Exploring event-based calibration approaches for semi-distributed urban hydrologic models in data scarce urban watersheds under climate change

Managing urban watersheds is a complex task that requires a deep understanding of climate variability, hydrological features, and the hydraulic characteristics of the system. Several semi-distributed urban hydrologic models are now available to simulate water flow, but accurately calibrating these models is essential to improving simulation reliability, especially in data-scarce urban watersheds. This study evaluates the performance and uncertainty of various event-based calibration strategies for a semi-distributed hydrologic model, aiming to enhance model accuracy and understand predictive uncertainty at ungauged locations. Using the Stormwater Management Model (SWMM), we conduct three experiments on the Banpo urban watershed in South Korea. Specifically, we explored three calibration strategies to assess their trade-offs: (1) determining whether incorporating input data from multiple gauges simultaneously or in a stepwise manner during model calibration, (2) examining the impact of increasing parameter complexity on model calibration, and (3) evaluating the potential to estimate ungauged flows using data from a single site. We also examine how these calibration strategies impact urban hydrologic projections in the context of climate change. The results indicate that using multiple site data simultaneously improves calibration accuracy compared to a stepwise approach. In addition, single-site calibration can lead to significant deviations in flow projections compared to multiple site calibration, suggesting caution is necessary when using semi-distributed models in data-scarce regions for climate change impact assessments. Lastly, we observe that using multiple calibration sites does not substantially increase the uncertainty in flow projections, even with higher parameter complexity. We believe our findings will contribute valuable insights for projecting future urban flow under climate change scenarios.