Dear Colleagues,

Over the last few decades, the understanding of water-related processes in natural/urban catchments and coastal areas has been significantly improved by means of physically-based distributed models, based on the fundamental laws of conservation of mass, energy and momentum at multiple spatio-temporal scales. These models are still evolving due to the 1) advances in mathematical derivation of hydrological and hydrodynamic processes, 2) the potentiality of mining flood data from several sources, such as the application of satellite-based products, the accessibility of range of sensors, the use of social media, etc., which reduce uncertainties in model parametrization and calibration, and 3) the increasing use of parallel computing techniques, especially for applications in large basins.

In addition to the advances in physically-based models, data-driven modeling based on computational intelligence and machine-learning methodologies has drawn mass research interest in hydrological and hydrodynamic simulation, since data availability has also increased. Machine learning techniques are powerful tools for understanding complex, nonlinear relations and have been applied in hydrological–hydrodynamic-related fields. For example, artificial neural networks, decision trees, and kernel methods (e.g., support vector machines; Gaussian process regression) have been widely explored.

The goal of this Special Issue is to collect high-quality and innovative scientific papers that describe cutting-edge research on the development and applications of physically-based and data-driven models in watershed science and technology in a broader sense.

The topics of interest include, but are not limited to, the following:

• Runoff mechanism;
• Short-term forecasting and inundation mapping of natural hazards;
• Urban flood modeling;
• Climate change impacts;
• Sediment and solute transport;
• Hydrometeorology ;
• Water losses in the catchment (evapotranspiration, infitration and interception).

Dr. Pierfranco Costabile
Dr. Vasilis Bellos
Dr. Mario Morales-Hernández
Dr. Reza Ahmadian
Guest Editors

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• physically-based models
• data-driven models
• machine learning
• high-performance computing
• watershed hydrology
• flood risk management
• high-resolution flood modeling