Data-Driven Assessment of Seasonal Impacts on Sewer Network Failures

Understanding the seasonal behaviour of sewer failures is essential for infrastructure reliability and sustainable asset management. This study presents a seasonality-centred, data-driven analysis of monthly sewer failures over a 15-year period (2010–2024) in a major city in south-eastern Poland. The assessment is based exclusively on operational failure records, allowing intrinsic temporal regularities to be extracted without the use of external meteorological covariates. Seasonal Decomposition of Time Series by LOESS (STL), Autocorrelation Function (ACF), Seasonal Index (SI) and the Winter–Summer Index (WSI) were applied to quantify periodicity, seasonal amplitude and long-term variability. The results confirm a pronounced annual cycle, with failures peaking around March and reaching minima in September, supported by a strong autocorrelation at a 12-month lag (r ≈ 0.45). The mean WSI value (1.05) indicates a nearly balanced but still winter-sensitive pattern, while annual WSI values ranged from 0.71 to 1.51. The STL seasonal amplitude remained structurally stable at ≈61 failures throughout the study period, while annual values showed a modest but statistically significant increasing tendency. Trend analysis showed no significant monotonic trend in the deseasonalized series (Z ≈ 0.89, p = 0.37), whereas the raw series exhibited a weak but significant upward trend (τ ≈ 0.33, p < 0.001), largely attributable to short-term operational variability rather than to changes in intrinsic failure rate. The study demonstrates that long-term operational data alone are sufficient to capture seasonal and long-term dynamics in sewer failures. The presented framework supports utilities in integrating seasonality diagnostics into preventive maintenance, resource allocation and resilience planning, even in the absence of detailed climatic datasets.