How to overcome hydrologic data scarcity in assessing potential groundwater risk to subterranean infrastructure: a finite-difference modelling approach

Groundwater infiltration (GWI) in sewer networks is an increasing challenge under climate change and human pressures, threatening the sustainability of sewer infrastructure. However, data scarcity and complex hydrogeology complicate its risk assessment. This study develops a finite-difference groundwater flow model (MODFLOW) to identify GWI susceptibility hotspots in non-pressurised sewer networks in Dawlish, southwest United Kingdom. To address limited hydrological data and hard-rock hydrogeology, several transferable approaches were integrated into the modelling framework. Fracture flow was represented using a porous-medium approximation, while borehole logs were interpreted to define the bedrock surface and estimate hydraulic properties. Groundwater recharge was derived from land-cover data, missing river stages were estimated from discharge–stage relationships, and initial node heads were assigned from river-network connectivity and upstream–downstream positions. These inputs, together with hydraulic properties and recharge, were refined through calibration. The model was calibrated for October 2007 to September 2020 and validated over the following four years using monthly time steps, achieving a mean error of − 0.05 m, a mean absolute error of 0.94 m, and a root mean square error of 1.62 m. Then, sewer sections were classified as having high (groundwater level (GWL) above the sewer), intermediate (GWL within 2 m below the sewer), or low (GWL more than 2 m below the sewer) GWI susceptibility. More than 85% of storm overflow spills occurred in sewers classified as highly susceptible. The resulting hotspot maps provide a practical decision-support tool for prioritising sewer inspection, maintenance, and rehabilitation, supporting more sustainable sewer network management.