Structural reliability analysis often considers failure mechanisms as correlated but non-interacting processes. Interacting failure mechanisms affect each others performance, and thereby the system reliability. We describe such interactions in the context of flood defenses, and analyze under which conditions such interactions have a large impact on reliability using a Monte Carlo-based quantification method. We provide simple examples and an application to levee failure due to landward slope instability and backward erosion piping (BEP). The examples show that the largest interaction effects are expected when the trigger mechanism is relatively likely to occur and the affected mechanism has a relatively large contribution to the system reliability. For the studied levee example, interactions between slope instability and BEP increased the failure probability up to a factor 4. Implications for the assessment and design of flood defenses are discussed.@en

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.

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