Radar Rainfall Input for Urban Stormwater Modelling

Accurate representation of radar-based rainfall inputs remains a critical challenge in urban stormwater modelling, particularly in densely urbanized environments exposed to short-duration intense storm events. While weather radar provides high spatio-temporal resolution precipitation estimates (Taromideh et al., 2025), its direct application in urban stormwater models is often affected by biases and spatial inconsistencies. Improving the integration of radar-derived rainfall information with hydrological observations is therefore essential to reliably simulate urban runoff and sewer system response.

The framework is applied to the coastal city of Portici, located within the metropolitan area of Naples in southern Italy. The study area is characterized by a highly urbanized combined sewer system serving a catchment of approximately 3.2 km², with an imperviousness of about 78% and elevations ranging from sea level to 144 m above sea level. The drainage network includes multiple regulators and combined sewer overflow structures that discharge excess stormwater to the sea during intense rainfall events (Marino et al., 2025). Discharge measurements at the main outlet are available at high temporal resolution over a multi-year period, providing a reliable dataset for model calibration and validation. While no rain gauges are installed within the catchment, nearby rain gauge stations and meteorological radar data are available. Radar precipitation is provided on a regular grid with 1 km × 1 km spatial resolution and 5-minute temporal resolution, enabling the estimation of spatially distributed rainfall fields over the study area. These data provide the necessary context for applying and evaluating the proposed optimization framework.

The objective of this study is to develop an optimization-based framework to adjust subcatchment-scale rainfall inputs in an urban stormwater model, using observed outlet discharge as an indirect reference for rainfall correction. Initial rainfall values for each subcatchment are derived from radar precipitation fields, and the optimization aims to ensure consistency with observed outlet discharges while preserving the spatial and temporal structure of radar-derived rainfall. The approach constrains rainfall adjustments to physically plausible patterns and prevents unrealistic hydrological responses, such as excessive runoff variability or flooding within the sewer network.

The proposed methodology couples the calibrated Storm Water Management Model (SWMM) with a genetic algorithm optimization scheme. Time-series rainfall values for each subcatchment are treated as decision variables over the duration of the storm event. Radar-derived precipitation fields are used both to initialize these variables and to activate them only during periods when radar precipitation is detected. The objective function integrates flow-based performance metrics, correlation measures between radar and subcatchment rainfall data, and runoff consistency indicators, while a flooding volume constraint penalizes solutions leading to surcharging or surface flooding. Model evaluations are parallelized to reduce computational cost. The proposed framework offers a robust methodology for improving the consistency between radar-based rainfall inputs and observed sewer system responses in urban environments, that can be used for the development of predictive models of rainfall-runoff transformation in urban catchments.