Combining remote sensing data and an inundation model to map tidal mudflat regions and improve flood predictions: A proof of concept demonstration in Cook Inlet, Alaska

Ezer, Tal; Liu, Hua

doi:10.1029/2008gl036873

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…Comprehensive sensitivity experiments (∼30 different runs) with a newly developed WAD scheme, WAD-POM, implemented in a widely used three-dimensional community ocean circulation model (Oey 2005(Oey , 2006Oey et al 2007;Ezer et al 2008;Ezer and Liu 2009), have been conducted in order to study tidal-driven mixing processes in the coastal environment. Model calculations are compared with a control model without the WAD scheme.…”

Section: Discussionmentioning

confidence: 99%

“…The details of the combined WAD-POM numerical model and recent applications can be found in Oey (2005Oey ( , 2006, Oey et al (2007), Ezer et al (2008), and Ezer and Liu (2009). An idealized exposed seamount (i.e., an island), with the maximum depth approximately 50 m, is used in this study; a mean sea level (MSL) is set to be 20 m below the maximum allowable sea level (Fig.…”

Section: Model Setupmentioning

confidence: 99%

“…The scheme was tested for oneand two-dimensional cases, including hydraulic jumps, dam-break problems, and a comparison with analytical solutions for non-linear long waves propagating up a sloping beach (Oey 2005(Oey , 2006. A full threedimensional version of WAD-POM, including stratification, rivers, winds, and realistic coastlines, has been applied to Cook Inlet, Alaska, where it has successfully simulated 10-m-range high tides, mudflat flooding, and even tidal bores (Oey et al 2007); the Cook Inlet model has also been used to simulate the movement of the Beluga whales with the tides (Ezer et al 2008) and for comparison with remote sensing data (Ezer and Liu 2009). It should be noted that various WAD schemes have also been implemented in POM (and similar models) independently by several groups, including, for example, the three-dimensional simulation of tidal wave in San Francisco Bay (Uchiyama 2004), simulations of Japanese bays (Kamiya et al 2006), and storm surge models (Xie et al 2004).…”

Section: Introductionmentioning

confidence: 99%

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Tidal-driven dynamics and mixing processes in a coastal ocean model with wetting and drying

Saramul

Ezer

2009

Ocean Dynamics

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A three-dimensional sigma coordinate numerical model with wetting and drying (WAD) and a Mellor-Yamada turbulence closure scheme has been used in an idealized island configuration to evaluate how tidally driven dynamics and mixing are affected by inundation processes. Comprehensive sensitivity experiments evaluate the influence of various factors, including tidal amplitudes (from 1-to 9-m range), model grid size (from 2 to 16 km), stratification, wind, rotation, and the impact of WAD on the mixing. The dynamics of the system involves tidally driven basin-scale waves (propagating anticlockwise in the northern hemisphere) and coastally trapped waves propagating around the island in an opposite direction. The evolutions of the surface mixed layer (SML) and the bottom boundary layer (BBL) under different forcing have been studied. With small amplitude tides, wind-driven mixing dominates and the thickness of the SML increases with time, while with large-amplitude tides, tidal mixing dominates and the thickness of the BBL increases with time. The inclusion of WAD in the simulations increases bottom stress and impacts the velocities, the coastal waves, and the mixing. However, the impact of WAD is complex and non-linear. For example, WAD reduces near-coast currents during flood but increases currents during ebb as water drains from the island back to the sea. The impacts of WAD, forcing, and model parameters on the dynamics are summarized by an analysis of the vorticity balance for the different sensitivity experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Model Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tidal-driven dynamics and mixing processes in a coastal ocean model with wetting and drying

Saramul

Ezer

2009

Ocean Dynamics

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“…The primary difference between the method of Horritt et al [14] and Chaouch et al [15] is the former computes the inundation extent as a vector feature (line) using a statistical active contour model where the latter computes a raster representation of cells as wet or dry. In addition to using active sensors such as SAR, passive optical sensors such as MODIS and Landsat have also been used to identify inundated areas [18,19]. In this paper, the approach of Chaouch et al [15], herein referred to as the "synergetic method", is used to validate a tidal model.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of a Northern Gulf of Mexico Tidal Model Using Satellite Imagery

et al. 2013

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Tidal harmonic analysis simulations along with simulations spanning four specific historical time periods in 2003 and 2004 were conducted to test the performance of a northern Gulf of Mexico tidal model. A recently developed method for detecting inundated areas based on integrated remotely sensed data (i.e., Radarsat-1, aerial imagery, LiDAR, Landsat 7 ETM+) was applied to assess the performance of the tidal model. The analysis demonstrates the applicability of the method and its agreement with traditional performance assessment techniques such as harmonic resynthesis and water level time series analysis. Based on the flooded/non-flooded coastal areas estimated by the integrated remotely sensed data, the model is able to adequately reproduce the extent of inundation within four sample areas from the coast along the Florida panhandle, correctly identifying areas as wet or dry over 85% of the time. Comparisons of the tidal model inundation to OPEN ACCESSRemote Sens. 2013, 5 5663 synoptic (point-in-time) inundation areas generated from the remotely sensed data generally agree with the results of the traditional performance assessment techniques. Moreover, this approach is able to illustrate the spatial distribution of model inundation accuracy allowing for targeted refinement of model parameters.

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“…For example, Ezer and Liu (2009) compared the topography data currently available for the CI inundation model (Oey et al 2007) with Landsat remote sensing data, demonstrating the much greater details that the remote sensing data can provide. So how can one use remote sensing data to study morphology and climatic changes, as well as improve flood prediction models?…”

Section: Introductionmentioning

confidence: 99%

On the dynamics and morphology of extensive tidal mudflats: Integrating remote sensing data with an inundation model of Cook Inlet, Alaska

Ezer

Liu

2010

Ocean Dynamics

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A new method of integrating satellite remote sensing data and inundation models allows the mapping of extensive tidal mudflats in a sub-Arctic estuary, Cook Inlet (CI), Alaska. The rapid movement of the shorelines in CI due to the large tides (~10 m range) is detected from a series of Landsat imagery taken at different tidal stages, whereas GIS tools are used to identify the water coverage in each satellite image and to extract the coordinates of the shoreline. Then, water level along the shoreline for each satellite image is calculated from the observed water level at Anchorage and the statistics of an inundation model. Several applications of the analysis are demonstrated: 1. studying the dynamics of a tidal bore and the flood/ebb processes, 2. identifying climatic changes in mudflats morphology, and 3. mapping previously unobserved mudflat topographies in order to improve inundation models. The method can be used in other regions to evaluate models and improve predictions of catastrophic floods such as those associated with hurricane storm surges and tsunamis.

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Combining remote sensing data and an inundation model to map tidal mudflat regions and improve flood predictions: A proof of concept demonstration in Cook Inlet, Alaska

Cited by 9 publications

References 8 publications

Tidal-driven dynamics and mixing processes in a coastal ocean model with wetting and drying

Tidal-driven dynamics and mixing processes in a coastal ocean model with wetting and drying

Assessing the Performance of a Northern Gulf of Mexico Tidal Model Using Satellite Imagery

On the dynamics and morphology of extensive tidal mudflats: Integrating remote sensing data with an inundation model of Cook Inlet, Alaska

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