This image shows the visual results of preprocessing the original digital elevation model data of each spatial resolution in ArcGIS to create the inputs needed to run the Coupled Routing and Excess Storage hydrologic model. Images created by the NASA Goddard Space Flight Center DEVELOP Choptank Watershed team.
Authors: Melissa Oguamanam, Scott Cook
Advisors/Science Mentors: Frederick Policelli, Megan Lang, Dimitar Ouzounov, Elizabeth Creamer, Huan Wu, Dan Slayback
Abstract: Surface water inundation is a key factor controlling the provision of multiple ecosystem services. Passive remotely sensed data have been used to map inundation, but these sensors are not well suited for the detection of inundation below the canopy level in forests. Active sensors, including LiDAR, have been successfully used to detect inundation below the forest canopy. LiDAR intensity data can be used to create accurate maps of forest inundation and LiDAR-based digital elevation models (DEMs) which are core components to hydrological models. Other types of remotely sensed data have been used to create DEMs, but the accuracy of these are limited by spatial resolution and the inherent nature of the sensors used to collect this information. The model used in this study is the Coupled Routing and Excess STorage (CREST) hydrological model. The inputs for the CREST model are the DEM, flow direction, flow accumulation, rainfall, and potential evapotranspiration data. These parameters are used to model potential flooding based on the slope and gradient of the contributing area. Using high resolution DEMs may increase the accuracy of the CREST model. This project seeks to quantify the impacts of DEM spatial resolution and quality on the ability to model potential inundation in forested ecosystems. Students will parameterize a terrain-based model of potential inundation using LiDAR, InSAR (SRTM), and NED-based DEMs at multiple spatial resolutions and validate the CREST model results using a field-validated, LiDAR intensity-based map of inundation developed for forested ecosystems within the Greensboro Watershed, Maryland. The resolution of rainfall and potential evapotranspiration data for Greensboro is coarse, and interns will investigate for finer resolution data inputs for modeling. Study results would contribute to an enhanced understanding of the drivers controlling surface water inundation and improved techniques for modeling inundation for the assessment of ecosystem services and mitigation of flood impacts.
Video transcript available here.