EGU22-1449, updated on 27 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Efficiency of different signal processing methods to isolate signature characteristics in altimetric water level measurements

Siavash Iran Pour1,2, Annette Eicker3, Kyriakos Balidakis4, Hamed Karimi1, Alireza Amiri-Simkooei1,5, and Henryk Dobslaw4
Siavash Iran Pour et al.
  • 1Department of Geomatics Engineering, University of Isfahan, Isfahan, Iran (
  • 2Research Institute of Environmental Studies, University of Isfahan, Isfahan, Iran
  • 3HafenCity University, Hamburg, Germany
  • 4German Research Centre for Geosciences (GFZ), Potsdam, Germany
  • 5Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands

Observed time-series of water transport in rivers can be perceived mathematically as a superposition of non-linear long-term trends, periodic variations, episodic events, colored instrument noise, and other components. Various statistical methods are readily available to extract and quantify both stationary and non-stationary components in order to subsequently attribute parts of the signal to underlying causal mechanisms. However, the available algorithms differ vastly in terms of computational complexity and implicit assumptions, and may thus have their own individual advantages and disadvantages. By employing a suite of time-series analysis methods for 1D (Wavelets, Singular Spectrum Analysis, Empirical Mode Decomposition) and additional statistical assessments like Pruned Exact Linear Time (PELT) tests for change point detection, we will analyze data from two virtual stations at Elbe River (Germany) and Urmia Lake (Iran) that are representative for the central European region with a rather humid climate, and the more arid conditions of Central Asia with much smaller hydrological signal variations, respectively. It is in particular the latter region with a much less developed in situ hydrometeorological observing system, where we expect that carefully processed geodetic data might contribute most to the monitoring of large-scale terrestrial water dynamics. This contribution will highlight the benefits of more advanced signal analysis methods for extracting relevant hydrometeorological information over more conventionally applied algorithms.

How to cite: Iran Pour, S., Eicker, A., Balidakis, K., Karimi, H., Amiri-Simkooei, A., and Dobslaw, H.: Efficiency of different signal processing methods to isolate signature characteristics in altimetric water level measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1449,, 2022.


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