Menyanthes is a unique programme that will enable its users to analyse different kinds of groundwater level data. With Menyanthes, uses can perform time series analyses as well as statistical analyses.

Time series analysis

Time series models model the course of the groundwater table at a single point in space. They do so by using the relationship between groundwater level observations and data on factors that influence the groundwater level, such as precipitation, evapotranspiration, groundwater abstraction, hydrological interventions, etc. Time series models have the advantage that they are easy to construct, but at the same time they have a high degree of accuracy. Some of the primary functions of these models are: 

  • Quantify the influence of factors or measures on the groundwater level;
  • Detect and quantify trends in the groundwater level;
  • Filter, lengthen or fill-up messy groundwater level data;
  • Correct groundwater level data for non-average meteorological circumstances in the observation period;
  • Pre-process groundwater level data before it is used to calibrate a groundwater model.

If necessary, Menyanthes can do all sorts of data-manipulations, like resampling or interpolation operations to make the data equidistant for ARMA / Box-Jenkins type time series models. The models are stored in the internal database of the program, so that they can be easily used for e.g. simulation, spatial  analysis, or can be analyzed and compared with respect to their respons functions and/or statistical properties.

What makes Menyanthes unique is the new method of modeling series of groundwater level observations which is implemented (the PIRFICT model, von Asmuth et al, 2002). Traditional methods still ask for a considerable amount of expertise and time investment. By using a different mathemical perspective, Menyanthes now brings time series analysis within the range of every practicioning hydrologist and/or ecologist, without giving in on the accuracy and reliability of the results. The difference with the traditional approach, the transfer-function noise models of Box and Jenkins, is that an elementary piece of knowledge about the dynamical behavior of groundwatersystems is already implemented in the model. Because of this, a user does not have to get into the complex structure and theory behind the (X)ARIMA TFN-models of Box and Jenkins. Moreover, using this approach, time series models also prove be able to handle high-frequency data or 'messy' data with missing observations, irregular time steps, etc., in a better and far more flexible manner. More information on the methods behind the program can be found in our list of publications on Menyanthes.

Statistical analysis

Menyanthes offers tools to calculate all kinds of statistics that define and characterise the groundwater level fluctuations or dynamics at a certain location. These include:

  • Mean Highest, Lowest and Spring Groundwater Levels
  • Frequency of exceedence graphs
  • Groundwater regime curves
  • Percentiles for the distributions and regime graphs
  • So-called 'GT' or groundwater classes

All statistics can be calculated simultaneously for multiple groundwater level series, and can be exported to csv-file for import in e.g. a GIS system. Groundwater level statistics can also be presented in various convenient graphs. Using the time series modeling tools, short or messy groundwater level series can be cleaned, lengthened and corrected for the meteorological situation in the observation period. By doing so, the accuracy with which the groundwater dynamics can be quantified using data collected in observation wells is greatly enhanced. More information on various methods to characterize groundwater dynamics can be found in:

Von Asmuth, J.R. & Knotters, M., 2004: 'Characterising spatial differences in groundwater dynamics based on a system identification approach' , Journal of Hydrology, 296(1-4), 118-134.

 Time series 



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