Flood hazard and risk are increasing and so are the associated costs. It is therefore important to collect data, monitor floods over various scales, and provide accurate and reliable model predictions. Global Flood Hazard: Applications in Modeling, Mapping and Forecasting, a new book just published by the American Geophysical Union, describes the latest tools and technologies for modeling, mapping, and predicting large-scale flood risk. There are many challenges that exist at present and need to be overcome, but the science and applications of global flood hazard estimation is currently a fast‐moving field with much more significant progress to be expected in upcoming years. Here one of the editors answers some questions about this field.
Where does flooding rank alongside other natural hazards in terms of severity, frequency, impact and cost?
According to Munich Re, one of the world’s leading insurance companies, beside windstorms, floods are the most frequent cause of natural hazard losses. Worldwide, about a third of all reported events and a third of economic losses resulting from natural catastrophes are attributable to floods.
The United Nations report that flooding has accounted for nearly half of all weather-related disasters worldwide since 1995 and has killed an estimated 157,000 people and affected some 2.3 billion others.
In the world’s largest coastal cities alone, if flood protection measures are not implemented, flood damages could amount to $1 trillion per year by 2050.
How is satellite imagery used to map and monitor floods?
With the recent proliferation of space-based image data, floods can now be mapped almost at daily frequency around the world, and at many different spatial resolutions and coverages.
In the presence of cloud cover, radar imagery, has many notable advantages for flood mapping given its all-weather, day and night capability.
With the recent constellations of radar as well as optical sensors, from both private and public sectors, our capability to map floods more reliably is steadily increasing.
How have developments in remote sensing technology improved the accuracy of flood monitoring and prediction?
Satellite as well as sensor technologies have greatly improved over the last decade, making geolocation, geometric and spectral properties of satellite images much more precise and accurate. Consequently, flood mapping algorithms have been made more robust and efficient and in turn the value of the information content that can now be extracted from satellite data has increased greatly. This development has led to better flood monitoring capabilities and also, through assimilation into forecast models, to better flood prediction at various scales.
What kind of models are used to understand flood hazards?
Understanding flood hazard and thus risk is dependent on many factors. Meteorological forcing data, such as precipitation, needs to be analyzed and processed to feed correctly into hydrology models that simulate discharge characteristics. Those numbers, in turn, are then used as inputs to hydrodynamic models to simulate the movement of a flood wave in rivers and across adjacent floodplain lands. Running such a cascade of models and producing meaningful results of flood hazard and associated risk is not without significant challenges.
What have been the major advances in flood hazard modeling over recent decades?
Major advances in computational efficiency and numerical modeling codes over the past decade have enabled better quantification of flood hazard and risk. Also, global data sets needed to estimate hazard and risk have been made more accurate and more readily available. Here, a steady increase of better open data and a clear push towards implantation of interoperability standards and protocols for models and their data have led to major advances in our ability to understand, quantify and predict flood hazard and risk at local to global levels.
How can remote sensing and modeling efforts complement one another?
Remote sensing, in particular from satellites, albeit an observation, is only a snapshot in time while a flood is a very dynamic process. Hence, it is very difficult to capture, quantify and understand flood hazard in full by using remote sensing technology only. In contrast, computer models are only a more or less complex simplification of reality but can simulate a dynamic event, continuously in space and time. This complementarity between remote sensing and models can be most effectively leveraged by integrating both. There are several approaches to achieve the “best of both worlds” and advanced so-called data assimilation techniques show promising results in this context.
What further efforts are required to improve the accuracy of flood models and flood forecasts?
With all the technological and scientific advances in modeling and remote sensing over recent years, significant progress has been made in the fields of flood hazard and risk to the point where quantification, prediction and projections of such are now possible at the global scale. This said, many challenges remain to improve the accuracy of estimates and forecast of flood hazard and therefore risk.
Among those challenges, the most important ones are without a doubt the provision of more accurate and open-access model forcing (precipitation, discharge) and boundary data at the global level, such as more accurate global topography in particular in urban areas where assets at risk are located. Also, better interoperability between data, models and output products is needed to increase reliability and impact of the science of flood hazard and risk.
Global Flood Hazard: Applications in Modeling, Mapping and Forecasting, 2018, 300 pp., ISBN: 978-1-119-32586-4, list price $189.95 (hardcover), $151.99 (e-book)
—Guy J.-P. Schumann, Remote Sensing Solutions, Inc., California, USA, and School of Geographical Sciences, University of Bristol, UK; email: email@example.com
Schumann, G. J.-P. (2018), The challenges of global flood hazard mapping and prediction, Eos, 99, https://doi.org/10.1029/2018EO101241. Published on 09 July 2018.
Text © 2018. The authors. CC BY-NC-ND 3.0
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