Flooding is a major hydro-meteorological event that impacts billions of people across the world daily. Models and Earth observation data are used for forecasting flood severity, extent, and depth, but these models and derived products are often not globally operational, and they often provide different outputs. Looking at recent disastrous events at a global level and the importance recently attributed to the need for early warning systems, it may seem that not much is being done to warn the public early enough, respond appropriately, or mitigate impacts. This is far from the truth. Worldwide, many organizations monitor hydrometeorological, geological and other types of hazards in order to allow the design, preparation and execution of an adequate response. The Model of Models (MoM) approach that leverages hydrologic models and Earth Observation datasets in an integrated manner is designed to provide flood risk information to assist emergency responders.

This research presents Earth Observation (EO) based methods and data developed for flood risk management and investigates the impact on critical infrastructure. Using Vadodara as a pilot city, this research aims to establish the utility of the flood products in risk mitigation and response. This research is a collaboration between two NASA funded Disasters program ROSES grants, “Open Critical Infrastructure Exposure for Disaster Forecasting, Mitigation and Response,” and “Advancing Access to Global Flood Modeling and Alerting.”

Urban areas are home to majority of the world's population. According to the U.S. Census, more than 80% of the US population was urban in 2020. In 2020, 40% of the energy in the US was consumed by residential and commercial sectors (EIA 2022). Urban Heat Island (UHI), a consequence of urbanization (and use/cover distribution, impervious surface, urban morphology, building type and materials, vegetation and green infrastructure, transportation, among others), is characterized by temperature difference between an urban area and its surroundings. UHI also influences energy consumption. Li et al. (2019) reported that UHI could result in a median increase of 19% in cooling energy consumption and a median decrease of 18.7% in heating energy consumption. The authors reported that UHI impacts could contribute to an increase in cooling energy consumption (by 10-120% and a decrease of heating energy consumption (by 3-45%) among cities.

Monitoring progress of power restoration following extreme events is essential for situational awareness about spatiotemporal distribution of populations without power and to help with response efforts. Because of the proprietary nature of restoration data, and the difficulty in obtaining power outage data from utility companies in near real-time (e.g., during Hurricane Maria (2017)), this project used satellite derived nighttime lights data from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) to (i) monitor wide area power outage and (ii) estimate impacted customers over time to assist with restoration. A discussion of the methodology and its implementation during hurricanes Maria (2017) and Eta (2020) is presented in this paper. Future work will focus on calibrating estimated customers based on light intensity and density distribution, and generation of restoration profiles for emergency response.

The rising number of flooding events combined with increased urbanization is contributing to significant economic losses due to damages to structures and infrastructures. Here we present a method for producing all weather maps of flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods that can be used to provide information on the evolution of flood hazards to DisasterAware©, a global alerting system, that is used to disseminate flood risk information to stakeholders across the globe. While these efforts are still in development, a case study is presented for the major flood event associated with Hurricane Harvey and associated floods that impacted Houston, TX in August of 2017.

Summary Risk communication is the exchange of information among stakeholders about an impending disaster and its risks to help individuals take appropriate actions to mitigate hazard impacts. While traditional risk communication follows a command and control structure such that information from hierarchical and vertically integrated organizations is disseminated to broader community, social media uses a decentralized, collaborative, and network based communication approach. The growth of information and communication technologies has made social media a popular channel for disseminating alert and warning messages both by citizens and agencies. However, social media suffers from spreading rumors and hoaxes. To minimize rumors and increase citizen communication, a science gateway (Cyber‐Infrastructure for GeoInformatics and Community Resilience) has been deployed. This gateway, resulted from a research conducted along the Mississippi Gulf Coast communities, incorporates citizen science to evaluate warning message sources, message contents and dissemination channels to increase public response to warnings. This gateway built on the social construct of risk communication provides opportunities to citizens to share data and information about a hazard, and participate in building community resilience. Copyright © 2015 John Wiley & Sons, Ltd.