Software for hydrogeologic time series analysis, interfacing data with physical insight

  • Authors:

  • Jos R. Von Asmuth;Kees Maas;Martin Knotters;Marc F. P. Bierkens;Mark Bakker;Theo N. Olsthoorn;D. Gijsbert Cirkel;Inke Leunk;Frans Schaars;Daniel C. Von Asmuth

  • Affiliations:

  • Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands and KWR Watercycle Research Institute, P.O. Box ...;Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands and KWR Watercycle Research Institute, P.O. Box ...;Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands;Utrecht University, Department of Physical Geography, Faculty of Geosciences, P.O. Box 80.115, 3508 TC Utrecht, The Netherlands;Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands;Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands;KWR Watercycle Research Institute, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands;KWR Watercycle Research Institute, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands;Artesia, Korte Weistraat 12, 2871 BP Schoonhoven, The Netherlands;Jutfaseweg 166, 3522 HN Utrecht, The Netherlands

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2012

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Abstract

The program Menyanthes combines a variety of functions for managing, editing, visualizing, analyzing and modeling hydrogeologic time series. Menyanthes was initially developed within the scope of the PhD research of the first author, whose primary aim was the integration of data and physically-based methods for modeling time series of groundwater heads. As such, time series analysis forms the heart of Menyanthes. Within Menyanthes, time series can be modeled using both the ARMA and PIRFICT methods. The PIRFICT method is a new method of time series analysis that has practical advantages and facilitates physical interpretation and implementation of knowledge on physical behavior. Analytic solutions to specific hydrogeologic problems may be used as response function, along with their physically-based parameters. A more general approach is possible using Skew-Gaussian distribution functions, which prove to fit the behavior of hydrogeologic (and other) systems well. Use of such functions within the PIRFICT method substantially simplifies the model identification procedure, as compared to the traditional Box-Jenkins procedure. PIRFICT models may be fitted to a large number of time series in batch. Spatial patterns that emerge in the results provide useful, additional, and independent information, which adds another dimension to time series analysis. Their interpretation is supported by the spatial visualization and analysis tools of Menyanthes. The PIRFICT method also facilitates the integration of time series and spatially-distributed models via, e.g., moment-generating differential equations. The PIRFICT method may prove to be of use for other types of time series as well, both within and outside the realm of environmental sciences.