Natural Hazards: Links between Science and Practice

What is it about?

Natural hazards are the subject of continous societal interest, often requiring integrated information and knowledge management for better understanding of risks and mitigation of natural disasters. Although sources of natural hazards can be different, such as atmospheric, hydrologic, volcanologic, seismic, to name but a few, their environmental impacts are almost always catastrophic. Furthermore, the rapid urbanization in regions prone to natural hazards places more people at risk than ever before. Regarding the natural hazards addressed, this special issue has a narrower focus, dealing with the application of information technology, methods and tools for the detection, monitoring, modelling and forecasting of natural hazards.

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Why is it important?

The paper by Stanić et al. presents GIS-oriented hydro-informatics platform 3DNET and the associated hydrologic model, with the focus on the platform and model features that are relevant for flood simulations. They find the presented features of the platform and the model, their application and the results interesting not only to scientific circles, but also to people dealing with such simulations in practice. The paper by Alabyan and Lebedeva deals with the modeling of the Northern Dvina River, a river with a large anastomosed delta subject to tidal effects. The proposed approach uses the horizontal 2D numerical model based on the shallow-water equations. The influence of the roughness and the bathymetry of delta arms seems to be less important than the water level regime along the marine boundary. The paper by Ivković et al. examines the possibility to include daily rainfall data from a dense observation network in the flood forecasting system with subdaily data. The method is to the extreme flood event in the Kolubara catchment in May 2014. The results of hydrologic simulations show a moderate improvement when the disaggregated rainfall from the denser network is used, thus indicating the significance of better representation of rainfall temporal and spatial variability for flood forecasting. The paper by Yamashkin et al. describes a methodology for decoding of remotely sensed multispectral space images based on the Ensemble Learning concept, which allows for solving effectively important problems of mapping geosystems. The performed experiments confirmed that the use of ensemble-systems allows good accuracy and reliability of the analysis.

Perspectives