Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding

As coastal circulation models have evolved to predict storm-induced flooding, they must include progressively more overland regions that are normally dry, to where now it is possible for more than half of the domain to be needed in none or only some of the computations. While this evolution has improved real-time forecasting and long-term mitigation of coastal flooding, it poses a problem for parallelization in an HPC environment, especially for static paradigms in which the workload is balanced only at the start of the simulation. In this study, a dynamic rebalancing of computational work is developed for a finite-element-based, shallow-water, ocean circulation model of extensive overland flooding. The implementation has a low overhead cost, and we demonstrate a realistic hurricane-forced coastal flooding simulation can achieve peak speed-ups near 45% over the static case, thus operating now at 80−90% efficiency.

KJ Roberts, JC Dietrich, D Wirasaet, WJ Pringle, JJ Westerink (2021). “Dynamic load balancing for predictions of storm surge and coastal flooding.” Environmental Modelling & Software, DOI: 10.1016/j.envsoft.2021.105045.

Downscaling of Real-Time Coastal Flooding Predictions for Decision Support

During coastal storms, forecasters and researchers use numerical models to predict the magnitude and extent of coastal flooding. These models must represent the large regions that may be affected by a storm, and thus, they can be computationally costly and may not use the highest geospatial resolution. However, predicted flood extents can be downscaled (by increasing resolution) as a post-processing step. Existing downscaling methods use either a static extrapolation of the flooding as a flat surface, or rely on subsequent simulations with nested, full-physics models at higher resolution. This research explores a middle way, in which the downscaling includes simplified physics to improve accuracy. Using results from a state-of-the-art model, we downscale its flood predictions with three methods: (1) static, in which the water surface elevations are extrapolated horizontally until they intersect the ground surface; (2) slopes, in which the gradient of the water surface is used; and (3) head loss, which accounts for energy losses due to land cover characteristics. The downscaling methods are then evaluated for forecasts and hindcasts of Hurricane Florence (2018), which caused widespread flooding in North Carolina. The static and slopes methods tend to over-estimate the flood extents. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from a higher-resolution, full-physics model. These results are encouraging for the use of these downscaling methods to support decision-making during coastal storms.

CA Rucker, N Tull, JC Dietrich, TE Langan, H Mitasova, BO Blanton, JG Fleming, RA Luettich Jr (2021). “Downscaling of Real-Time Coastal Flooding Predictions for Decision Support.” Natural Hazards, DOI: 10.1007/s11069-021-04634-8.

Using a Multi-Resolution Approach to Improve the Accuracy and Efficiency of Flooding Predictions

This research describes a method to improve the accuracy and efficiency of coastal flooding predictions. First, an existing model is used to explore the effect of storm forward speed and timing on tides and storm surge during Hurricane Matthew (2016). It is hypothesized that the spatial variability of Matthew’s effects on total water levels is due to the surge interacting nonlinearly with tides. If the storm occurred a few hours earlier or later, then the largest surges would have been shifted to other regions of the U.S. southeast coast. A change in forward speed of the storm also should alter its associated flooding due to differences in the duration over which the storm impacts the coastal waters. If the storm had moved faster, then the peak water levels would have increased along the coast, but the overall volume of inundation would have decreased. Then this research explores ways to increase the model’s accuracy and efficiency. To better represent Matthew’s effects, a mesh with detailed coverage of the coastal regions from Florida to North Carolina was developed by combining regional meshes originally developed for floodplain mapping. Compared to predictions using the earlier model, the new mesh allows for simulations of inundation that better match to observations especially inland.

Then, to best utilize this new mesh, a multi-resolution approach is implemented to use meshes of varying resolution when and where it is required. It is hypothesized that by `switching’ from coarse- to fine-resolution meshes, with the resolution in the fine mesh concentrated only at specific coastal regions influenced by the storm at that point in time, both accuracy and computational gains can be achieved. As the storm approaches the coastline and the landfall location becomes more certain, the simulation will switch to a fine-resolution mesh that describes the coastal features in that region. Application of the approach during Hurricanes Matthew and Florence revealed the predictions to improve in both accuracy and efficiency, as compared to that from single simulations on coarse- and fine-resolution meshes, respectively.

Finally, the efficiency of the approach is further improved in the case of Hurricane Matthew, by using multiple smaller fine-resolution meshes instead of a single high-resolution mesh for the entire U.S. southeast coast. Simulations are performed utilizing predicted values of water levels, wind speeds, and wave heights, as triggers to switch from one mesh to another. Results indicate how to achieve an optimum balance between accuracy and efficiency, by using the above-mentioned triggers, and through a careful selection of the combination meshes to be used in the approach. This research has the potential to improve the storm surge forecasting process. These gains in efficiency are directly a savings in wall-clock time, which can translate into more time to invest in better models and/or more time for the stakeholders to consider the forecast guidance.

A Thomas (2020). “Using a Multi-Resolution Approach to Improve the Accuracy and Eficiency of Flooding Predictions,” North Carolina State University.

Differences between SWAN v41.31 and v41.10

Updated 2020/06/23: Adjusted for new SWAN setting with NSWEEP=1.

In late May 2019, the SWAN developers released a new version. Whenever this happens, the new version needs to be implemented into the coupled SWAN+ADCIRC, thus replacing an older version in the coupled model.

Starting with the upcoming release version 55 of ADCIRC, the coupled SWAN has been upgraded to its latest release version 41.31. It replaces the older version 41.10.

This upgrade is mostly a benefit to users of SWAN+ADCIRC. It has been almost 4 years since the last upgrade, and we had skipped a new SWAN version (41.20) during that time. Thus, this upgrade is adding features and bug fixes from two newer versions (41.20 and 41.31). SWAN has added several capabilities that will be advantageous to users of SWAN+ADCIRC.

However, a few of its changes will cause differences in the wave predictions, as described below. Users will likely need to re-calibrate their input settings for SWAN.

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Downscaling with Head Loss due to Land Cover in Kalpana

Originally developed as a tool for visualizing ADCIRC output, Kalpana has evolved to include methods for downscaling ADCIRC water elevation results. The first method, now referred to as the static method, extrapolated ADCIRC water elevations horizontally until intersecting an equivalent DEM elevation. More information about the static method and about downscaling ADCIRC results with Kalpana can be found on an earlier post.

The static method has proven to be a useful tool but incorporates minimal physics. Therefore, a new method, referred to as the head loss method, has been introduced to include energy dissipation due to land cover during overland flow events. In this page, we describe the theory of the head loss method and provide examples for how to apply it using Kalpana.

Side-view schematic of downscaling methods. A one-dimensional schematic is displayed for each of the two downscaling methods, where the top figure is the static method and the bottom is the head loss method. In the static method, the water elevations from ADCIRC (blue hatched portion) are extrapolated as a flat surface until they intersect the DEM. In the head loss method, these water elevations are extrapolated to an energy cost surface (elevation plus cumulative head loss).

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