Author(s): B. F. Sanders; A. Luke; J. E. Schubert; H. R. Moftakhari; A. Agha Kouchak; R. A. Matthew; K. Goodrich; W. Cheung; D. L. Feldman; V. Basolo; D. Houston; K. Serrano; D. Boudreau; A. Eguiarte
Linked Author(s): Brett F. Sanders
Keywords: No keywords
Abstract: Metric resolution “urban” flood models are emerging as powerful tools for analyzing and communicating flood risk at fine spatial and temporal scales which align with personal awareness of geographical areas, and differentiate across individual assets vulnerable to flooding such as homes, businesses, industrial facilities, health care facilities, schools, parks, places of worship, and environmental resources. A combination of trends such as urbanization, intensification of the hydrologic cycle, and higher sea levels portends a significant increase in urban flooding hazards (Hanson et al. 2011). Furthermore, development pressures in many communities mean greater willingness to build in high hazard areas such as floodplains. Urban flood models have potential to provide valuable information about the impacts of development decisions and flood mitigation measures on flood risk, and to support planning for and responding to severe flooding events. However, there is a dearth of knowledge regarding how to transform dense spatiotemporal flood model output data into information that can be used by decision makers. To develop new and improved flood modeling systems, engineers also need to deepen understanding of flooding as a coupled human-water system (Sivapalan et al. 2012). The Flood Resilient Infrastructure and Sustainable Environments (Flood RISE) project funded by the US National Science Foundation (#1331611) has resulted in the codevelopment of metric resolution flood models (e.g., Gallien et al. 2011,2014) in three communities: Newport Beach, Calif. ; Tijuana River Valley, Calif. ; and Los Laureles in Tijuana, Baja Calif. Co-development of flood models refers to a two-way communication and development process involving the research team and personnel working and living in the study areas, including residents, government officials, emergency managers, civil society groups and business leaders. Activities include in-depth interviews to gather qualitative data about flooding, a formal field survey to gather information about community awareness of and preparedness for flooding, and focus groups to deepen understanding of the decision-points that can be served by flood models and the map formats that best serve decision-making needs. Examples include maps of the 100-year flood depth, the annual probability of flooding, flood intensity (i.e., depth times velocity), and future vs. present flood risk. Focus groups revealed strong preferences for visualized flood risk information based on decision-making needs. Officials responsible for city planning and regulatory compliance were most interested in the 100-year flood zone presumably as a consequence of flood insurance policy in the USA. Water rescue personnel were most interested flooding intensity and valued greater awareness of the geographical scope of swift water hazards. Civil society groups were most interested the frequency and duration of flooding to assess potential flood damage to local ecosystems. Collectively, results suggest that a diverse set of decision-support needs can be met by a single, fine-scale modeling tool when model output is transformed into a set of maps that communicate different aspects of the flood, and address decision points relevant to diverse users. Further, we conjecture that the process of co-developing the flood model with stakeholders builds confidence in the model among both experts and community members and also creates a solid foundation to plan and evaluate flood risk interventions.