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The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through the water with high precision and accuracy if the bottom peak intensity of the waveform is greater than the noise level. However, passive optical imaging of optically shallow water involves measuring the radiance after the sunlight undergoes downward attenuation on the way to the sea floor, and the reflected light is then attenuated while moving back upward to the water surface. The difficulty of satellite-derived bathymetry (SDB) arises from the fact that the measured radiance is a result of a complex association of physical elements, mainly the optical properties of the water, bottom reflectance, and depth. In this research, we attempt to apply physics-based algorithms to solve this complex problem as accurately as possible to overcome the limitation of having only a few known values from a multispectral sensor. Major analysis components are atmospheric correction, the estimation of water optical properties from optically deep water, and the optimization of bottom reflectance as well as the water depth. Specular reflection of the sky radiance from the water surface is modeled in addition to the typical atmospheric correction. The physical modeling of optically dominant components such as dissolved organic matter, phytoplankton, and suspended particulates allows the inversion of water attenuation coefficients from optically deep pixels. The atmospheric correction and water attenuation results are used in the ocean optical reflectance equation to solve for the bottom reflectance and water depth. At each stage of the solution, physics-based models and a physically valid, constrained Levenberg–Marquardt numerical optimization technique are used. The physics-based algorithm is applied to Landsat Operational Land Imager (OLI) imagery over the shallow coastal zone of Guam, Key West, and Puerto Rico. The SDB depths are compared to airborne lidar depths, and the root mean squared error (RMSE) is mostly less than 2 m over water as deep as 30 m. As the initial choice of bottom reflectance is critical, along with the bottom reflectance library, we describe a pure bottom unmixing method based on eigenvector analysis to estimate unknown site-specific bottom reflectance.

We developed the first-ever bathymetric module for the NASA Ames Stereo Pipeline (ASP) open-source topographic software called Satellite Triangulated Sea Depth, or SaTSeaD, to derive nearshore bathymetry from stereo imagery. Correct bathymetry measurements depend on water surface elevation, and whereas previous methods considered the water surface horizontal, our bathymetric module accounts for the curvature of the Earth in the imagery. The process is semiautomatic, reliable, and repeatable, independent of any external bathymetry data eliminating user bias in selecting bathymetry calibration points, and it can generate a fully integrated and seamless topo-bathymetry digital elevation model (TBDEM) in the same coordinate system, comparable with the band-ratio method irrespective of the regression method used for the band-ratio algorithm. The ASP output can be improved by applying a camera bundle adjustment to minimize reprojection errors and by alignment to a more accurate topographic (above water) surface without any bathymetric input since the derived TBDEM is a rigid surface. These procedures can decrease bathymetry root mean square errors from 30 to 80 percent, depending on environmental conditions, the quality of satellite imagery, and the spectral band used (e.g., blue, green, or panchromatic).

Many coral reef-lined coasts are low-lying with elevations 30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.

Tropical coral reef-lined coasts are exposed to storm wave-driven flooding. In the future, flood events during storms are expected to occur more frequently and to be more severe due to sea-level rise, changes in wind and weather patterns, and the deterioration of coral reefs. Hence, disaster managers and coastal planners are in urgent need of decision-support tools. In the short-term, these tools can be applied in Early Warning Systems (EWS) that can help to prepare for and respond to impending storm-driven flood events. In the long-term, future scenarios of flooding events enable coastal communities and managers to plan and implement adequate risk-reduction strategies. Modeling tools that are used in currently available coastal flood EWS and future scenarios have been developed for open-coast sandy shorelines, which have only limited applicability for coral reef-lined shorelines. The tools need to be able to predict local sea levels, offshore waves, as well as their nearshore transformation over the reefs, and translate this information to onshore flood levels. In addition, future scenarios require long-term projections of coral reef growth, reef composition, and shoreline change. To address these challenges, we have formed the UFORiC (Understanding Flooding of Reef-lined Coasts) working group that outlines its perspectives on data and model requirements to develop EWS for storms and scenarios specific to coral reef-lined coastlines. It reviews the state-of-the-art methods that can currently be incorporated in such systems and provides an outlook on future improvements as new data sources and enhanced methods become available.

Coastal development and climate change are dramatically increasing the risks of flooding, erosion, and extreme weather events. Coral reefs and other coastal ecosystems act as natural defenses against coastal hazards, but their degradation increases risk to people and property. Environmental degradation, however, has rarely been quantified as a driver of coastal risk. In Quintana Roo, Mexico, a region on the Mexican Caribbean coast with an annual tourism economy of 10 billion USD, coral reefs constitute a natural barrier against flooding from hurricanes. This study spatially quantifies the risk reduction benefits of the Mesoamerican Reef in Quintana Roo for people, buildings, and hotel infrastructure. The risk reduction benefits are substantial. For example, the reefs prevented 43% additional damage during Hurricane Dean in (2007) and provide nowadays hazard risk reduction for 4.3% of the people, 1.9% of the built capital, and 2.4% of the hotel infrastructure, per year. The annual benefits are estimated in 4,600 people, 42 million USD damage prevention for buildings, and 20.8 million USD for hotel infrastructure. The study also compares the risk reduction of coral reefs with (i) the protection offered by dunes and (ii) the increase in coastal risk from sea-level rise (SLR). The risk reduction of dunes is more critical where there are no coral reefs offshore and for small return-periods storms. Sea-level rise, however, will make the more frequent storms more impactful and will drive significant increases in annual expected damages across the region. However, we demonstrate that, in coral reef environments, the contribution of reef degradation to coastal risk is larger than the expected increase in risk from SLR. However, the spatial distribution of the risk reduction benefits from reefs differs for people and infrastructure, and in particular for hotels, which receive the most protection from reefs. Furthermore, many sections present larger benefits than the typical costs of restoration. This valuation makes a compelling case for protecting and maintaining this natural infrastructure for its risk reduction service, but also allows the development of piloting innovative strategies, such as risk finance and insurance strategies, that can align environmental and risk management goals.