Water research · 2025
Prioritising sewersheds based on groundwater infiltration probability: A geospatial approach.
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Zeydalinejad Nejat, Javadi Akbar A, Jacob Mark, Baldock David, Webber James L
Аннотация
Evaluating groundwater infiltration (GWI) in sewer networks is essential for managing network capacities, especially amid growing pressures on network maintenance and operation caused by increasing domestic and storm water inputs. Despite this significance, GWI assessments have received limited attention, especially at large scales. In fact, no previous study has comprehensively evaluated sewersheds based on GWI scores. This study addresses this gap by focusing on prioritising sewersheds based on a GWI metric across South West England. Geospatial technology, incorporating a fuzzy-analytic hierarchy process (F-AHP), was employed to assess GWI scores at a high resolution (10 m × 10 m). The analysis incorporated sewer properties alongside hydrological factors through 13 thematic layers: sewer material, depth, length, diameter, installation date, sewerage type, combined sewer overflow counts and durations, groundwater productivity, river proximity, elevation, and individual days and durations of reduced asset performance in periods of no to low precipitation. Sensitivity analysis identified five key influencing factors-installation date, groundwater productivity, river proximity, individual days and durations of reduced asset performance in periods of no to low precipitation-that closely aligned with the final AHP-weighted prioritisation map derived using all layers. Challenges such as data scarcity, scale inconsistencies-27 groundwater catchments encompassing 671 sewersheds, ranging from 173 m² to 72.6 km²-, limited observation wells, and low groundwater productivity complicate GWI analysis in sewer networks. This study highlights priority areas for GWI research and field surveys, enabling more efficient resource allocation. Future research should focus on expanding monitoring points and enhancing hydrological models to function effectively in data-scarce areas.