Is it possible to forecast natural disasters such as flooding? DEVELOP students at NASA’s Goddard…
Detecting Floods in Greensboro, Maryland
- Published on Thursday, 10 November 2011 00:10
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Team Location: NASA Goddard Space Flight Center
Authors: Melissa Oguamanam, John David, Mussie Kebede.
Advisors/Mentors: Frederick Policelli, Dr. Megan Lang, Dr. Dimitar Ouzounov, Elizabeth Creamer, Huan Wu.
Other Acknowledgements:Scott Cook, Ron Albright, Dr. Daniel Slayback, Dr. Xianwu Xue.
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 vegetative canopies. Active sensors, including light detection and ranging (LiDAR), have been successfully used to detect inundation below the forest canopy and to create accurate maps of forest inundation. Hydrological models often use LiDAR-based digital elevation models (DEMs) as primary inputs. However, the highest-resolution DEMs are obtainable through LiDAR. This project quantified the impacts of DEM spatial resolution on the ability to model inundation in forested ecosystems within the Greensboro sub-basin of the Choptank Watershed in Maryland. Available rainfall and potential evapotranspiration (PET) data for Greensboro were investigated for the use of multiple resolution data inputs. Using high resolution DEMs may benefit the accuracy of the Coupled Routing and Excess STorage (CREST) hydrological model used in this project. The inputs for CREST were a DEM, Flow Accumulation Map, Drainage Direction Map, rainfall, and PET data. These parameters were used to model potential flooding based on geographic aspects such as soil infiltration, runoff and interflow. Previously, the CREST 1.6 model was run using the low-resolution Tropical Rainfall Measuring Mission precipitation data. This term, we focused on using higher-resolution Next Generation Radar precipitation data in CREST 2.0. Study results would contribute to an enhanced understanding of the drivers controlling surface water inundation and improved techniques to model inundation for the assessment of ecosystem services and mitigation of flood impacts.
Video transcript available here.