North Carolina State University
Co-Authors: N. Nelson
Flood maps are often developed using remotely sensed imagery and high-water mark data to produce static maps of peak flooding conditions. Few approaches exist for generating time series of flood dynamics due to trade-offs in the spatial and temporal resolutions of satellite imagery, lack of hydrologic in situ measurements, and challenges associated with modeling flood dynamics in low topography landscapes. This project addresses the existing gap in our capacity to generate flood time series using a dynamic model framework that consists of delineating flood waters from remotely sensed Sentinel-1 radar imagery, building a Random Forest machine learning model, and applying the model at a daily time-step. Predictors in the Random Forest model included daily precipitation observations and geospatial data that captured twelve biophysical and socioeconomic variables (e.g. land cover, elevation, social vulnerability, and population) at the watershed scale. The dynamic model framework was developed in the context of eastern North Carolina, which experienced severe flooding due to Hurricane Florence in September 2018. Results from this work included quantified metrics of the relative importance of individual predictor variables, as well as an evaluation of model accuracy and predictability for future model iterations.