Spatiotemporal Data Analysis, Visualization, and Modelling in Water Resources

Dear Colleagues,

Water resources management problems have important characteristics in their spatial and temporal dimensions. In recent years, due to technological advancements, new research efforts have included data in water resources model representation and analysis from remote sensing and satellite sources. The study of water resources-associated problems requires advanced spatiotemporal methods for their analysis and prediction, including estimating the probability of their occurrence and the associated risk. Key issues are the management and mitigation of extreme hydrological phenomena (e.g., precipitation, runoff), floods, low flows, droughts, groundwater, as well as modelling the fate of pollution sources both in onshore and offshore environment. The spatiotemporal study of key topics aids the understanding of the relationship between their magnitude and the probability of these events occurring. This Special Issue aims to provide spatiotemporal methods to study and mitigate major problems associated with water resources based on space–time geostatistics, machine learning, statistical theory, hydrological modelling, risk assessment, etc.

Dr. Emmanouil Varouchakis
Dr. Gerald Corzo
Guest Editors

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