Climate Viewer Documentation Page

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Sea Level Scenarios

Rising seas in Hawaiʻi: Present and Long-Term Impacts

Marine flooding from rising seas is a pressing global issue, and its impacts are particularly pronounced in Hawaiʻi, where the delicate balance between land and sea is threatened by the rising waters. This phenomenon is primarily driven by a changing climate, specifically the melting of polar ice caps and the expansion of seawater as it warms. In the case of Hawaiʻi, a state composed of islands, the implications of marine flooding from rising seas are profound, affecting not only the natural environment but also the socio-economic landscape of the region.

The National Aeronautical and Space Administration (NASA), as a prominent space agency, plays a crucial role in monitoring and studying earth’s climate, including sea level rise. Their assessments contribute valuable data to our understanding of the phenomenon, providing insights that complement global estimates. NASA provides Intermediate and Intermediate High projection scenarios that offer additional granularity to the broader picture of sea level rise.

NASA’s Intermediate Scenario:

NASA’s Intermediate scenario is based on a combination of satellite observations, climate models, and historical data. According to these projections, the global mean sea level is expected to rise within a range that aligns with the mid-point of other global estimates, like the Intergovernmental Panel on Climate Change (IPCC). The Intermediate scenario accounts for various factors contributing to rising seas, including the melting of glaciers, ice sheets, and the expansion of seawater as it warms.

In the context of Hawaiʻi, these Intermediate estimates suggest that the islands will continue to experience a rise in sea levels 16% – 20% higher than the global average (Sweet et al., 2022). This information is valuable for regional planning and adaptation efforts, providing a more nuanced understanding of the challenges that Hawaiʻi faces in the coming decades.

NASA’s Intermediate High Scenario:

NASA’s Intermediate high scenario projects a scenario where the rate of rising seas is at the higher end of the spectrum. This projection takes into account potential accelerations in the melting of polar ice and increased warming of the oceans. While this scenario is considered less likely, it is crucial for planners and policymakers to incorporate such possibilities into their long-term strategies to ensure resilience of critical infrastructure in the face of uncertainty.

For Hawaiʻi, the Intermediate High scenario implies a more pronounced impact, with the sea rising faster than the global average. This higher rate would amplify the challenges faced by coastal communities, necessitating more robust and immediate adaptation measures. The vulnerability of low-lying coastal areas, critical infrastructure, and ecosystems would be exacerbated, underscoring the importance of proactive planning and mitigation strategies.

Ongoing monitoring and research efforts, such as the Gravity Recovery and Climate Experiment (GRACE) and the Ice, Cloud, and land Elevation Satellite (ICESat), provide invaluable data for refining projections. These technologies enable scientists to track changes in ice mass and sea level with unprecedented accuracy, contributing to more reliable projections.

NASA’s Intermediate and Intermediate High scenarios offer a nuanced perspective on sea level rise, aiding both the global scientific community and regional stakeholders like Hawaiʻi in understanding the potential range of future scenarios. Incorporating these projections into local planning efforts is essential to ensure that the islands are prepared for the varying degrees of sea level rise and can implement adaptive strategies to protect their communities, ecosystems, and infrastructure.

Present Sea Level Rise in Hawaiʻi:

Hawaiʻi is already experiencing the effects of rising sea levels, with the current rate estimated to be around 3.2 millimeters per year. This may seem insignificant at first glance, but the cumulative impact is substantial, particularly in low-lying coastal areas. The primary drivers of this rise are the global increase in temperature and the associated thermal expansion of seawater. Coastal erosion, saltwater intrusion into freshwater resources, and damage to coastal infrastructure are some of the immediate consequences faced by Hawaiian communities.

Global Estimates and Projections:

Various global estimates contribute to our understanding of changing sea levels, and organizations like the IPCC, NASA, and the National Ocean and Atmospheric Administration (NOAA) provide comprehensive assessments. According to the IPCC’s latest report in 2022, the global mean sea level is expected to rise between 0.26 to 0.77 meters by 2100, depending on various emission scenarios. These estimates are based on the best available scientific data and models that take into account factors such as glacial melt, thermal expansion, and changes in land ice.

Regional Variability in Sea Level Rise:

While global estimates offer a broad perspective, regional variability plays a crucial role in understanding the specific impacts on a local scale. In the case of Hawaiʻi, the Pacific Islands are exposed to different oceanographic and climatic conditions, leading to variations in sea level rise. Some studies suggest that the Hawaiian Islands may experience a slightly higher rate of sea level rise compared to the global average due to factors such as ocean currents and gravitational effects from melting ice.

Impacts on Hawaiian Ecosystems:

Higher sea levels pose a significant threat to Hawaii’s diverse ecosystems. Coastal habitats, including coral reefs, wetlands, and mangroves are particularly vulnerable. Coral reefs, vital for the islands’ biodiversity, food security and tourism, face bleaching and degradation as they struggle to adapt to changing conditions. Beaches and mangroves, acting as natural buffers and sand storage against episodic events like storms and hurricanes, may face increased stress, impacting their ability to protect coastal areas from erosion and flooding.

Socio-Economic Consequences:

The socio-economic consequences of higher sea levels in Hawai’i are multifaceted. Tourism, a cornerstone of the Hawai’i economy, could be adversely affected as iconic beachfronts disappear and public trust beaches and recreational areas are compromised. Furthermore, the loss of arable land due to saltwater intrusion and erosion could impact agriculture, challenging food security on the islands. Additionally, the increased frequency and intensity of storms associated with sea level rise may result in more frequent damage to infrastructure, disrupting daily life and straining resources for disaster response and recovery.

Adaptation and Mitigation Efforts:

To address the challenges posed by higher sea levels, Hawaiʻi is actively engaged in adaptation and mitigation efforts. These include coastal protection measures such as seawalls and beach nourishment, managed retreat, sustainable land-use planning, and community outreach programs to raise awareness and promote resilience. The state is also investing in renewable energy sources to mitigate the broader impacts of climate change and reduce its carbon footprint with the goals of net-negative carbon production and achieving 100% power generation from renewable energy by 2045.

Rising sea levels in Hawaiʻi is a complex and multifaceted issue with present and long-term consequences. The global and regional estimates provide valuable insights into the scale of the challenge, emphasizing the need for immediate action to mitigate the impacts and adapt to the changing conditions. The integration of scientific research, community engagement, and policy initiatives is essential to ensuring the resilience and sustainability of Hawaiʻi in the face of rising sea levels.

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The Local Tidal Datum – MHHW

Honolulu Tide Gauge:

The Honolulu Tide Gauge serves as a critical tool in monitoring sea level changes around the Hawaiian Islands, providing localized data that complements global assessments. Located at the Honolulu Harbor, this tide gauge records tidal fluctuations and long-term sea level trends, offering valuable insights into the specific conditions faced by Oahu and the broader Hawaiian archipelago. Operated by the University of Hawaiʻi Sea Level Center in collaboration with the NOAA, the gauge provides real-time data that contributes to our understanding of sea level rise in the region.

Contributions to Understanding Sea Level Rise:

The Honolulu Tide Gauge plays a pivotal role in validating and refining sea level rise projections, offering a high-resolution dataset specific to Hawaiʻi. Researchers utilize this data to assess local factors influencing sea level, such as ocean currents, episodic water levels, tides, and geological processes. The gauge enhances the accuracy of global and regional estimates, enabling policymakers and scientists to tailor adaptation and mitigation strategies to the unique conditions of Hawaiʻi.

Future Projections:

Based on the data collected from the Honolulu Tide Gauge, future projections for sea level rise in Honolulu and the surrounding areas are derived in 1 foot increments. These projections take into account a combination of factors, including global climate models, local geological conditions, and historical sea level trends. While the global estimates provide a broad framework, the localized data from the tide gauge refines projections, offering a more precise understanding of how sea level rise will impact Hawaiʻi in the coming decades.

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The CRC sea level rise impacts viewer

This viewer shows results of flood modeling (ʻExposureʻ) from a variety of sources in the legend on the right side of the window. By default, the “Passive Flooding” layers “Marine Connected Flooding” and “Low-lying Areas” are toggled on. Each section can be minimized by clicking the horizontal arrow head next to the section name.

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Passive Marine and Low-lying flooding

Passive flooding was modeled by the Climate Resilience Collaborative at the University of Hawaiʻi at Mānoa School of Ocean and Earth Science and Technology. Using a modified “bathtub” approach following methods described in Anderson et al. 2018, passive flooding describes using color to indicate potential water depth, those portions of a digital elevation model (DEM) equal to or less than the sea level scenario at MHHW tidal datum. 

The passive flooding model provides an initial assessment of low-lying areas susceptible to flooding by sea level rise. Passive flooding includes areas that are hydrologically connected to the ocean (marine flooding) and areas that are not directly hydrologically connected to the ocean (low lying)*. Data used in modeling passive flooding include the DEM at 2 meter resolution and the MHHW datum from local tide gauges. DEMs used in this study are modified from freely available sources like the NOAA, U.S. Army Corps of Engineers (USACE) and U.S. Geological Survey (USGS). DEMs are derived from aerial based light detection and ranging (LiDAR) data. Edits are made to correct streams and canals that were mischaracterized in the original data. The horizontal and vertical positional accuracies of the DEMs conform to flood hazard mapping standards of the Federal Emergency Management Agency (FEMA 2012). Passive flooding is modeled at 0 – 10 ft sea level rise scenarios. Please refer to SLR scenario documentation to apply an appropriate timeline.

Passive flooding was modeled using the DEMs in geographic information systems software to identify areas below a certain sea level height (flooded by sea level rise) when raising water levels above a tidal datum surface. In other words, water levels are shown as they would appear during MHHW, or the average higher high water height of each tidal day. The area flooded was derived by subtracting a tidal surface model from the DEM.

Assumptions and Limitations:

In many areas around the State, representing sea level rise from passive marine flooding will likely produce an underestimate of the area inundated or permanently submerged because the model does not account for waves and coastal erosion, important processes along Hawaiʻi’s highly dynamic coasts. For this reason, coastal erosion and annual high wave flooding are also modeled to provide a more comprehensive picture of the extent of hazard exposure with sea level rise.

Note that the passive flooding model does not explicitly include flooding through storm drain systems and other underground infrastructure, which would contribute to flooding in many low-lying areas identified in the model (see drainage backflow and storm drain structure failure layers and descriptions). The DEMs used in the modeling depict a smoothed topographic surface and do not identify basements, parking garages, and other development or infrastructure below ground that would be affected by marine and groundwater flooding associated with sea level rise. Detailed hydrologic and engineering modeling may be necessary to fully assess passive marine flooding hazards at the scale of individual properties. Mapping errors may be found in some areas due to clipping (subsetting) of the original map layers using a shoreline (Special Management Area) boundary.

*Low lying areas may be indirectly connected to the ocean through soils and sediments.

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Groundwater Inundation

MONITORING AND MODELING COASTAL GROUNDWATER

This dataset was developed by the University of Hawai‘i at Mānoa Climate Resilience Collaborative (CRC) to advance understanding of the present-day water table position, the reduction of unsaturated space, and the increased emergence of groundwater under conditions of sea-level rise. Steady-state groundwater levels were simulated, considering 1ft increments of sea level rise up to 10 ft.

The groundwater inundation layer identifies areas where the groundwater table intersects or exceeds the land surface (depicted in green). The emergent (<1 ft) layer (depicted in orange) highlights locations where groundwater depths are less than 1 foot below the surface, indicating potential groundwater emergence during heavy rainfall or other events that elevate groundwater levels. The impacting utilities (1 – 5 ft) layer (depicted in yellow) identifies areas with groundwater depths less than 5 feet below the surface—a threshold known to adversely impact buried or partially buried urban infrastructure (e.g., roadways, on-site sewage disposal systems, gravity-fed drainage systems) through processes such as increased corrosion and submersion (Habel et al., 2024). These layers were generated using individual simulations of groundwater table elevations under specified sea-level conditions.

The groundwater inundation layer can be analyzed independently; however, the emergent (<1 ft) layer and the impacting utilities (1 – 5 ft) layer rely on the context provided by the preceding layers for proper interpretation. The maps focus on coastal areas below the 10-meter elevation contour and represent only the water table levels of unconfined coastal aquifers.

Simulations have been produced for Honolulu’s Primary Urban Core and the moku of Ko‘olaupoko and Ko‘olauloa. This dataset provides a tool to assess flood vulnerabilities from groundwater inundation, evaluate risks to buried and low-lying infrastructure, and guide strategies for enhancing long-term resilience in coastal communities affected by sea-level rise.

Groundwater Inundation (GWI) refers to flooding that occurs when groundwater levels rise above the ground surface and/or buried infrastructure. Managing GWI presents significant challenges, as it can bypass coastal defense systems designed to mitigate direct marine flooding, such as seawalls, revetments, and other forms of shoreline hardening.

Model Development and Methodology
Simulations of the shallowing of the unsaturated zone and increased groundwater emergence under scenarios of sea-level rise were initially developed based on the methodologies outlined by Habel et al. (2017) and were subsequently expanded in accordance with Habel et al. (2020). In 2023, simulations for the Primary Urban Core (PUC) were updated to integrate additional data from monitoring efforts in Waikīkī, supported by the Honolulu Board of Water Supply.
For the Ko‘olaupoko and Ko‘olauloa moku, and updates to modeling for the PUC domain, model methodologies were refined by incorporating aquifer drain modeling in alignment with Whittier et al. (2009). Drains were used to improve the simulation of streams across the study areas, following Whittier et al. (2010) and Izuka et al. (2023). Instead of the standard drain package, the drain return package was employed to more realistically simulate the partial influx of water into coastal aquifers from basal aquifers via stream channels.

3D Model Construction
The method employs MODFLOW-2005, a three-dimensional finite-difference flow model, to simulate steady-state water table conditions under various magnitudes of sea-level rise. Subsurface hydrogeologic conditions were modeled based on findings from regional studies. The model comprises layers representing caprock and basal basalt hydrogeological units. Pumping well locations and withdrawal rates, as provided by the State Commission on Water Resource Management, were adopted from existing groundwater-flow models representative of the respective aquifer. Only wells extracting water from the caprock aquifer were considered in the simulations. Recharge data were derived from a mean annual water-budget model for the Island of O‘ahu, Hawai‘i. This model accounts for hydrological processes such as rainfall, fog interception, evapotranspiration, direct runoff, irrigation, and return flow from septic systems. Seaward of the 0-meter land-surface elevation contour, mean sea level conditions were simulated using a specified general-head boundary at the ocean bottom, with a conductance of 10 m²/day and a general-head elevation of 0 meters.

Model Calibration
Calibration was performed by comparing model outputs to discrete water-level observations from the Hawai‘i Department of Health’s Leaky Underground Storage Tank records and regional monitoring efforts. Observations were corrected for tidal influence and anomalous sea-surface heights using methods from Habel et al. (2017). The nonlinear inverse modeling tool, PEST, with Tikhonov preferred homogeneous regularization, was used to estimate hydraulic conductivity for caprock layers. Zonal PEST was applied instead of pilot points to avoid overfitting. Post-calibration hydraulic conductivity values for the respective zones aligned with observed ranges in each study area. Additionally, wetlands identified via satellite imagery were used as a calibration metric, as they represent the surface expression of the water table. This method was particularly valuable in the Ko‘olaupoko and Ko‘olauloa moku, where discrete measurements are sparse, but wetlands are abundant.

Simulation of Sea Level Rise
Sea-level rise was simulated by adjusting the general-head boundary to match the specified increase. For instance, a 1 ft rise in mean sea level was simulated by setting the general-head value to 1 ft. The landward extent of the general-head boundary was re-evaluated to reflect the land-surface elevation contour corresponding to the simulated sea-level rise.

Calculation of Tidal Influence
Groundwater-level oscillations are attenuated relative to ocean water levels, with increased temporal lag and decreased amplitude inland. Tidal influence was quantified by cross-correlating tidal signals from the nearest NOAA tide station with observed groundwater data. Tidal efficiency was calculated through linear regression of lag-corrected groundwater time series against tidal signal data.

Production of Flood Maps
Flood maps were produced by comparing simulated water table elevations, including tidal influence, to a Digital Elevation Model (DEM). Flood depths were calculated where the simulated water table exceeded the ground surface, while depths to groundwater were calculated where the opposite occurred.

Simulation Uncertainty

Uncertainty in GWI simulations was quantified for each modeling domain:

• Honolulu PUC: Mean residual water level: 0.09 m; Root-mean-squared error: 0.12 m; Total vertical error considering the quadrature sum of LiDAR and calibrated MODFLOW errors: 0.32 m.
• Ko‘olaupoko moku: Mean residual water level: 0.09 m; Root-mean-squared error: 0.30 m; Total vertical error considering the quadrature sum of LiDAR and calibrated MODFLOW errors: 0.42 m.
• Ko‘olauloa moku: Mean residual water level: 0.25 m; Root-mean-squared error: 0.51 m; Total vertical error considering

Limitations of the Groundwater Model
Limitations of the modeling approach are detailed in Habel et al (2017) in which main limitations
include:

• The model is steady-state and thus does not assess time-dependent processes (i.e., variations in boundary flows, recharge, pumping rates, and groundwater storage, aperiodic short-term changes in sea level by phenomena such as tsunamis, storm-surges, etc.);
• MODFLOW-2005 assumes uniform density of water, and thus does not assess the influence of density-driven fluid flow including mixed seawater and freshwater flows;
• The model does not consider the following: flow that occurs in the unsaturated zone, surface-water flow, evaporation from surface-water sources, ponding or routing of waters that occurs once groundwater breaches the ground surface, dynamic changes in landscape (i.e., erosion).

Figure 5. Schematic diagram showing passive marine and groundwater flooding from current sea level (blue) to future sea level (red) (adapted from Rotzoll and Fletcher 2012).

Note that in certain wetland areas featuring elevated groundwater levels, an increase in sea level may counterintuitively lead to a decline in groundwater levels. This phenomenon is attributed to heightened exposure to open ocean waters and increased opportunities for drainage.

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Emergent and Shallow Groundwater

Shallow and emergent groundwater layers are derived from groundwater modeling discussed in the groundwater inundation section above.

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Drainage Backflow

This layer shows flooding from passive marine flooding coupled with storm drain locations that are at or below the passive SLR scenario. The layer reveals the potential impact of urban coastal drainage infrastructure on the extent of passive SLR flooding.

Storm-drain backflow is similar to direct marine flooding, as both involve flood waters originating from the ocean. However, storm-drain backflow specifically occurs due to the presence of gravity-flow drainage networks, which are commonly used in coastal cities globally. These drainage systems rely on differences in elevation between the drainage and outflow areas (ocean waters). In coastal regions with lower elevations, high tides can decrease these elevation differences, leading to a potential slowdown or even reversal of drainage.

Methods:

Areas prone to flooding due to storm-drain backflow were identified using a modified version of the bathtub approach. This method was adapted to exclude flooded regions lacking direct surface connections to drainage systems. Exclusions were implemented for flood areas not interfacing with drainage infrastructure, based on the assumption that such areas wouldn’t experience drainage-facilitated flooding. To identify locations where drainage infrastructure facilitates water flow from the marine environment, we use geospatial data that characterize drainage inlet locations. These datasets, endorsed by the responsible agency, have high confidence in the spatial accuracy of the data such that it is extensively used for planning, management, and operational decision-making.

Note that groundwater contributions were not considered in the simulation of storm-drain backflow. Additionally, dynamic effects, such as variations in flow rate due to conduit radii (i.e. pipe size), were omitted from the simulation. This omission may produce a slight overestimation in simulated flood depth and area since water is assumed to be able to flow unrestricted.

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Annual High Wave-Driven Flooding

Hawaiʻi is exposed to large waves annually on all open coasts due to our location in the Central North Pacific Ocean. The distance over which waves run-up and wash across the shoreline will increase with sea level rise. As water levels increase, less wave energy will be dissipated through breaking on nearshore reefs and waves will arrive at a higher elevation at the shoreline.

We use the phase-resolving numerical model Boussinesq Ocean and Surf Zone (BOSZ) to simulate wave-driven flooding for annually-recurring wave conditions under a range of SLR levels. Modeling the annually-recurring wave conditions is relevant to this modeling effort since it represents wave conditions that, on average, could occur in any given year.     

The simulations are done over a high resolution (5 m x 5 m horizontal grid spacing) digital surface model, and the final product is projected onto a 2 m x 2 m grid. 

Histograms of wave directions generated by lengthy hindcast records (40-yr-long) are used to determine the dominant wave directions that need to be modeled. Our directional bands are 30-degrees wide (+/- 15 deg). If for a given domain we clearly identify multiple wave directions from which the wave amplitudes are sufficiently high, we will run our model simulations separately for each of these wave directions. The annually-recurring wave height is determined from a Generalized Extreme Value (GEV) analysis.  

All simulations use the MHHW tidal datum. We directly calculate the MHHW from a lengthy (18.6-yr-long) record of water level at the nearest tide gauge. Sea level rise scenarios (0-10ft) are added as a water state assuming the same tidal epoch and wave direction and amplitudes. Each SLR value requires a separate model simulation.

These results reflect merged maximum modeled 2D wave inundation water depths around the island of Oahu, Hawaii. Modeling was carried out by Assaf Azouri and Fatima-Zahra Mihami and delivered to the CRC. Modelling occurred on a domain basis using a CRC provided Oahu DSM, hydro enforced with building structures below 20 m elevation and extruded to 5 m height. The effect is to not allow water to pass through building/structures where elevations do not exist.
The raster values represent classified water depth (00-10 feet) with classes 1 – 11. Class 11 represents depths greater than 10 feet. All other values represent no data.

The flood depth at a given grid cell is the mean of the five highest water depth values out of the entire simulation.

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Compound Flooding

Compound flooding occurs when multiple flood drivers occur simultaneously or in close succession, resulting in more significant impacts. In Hawai’i, these events are often a result of heavy rainfall coinciding with high tide. In the coastal zone, these impacts are further exacerbated by climate change through SLR and the increasing likelihood of more intense storms reaching the islands.

Methods:

Floodwater depth and extent were simulated using WRF-Hydro-CUFA, an expanded version of the Weather Research and Forecasting Hydrological (WRF-Hydro) modeling system adapted for coastal-urban flood applications. The model configuration includes the Noah-MP land surface model, WRF-Hydro terrain routing modules, and a coastal boundary component developed by Son et al. (2023). Static geographical inputs include a 10-meter LiDAR-based bare-earth topobathymetric digital elevation model (DEM) composite, NOAA Coastal Change Analysis Program (C-CAP) land cover data at 2.4-meter resolution, and USDA gridded Soil Survey Geographic Database (gSSURGO) soil texture data at 30-meter resolution.

Design storm simulations were forced using precipitation from a 100-year rainfall event based on NOAA Atlas 14 (Volume 4, Version 3) depth-duration-frequency data. Rainfall depths were assigned using the centroid of each watershed, and synthetic 24-hour hyetographs were generated using the alternating block method. All remaining atmospheric forcing variables were prescribed using idealized diurnal cycles and global constants hardcoded within WRF-Hydro. Coastal water level boundary conditions were defined using the NOAA Tidal Constituents and Residual Interpolation (TCARI) dataset to represent high tide conditions.
Simulations were performed on a 10 x 10 meter grid and validated against observations from historical flood events. Future SLR conditions were included for scenarios ranging from 0 to 10 feet in 1-foot increments. Resulting compound flooding layers represent the maximum floodwater depth produced from the combined effects of a 100-year, 24-hour design storm and each passive SLR scenario.

Citations:

Gochis, D.J., M. Barlage, R. Cabell, M. Casali, A. Dugger, K. FitzGerald, M. McAllister, J. McCreight, A. RafieeiNasab, L. Read, K. Sampson, D. Yates, Y. Zhang (2020). The WRF-Hydro® modeling system technical description, (Version 5.1.1). NCAR Technical Note. 108 pages. Available online at: https://ral.ucar.edu/sites/default/files/public/WRFHydroV511TechnicalDescription.pdf

Son, Y., Di Lorenzo, E., & Luo, J. 2023. WRF-Hydro-CUFA: A Scalable and Adaptable Coastal-Urban Flood Model Based on the WRF-Hydro and SWMM Models. Environ. Model. & Software 167, 105770-. https://doi.org/10.1016/j.envsoft.2023.105770

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Future Erosion Hazard Zones

We use a data-assimilated shoreline change model called CoSMoS-COAST (Vitousek et al. 2023; Moskvichev et al. 2025) to forecast coastal change on Hawai‘i’s sandy beaches. The model uses a high resolution wave-hindcast and the NOAA interagency task force sea level rise projections for Honolulu to simulate chronic, long-term shoreline recession, and short-term change due to wave climate. Our model is calibrated and validated using aerial photos, and satellite-derived shorelines from Landsat 5,7,8 and 9, Sentinel-2, and PlanetScope, between 1990 and 2023. Detailed information on the erosion modeling approach is provided in Moskvichev et al. (2025).
For satellite image processing, we use two toolkits. CoastSat (Vos et al. 2019), an open-source toolkit, is used for Landsat and Sentinel-2 images with 30 and 10-meter resolution. For PlanetScope images with 3-meter resolution, we use CoastVision, a framework developed by CRC. Both frameworks leverage machine learning methods for automated identification of the shoreline in each satellite image. The identified shorelines are corrected to adjust for water level at the time the satellite image was captured, and to remove outliers caused by image misclassification, or hazy, cloudy, or blurry images. Further details on CoastSat and CoastVision processing and benchmarking are provided in Mikkelsen et al. (2026).

The CoSMoS-COAST model outputs either 50th-percentile or 80th-percentile (user-defined) projected erosion hazard lines in 1 ft increments of sea level rise. These lines delineate the point beyond which everything seaward is exposed to the risk of erosion. This modeling approach improves the spatial and temporal resolution of coastal erosion modeling in Hawaii, and can be used to inform future policies around coastal infrastructure, such as setback laws or coastal zone management boundaries. 

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Flooded Roads

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Stormwater Structure Failure

Stormwater inlets include structures that are intended, through gravity, to move water into the ocean from land. These are common structures in the built environment throughout Hawaiʻi as entry points for the storm drain network intended to drain upland sources of flooding in the built environment. When sea level rise exceeds the elevation of the stormwater structure, the structure will no longer be able to receive water – it fails. Coupled with drainage backflow, these locations provide an exit point for marine flooding that can be significantly more inland than direct passive marine flooding. With each increment of sea level rise, stormwater structures are highlighted that are at or below the selected sea level scenario. Infrastructure managers can use this information as a first assessment of potential drainage network failure. Please also see the drainage backflow layer and description for more information about potential flooding through existing infrastructure.

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Critical Facilities

Critical facilities include public infrastructure for emergency services (Hospitals and Clinics, Fire and Police) as well as Public Schools. The locations of these are provided as overlays to show facility locations threatened by each modeled exposure. Note that both direct (to the facilities themselves) or indirect (to the transportation, wastewater or electrical infrastructure that they may rely on) can be visualized.

Information and metadata about each dataset is available from the source department cited in the links:

Hospitals and Clinics: Source data Metadata 

Fire Stations: Source data Metadata

Police Stations: Source data   MetadataPublic Schools: Source data   Metadata (none)

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Wastewater Infrastructure

Wastewater infrastructure includes treatment plants, pump stations and known cesspool locations. The locations of these are provided as overlays to show facility and point source (cesspools) locations threatened by each modeled exposure. Note that both direct (to the facilities themselves) or indirect (to the transportation, or electrical infrastructure that they may rely on) can be visualized. Facilities impacted directly or indirectly by fooding may become pollution sources due to reduced capacity, failure or submergence. 

Information about each dataset is available from the source department cited in the links. Metadata is limited for this data:

Treatment Plants: Source data   Metadata

Pump Stations: Source data   Metadata (none)Cesspool locations: Source data Metadata

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Electrical Infrastructure

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Oahu Shoreline Setback

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Geology

The geology map layer serves as a useful guide to understanding the physical setting of coastal areas around the State and how these areas may be affected by increased flooding and erosion with sea level rise. For the purposes of the Viewer, we have categorized the geology into beach and dune deposits, marine and lagoon deposits, alluvium deposits, and volcanic deposits. Volcanic and marine limestone deposits may be more resistant to coastal erosion. In contrast, deposits of sand and alluvium may be more susceptible to coastal erosion. However, it should be noted that beach environments may be sustained if they are allowed to migrate landward and erode into upland deposits of beach and dune sand, releasing this sediment into the littoral system. The Geology layer identifies surficial deposits only. It should be used as an initial screening tool and may require verification at the site level.

Sherrod, David R., Sinton, John M., Watkins, Sarah E., and Brunt, Kelly M. 2007. Geologic map of the State of Hawaiʻi: U.S. Geological Survey Open-File Report 2007-1089. http://pubs.usgs.gov/of/2007/1089/. Accessed [date].

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Public Safety Power Shutoff

This PSPS Estimated Outage Areas feature layer displays the generalized areas where power may be shut off due to potential wildfire conditions. Using advanced technology and weather monitoring systems, we have identified and marked those regions most at risk. Due to the dynamic nature of our electrical grid and environmental conditions, areas affected by power shutoffs may vary.

Please note that the borders of the displayed areas are approximate and may change based on the grid configuration, electric system status, and changing environmental and weather conditions. The highlighted communities are at higher risk, and therefore, more likely to experience a power shutoff due to potential wildfire conditions. 

For detailed information on our PSPS program and the latest updates, please refer to the Company’s official PSPS website.

Citations:

Anderson, T., Fletcher, C., Barbee, M., Romine, B., & Lemmo, J. 2018. Modeling multiple sea level rise stresses reveals up to twice the land at risk compared to strictly passive flooding methods. Nature Scientific Reports 8: 14484 DOI:10.1038/s41598-018-32658-x

Habel, S., Fletcher, C., Anderson, T., & Thompson, P. 2020. Sea-Level Rise Induced Multi-Mechanism Flooding and Contribution to Urban Infrastructure Failure. Nature Scientific Reports, 10: 3796 DOI:10.1038/s41598-020-60762-4

Habel, S., Fletcher, C., Rotzoll, K., El-Kadi, A., & Oki, D. 2019. Comparison of a simple hydrostatic and a data-intensive 3D numerical modeling method of simulating sea-level rise induced groundwater inundation for Honolulu, Hawai’i, USA. Environmental Research Communications, 1(4), 041005. DOI:10.1088/2515-7620/ab21fe

Habel, S., Fletcher, C.H., Rotzoll, K. and El-Kadi, A. 2017. Development of a model to simulate groundwater inundation induced by sea-level rise and high tides in Honolulu, Hawaii. Water Research. ISSN 0043-135.http://dx.doi.org/10.1016/j.watres.2017.02.035

Hawaiʻi Climate Change Mitigation and Adaptation Commission. 2021. State of Hawaiʻi Sea Level Rise Viewer. Version 1.12. Prepared by the Pacific Islands Ocean Observing System (PacIOOS) for the University of Hawaiʻi Sea Grant College Program and the State of Hawaiʻi Department of Land and Natural Resources, Office of Conservation and Coastal Lands, with funding from National Oceanic and Atmospheric Administration Office for Coastal Management Award No. NA16NOS4730016 and under the State of Hawaiʻi Department of Land and Natural Resources Contract No. 64064. http://hawaiisealevelriseviewer.org. Accessed [date].

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