Climate Vulnerability of Wastewater Treatment Plants to Extreme Weather: An Effect-Size-Based Sensitivity Analysis of Influent and Performance

 Climate change has intensified the frequency and severity of extreme meteorological events, placing growing pressure on the stable operation of wastewater treatment plants (WWTPs). Heavy rainfall and elevated temperatures can trigger abrupt changes in influent flow and pollutant loading, thereby challenging both hydraulic and operational stability of WWTPs. Although these responses are driven by meteorological forcing, their magnitude and manifestation differ across WWTPs. Such differences may be associated with non-climatic characteristics, including urbanization and plant capacity.

 Accordingly, this study aims to evaluate the climate vulnerability of WWTPs by (1) characterizing relationships between meteorological conditions and influent dynamics and quantifying their sensitivity under extreme and non-extreme climate clusters, and (2) projecting future influent conditions under climate change scenarios using predictive deep learning models.

 Daily operational and meteorological data collected from January 2016 to July 2025 were analyzed for four representative WWTPs located in a major metropolitan area in Republic of Korea. Meteorological variables were derived from Automated Weather System (AWS) observations and spatially aligned with service areas of each treatment plant. Meteorological conditions were classified using K-means clustering, and climate sensitivity was quantified by comparing extreme and non-extreme clusters using Cohen’s d effect size. Future influent conditions were projected by applying SSP5-8.5 climate scenario to a gated recurrent unit (GRU) trained on historical meteorological observations.

 Meteorological clustering identified five distinct climate clusters, among which hot–wet (extreme event) conditions exerted the strongest impacts across all WWTPs. Under hot–wet conditions, influent volumes increased by approximately 38–86% relative to cool–dry clusters. In contrast, influent concentrations (mg/L) of organic matter and nutrients generally decreased by 20–40%, reflecting dilution effects. Conversely, suspended solids (SS) loads (kg/d) increased by an average of approximately 80% across WWTPs, indicating a strong linkage between rainfall and sediment transport.

 In terms of treatment performance, nutrient removal efficiencies (total nitrogen (TN) and total phosphorus (TP)) declined markedly than those of organic matter and SS. Effect-size-based analysis revealed pronounced climate sensitivity, with very large effect sizes for influent flow (Cohen’s d ≈ 2.0–3.0) and consistently large sensitivities for SS load and nutrient removal (d > 1.0). In contrast, organic matter removal showed relatively smaller sensitivities. These response patterns were subsequently used to assess climate vulnerability across WWTPs with different levels of urbanization and plant capacities, highlighting substantial inter-plant variability in climate sensitivity.

 Building on the climate sensitivity patterns derived from historical observations, scenario-based projections suggest that increasing frequencies of extreme weather were likely to further amplify influent variability and pollutant loading under future climate conditions. Taken together, the historical analysis demonstrates that the vulnerability of WWTPs to climate change is influenced not only by extreme weather patterns but also by intrinsic system characteristics. The scenario-based projections extend these insights by highlighting potential future risks under climate forcing. By integrating meteorological clustering, effect-size-based sensitivity analysis, and scenario-driven influent projections, this study provides a practical framework for identifying vulnerable facilities and informing climate adaptation strategies, including capacity planning, nutrient management under extreme influent conditions, and prioritization of infrastructure upgrades.