The SPoRT Land Information System as a Tool for Assessing Areal/River Flooding Threat

The Weather Forecast Office (WFO) in Huntsville, Alabama has played host to a number of experimental remote-sensing products and model data sets since 2002 thanks to a long-standing partnership with the NASA Short-term Prediction Research and Transition (SPoRT) Center. One such data set is the SPoRT-Land Information System (SPoRT-LIS), a real-time configuration of the Noah land surface model within the NASA Land Information System framework. It is updated four times daily with hourly temporal and ~3-km spatial resolution. SPoRT-LIS data were initially utilized for drought monitoring and local Weather Research and Forecast model initialization. These data were successfully ingested into AWIPS II in 2011, permitting subjective pattern recognition for flooding events. This presentation will outline a project to obtain a more robust, objective data set for flood threat assessment of select tributary basins of the Tennessee River and ultimately, the creation of a statistical-based flood model.

SPoRT-LIS relative soil moisture output at several sub-surface levels are utilized for initial analysis for the cold season in the Tennessee Valley (defined as November through April), over several specific Tennessee River tributaries with historical USGS streamflow measurements. For the first phase of this project, we employ a random forest regression analysis spanning at least 10 years of drainage basin rainfall estimates, relative soil moisture (predictors) and resulting basin streamflow (predictand). Because streamflow forecasts from the Lower Mississippi River Forecast Center do not incorporate quantitative precipitation forecasts beyond 24 hours (sometimes longer if coordinated with local WFOs), this statistical model will focus on the period beyond Day One for the assessment of flood risk along the select Tennessee River tributaries. Areas at significant risk for flooding will be plotted in the Graphical Forecast Editor within AWIPS to enhance forecaster situational awareness and for impact-based decision support services.