Definition of the Local Geoid Undulation Using Non-contemporary GNSS-Levelling Data on Subsidence Area: Application on the Adriatic Coastline

Abstract

The knowledge of the so-called geoid undulation, which represents the height of the geoid above a reference ellipsoid, is a fundamental step to link ellipsoidal heights measured using satellite systems and orthometric heights. Several geoid models are available at the time both at the national or global scale, which can be used for the purpose. Another way to define the geoid undulation is to perform joint measures with GNSS and spirit levelling over common benchmarks. This requires onerous measurements that is seldom possible to perform due to their cost. In this work, we evaluated the possibility to define a local model of the geoid undulation using already available spirit levelling orthometric heights and GNSS ellipsoidal heights measured about 13 years later. The test area is the Emilia-Romagna Adriatic coastline, an area of great interest both from the environmental and economic point of view, which is also undergoing consistent subsidence phenomena.Test results show that the available measurements allow defining a geoid undulation that is coherent with the shape defined by the gravimetric models and also allows to transform ellipsoidal heights into orthometric ones more consistent with the height reference available on the surveyed area. A 7 cm overall bias with respect to the ITALGEO05 was found, whereas ITG2009 and EGM2008 have higher differences. The use of subsidence models to align over time the coordinates used to define the geoid undulation has proven to be a fundamental step. The analysis on the a-priori uncertainty in the geoid height definition shown that the combined use of much more precise GNSS coordinates and contemporary spirit levelling campaign is necessary to significantly improve the resulting geoid height.

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“…The differences influenced by a different geoid 'undulation' produce different orthometric heights. Our findings are in line with the mean biases estimated by Tavasci et al (2022). Such findings explain the differences observed when analyzing the point-wise vertical values shown in Figure 6.…”