H036
Bridging Flood Inundation Mapping Scales between Local and Continental
Session ID#: 161050
Session Description:
The recent explosion of continental and regional flood forecasting methods (e.g. NOAA FIM, FirstStreet, FLDPLN) quickly exposed the divide between prediction and operations, posing challenges at both ends of these scales. This lack of communication and interoperability between the model and local observations, exacerbated by the digital literacy divide, requires insight and input from both communities in order to make meaningful progress. This session invites research that explores any aspect of this , particularly studies that: (1) address the integration and ingestion of local observations into national scale systems to improve skill, (2) demonstrate how national scale models are transformed into actionable information. (3) showcase technologies that aid in data use and application (4) provide methods for validating and assessing reliability and (5) that demonstrate novel communication and dissemination techniques.
Index Terms:
1821 Floods [HYDROLOGY]
4303 Hydrological [NATURAL HAZARDS]
4334 Disaster risk communication [NATURAL HAZARDS]
Primary Convener: James Matthew Matthew Coll, University of Alabama, Tuscaloosa, AL, United States
Convener: Mike Johnson, Lynker Technologies LLC, Boulder, United States
Student/Early Career Convener: James Matthew Matthew Coll, NOAA Affiliate, Lynker, Lawrence, United States
Identifying the Optimal Spatial Resolution of LiDAR Derived Elevation Data for Continental Scale Flood Inundation Mapping Applications (1048880)
Fernando Aristizabal, Lynker, Leesburg, United States; Office of Water Prediction, NWS/NOAA, Tuscaloosa, AL, United States, Fernando Renzo Salas, NOAA/NWS Office of Water Prediction, Geo-Intelligence Division, Tuscaloosa, AL, United States, Taher Chegini, University of Houston, Houston, United States and Jasmeet Judge, Center for Remote Sensing, Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States
Ensemble Flood Inundation Maps for use in Local- to Continental-Scale Forecasting Frameworks. (1092463)
Michael L Follum, Follum Hydrologic Solutions, Casper, WY, United States, Ahmad Tavakoly, US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States, Joseph Gutenson, US Army Engineer Research and Development Center, Cottondale, AL, UNITED STATES and Laura Read, Upstream Tech, Boulder, CO, United States
Computationally-efficient, High-Resolution Inland Flood Inundation Depth Prediction at the Regional Scale (1127720)
Fariborz Daneshvar1, Rahul Dhakal1, Jared Oyler2, Hannah Hampson3, Justin Rogers1 and Marco Maneta1, (1)Jupiter Intelligence, San Mateo, United States, (2)Jupiter Intelligence, State College, United States, (3)Jupiter Intelligence, New York, United States
Flood maps across CONUS using the U.S. National Water Model, satellite observations and convolutional neural networks (1175305)
Jonathan M Frame1, Veda Sunkara2, Tanya Nair2, Philip Popien1, Max Goodman1, Subit Chakrabarti1, Colin Doyle1 and Beth Tellman2, (1)Cloud to Street, New York, United States, (2)Cloud to Street, New York, NY, United States
Introducing a building level, continental scale, flood risk forecast system (1197636)
J Michael Johnson, NOAA Affiliate, Lynker, Office of Water Prediction, Tuscaloosa, United States, James Matthew Matthew Coll, NOAA Affiliate, Lynker, Lawrence, United States, Shiqi Fang, North Carolina State University Raleigh, Raleigh, NC, United States and Justin SinghMohudpur, University of California Santa Barbara, Geography, Santa Barbara, United States
