Assimilation of Earth Observation-Derived Hydrological Information into Flood Inundation Models

Dear Colleagues,

The monitoring and modeling of river water levels, river discharges, extent of water bodies, storage in soil, lakes and reservoirs, soil moisture, flooding, and floodplain dynamics play a key role in assessing water resources, understanding surface water dynamics, characterizing and reducing disaster risks, and enabling the integrated management of water resources and aquatic ecosystems.

Powerful flood prediction tools, such as hydrological, land surface, and hydraulic models, are essential for assessing flood risk at a large scale. However, in spite of the recent advances in this field, decision-making in flood management is still hampered by uncertainties inherent to numerical modeling. To reduce prediction uncertainty, models are oftentimes constrained using in situ distributed data. However, large parts of the globe are not equipped with the required gauging stations, and the number of available stations is currently in decline. In this context, an inviting alternative that has obtained increased attention over the last years is to improve model predictions by assimilating hydrology-related data derived from globally available Earth observation satellite data. Indeed, as the number of available satellite data products (images or essential hydrological variables) is increasing rapidly—with many contributions from recently launched or planned missions—there are emerging opportunities for using these ever-growing data collections towards improving model predictions. In this context, remote sensing techniques are expected to contribute to the improvement of models as they provide homogeneous and high frequency measurements over large areas and at high spatial resolution.

Remote sensing, therefore, offers an opportunity to alleviate the decline in field surveys and gauging stations, especially in remote areas and developing countries. The integration of remotely sensed variables (such as floodplain topography, river width, flood extent, water level, land cover, etc.) with numerical flood inundation models shows a high potential for improving our understanding of processes and reducing the predictive uncertainty of such models. During the last decades, an increasing amount of research has been undertaken to better exploit the potential of current and future satellite observations. In particular, the scientific community has demonstrated how remotely sensed variables have the potential to play a key role in the calibration and validation of flood inundation models, as well as for the large-scale and near-real-time monitoring of terrestrial water bodies. However, with the exception of a few pioneering studies, the potential of remotely sensed data to enhance water-related modeling and applications has not yet been fully realized, and the use of such data for operational decision-making is far from being consolidated. The forthcoming satellite missions dedicated to global water surface monitoring—with unprecedented quality and spatial and temporal resolutions—are thus offering new opportunities to enhance the understanding and prediction of flood risk and water resources.

In this Special Issue, we welcome studies presenting the most recent advances in the enhancement of remote sensing datasets and their error characterization, and in the assimilation of these remote sensing-derived observations into flood inundation models, including, for example:

• Reservoir levels and storage;
• Flood extent and levels;
• Soil moisture;
• Ground water storage;
• River discharge, water levels, slope and width.

Dr. Renaud Hostache
Dr. Patrick Matgen
Dr. Guy Schumann
Dr. Jérôme Benveniste
Dr. Ben Jarihani
Dr. Alessio Domeneghetti
Guest Editors

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• Earth Observation
• remote sensing
• discharge
• flood extent
• water levels monitoring
• soil moisture
• groundwater monitoring
• flood inundation modelling
• data assimilation
• uncertainty reduction