EGU2020-22125
https://doi.org/10.5194/egusphere-egu2020-22125
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Dissemination of modeled and satellite derived flood products: Global coverage to support local needs

Albert Kettner1, Guy Schumann1,2,3, Robert Brakenridge1, Bob Adler4, Fritz Policelli5, Daniel Slayback5, Patrick Matgen6, Michael Souffront7, and Xinyi Shen8
Albert Kettner et al.
  • 1Univ. of Colorado, INSTAAR, DFO - Flood Observatory, Boulder CO, United States of America (albert.kettner@gmail.com)
  • 2Remote Sensing Solutions (RSS), United States of America
  • 3School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
  • 4University of Maryland, United States of America
  • 5NASA Goddard Space Flight Center, Maryland, United States of America
  • 6LIST, Luxembourg Institute of Science and Technology, Luxembourg
  • 7Aquaveo, Utah, United States of America
  • 8University of Connecticut, Connecticut, United States of America

The demand for timely and accurate flood information is well understood and more urgent than ever as flooding has become the most common natural hazard worldwide, impacting people of all continents in both developed and less developed countries. Population and total exposed assets by river flooding are certain to increase in the coming century making the need for flood information even more pressing. Unlike the World Meteorological Organization (WMO), the hydrological community hasn’t been very successful in establishing a global hydrological network of observations through which model simulations and measurements and novel measurement technologies could be exploited. Countries that can afford have departments in place that are tasked to develop flood risk maps and are involved in flood forecasting and relief efforts. However, the majority of countries do not or cannot allocate sufficient funds to support such efforts, nor has there been a global initiative to identify and determine global flood risk areas.

Due to the lack of objective knowledge of the impact of flooding during or after an event, first relief agency assistance is often constrained and therefore less effective. These humanitarian catastrophes could be reduced with better transformation of existing observational and modeling technologies into information useful to local populations and decision makers.

Here I present new efforts to produce a state-of-the-art, globally-scoped, flood prediction, monitoring capabilities and risk evaluations platform that is interactive and includes high resolution flood information to better serve local needs. The platform builds upon already operational or quasi-operational NASA-supported global flood systems, including the DFO - Flood Observatory satellite-based hydrological gauging stations, UMD Global Flood Monitoring System (GFMS) and have these integrated with the European Commission’s GloFAS, and SAR-based high-resolution flood mapping. This with the intension to have these data layers (flood forecasting, flood extent, and flood history) available to everybody.

How to cite: Kettner, A., Schumann, G., Brakenridge, R., Adler, B., Policelli, F., Slayback, D., Matgen, P., Souffront, M., and Shen, X.: Dissemination of modeled and satellite derived flood products: Global coverage to support local needs, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22125, https://doi.org/10.5194/egusphere-egu2020-22125, 2020

Comments on the presentation

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Presentation version 1 – uploaded on 28 Apr 2020
  • AC1: Comment on EGU2020-22125, Robert Adler, 01 May 2020

    Good stuff, but we have to get more specific.  PDC is doing things with our coarse resolution estimation and forecasts.  Need to discuss.

    Bob A.

    • AC2: Reply to AC1, Albert Kettner, 01 May 2020

      Hi Bob,

      I agree, and sorry for being not being very responsive. I have 2 deadlines I have to take care off but will work on this. Talk sounds good. I should have a look at the PDC site as well to see what works and what doesn't.

      Thanks, Albert.