Real-Time Data in Operational Hydrology - Ways of Visualizations in an Online Cartographic Application

Lienert, Christophe; Weingartner, Rolf; Hurni, Lorenz (2008). Real-Time Data in Operational Hydrology - Ways of Visualizations in an Online Cartographic Application (Unpublished). In: Autocarto, the 17th International Research Symposium on Computer-based Cartography. Shepherdstown, West Virginia. 8.-11. September 2008.

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Early warning and monitoring activities in the field of operational hydrology – and any other natural hazard management field – are crucial in order to limit damages to life and assets caused by extreme natural events. Statistics show that hydro-meteorological events both on a global and a local scale are increasing in their number of occurrence as well as in their intensities. Decision makers such as operational hydrologists must be enabled to quickly and comprehensively overlook an on-going situation, to go more into its details and then take further actions in collaboration with crisis management groups based on the information they have. Thus, more than ever, real-time data from different measurement networks and sources have to be compiled, processed and presented in real-time to these decision makers.
The scientific, cartographic challenge is to deliver automated, real-time visualizations in consideration of sound cartographic rules and standards. In this paper, the conceptualization of a web-based cartographic real-time information system is presented. Based on this concept, a prototype has been tested that integrates acquisition, processing and visualizations of real-time hydro-meteorological data in different ways, depending on the chosen timeframe and level of detail. Besides monitoring visualizations, data are retraceable using visualizations that clearly stress the course of the event and the data’s spatio-temporal character. Further visualizations and functionalities are provided that allow for putting real-time data in relation to a broader historical context. Data are instantaneously retrievable from a data archive, classified and visualized in order to learn from former events and experiences.

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