06 Feb 2024
 | 06 Feb 2024
Status: this preprint is currently under review for the journal NHESS.

Water depth estimate and flood extent enhancement for satellite-based inundation maps

Andrea Betterle and Peter Salamon

Abstract. Floods are extreme hydrological events that can reshape the landscape, transform entire ecosystems, and alter the relationship of humans and animals with the surrounding environment. Every year, fluvial and coastal floods claim thousands of human lives and cause billions of euros in direct damages and inestimable indirect losses, in both economical and in life-quality terms. Monitoring the spatio-temporal evolution of floods is of fundamental importance in order to reduce their devastating consequences. Observing floods from space can make the difference: from this distant vantage point it is possible to monitor large areas consistently and, by leveraging multiple sensors on different satellites, it is possible to acquire a comprehensive overview on the evolution of floods at a global scale. Synthetic Aperture Radar (SAR) sensors in particular have proven extremely effective for flood monitoring, as they can operate day and night and in all weather conditions, with a highly discriminatory power. On the other hand, SAR sensors are unable to reliably detect water in certain conditions, the most critical being urban areas. Furthermore, flood water depth – which is a fundamental variable for emergency response and impact calculations – cannot be estimated remotely. In order to address such limitations, this study proposes a framework for estimating flood water depths and enhancing satellite-based flood delineations, based on readily available topographical data. The methodology is specifically designed to accommodate, as additional inputs, masks delineating water bodies and/or areas that are excluded from flood mapping. In particular, the method relies on simple morphological arguments to expand flooded areas to cover excluded regions, and to estimate water depths based on the terrain elevation of the boundaries between flooded and non-flooded areas. The underlying algorithm – named FLEXTH – is provided as Python code and is designed to run in an unsupervised mode in a reasonable time over areas of several hundred thousand square kilometers. This new tool aims to quantify and ultimately to reduce the impacts of floods, especially when used in synergy with the recently released Global Flood Monitoring product of the Copernicus Emergency Management Service.

Andrea Betterle and Peter Salamon

Status: open (until 19 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Formal Review Comments on nhess-2024-22', Anonymous Referee #1, 21 Feb 2024 reply
Andrea Betterle and Peter Salamon
Andrea Betterle and Peter Salamon


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Short summary
The study proposes a new framework, named FLEXTH, to estimate flood water depths and improve satellite-based flood monitoring using topographical data. FLEXTH aims to reduce the impact of floods and is readily available as a computer code, offering a practical and scalable solution for estimating flood depths quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.