Global Flood Map is a
‘GIFFT’ to the World
Funded by the NASA Disasters Program under its Global Initiative for Flood Forecasting and Alerting (GIFFT), Global Flood Map models global flood forecast and inundation information in the world
INTRODUCTION
What is Global Flood Map?
A product of Model of Models (MoM), an ensemble model, that integrates hydrologic models along with earth observation data to forecast flood inundation information globally.
Using DisasterAWARE to provide flood warnings.
Based on MoM, the new global flood information will reach tens of thousands of disaster management decision makers and humanitarian assistance practitioners through PDC’s DisasterAWARE platform as flood emergencies unfold. DisasterAWARE® will provide flood early warnings and supplemental advanced analytical products to help assess potential impacts.
APPROACH
Model-of-models
The model-of-models approach combines various flood products from open-source hydrologic models and Earth Observation data to determine flood severity risk at sub-watershed level daily and globally. In the current version of this model, flood outputs are being leveraged, including:
Forecasting flood severity
Global Flood Awareness System (GloFAS) and Global Flood Monitoring System (GFMS) data are used to forecast flood severity.
Flood severity in near real-time
MODIS Flood Products from Dartmouth Flood Observatory (DFO/MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data are used to determine flood severity in near real-time based on potential inundation.
Forecasting flood severity from changing tropical storms
The Hurricane Weather Research and Forecasting (HWRF) model allows forecasting flood severity from rapidly changing tropical storm events.
Trigger alert messages via DisasterAWARE
The flood severity is used to trigger alert messages for high-severity flood events via PDC’s DisasterAWARE global platform.
Trigger impact assessment
The flood severity is used to trigger processing of Sentinel 1 (Synthetic Aperture Radar imagery) and Sentinel 2 (optical imagery) as well as population and infrastructure data to assess potential impacts of flood events.
SAR-Based Flood Extent and Depth
Two techniques will be implemented to determine observed flood information from SAR data:
HydroSAR provides information for post-event flood mapping, flood depth estimation, and resulting flood severity using remote sensing-based flood information derived from SAR.
Water depth and extent is also being calculated using a thresholding technique.
The thresholding technique and HydroSAR are being compared and cross-validated in GIFFT.
Triggering of SAR flood extent product generation will use event information provided by MoM severity alerts from PDC.
Currently SAR-based surface water extent algorithms are fully implemented and validated in cloud-based platforms.
Optical Remote Sensing for Flood Severity and Damage Mapping
MoM triggered application of optical remote sensing for global flood severity and built environment damage identification and mapping.
Operation ready pre-flood water body and built-environment classification and mapping using Sentinel-2 images.
Development of pixel-level machine learning of flood water for rapid flood severity mapping and online gateway.
Development of deep metric learning of built objects (buildings and other civil infrastructure categories) detection.
Large-scale machine vision models for complex built environment learning, segmentation, and mapping.
Infrastructure and Exposure – The Global Economic Disruption Index
The GEDI scale is designed to provide simplified qualitative estimates of the severity of a disaster with regards to the global economy.
Global economic exposure data is first generated using classification of various EO datasets to detect areas of critical infrastructure (CI) or industrial activity. The detected areas are assigned economic value based on a global economic input/output model.
Real-time disaster data is then applied to the exposure to estimate economic impact, and the result is aggregated to a regional level.
The GEDI score for the disaster is calculated by comparing the pre-event and post-event values for these regional aggregates.
The system is currently being tested using hurricane event data as input. The plan is to expand this to flood events using products derived from the Model of Models process, with eventual dissemination of GEDI results through PDC.
DisasterAWARE®
Currently, PDC’s DisasterAWARE platform incorporates Model of Models (MoM) outputs as flood “incidents,” visually depicting potential floods in the context of population and infrastructure that may become affected. Automated procedures have been developed to categorize MoM outputs as DisasterAWARE “hazards,” allowing for their dissemination to users along with other flood products that assess potential impacts.
PDC’s stakeholders include UN, DoD, FEMA and their equivalents around the world. Additionally, the DisasterAWARE platform has 2 million users worldwide and is a fully functional and sustained platform used to disseminate alerts and analytical products for 18 different hazard types.
BENEFITS
Impacts of the Global Flood Map
Flood extent, depth resolution
This new technology will be used to create much-needed products that include flood extent and depth at finer resolution using Synthetic Aperture Radar (Sentinel 1) data.
Anticipate flood impacts
It will also help anticipate flood impacts on population and critical infrastructures using optical imagery as well as geospatial socioeconomic and infrastructure data.
Informed response decisions
These products aim to allow officials to make more informed disaster response decisions by quickly understanding flood severity and extent and their potential impacts to people and property—specifically in other parts of the world where flood information is not readily available.
TEAM
Contributors
Margaret Glasscoe
PIUniversity of Alabama in Huntsville
Doug Bausch
MoMNiyam IT/PDC
Bandana Kar
MoMDepartment of Energy
Marlon Pierce
MoMIndiana University
Jun Wang
MoMIndiana University
Guy Schumann
MoMImageCat Inc.
Marina Mendoza
MoMImageCat Inc.
Greg Hampe
PDCUniversity of Hawaii/PDC
Chris Chiesa
PDCUniversity of Hawaii/PDC
Kristy Tiampo
Remote SensingUniversity of Colorado, Boulder
Franz Meyer
Remote SensingUniversity of Alaska Fairbanks
Joseph Kennedy
Remote SensingUniversity of Alaska Fairbanks
Lori Schultz
Remote SensingNASA Marshall Space Flight Center
Alexander Melancon
Remote SensingUniversity of Alabama in Huntsville
MinJeong Jo
Remote SensingNASA Goddard Space Flight Center
Batuhan Osmanoglu
Remote SensingNASA Goddard Space Flight Center
ZhiQiang Chen
Remote SensingUniversity of Missouri-Kansas City
Charlie Huyck
Infrastructure ExposureImageCat Inc.
Ron Eguchi
Infrastructure ExposureImageCat Inc.
Paul Amyx
Infrastructure ExposureImageCat Inc.
TEAM
Students
Dinuka De Silva
Indiana University
Molan Zhang
University of Missouri Kansas City
TEAM
Alumnus
Prativa Sharma
University of Missouri Kansas City
PROJECT SUPPORT
Our Partners
Pacific Disaster Center
National Oceanic and Atmospheric Administration
Committee on Earth Observation Satellites
United Nations
ACHIEVEMENTS
Publications
Kar, B., Sharma, P., Chen, Z., Wang, J., Bausch, D., Schumann, G., Pierce, M., Eguchi, R.,Glasscoe, M. An ensemble approach to global flood severity forecasting and alerting in near real-time, Proceedings, International Society for Photogrammetry and Remote Sensing Congress, 2022.
Andrew Kruczkiewicz, Fabio Cian, Irene Monasterolo, Giuliano Di Baldassarre, Astrid Caldas, Moriah Royz, Margaret Glasscoe, Nicola Ranger and Maarten van Aalst, 2022, Multiform flood risk in a rapidly changing world: what we do not do, what we should and why it matters, Environ. Res. Lett. 17 081001
Tiampo, K.F., Huang, L., Simmons, C., Woods, C., Glasscoe, M.T. Detection of flood extent using Sentinel-1A/B synthetic aperture radar: An application for Hurricane Harvey, Houston, TX, Remote Sensing, 14, 2261, doi:10.3390/rs14092261, 2022.
Kar, B. and Schumann, G. (invited commentary paper). 2022. “Reproducibility and Replicability of Flood Models”, Hydrological Processes Today, 36(9). https://doi.org/10.1002/hyp.14666
Ghosh, S., Glasscoe, M., Kar, B., Tiampo, K., Green, D., Mendoza, M. and Hyuck, C., 2021. “Detection and Alert for the City of Vadodara: A Space-based Flood Approach for Mitigating Impacts on Critical Infrastructures and Reducing Economic Losses”, Know Disasters, July-August 2021.
Sharma, P., Wang, J., Zhang, M., Woods, C., Kar, B., Bausch, D., Chen, Z., Tiampo, K., Glasscoe, M., Schumann, G., Pierce, M. and Eguchi, R. 2020. “DisasterAWARE-A Global Alerting Platform for Flood Events”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, VI-3/W1-2020, 107-113. https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-107-2020
Sharma, P, Kar, B., Wang, J. and Bausch, D. 2021. “A Machine Learning Approach to Flood Severity Classification and Alerting” In Proceedings of 4th ACM SIGSPATIAL International Workshop on Advancements in Resilient and Intelligent Cities. https://doi.org/10.1145/3486626.3493432
K. Tiampo, C. Woods, L. Huang, P. Sharma, Z. Chen, B. Kar, D. Bausch, C. Simmons, R. Estrada, M. Willis, M. Glasscoe. 2021. “A Machine Learning Approach to Flood Depth and Extent Detection Using Sentinel 1A/B Synthetic Aperture Radar”, In Proceedings of the 2021 IGARSS.
Kar, B., Bausch, B., Wang, J., Sharma, P., Chen, Z., Schumann, G., Pierce, M., Tiampo, K., Eguchi, R. and Glasscoe, M. 2020. “An Integrated Model of Models for Global Flood Alerting”, WIT Transactions on the Built Environment, 194: 73-86. ISSN# 1743-3509, WIT Press.
ACHIEVEMENTS
Presentations
Glasscoe, Margaret, Kar, Bandana, Meyer, Franz, Tiampo, Kristy, Huyck, Charles, Chiesa, Chris, Hampe, Greg, Osmanoglu, Batuhan, Schultz, Lori. 2021, Dissemination of Global Surface Water Mapping from SAR and Optical Data to Global Stakeholders, American Geophysical Union Fall Meeting.
Ghosh, Shubharoop , Glasscoe, Margaret , Kar, Bandana, Tiampo, Kristy, Huyck, Charles, Hampe, Greg ;, and Amyx, Paul , 2021, A framework for flood detection and alerting for Vadodara to mitigate impacts on critical infrastructure and reduce economic losses, American Geophysical Union Fall Meeting
Tiampo, K., Woods, C., Huang, L., Sharma, P., Chen, Z., Kar, B., Bausch, D., Simmons, C., Estrada, R., Willis, M., Glasscoe, M. A Machine Learning Approach to Flood Depth and Extent Detection Using Sentinel 1A/B Synthetic Aperture Radar, oral presentation, IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021), July 2021.
Glasscoe, M., Tiampo, K., Woods, C., Kar, B., Bausch, D., Sharma, P., Zhang, M., Chen, Z., Wang, J., Pierce, M., Schumann, G., Eguchi, R., Integrating Hydrologic Models and Earth Observation Data for Global Flood Forecasting and Alerting, ASPRS – IMAGING AND GEOSPATIAL SOCIETY Annual meeting (virtual), 2021.
Kar, B., Glasscoe, M., Bausch, D., Sharma, P., Wang, J., Schumann, G., Tiampo, K., Chen, Z., Eguchi, R., Pierce, M., 2022, AN ENSEMBLE APPROACH TO GLOBAL FLOOD SEVERITY FORECASTING AND ALERTING IN NEAR REAL-TIME, ISPRS Congress
Ghosh, S., Glasscoe, M., Kar, B., Tiampo, K., and Huyck, C., 2022, Earth observation (EO) based approaches for developing flood risk products to support mitigation and response activities: A demonstration study for Vadodara, India, IGARSS.
Kristy Tiampo, Clay Woods, Lingcao Huang, Prativa Sharma, ZhiQiang Chen, Bandana Kar, Doug Bausch, Conor Simmons, Michael Willis, Margaret Glasscoe, 2021. "Advanced Flood Extent Detection Algorithms Applied to Sentinel 1A/B Synthetic Aperture Radar Data." Western-ICLR Multi-hazard Risk and Resilience Workshop, Nov 2021.
Molan Zhang and ZhiQiang Chen, “Urban Road Network and Flood Damage Assessment using Optical Remote Sensing Data and Deep Metric Learning”. Abstract submit to 2022 DSPS (1st International Data Science for Pavements Symposium)
Chen, Z., Zhang, M., Glasscoe, M., Tiampo, M. and Kar, B.. 2022. “Resilience Assessment of Urban Road Network with Flood Damage Using Remote Sensing Data,” AGU, Chicago, IL.
Schumann, G., Glasscoe, M. and Kar, B. 2022. “Global Satellite-based Flood Monitoring Post-MODIS”, LANCE UWG (https://wiki.earthdata.nasa.gov/display/LANCE/LANCE+UWG%3A+3-4+May+2022).
Ghosh, S., Glasscoe, M., Kar, B., Tiampo, K., Huyck, C., Amyx, P. and Hempe, G. 2022. “Stakeholder Engagement to Disseminate Flood Severity, inundation, and Critical Infrastructure Exposure Information for Mitigation”, ASPRS.
Sharma, P., Kar, B., Wang J. and Bausch, D.. 2021. “A Machine Learning Approach to Flood Severity Classification and Alerting”, 4th ACM SIGSPATIAL International Workshop on Advancements in Resilient and Intelligent Cities.
Glasscoe, M., Tiampo, K., Woods, C., Kar, B., Bausch, D., Sharma, P., Zhang, M., Chen, Z., Wang, J., Pierce, M., Schumann, G. and Eguchi. R., 2021 . “Integrating Hydrologic Models and Earth Observation Data for Global Flood Forecasting and Alerting”, American Society of Photogrammetry and Remote Sensing Annul Conference (ASPRS).
Sharma, P, Zhang, M., Wang, J., Wods, C., Kar, B., Chen, Z., Tiampo, K., Schumann, G., Pierce, M. and Eguchi R. 2020. “DisasterAWARE-A Global Alerting Platform for Flood Events”, GI4DM.
Glasscoe, M., Kar., B., Bausch, D., Hampe, G., Chisea, C., Tiampo, K., Chen, Z., Schumann, G., Eguchi, R., Huyck, C., Pierce, M. and Wang, J. 2020. “Rapid Flood Severity Classification and Alerting for the Spring 2020 Africa Floods: A Case Study”, AGU.
Kar, B., Bausch, D., Wang, J., Sharma, P., Chen, Z., Schumann, G., Pierce, M., Tiampo, K., Eguchi, R. and Glasscoe, M. 2020. “An Integrated Model of Models for Global Flood Alerting”, 7th International Conference on Flood and Urban Water Management (FRIAR 2020).
Schumann, G., Glasscoe, M., Bausch, D., Pierce, M., Wang, J. Chen, Z., Eguchi, R., Huyck, C., Tiampo, K. and Kar, B. 2020. “Using a Model-of-Models Approach and Remote Sensing Technologies to Improve Flood Disaster Alerting”, EGU General Assembly 2020, Virtual, https://doi.org/10.5194/egusphere-egu2020-13113.
ACHIEVEMENTS
Posters
Glasscoe, M. T., Kar, B., Meyer, F. J., Tiampo, K. F., Pierce, M. E., Huyck, C. K., Chiesa, C., Hampe, G., Osmanoglu, B., Schultz, L. A. (2022). Dissemination of Global Flood Information Through NASA Disasters Program Global Initiative for Flood Forecasting and Alerting (GIFFT). AGU Fall Meeting 2022. https://ui.adsabs.harvard.edu/abs/2022AGUFMIN45D0395G
Ghosh, S., Huyck, C., Eguchi, R., Glasscoe, M., Kar, B., Tiampo, K., Chen, Z. and Bausch, D. 2020. “Earth Observation (EO) Based Critical Infrastructure Exposure Models and Flood Forecasting Techniques for Risk Monitoring and Management for the City of Vadodara, India”, AGU.
Glasscoe, M., Bausch, D., Tiampo, K., Eguchi, R., Pierce, M., Chen, Z., Kar, B. and Schumann, G. 2019. “Advancing Access to Global Flood Modeling and Alerting Using the PDC DisasterAWARE Platform and Remote Sensing Technologies”, AGU. San Francisco, CA.
ACHIEVEMENTS
Media Releases
PreventionWeb NASA AND PACIFIC DISASTER CENTER PARTNER TO DEVELOP GROUND-BREAKING GLOBAL FLOOD EARLY WARNING SYSTEM
https://www.preventionweb.net/news/nasa-and-pacific-disaster-center-partner-develop-ground-breaking-global-flood-early-warning
PDC | Global NASA and Pacific Disaster Center partner to develop ground-breaking global flood early warning system
https://www.pdc.org/nasa-pdc-flood-early-warning-system/
PDC | Global NASA and Pacific Disaster Center link experts in development of new life-saving technologies
https://www.pdc.org/nasa-pdc-develop-life-saving-technologies/