Floods are one of the most frequently occurring natural disasters. There are numerous studies devoted to comprehending and forecasting flooding in order to aid in preparedness and response. It is critical to share and communicate datasets generated by various systems and organizations for flood forecasting and modeling. The majority of organizations share limited metadata and details for flood risk data to support research and operational purposes. However, there is no standardized way for various stakeholders and automated systems to exchange flood forecast and alert data. This article proposes the Flood Markup Language (FloodML) as a data communication specification for extensively describing and exchanging flood forecasts and alerts with corresponding stakeholders.
This markup language improves the consistency for flood risk communication across different institutions by establishing a standardized format. It also improves the readability of alerts by providing structured semantics for the flood forecast data. Researchers can easily compare various forecast products for the purpose of (re)analysis. When a flood alert is issued, the emergency department can also obtain information more quickly in order to prepare for a flood. It increases processing efficiency by connecting forecast data and alerts, as well as the speed with which critical information is distributed to the public through automated data processing. The public can gain a more intuitive understanding of the changing trend of nearby rivers' water levels and possible future floods using FloodML's automated visualization capabilities. This will assist the public, local governments, and the federal government in reducing injury and property loss caused by flooding.
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Visualization of observed and three forecasted streamflow for Iowa River near Rowan in FloodML
