A review on implementing infiltration-based green infrastructure in shallow groundwater environments: Challenges, approaches, and progress

Abstract

Abstract Urban water problems (e.g., increased runoff, inhibited infiltration, and groundwater recharge) are becoming increasingly serious, and green infrastructure (GI) has been demonstrated to be effective in tackling these problems and restoring the pre-development hydrologic cycle. However, shallow groundwater limits the implementation of infiltration-based GI. Although projects and studies have been conducted, knowledge of the hydrologic performance of infiltration-based GI, and the impact on shallow groundwater environments, has not been comprehensively summarized. In this review, we first identify the challenges of implementing infiltration-based GI in shallow groundwater environments, and then evaluate and compare the potential approaches to evaluating GI in such environments. We also summarize progress in the understanding of the performance and impact of GI in shallow groundwater environments from previous and ongoing engineering projects and academic studies. The main topics include the evaluation of the potential reduction in runoff control performance of GI, the formation of groundwater mounds, and groundwater contamination. We also assess the exploration of strategies for optimally allocating and designing GI in shallow groundwater environments. The distance between the bottom of the GI and the groundwater table, the selection of the media soil, and the design of underdrain pipes are the main considerations when implementing GI in shallow groundwater environments. The review is concluded with recommendations for future studies, which include conducting tracer monitoring and variably saturated modeling to track subsurface flows of GI, improving existing hydrological models, developing new multi-scale models, and utilizing more advanced data-driven artificial intelligence models to predict the performance and impact of GI and to process the monitoring data for model calibration.