- 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.
- Participant input, including eliciting facility level vulnerability information as a credible non-aggregated basis for modeling specific impacts . Developing highly specific granular data (e.g., the wind velocity at which a communication tower may be compromised) requires the engagement of stakeholders . 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
- 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.
- 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
- 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 . 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 . These practices will be further developed and refined based on the outcome of this work.
- 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.
Conflicts of Interest
- Haraguchi, M.; Kim, S. Critical infrastructure interdependence in New York City during Hurricane Sandy. Int. J. Disaster Resil. Built Environ. 2016, 7, 133–143. [Google Scholar] [CrossRef]
- Brecht, H. Geo-technologies in hurricane research. Cartogr. Geogr. Inf. Sci. 2007, 34, 153–154. [Google Scholar] [CrossRef]
- Ginis, I.; Kincaid, C.; Hara, T.; Rothstein, L.; Ullman, D.S.; Huang, W.; Rosa, K.; Chen, X.; Zhou, X.; Rubinoff, P.; et al. Modeling the Combined Coastal and Inland Hazards from High-Impact Hypothetical Hurricanes. Appendix to the Annual Project Performance Report Prepared for the DHS Coastal Resilience Center; University of Rhode Island: South Kingstown, RI, USA, 2017. [Google Scholar]
- Aerts, J.C.J.H.; Botzen, W.J.; Clarke, K.C.; Cutter, S.L.; Hall, J.W.; Merz, B.; Michel-Kerjan, E.; Mysiak, J.; Surminski, S.; Kunreuther, H. Integrating human behaviour dynamics into flood disaster risk assessment. Nat. Clim. Chang. 2018, 8, 193–199. [Google Scholar] [CrossRef][Green Version]
- Allen, T.R.; McLeod, G.; Sanchagrin, S. Visualization for Hurricane Storm Surge Risk Awareness and Emergency Communication; INTECH Open Access Publisher: London, UK, 2013. [Google Scholar]
- Allen, T.; Sanchagrin, S.; Lee, N. Geovisualization Strategies to Analyze and Communicate Inundation Risk from Storm Surges. In Proceedings of the Coastal Society’s 22nd International Conference, Wilmington, NC, USA, 13–16 June 2010; pp. 1–5. [Google Scholar]
- Vickery, P.J.; Skerlj, P.F.; Lin, J.; Twisdale, L.A., Jr.; Young, M.A.; Lavelle, F.M. HAZUS-MH hurricane model methodology. II: Damage and loss estimation. Nat. Hazards Rev. 2006, 7, 94–103. [Google Scholar] [CrossRef]
- Kopp, R.E.; Horton, R.M.; Little, C.M.; Mitrovica, J.X.; Oppenheimer, M.; Rasmussen, D.; Strauss, B.H.; Tebaldi, C. Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth’s Future 2014, 2, 383–406. [Google Scholar] [CrossRef][Green Version]
- Spaulding, L.M.; Grilli, A.; Damon, C.; Crean, T.; Fugate, G.; Oakley, A.B.; Stempel, P. STORMTOOLS: Coastal Environmental Risk Index (CERI). J. Mar. Sci. Eng. 2016, 4, 54. [Google Scholar] [CrossRef]
- Fenech, A.; Chen, A.; Clark, A.; Hedley, N. Building an adaptation tool for visualizing the coastal impacts of climate change on Prince Edward Island, Canada. In Climate Change Adaptation in North America; Springer: Cham, Switzerland, 2017; pp. 225–238. [Google Scholar] [CrossRef]
- Sneath, S. Predicting flood locations, depths, is aim of new computer simulation. In NOLA.com | The Times Picayune; NOLA Media Group: New Orleans, LA, USA, 2017. [Google Scholar]
- Becker, A. Using boundary objects to stimulate transformational thinking: storm resilience for the Port of Providence, Rhode Island (USA). Sustain. Sci. 2017, 12, 477–501. [Google Scholar] [CrossRef]
- Stempel, P.; Becker, A. Data Driven Visualization. In Proceedings of the Estuarine and Coastal Modeling Conference, South Kingstown, RI, USA, 14–15 June 2016. [Google Scholar]
- Laniak, G.F.; Olchin, G.; Goodall, J.; Voinov, A.; Hill, M.; Glynn, P.; Whelan, G.; Geller, G.; Quinn, N.; Blind, M. Integrated environmental modeling: a vision and roadmap for the future. Environ. Model. Softw. 2013, 39, 3–23. [Google Scholar] [CrossRef][Green Version]
- Kelly, R.A.; Jakeman, A.J.; Barreteau, O.; Borsuk, M.E.; ElSawah, S.; Hamilton, S.H.; Henriksen, H.J.; Kuikka, S.; Maier, H.R.; Rizzoli, A.E. Selecting among five common modelling approaches for integrated environmental assessment and management. Environ. Model. Softw. 2013, 47, 159–181. [Google Scholar] [CrossRef]
- Biljecki, F.; Stoter, J.; Ledoux, H.; Zlatanova, S.; Çöltekin, A. Applications of 3D city models: State of the art review. ISPRS Int. J. Geo-Inf. 2015, 4, 2842–2889. [Google Scholar] [CrossRef][Green Version]
- Schroth, O.; Pond, E.; Sheppard, S.R. Integration of spatial outputs from mathematical models in climate change visioning tools for community-decision making on the landscape scale. In Peer Reviewed Proceedings Digital Landscape Architecture 2011: Teaching & Learning with Digital Methods & Tools, Anhalt University of Applied Sciences; Buhmann, E., Ervin, S., Tomlin, D., Pietsch, M., Eds.; Wichmann: Berlin, Germany, 2011; pp. 246–255. [Google Scholar]
- Luettich, R.A., Jr.; Westerink, J.J.; Scheffner, N.W. ADCIRC: An Advanced Three-Dimensional Circulation Model for Shelves, Coasts, and Estuaries. Report 1. Theory and Methodology of ADCIRC-2DDI and ADCIRC-3DL; Coastal Engineering Research Center: Vicksburg, MS, USA, 1992. [Google Scholar]
- Sheppard, S.R.; Cizek, P. The ethics of Google Earth: Crossing thresholds from spatial data to landscape visualisation. J. Environ. Manag. 2009, 90, 2102–2117. [Google Scholar] [CrossRef] [PubMed]
- Liu, S.B.; Palen, L. The new cartographers: Crisis map mashups and the emergence of neogeographic practice. Cartogr. Geogr. Inf. Sci. 2010, 37, 69–90. [Google Scholar] [CrossRef]
- Couclelis, H. The certainty of uncertainty: GIS and the limits of geographic knowledge. Trans. GIS 2003, 7, 165–175. [Google Scholar] [CrossRef]
- Kostelnick, J.C.; McDermott, D.; Rowley, R.J.; Bunnyfield, N. A cartographic framework for visualizing risk. Cartogr. Int. J. Geogr. Inf. Geovis. 2013, 48, 200–224. [Google Scholar] [CrossRef]
- Gesch, D.B. Analysis of lidar elevation data for improved identification and delineation of lands vulnerable to sea-level rise. J. Coast. Res. 2009, 49–58. [Google Scholar] [CrossRef]
- White, D.D.; Wutich, A.; Larson, K.L.; Gober, P.; Lant, T.; Senneville, C. Credibility, salience, and legitimacy of boundary objects: water managers’ assessment of a simulation model in an immersive decision theater. Sci. Public Policy 2010, 37, 219–232. [Google Scholar] [CrossRef]
- Gao, K.; Ginis, I. On the equilibrium-state roll vortices and their effects in the hurricane boundary layer. J. Atmos. Sci. 2016, 73, 1205–1222. [Google Scholar] [CrossRef]
- Gao, K.; Ginis, I. On the Characteristics of Linear-Phase Roll Vortices under a Moving Hurricane Boundary Layer. J. Atmos. Sci. 2018, 75, 2589–2598. [Google Scholar] [CrossRef]
- Booij, N.; Ris, R.C.; Holthuijsen, L.H. A third-generation wave model for coastal regions: 1. Model description and validation. J. Geophys. Res. Oceans 1999, 104, 7649–7666. [Google Scholar] [CrossRef][Green Version]
- Dietrich, J.C.; Zijlema, M.; Westerink, J.J.; Holthuijsen, L.H.; Dawson, C.; Luettich, R.A.; Jensen, R.E.; Smith, J.M.; Stelling, G.S.; Stone, G.W. Modeling hurricane waves and storm surge using integrally-coupled, scalable computations. Coast. Eng. 2011, 58, 45–65. [Google Scholar] [CrossRef]
- Coulbourne, B.; Headen, F.L.; Jones, C.; Kennedy, A.; Pagano, M.; Ramanathan, K.; Rogers, S.; Soucy, J.; Young, J. North Atlantic Coast Comprehensive Study: Resilient Adaptation to Increasing Risk; Physical Damage Function Summary Report; United States Army Corps of Engineers: Washington, DC, USA, 2015. [Google Scholar]
- Sheppard, S.R.; Shaw, A.; Flanders, D.; Burch, S. Can visualization save the world? Lessons for landscape architects from visualizing local climate change. In Peer Reviewed Proceedings Digital Landscape Architecture 2008: Introduction, Anhalt University of Applied Sciences; Buhmann, E., Heins, M., Pietsch, M., Eds.; Wichmann: Berlin, Germany, 2008; pp. 2–21. [Google Scholar]
- Chen, L.; Xu, J.-C. Optimal delaunay triangulations. J. Comput. Math. 2004, 22, 299–308. [Google Scholar]
- Padgett, J.; DesRoches, R.; Nielson, B.; Yashinsky, M.; Kwon, O.S.; Burdette, N.; Tavera, E. Bridge damage and repair costs from Hurricane Katrina. J. Bridg. Eng. 2008, 13, 6–14. [Google Scholar] [CrossRef]
- Robertson, I.N.; Riggs, H.R.; Yim, S.C.; Young, Y.L. Lessons from Hurricane Katrina storm surge on bridges and buildings. J. Waterw. Port Coast. Ocean Eng. 2007, 133, 463–483. [Google Scholar] [CrossRef]
- Chang, J.I.; Lin, C.-C. A study of storage tank accidents. J. Loss Prev. Process Ind. 2006, 19, 51–59. [Google Scholar] [CrossRef]
- Lange, E. The limits of realism: perceptions of virtual landscapes. Landsc. Urban Plan. 2001, 54, 163–182. [Google Scholar] [CrossRef]
- Schroth, O.; Hayek, U.W.; Lange, E.; Sheppard, S.R.; Schmid, W.A. Multiple-case study of landscape visualizations as a tool in transdisciplinary planning workshops. Landsc. J. 2011, 30, 53–71. [Google Scholar] [CrossRef]
- Hayek, U.W.; Halatsch, J.; Kunze, A.; Schmitt, G.; Grêt-Regamy, A. Integrating natural resource indicators into procedural visualisation for sustainable urban green space design. Peer Rev. Proc. Digit. Landsc. Archit. 2010, 361–369. [Google Scholar]
- Wynne, B. Sheep farming after Chernobyl: A case study in communicating scientific information. When Science Meets the Public; American Association for Advancement in Science: Washington, DC, USA, 1992; pp. 43–68. [Google Scholar] [CrossRef]
- Schneider, P.J.; Schauer, B.A. HAZUS—Its development and its future. Nat. Hazards Rev. 2006, 7, 40–44. [Google Scholar] [CrossRef]
- Ranger, N.; Reeder, T.; Lowe, J. Addressing ‘deep’uncertainty over long-term climate in major infrastructure projects: four innovations of the Thames Estuary 2100 Project. EURO J. Decis. Process. 2013, 1, 233–262. [Google Scholar] [CrossRef]
- Brown, C.; Werick, W.; Leger, W.; Fay, D. A decision-analytic approach to managing climate risks: Application to the Upper Great Lakes. JAWRA J. Am. Water Resour. Assoc. 2011, 47, 524–534. [Google Scholar] [CrossRef]
- Becker, A.; Matson, P.; Fischer, M.; Mastrandrea, M. Towards seaport resilience for climate change adaptation: Stakeholder perceptions of hurricane impacts in Gulfport (MS) and Providence (RI). Prog. Plan. 2014. [Google Scholar] [CrossRef]
- Moser, S.C.; Dilling, L. Communicating climate change: closing the science-action gap. In The Oxford Handbook of Climate Change and Society; Oxford University Press: Oxford, UK, 2011; pp. 161–174. [Google Scholar]
- Sheppard, S.R. Making climate change visible: A critical role for landscape professionals. Landsc. Urban Plan. 2015, 142, 95–105. [Google Scholar] [CrossRef]
- Bostrom, A.; Anselin, L.; Farris, J. Visualizing seismic risk and uncertainty. Ann. N. Y. Acad. Sci. 2008, 1128, 29–40. [Google Scholar] [CrossRef] [PubMed]
- Schroth, O. From Information to Participation: Interactive Landscape Visualization as a Tool for Collaborative Planning; vdf Hochschulverlag AG: Zürich, Switzerland, 2010; Volume 6. [Google Scholar]
- Allen, E.S. A Wind to Shake the World: The Story of the 1938 Hurricane; Little, Brown: Boston, MA, USA, 1976. [Google Scholar]
- Blake, E.S.; Rappaport, E.N.; Jarrell, J.D.; Landsea, C.; Center, T.P. The Deadliest, Costliest, and Most Intense United States Tropical Cyclones from 1851 to 2006 (and Other Frequently Requested Hurricane Facts); NOAA/National Weather Service, National Centers for Environmental Prediction, National Hurricane Center: Miami, FL, USA, 2007.
- Pérez-Peña, R.; Chokshi, N.; Fountain, H.; Astor, M.; Turkewitz, J.; Thrush, G.; Hirschfeld-Davis, J. With Death Toll at 30, Storm Makes 2nd Landfall. New York Times, 29 August 2017. [Google Scholar]
- Lin, N.; Emanuel, K. Grey swan tropical cyclones. Nat. Clim. Chang. 2016, 6, 106–111. [Google Scholar] [CrossRef]
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).