Introduction: Climate-resilient stormwater infrastructure requires assessment beyond conventional design-storm adequacy. This study demonstrates how a validated Digital Twin can be repurposed as a controlled climate stress-testing environment to identify infrastructure-specific resilience thresholds in a wetland catchment.
Methods: The prototype Digital Twin was applied to the 128.2 ha Yaralpat wetland catchment, Northeast India, integrating Empirical Quantile Mapping (EQM)-corrected PERSIANN-CDR rainfall, a calibrated EPA SWMM 5 hydrological–hydraulic model, and NSGA-II-based retention-basin optimization. EQM was selected from five bias-correction methods based on validation against 23 years of gauge observations and hydrological consistency. Two fixed infrastructure configurations were evaluated: Configuration A, comprising two basins with 92,610.32 m³ total capacity, and Configuration B, comprising five basins with 224,698.22 m³ capacity reconciled to SWMM-simulated event runoff. The validated 25-year, 24-hour rainfall of 233.06 mm was progressively amplified by 10%, 20%, 25%, 30%, 40%, and 50%, while model parameters and infrastructure geometry remained unchanged.
Results: Configuration A produced 18,740 m³ of flooding under the baseline event, increasing to 97,682 m³ at +50% rainfall. Configuration B eliminated baseline flooding and maintained zero simulated flooding through +25% amplification, although storage utilization increased from 74.5% to 98.9%. Flooding commenced at +30%, reaching 9,335 m³, and increased to 59,125 m³ at +50%. Thus, the resilience threshold was bracketed between +25% and +30% rainfall amplification.
Conclusions: The results demonstrate that Digital Twin stress testing can convert conventional design verification into a quantitative resilience assessment by revealing degradation trajectories and system-specific failure thresholds. The framework provides a transferable basis for climate-adaptive wetland stormwater planning and for incorporating explicit resilience margins into future infrastructure optimization.