Dear Colleagues,

Extreme flooding is increasing worldwide and remains the deadliest weather-related hazard, especially in densely populated areas. Real-time forecasting with sufficient lead time is paramount in order to significantly mitigate damage from flooding. Such results must be delivered within a predetermined time horizon and with enough accuracy to promote community confidence in actions taken to prepare for an emergency.

Despite extensive efforts to improve forecasting accuracy, predictability, and efficiency, real-time flood forecasting is still being hindered due to the complexity of natural phenomena represented by equifinality, hysteresis, non-uniqueness, non-linearity, and internal variability. Application in urban environments can be more challenging, as much finer spatial resolution is needed in the models to resolve interactions among streets, buildings, and other infrastructures.

This Special Issue aims to collect papers on current efforts to simulate real-time flood forecasting in watersheds of varying scales and environments with urban characteristics. The following list provides an overview of the topics we are looking for, but is not exhaustive.

• Techniques to improve model accuracy and quantify model uncertainties, such as data assimilation, model calibration, and optimization.
• Data-driven methods to increase model efficiency while preserving model accuracy, such as deep learning and surrogate modeling.
• Reduced modeling techniques to reduce dimensionalities at larger spatial and finer temporal scales.
• Remote sensing techniques relevant to enriching the availability of model inputs and outputs.
• Application of real-time flood forecasting with a particular interest in developing countries and data-poor regions.

Prof. Dr. Jongho Kim
Dr. Kyongho Son
Dr. Seongho Ahn
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• real-time flood forecasting
• urban flood
• uncertainty quantification
• deep learning
• surrogate modeling
• data assimilation
• remote sensing
• numerical models