Real-Time Chronological Hazard Impact Modeling
Abstract
:1. Introduction
- (1)
- Overview of the storm surge modeling system, including: generation of the meteorological forcing, the hydrodynamic simulation model, and steps taken to validate the model using a historic storm (Hurricane Carol, 1954) and tide gauge data.
- (2)
- (3)
- (4)
- Participant input, including eliciting facility level vulnerability information as a credible non-aggregated basis for modeling specific impacts [7]. Developing highly specific granular data (e.g., the wind velocity at which a communication tower may be compromised) requires the engagement of stakeholders [4]. Incorporation of stakeholder input has been shown to increase the transparency of processes as well as enhancing trust and perceived legitimacy of model outputs [17,24].
2. Overview of the Storm Surge Modeling System
2.1. Generation of the Meteorological Forcing
2.2. Hydrodynamic Simulation Model
3. Architecture of the All Numerical Method
3.1. Overview
3.2. Interpolation
- (1)
- Geographic point with three adjacent wet nodes (nodes which are reported to be inundated by the ADCIRC model): interpolate sea surface elevation, water direction, and velocity based on the geometric relationship of the point to the planar surface described by the three points.
- (2)
- Geographic point beyond the last wet node: use nearest adjacent node without interpolation (Figure 10).
4. Quality of Spatial Data
5. Participant Input
6. Next Steps
- (1)
- While the potential of 3D visualizations to make it difficult to imagine impacts seem more tangible is widely acknowledged [43,44], the effects of such visualizations on perceptions of risk, however, is less clear [22,45]. There are concerns that highly detailed depictions of impacts may make uncertain outcomes appear more certain than they are by virtue of contextualizing less detailed information in highly specific contexts [22]. Further research is needed to better understand the effects of these visualizations on risk perception. The development of the thresholds database, and the implementation of iterative processes involving end users is based in part on practices that are intended to contextualize and support the use of visualizations [46]. These practices will be further developed and refined based on the outcome of this work.
- (2)
- At the time of the IEMC, databases had only been developed for a limited number of sites and facilities. Representations that mix structures for which there is highly detailed information available with structures for which there is no data may create misleading impressions due to the absence of reported effects. To the extent that specific vulnerability information is gathered from multiple emergency managers, there is also a concern regarding the consistency of the reported data for modeling purposes. This requires further development of consistent methodologies to elicit vulnerability data. The implementation of the databases as part of the IEMC has led to an ongoing collaboration between RIEMA and URI to develop more comprehensive databases for critical facilities in the state.
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Range | Existing-Lowest | Highest-Existing | |
---|---|---|---|
Max | 14.18 | 5.42 | 0.82 |
Mean | 4.54 | 2.33 | −0.92 |
Median | 3.34 | 1.55 | −0.73 |
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Stempel, P.; Ginis, I.; Ullman, D.; Becker, A.; Witkop, R. Real-Time Chronological Hazard Impact Modeling. J. Mar. Sci. Eng. 2018, 6, 134. https://doi.org/10.3390/jmse6040134
Stempel P, Ginis I, Ullman D, Becker A, Witkop R. Real-Time Chronological Hazard Impact Modeling. Journal of Marine Science and Engineering. 2018; 6(4):134. https://doi.org/10.3390/jmse6040134
Chicago/Turabian StyleStempel, Peter, Isaac Ginis, David Ullman, Austin Becker, and Robert Witkop. 2018. "Real-Time Chronological Hazard Impact Modeling" Journal of Marine Science and Engineering 6, no. 4: 134. https://doi.org/10.3390/jmse6040134