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Emerging Flood Model Validation Frameworks for Street-Level Inundation Modeling with StormSense

Published

Author(s)

Cuong Nguyen, Sokwoo Rhee, Jon 'Derek' Loftis

Abstract

Technological progression in flood monitoring methods and the proliferation of cost-efficient Internet of Things (IoT)-enabled water level sensors is opening the door for new streams of information to be at the disposal of today's smart cities. StormSense is an inundation forecasting research initiative and an active participant in the Global City Teams Challenge seeking to enhance flood preparedness in the Smart Cities of Hampton Roads, VA for flooding resulting from storm surge, rain, and tides and demonstrating replicability of the solution across cities. Herein, we present street-level hydrodynamic modeling results at 5m resolution with conventional flood validation sources alongside new emergent techniques for validating model predictions during three prominent recent flooding events in Hampton Roads, VA, during Fall 2016: Hurricane Hermine, Tropical Storm Julia, and Hurricane Matthew. Emerging validation techniques explored are: (1) IoT-water level sensors, (2) crowd-sourced GPS maximum flood extent measurements, (3) geospatial flooded area comparisons with drone-surveyed flood extents via ESRI's Drone2Map, and (4) location inference algorithms via open source entity extraction of social media posts about flooding in Hampton Roads during the events. Model open boundary inputs were prescribed by recently-established water level sensors, and precipitation inputs were derived from 128 rain gauges owned by individual municipalities' public utilities and waterworks divisions. Observations were interpolated to form a spatially and temporally-varying hydrodynamic model rainfall input during 2016 Hurricane Matthew (10/8-9) and Tropical Storm Julia in the weeks prior (9/19-22) to Matthew. The model was validated via 5 newly-established tide gauges within the domain for an aggregate vertical RMSE of 8.19cm, and was geospatially validated via 206 crowd-sourced GPS flood extents from the 'Sea Level Rise' App for a mean horizontal distance difference of 4.97m.
Conference Dates
April 21, 2017
Conference Location
Pittsburg, PA, US
Conference Title
Second International Workshop on Science of Smart City Operations and Platforms Engineering
(SCOPE)

Keywords

Hurricane Matthew, Hydrodynamic Modeling, Internet of Things, Smart City, Global City Teams Challenge, Replicability, Citizen Science, Sea Level Rise, Drone2Map

Citation

Nguyen, C. , Rhee, S. and Loftis, J. (2017), Emerging Flood Model Validation Frameworks for Street-Level Inundation Modeling with StormSense, Second International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE), Pittsburg, PA, US, [online], https://doi.org/10.1145/3063386.3063764, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922844 (Accessed August 10, 2022)
Created April 25, 2017, Updated April 19, 2022