Rüppel, Uwe ; Göbel, Peter
Hryniewicz, Olgierd ; Studzinski, Jan ; Romaniuk, Maciej ; Szediw, Anna (eds.) (2007):
Semiautomatic verification of groundwater measured data.
In: EnviroInfo 2007: Environmental Informatics and Systems Research, pp. 569-576,
Aachen, Shaker Verlag, 21st International Conference on Informatics for Environmental Protection (EnviroInfo), Warschau, Polen, 12.-14. September 2007, ISBN 978-3-8322-6397-3,
[Conference or Workshop Item]

Abstract

Groundwater is an unseen yet vital resource. It is the most important resource for potable water supply in Germany. Particularly in the state of Germany Hesse, where drinking water originates to 95.2% from groundwater, there is no substitute (HLUG 2007). However, groundwater does not only play an extraordinarily decisive role in watereconomical regards, but as a substantial component of the hydrological cycle, it must be seen with its great importance for the ecological system. The main objectives of groundwater monitoring are to study the variations and long-term trends in the quantitative and qualitative condition of groundwater. The provided information is intended to serve as basis for assessment of environmental quality goals and norms, to ensure compliance with regulations and to prevent excessive use of the groundwater supplies for a sustainable groundwater management. In order to give a representative picture of the groundwater status in consideration of the complexity of hydro-geological systems and heterogeneous groundwater bodies, the net of measuring points is very dense. In a large scaled catchment area, there will be easily some thousands of these groundwater objects, each of them producing groundwater data. In addition with the trend of data loggers in crucial or hard accessible rough nature environments for an automated retrieval and storage of information from one or more sensors, enormous data pools are the result. Due to various reasons, these data do not always have the required quality in order to accomplish the necessary analyses. Since the data pools are too big a manual check up is not suitable. For these reasons series of measurements are in principle to be automatically examined for plausibility – whereas a skilled worker still has the opportunity to make the final decision to obtain high quality data.

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