WIT Press


INTEGRATED MODEL OF MODELS FOR GLOBAL FLOOD ALERTING

Price

Free (open access)

Volume

194

Pages

11

Page Range

73 - 83

Published

2020

Paper DOI

10.2495/FRIAR200071

Copyright

Author(s)

BANDANA KAR, DOUG BAUSCH, JUN WANG, PRATIVA SHARMA, ZHIQIANG CHEN, GUY SCHUMANN, MARLON PIERCE, KRISTY TIAMPO, RON EGUCHI, MARGARET GLASSCOE

Abstract

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures, specifically, by providing situational awareness in near real-time about flood impacts to enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions to forecast flood events at regional to global scale. Given the global coverage of two operational flood models – GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the purpose of this project is to implement a Model of Models (MoM) approach to integrate the outputs from these two models to classify flood severity at watershed level worldwide, and send alerts based on severity similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes) to flood impacted communities. The alerts containing flood impacts and severity information will be disseminated through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. The current version of the MoM approach was implemented for a case study flood event that occurred during January and February of 2020 in South and Central Africa. The findings of the case study event reveal that the approach is effective in identifying potential flood impact areas and the spatio-temporal variation of flood severity, flood depth and extent at watershed level, which will be used to assess infrastructure and societal impacts using earth-observation data and for alerting.

Keywords

global flood forecasting, flood modelling, model of models, alerting, DisasterAWARE