While existing studies on sewer networks have explored topics such as surface water inflow, limited research has delved into groundwater infiltration (GWI). This study aims to fill this void by providing a comprehensive overview of quantitative analyses of GWI in sewer networks plus current status, limitations and future perspectives, considering the most relevant peer-reviewed research, including 83 studies. We propose dividing the existing research into two main groups: (1) phreatic zone, and (2) vadose zone. Most research has focused on the latter, mainly considering Rainfall-Derived Inflow and Infiltration (RDII), including surface water inflow and GWI. The ratio of each is not frequently separated; otherwise, there may be some assumptions, e.g. in dry weather and assuming zero surface water inflow. We also divided the employed approaches in different categories from physically-based numerical models, to simpler ones, e.g. water budget analysis. In fact, a combination of approaches may be applied to find the intricate characteristics of 'urban groundwater' or 'urban karst.' The findings revealed a heightened vulnerability of sewer networks to GWI, due to climate change (CC) and its associated repercussions, e.g. sea level rise (SLR), making the coastal cities the most vulnerable regions. In future research, the criticality of pre-emptive measures and monitoring of networks, especially near the coastline, is emphasised to ensure the resilience and adaptability of sewer networks in the context of GWI amid the potential impacts of CC. However, current monitoring practices lack widespread evidence for spatiotemporal analysis of GWI quantity.

The incremental impacts of climate change on elements within the water cycle are a growing concern. Intricate karst aquifers have received limited attention concerning climate change, especially those with sparse data. Additionally, snow cover has been overlooked in simulating karst spring discharge rates. This study aims to assess climate change effects in a data-scarce karst anticline, specifically Khorramabad, Iran, focusing on temperature, precipitation, snow cover, and Kio spring flows. Utilizing two shared socioeconomic pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, extracted from the CMIP6 dataset for the base period (1991–2018) and future periods (2021–2040 and 2041–2060), the research employs Landsat data and artificial neural networks (ANNs) for snow cover and spring discharge computation, respectively. ANNs are trained using the training and verification periods of 1991–2010 and 2011–2018, respectively. Results indicate projected increases in temperature, between + 1.21 °C (2021–2040 under SSP245) and + 2.93 °C (2041–2060 under SSP585), and precipitation, from + 2.91 mm/month (2041–2060 under SSP585) to + 4.86 mm/month (2021–2040 under SSP585). The ANN models satisfactorily simulate spring discharge and snow cover, predicting a decrease in snow cover between − 4 km²/month (2021–2040 under SSP245) and − 11.4 km²/month (2041–2060 under SSP585). Spring discharges are anticipated to increase from + 28.5 l/s (2021–2040 under SSP245) to + 57 l/s (2041–2060 under SSP585) and from + 12.1 l/s (2021–2040 under SSP585) to + 36.1 l/s (2041–2060 under SSP245), with and without snow cover as an input, respectively. These findings emphasize the importance of considering these changes for the sustainability of karst groundwater in the future.

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities.

Groundwater infiltration (GWI) in sewer networks poses significant challenges to infrastructure. However, limited research has assessed the risk of GWI to sewer networks under climate change. This study addresses this gap by developing a numerical model to simulate groundwater flow in the Lower River Otter catchment, United Kingdom. The model was calibrated and verified using monthly data representing an observation period from 2011 to 2023, resulting in a mean error of − 0.12 m, a mean absolute error of 0.48 m, and a root mean square error of 0.64 m after verification. Next, distinct risk zones were delineated based on groundwater depth (GWD) and GWI in sewer networks. This delineation accounted for the observation period with current precipitation levels, and projected increases of 20% and 40% in precipitation (indicative of climate change), across a base period (1961–1990) and future period (2021–2060), under the shared socio-economic pathways SSP245 and SSP585. The model found groundwater flooding affects 13.2% of the region during the observation period, mainly occurring near the river and in areas where the aquifer is sandwiched by aquicludes. Future scenarios indicated that flooding could increase to 15.4% under SSP245 in the future. The area with GWD shallower than 4 m ranges from 17.5% in the base period to 18.1% with a 40% increase in observed precipitation. The model reveals that during the observation period and a 40% increase in precipitation, the area showcases the minimum (26.7%) and maximum (27.6%) proportion of high-risk areas for GWI in sewer networks, respectively. This prioritisation indicates the potential risk of GWI and the need for investigations, such as focused site surveys, to minimise risks of GWI into networks and ensure future sustainability of services.

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.