To retrieve the full displacement vector from InSAR, three line-of-sight (LoS) observations from different viewing geometries are required. However, often, at most two LoS observations are available. Within the literature, we encounter different approaches to address for this problem, unfortunately often with either mathematical or semantic flaws. Their impact reaches from quantitative errors in the reported studies, mismatches in comparative studies with other geodetic techniques, a lack of trust in the technology by end-users, to plain confusion. We propose both a uniform nomenclature and an alternative approach to the standard 3D decomposition problem using the concept of a strap-down reference system.

Like other cities in the Kathmandu Valley, Bhaktapur faces rapid urbanisation and population growth. Rivers are negatively impacted by uncontrolled settlements in flood-prone areas, lowering permeability, decreasing channels widths, and waste blockage. All these issues, along with more extreme rain events during the monsoon due to climate change, have led to increased flooding in Bhaktapur, especially by the Hanumante River. For a better understanding of flood risk, the first step is a return level analysis. For this, historical data are essential. Unfortunately, historical records of water levels are non-existent for the Hanumante River. We measured water levels and discharge on a regular basis starting from the 2019 monsoon (i.e., June). To reconstruct the missing historical data needed for a return level analysis, this research introduces the Classical Model for Structured Expert Judgment (SEJ). By employing SEJ, we were able to reconstruct historical water level data. Expert assessments were validated using the limited data available. Based on the reconstructed data, it was possible to estimate the return periods of extreme water levels of the Hanumante River by fitting a Generalized Extreme Value (GEV) distribution. Using this distribution, we estimated that a water level of about 3.5 m has a return period of ten years. This research showed that, despite considerable uncertainty in the results, the SEJ method has potential for return level analyses.