Development of a Methodology for Estimating the Storage of Deep Tunnel

Rapid urbanization and climate change are shifting the urban water cycle and intensifying pluvial flood risks, whereas design rainfall concepts inadequately capture climate variability and facility operating conditions. Focusing on the Shinwol Deep Tunnel Facility in Seoul, this study introduces a methodology for estimating the storage volume in deep tunnels by deriving a rating curve that reflects the dimensions and cross-sectional geometry. From an EPA-SWMM input file constructed with design information, the structural attributes were programmatically extracted; and the effective length, cross-sectional area, and cumulative volume were computed using the water level to generate the rating curve. The resulting curve closely matched the design reference (R<sup>2</sup> = 0.9984; the relative error of storage volume by water level was < 0.5%). Applying the curve to SWMM-simulated water levels at the outlet produced storage estimates that were strongly correlated with historical data (≈ 0.91). Quantitative biases were larger than desired, likely due to uncertainty in the initial conditions, such as persistent base water levels prior to rainfall events and limited reliability of the observation data. With improved data quality, the proposed methodology can support the design and operational strategy development by enabling quantitative storage estimation for deep tunnel facilities.