Innovating global flood alerting with an ensemble of models and remotely sensed observations
- 1AAAS Science, Technology and Policy Fellow, U.S. Department of Energy – Office of EERE, USA
- 2University of Missouri, Kansas City, MO, USA
- 3Niyam IT, Inc., Maui, HI, USA
- 4Indiana University, Bloomington, IN, USA
- 5ImageCat Inc., Long Beach, CA, USA
- 6School of Geographical Sciences, University of Bristol, Bristol, UK
- 7University of Alabama in Huntsville, Huntsville, AL, USA
At the global level, several flood related tools are available for free, ranging from observations to modeling and forecasting, using field data, remotely sensed observations as well as hydrologic and hydrodynamic models (for more details of available tools, see EOTEC DevNet’s tool tracking capacity building resources for flooding at https://eotec-dev.ceos.org/tools/). In this context, the Global Flood Awareness System (GloFAS) managed by Copernicus, for instance, aims to facilitate response to flooding, particularly in countries that cannot forecast these events on their own.
However, having an EWS available to all globally, with consistent accuracy and reliability, for alerting at different severity levels, will not only aid with reduction of flood impacts, but also assist with improving resilience of these counties.
In this paper, we present the model of models (MoM), which is an ensembled model that forecasts flood severity daily, globally at sub-watershed level. MoM integrates the outputs of GloFAS, GFMS, and HWRF models to forecast severity and uses MODIS and VIIRS outputs for calibration and validation of severity scores.
The flood severity risk score is used to obtain and process high-resolution Earth observation data to assess flood depth and extent at granular level and estimate flood impact on critical infrastructure.
The flood severity score is used to trigger dissemination of alerts using PDC’s DisasterAWARE® platform.
We present a number of real event cases where MoM has been activated to alert and assist with event response activities, including performance validation with high-resolution satellite flood maps.
How to cite: Kar, B., Sharma, P., Bausch, D., Wang, J., Schumann, G., and Glasscoe, M.: Innovating global flood alerting with an ensemble of models and remotely sensed observations, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17400, https://doi.org/10.5194/egusphere-egu23-17400, 2023.
