Leveraging Existing USGS Streamgage Data to Map Flood-Prone Areas
We will develop reproducible workflows in R and Python to combine already existing and underutilized field data collected as part of the USGS streamgage network with remotely sensed data to map flood-prone areas for various recurrence intervals in both gaged and ungaged stream reaches.
Knowing where floods occur is crucial for a wide range of stakeholder needs, from protecting people and infrastructure from flood risk, to managing and maintaining healthy riparian and floodplain ecosystems that frequently flood. The goal of this project is to develop automated workflows using open-source software and publicly available data to map flood extent in locations that are unmonitored. The output will be continuous digital representations of floodplains across stream networks for a range of annual exceedance probabilities. The floodplains will also include width and other metrics averaged by cross-section, reach, or watershed. A model to estimate flood extent in unmonitored locations will be calibrated using existing USGS streamgage data and field measurements, and elevation data derived from lidar. This project will develop methods for using streamgage data outside traditional applications and create workflows for more readily accessing intermediate datasets associated with streamgage maintenance that are relevant to floodplain mapping.
We will develop reproducible workflows in R and Python to combine already existing and underutilized field data collected as part of the USGS streamgage network with remotely sensed data to map flood-prone areas for various recurrence intervals in both gaged and ungaged stream reaches.
Knowing where floods occur is crucial for a wide range of stakeholder needs, from protecting people and infrastructure from flood risk, to managing and maintaining healthy riparian and floodplain ecosystems that frequently flood. The goal of this project is to develop automated workflows using open-source software and publicly available data to map flood extent in locations that are unmonitored. The output will be continuous digital representations of floodplains across stream networks for a range of annual exceedance probabilities. The floodplains will also include width and other metrics averaged by cross-section, reach, or watershed. A model to estimate flood extent in unmonitored locations will be calibrated using existing USGS streamgage data and field measurements, and elevation data derived from lidar. This project will develop methods for using streamgage data outside traditional applications and create workflows for more readily accessing intermediate datasets associated with streamgage maintenance that are relevant to floodplain mapping.