the Kahana, Maui, forecast region, located 100 m from shore at ~2 m water depth. The record mean and tidal signal were previously removed from the data to help visualize the shorter period waves. Panel (a) shows a 3-day-long data segment from November 25-28, 2018, having moderate SS events. During the two largest events the SL reaches a maximum crest-to-trough range of ~0.7 m. Panel (b) is a 1-hour data segment (see shaded blue region in panel (a)) during the SS event from November 26. The cyan curve is a low-pass filtered version of that segment; it reveals the oscillations of IG waves that have periods in the 7-10 min range. Note that these IG oscillations nearly reach a crest-to-trough range of 0.2 m. In panel (c), a 3-minute data segment from panel (b) is shown (see shaded red region in panel (b)), highlighting the variability at SS periods ranging from 5 sec to about 20 sec. The crest-to-trough range of these oscillations exceeds 0.4 m.

Figure 6

Figure 6. (Click on image for larger view.) Time series of the de-tided and de-meaned SL at a nearshore site in the Kahana forecast region, located 100 m from shore at 2 m depth. (a) 3-day-long data segment from November 25-28, 2018, showing moderate SS events where the SL reaches a maximum crest-to-trough range of ~0.7 m; (b) 1-hour data segment from panel (a), during a SS event from November 26; the cyan curve is a low-pass filtered version of that segment which reveals oscillations of IG waves with periods in the 7-10 min range, and amplitudes that nearly reach a crest-to-trough range of 0.2 m; (c) 3-minute data segment from panel (b), highlighting the variability at SS periods ranging from 5 sec to about 20 sec, and crest-to-trough range exceeding 0.4 m.

The BOSZ simulations are applied to two overlapping rectangular domains that are aligned along the coastline to cover West Maui's coastal region of interest for this run-up forecast (see the red rectangles in the BOSZ map in Figure 4). Computations for the operational run-up forecasts are made over a grid resolution of 8 m (cross-shore) by 12 m (alongshore). A given BOSZ simulation requires input near the offshore boundary, which is provided by the SWAN simulations (200 m x 200 m grid resolution) at 42 equally-separated (1 km) locations along the 40 m depth contour (see the black circles in the BOSZ map in Figure 4). Another input that BOSZ requires is the tide plus long-period sea level height anomalies, which are provided by a separate forecast of SL at Lahaina, described in section B.4 below.

Back to Top

B.3. Model Validation

The notion of sequencing seven global-to-local-scale numerical models (Figure 4) in order to accurately simulate the variability of SS waves and their interaction products along the West Maui coast or any coast would seem to strain credulity. Consequently, the current modeling effort coincides with an observational program that began in October, 2018, and continues as of this writing. Observations of SL variations and currents are being made all along West Maui for the purpose of checking the accuracy of the SL and current simulations produced by BOSZ, the last model in the sequence.

Two useful metrics of whether a wave model like BOSZ is simulating the real ocean well are comparisons of observations and model outputs using auto-spectra and horizontal coherence functions. If the model is producing the correct levels of energy for each of the types of wave motions created by the SS near the shore, then the modeled and observed auto-spectra of SL and currents should be quite similar in amplitude and structure as a function of wave frequency. If the model is producing the correct mix of different horizontal structures for the types of wave motions created by the SS near the shore, then the horizontal coherence amplitudes and phases (alongshore and cross-shore) within the observed SL and currents and within the model output SL and currents should be quite similar as a function of wave frequency.

Figure 7 shows a comparison of modeled and observed SL auto-spectra during a significant SS event on January 9, 2019 (13:00 - 15:00 HST) at two locations offshore of Kahana in 3 m of water. The northern site, KH08, is just at the edge of the wave breaking zone, while KH11 is well inside the breaker zone. The dominant period of the SS at this time was about 17 sec. A peak at this period is clear in the KH08 spectrum, but by the time these waves propagate closer to shore, at KH11, their energy has been quite diminished by turbulent dissipation and nonlinear energy transfers to IG waves. It can be seen in Figure 7 that the model is able to simulate the various wave energies quite well at both sites.

Figure 7

Figure 7. (Click on image for larger view.) Modeled and observed SL auto-spectra are compared for a significant SS event on January 9, 2019 (13:00 - 15:00 HST). The two locations are offshore of the Kahana region in 3 m of water. Plot (a) is for site KH08, at the edge of the wave breaking zone just outside the nominal reef edge, while plot (b) is for site KH11, well inside the breaking zone. The tides and other long-period oscillations were filtered out before calculating the spectra in order to clearly visualize the IG wave energy levels at periods of 30 sec to 10 min. The BOSZ model used for this comparison had horizontal resolution of 5 m by 5 m. The disagreement in the spectral levels at periods shorter than 8 seconds is a result of the necessary trimming (in order to improve computational speed) of the wave directional spectra used to force the BOSZ model. These very short period waves have little impact on wave run-up in any case. The 95% confidence intervals for independent spectral estimates are shown at the bottom of the figure. The confidence intervals apply to each independent point. Every other point plotted is independent.

Figure 8 shows examples of alongshore and cross-shore coherence functions for both observed and modeled SL for another significant SS event on November 26, 2018, 18:00 - 22:00 HST. The three locations incorporated in these coherences are offshore of Kahana. The coherence amplitude indicates the degree of similarity of the oscillations at a given frequency, and the coherence phase contains information about wave structure and propagation tendency. In general, the variation in coherence amplitude from one frequency to the next is due to the different structures of the SS or IG waves at each frequency over the 200-300 m distances. The observed and modeled coherences for each pair of stations are remarkably similar, indicating that the model is correctly simulating the SS waves propagating toward shore as well as the structures of the IG waves.

Figure 8

Figure 8. (Click on image for larger view.) Coherence functions—amplitude and phase versus frequency—for observed and modeled SL offshore of the Kahana region for a significant wave event on November 26, 2018, 18:00 - 22:00 HST. The coherence amplitude indicates the degree of similarity of the oscillations at a given frequency in a pair of time series, and the phase contains information about the wave structure and propagation tendency between the two sites. The upper two frames show coherence amplitude and phase between sites KH08 and KH09 oriented roughly cross-shore and 200 m apart, and the lower two frames show coherence amplitude and phase between sites KH08 and KH02 oriented diagonally cross-shore and 300 m apart. In general, the variation in coherence amplitude from one frequency to the next is due to the different structures (over the 200 - 300 m distances) of IG or SS waves at the different frequencies. The 95% level of no significance is indicated by the red line for the coherence amplitudes. Every other point plotted is independent.

Back to Top

B.4. Tides, Long-Period Water Level Anomalies, and Secular Sea Level Change

In the preceding sections, we described wave-driven changes in SL due to short-period SS waves, and their corresponding longer period products (IG waves and setup), that occur on relatively short time scales ranging from tens of seconds up to several hours. These wave-driven SL changes are "riding" on top of the oceanic tides, as well as other slowly-varying phenomena that contribute to SL (the latter will be referred to as SL anomalies). To account for the contribution of tides, the tidal analysis methodology of Guiles et al. (2012), was applied to historical multi-year 2-min SL data from a tide gauge located in Lahaina Harbor. Three 18-month-long data segments from the years 2006 through 2008 were employed to estimate 105 tidal constituents. Currently, this Lahaina tidal analysis is used as a proxy for the tides along the entire West Maui coastline. Doing so results in small errors in the times and heights of high and low tides towards the ends of the West Maui region (Honolua Bay in the north and Pāpalaua Wayside Park in the south, which are roughly equidistant from Lahaina). In the next version of the forecasts, an improved tide model based on analyses of SL data currently being collected along the length of West Maui will be incorporated.

Sea level anomalies in the Hawaiʻi region can result from a variety of sources with variabilities ranging from days to years, such as: daily land/sea-breezes, the annual cycle of winds, atmospheric pressure, and ocean temperatures, salinities and circulation around Hawaiʻi (e.g., 0.3 ft SL range at Kahului, Maui; see the Average Seasonal Cycle tab at this link); and, multi-month circulation changes resulting from such phenomena as the El Niño (1.0+ ft SL ranges). An example of a high SL anomaly due to El Niño (Long et al., 2020) that persisted for several months in 2017 is shown in Figure 9. This figure displays a large discrepancy between the predicted (blue curve) and observed and verified water levels (green curve) at Kahului Harbor, Maui, in August 2017; the anomaly lasted for several months. Note that the observed SL reaches nearly a foot higher than the prediction in the middle of the month.

Figure 9

Figure 9. (Click on image for larger view.) Sea level anomaly recorded by the NOAA tide gauge at Kahului Harbor, Maui, during August, 2017. Note how the observed water level (green curve) is much higher than the predicted tides (blue curve), exceeding the predictions by nearly a foot around mid-August. This anomaly was caused by a change in the ocean circulation in the central Pacific that was caused by El Niño and lasted many months. Plot taken from NOAA Tides and Currents.

In order to include appropriate long-period SL anomalies in the run-up forecast, the long-period component of the SL forecast for Kahului Harbor (Guiles et al., 2012) is used as a proxy for the long-period variability along West Maui. In this run-up forecast, that component is updated once every hour. A coherence analysis between historical SL records from Lahaina and Kahului confirmed the reasonableness of this approach, although it is not perfect. To improve the estimate of long-period anomalies along West Maui in the future, the establishment of a real-time SL gauge at Lahaina Harbor is being planned. The local-scale phenomena of land/sea-breeze is accounted for in our modeling through the coupling of the local-scale wind (WRF) and wave (SWAN) models.

According to SL rise projections from the 5th Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC; Pachauri et al., 2014), assuming that future greenhouse gas emissions continue to increase at their current rate (the "business as usual" scenario; RCP8.5), the Global Mean Sea Level (GMSL) could rise by 2-3 ft (up to 1 m) by the year 2100 (see Figure 10). Recent observations and projections suggest that under more extreme scenarios this level of GMSL rise could be reached much earlier, as early as the 2060s (Sweet et al., 2017). As part of the work here, a database of run-up statistics is being developed using numerous wave amplitude and propagation direction scenarios for West Maui as input to the modeling methodology described above. This database will be created for four mean sea level scenarios: no change; 0.3 m (1 ft) rise; 0.6 m (2 ft) rise; and, 1.0 m (3.3 ft) rise.

Figure 10

Figure 10. (Click on image for larger view.) Projected Global Mean Sea Level rise under different greenhouse gas emissions scenarios from the IPCC AR5 Report (IPCC).

Back to Top

C. The Run-up Metric

For each run-up forecast that is needed for a particular day, the BOSZ model is executed to create and store 60 minutes of output in each of two domains (see the red rectangles in the BOSZ map in Figure 4). The stored output includes time series of the free water surface, relative to the initial sea level (hereafter, called free surface), and horizontal velocity components extracted at nearshore locations (depths shallower than ~1.5 m) not fronted by rocky bluffs and separated by at least 100 m alongshore (hereafter, called flux sites).

The data series from the flux sites are processed to create what is called a 2% run-up exceedance (R2%) metric in 12 separate West Maui regions (Figure 11). There are many flux sites in each of the 12 regions. The 2% run-up exceedance is the run-up height that will be exceeded by 2% of the individual bores running up the shore in any given time interval. Previous studies have established approximate empirical relationships between R2% and the summation of estimates of the background sea level (tides and long-period SL anomalies) and the wave-driven components (setup, IG, and swash) in several coastal settings (e.g., Stockdon et al., 2006; Merrifield et al., 2014). The novelty of the modeling approach presented here is in the direct numerical simulation of the physical processes that are implicit in the approximate empirical methods.

Figure 11

Figure 11. (Click on image for larger view.) Map of the 12 coastal regions for which a run-up forecast is provided.

The run-up calculation proceeds by first using the free surface and water velocity variables to determine the incoming (shoreward propagating) energy flux, F+(hlp), following Sheremet et al. (2002):

equation 1

where Sηη(f) and Suu(f) are the auto-spectra of the free surface and cross-shore velocity component, respectively; Sηu(f) is the cross-spectrum between the free surface and cross-shore velocity component, and f is frequency. The indicated integration of the sum of these three terms (in brackets), over the period range 1 ≤ T ≤ 30 sec, determines the contribution of SS waves to the energy flux. The somewhat longer-period IG waves and setup enter the calculation through their alteration of the total water depth, hlp, which is a low-pass filtered version of the full time-varying water depth at a given flux site; the filter eliminates variations in water depth due to the short-period SS. R2% is then calculated as follows:

equation 2

where SLLah is a constant SL value obtained from the Lahaina SL forecast (described previously) for the appropriate day and time of the SS forcing of the BOSZ model. SLLah includes the background SL contributions from tides, long-period SL anomalies, and secular SL rise. For a given forecast region, [F+(hlp)]|2% is the 98th percentile of a statistical distribution of F+(hlp) fluxes; that is, the value separating the largest 2% of energy flux values from the lower 98%. To obtain the statistical distribution of F+(hlp) in a given forecast region, F+(hlp) is calculated over 2-min-long segments from each 60-min-long data series from all the flux sites in that region. The 100 m or more separation of the flux sites was found to ensure statistical independence of the F+(hlp) variable.

Finally, C is a calibration coefficient that accounts for the regional variations of the shore structure (for example, slope, rugosity, porosity, and composition of the natural shore, as well as man-made infrastructure). A given energy flux can result in quite different wave run-ups for different coastal environments. The determination of C for each region is achieved by calibrating the calculated R2% heights with actual field observations of run-up by a cadre of citizen scientists armed with cameras, in conjunction with known elevation benchmarks. The 12 regions delineated along West Maui were determined after considering many natural and anthropogenic affinity categories. A summary of the most important affinity characteristics for each region can be found under the "Map" tab on each region's web page.

In addition to affecting the numerical run-up height that can occur for a given energy flux value, the coastal structure dictates the inherent impact of the wave run-up. A gently sloping beach backed by low-lying land with infrastructure such as a road near the water's edge (for example, the Ukumehame region) may experience a significant hazard from a relatively small run-up, whereas the same run-up at a steeply sloping beach backed by natural bluffs (for example, the Honolua region) would likely be completely unremarkable. These differences necessitate a second type of calibration for the run-up forecasts whereby the citizen scientist observations are used to determine at which run-up heights particular impacts will occur in each region. Three impact levels have been defined for each region: (a) light impact; (b) hazardous impact; and, (c) critical impact. Each region's impact definitions are provided under its "Run-up Examples" tab. The letter code (a, b, and c) appear on the forecasts at the appropriate numerical run-up values determined by the field observations.

Back to Top

References

  • Booij, N. R. R. C., Ris, R. C., & Holthuijsen, L. H. (1999). A third-generation wave model for coastal regions: 1. Model description and validation. Journal of Geophysical Research: Oceans, 104(C4), 7649-7666.
  • Environmental Modeling Center (2003). The GFS Atmospheric Model. NOAA/NCEP/Environmental Modeling Center Office Note 442, 14 pp. Available online.
  • Guiles M., Luther, D.S., Merrifield, M.A. (2012). PacIOOS - Sea Level Information Products; Part 1 - Sea Level Forecasts at Harbors in Hawaiʻi and on Other Pacific Islands. SOEST Technical Report #12-02; July, 2012; 72 pp.
  • Long, X., Widlansky, M. J., Schloesser, F., Thompson, P. R., Annamalai, H., Merrifield, M. A., & Yoon, H. (2020). Higher sea levels at Hawaii caused by strong El Niño and weak trade winds. Journal of Climate, 33(8), 3037-3059.
  • Merrifield, M. A., Becker, J. M., Ford, M., & Yao, Y. (2014). Observations and estimates of wave-driven water level extremes at the Marshall Islands. Geophysical Research Letters, 41(20), 7245-7253.
  • Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., ... & Dubash, N. K. (2014). Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, 151 pp.
  • Roeber, V., & Cheung, K. F. (2012). Boussinesq-type model for energetic breaking waves in fringing reef environments. Coastal Engineering, 70, 1-20.
  • Sheremet, A., Guza, R. T., Elgar, S., & Herbers, T. H. C. (2002). Observations of nearshore infragravity waves: Seaward and shoreward propagating components. Journal of Geophysical Research: Oceans, 107(C8), 10-1.
  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G Duda, X.-Y. Huang, W. Wang, and J. G. Powers (2008). A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp.
  • Stockdon, H. F., Holman, R. A., Howd, P. A., & Sallenger Jr, A. H. (2006). Empirical parameterization of setup, swash, and runup. Coastal Engineering, 53(7), 573-588.
  • Sweet, W. V., Kopp, R. E., Weaver, C. P., Obeysekera, J., Horton, R. M., Thieler, E. R., & Zervas, C. (2017). Global and regional sea level rise scenarios for the United States. NOAA Technical Report NOS CO-OPS 083.
  • Tolman, H. L. (2014). User manual and system documentation of WAVEWATCH III TM version 4.18. Technical note, MMAB Contribution, 316, 311.

Back to Top

The development of this forecast would not have been possible without the generous support from the West Maui community. We would like to thank our local partners for their continued in-kind support for this project, including accommodations at Sands of Kahana from Soleil Management (Wayne Cober and Gary Mano); boat support for instrument deployments from Ultimate Whale Watch (Peter and Toni Colombo, Lee James and Captain Amy Venema); field work staging support and accommodations from Donna Brown, Marine Options Coordinator and Lecturer, University of Hawaiʻi Maui College; and diving support from University of Hawaiʻi Maui College student Caroline Sabharwal.

In order to ground-truth the wave run-up model and to better understand thresholds and associated impacts, detailed photo documentation is needed for all 12 regions. Many thanks to our volunteer citizen scientists who greatly contribute to the development of the forecast by submitting photos of impacts along the shoreline. In addition to the valuable photo database collected by citizen scientists of the Hawaiʻi and Pacific Islands King Tides Project, listed below are our dedicated volunteers (ordered alphabetically) who are donating their time and skills to provide us with critical photo documentation of wave run-up events along the West Maui coastline. We greatly appreciate your commitment to this project and are grateful for your flexibility to respond to last minute requests. Mahalo!

  • Lisa Agdeppa
  • Paul Alcoseba
  • Chris Brosius
  • Tracey Cannon
  • Ikaika Cosma
  • Kevin Dale
  • Ingrid Eichenbaum
  • Joseph Eichenbaum
  • Asa Ellison
  • Yvonne Fisher
  • Gene Gay
  • Dawn Hegger-Nordblom
  • Brenda Jarmakani
  • Pat Lindquist
  • Dane Maxwell
  • Don McLeish
  • Tara Owens
  • Shawn Racoma
  • Chris Ratchford
  • Holly Rindge
  • Louise Rockett
  • Norm Runyan
  • Terry Schroeder
  • John Seebart
  • Heidi Sherman
  • Ananda Stone
  • Tano Taitano
  • Penny Wakida
  • Don Whitebread
  • Hyang Yoon

Valuable photo documentation of events was also provided by web cams hosted by the following properties (ordered from north to south):

  • Nāpili Kai Beach Resort
  • Nāpili Sunset Beachfront Resort
  • Honokeana Cove Condos
  • Kahana Village
  • The Royal Kahana Maui
  • Hale Mahina Sea Shell Condo, A305
  • Maui Sands
  • Papakea Resort
  • Maui Kai
  • Westin Nanea
  • Westin Kāʻanapali South
  • Maui Eldorado
  • Sheraton Maui Resort & Spa
  • Westin Maui Resort & Spa Kāʻanapali

We would also like to thank staff from the following local, state, and federal agencies and organizations who provided valuable feedback and input during the development of the West Maui Run-up Forecast (ordered alphabetically):

  • County of Maui
    • Emergency Management Agency
    • Mayor's Office of Economic Development
    • Parks and Recreation
    • Planning Department
    • Public Works
  • National Oceanic and Atmospheric Administration (NOAA)
    • National Weather Service
    • Office for Coastal Management
  • Pacific Islands Climate Adaptation Science Center
  • State of Hawaiʻi Department of Land and Natural Resources, Office of Conservation and Coastal Lands
  • University of Hawaiʻi at Mānoa, School of Ocean and Earth Science and Technology
    • Department of Earth Sciences
    • Department of Oceanography
    • Sea Level Center
    • University of Hawaiʻi Sea Grant College Program
  • U.S. Army Corps of Engineers, Honolulu District
  • West Maui Ridge to Reef Project