Interactions between shallow groundwater and low‐impact development underdrain flow at different temporal scales

Abstract

Low‐impact development (LID) practices are effective in managing surface run‐off, mitigating nonpoint source pollution, and recharging groundwater. However, they are often less effective in shallow groundwater environments as their surface infiltration and bottom exfiltration rates could be reduced, and the underdrains (i.e., underground drains within the media of the LID practices) may drain groundwater and alter groundwater dynamics. To evaluate and better understand the interactions between shallow groundwater and LID underdrain flow at different temporal scales, multiple statistical analyses were conducted on the monitored groundwater table depth and underdrain flow of porous pavements at the Central Kitsap Community Campus (Washington) and both porous pavements and bioretention cells at the IMAX parking lot (Ontario, Canada). The wavelet power, which represents oscillation behaviour and is obtained by continuous wavelet transform, of underdrain flow was a hybrid of that of rainfall and groundwater table depth. Rainfall variations at finer temporal scales (5 min to 10 days) resulted in finer scale underdrain flow, whereas groundwater fluctuations at coarser temporal scales (10 to 30 days) accounted for coarser scale underdrain flow and extended the duration of it. The impact of groundwater on underdrain flow was greater when the groundwater table was shallower than the underdrain (e.g., <0.6 m), particularly at finer temporal resolutions (5 min to 1 hr), which correspond to short‐term responses of LID practices and their corresponding effects on flooding. It was possible that the local groundwater mounds formed by infiltrated water instead of regional groundwater fluctuations that affected the underdrain flow in these conditions. Finally, through an evolutionary polynomial regression, two simple equations were obtained to predict the total amount and peak of LID underdrain flow using event‐based rainfall, groundwater table depth, and the hydraulic conductivity of media and in situ soils. The results may facilitate LID design and planning, and their implementation in shallow groundwater areas. The derived equations may benefit the real‐time control of LID‐related sewer and treatment systems in shallow groundwater urban catchments.

Measuring city-scale green infrastructure drawdown dynamics using internet-connected sensors in Detroit

“…A shallow groundwater table often suggests limited infiltration capacity due to the proximity of the water table to the surface. [56][57][58] In such cases, placing GI in areas with deeper soil profiles or utilizing subsurface systems might be more effective. Conversely, areas with deeper groundwater tables offer increased potential for water storage and infiltration, making them suitable candidates for various GI installations.…”

Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations

“…Storm Water Management Model (SWMM) developed by the US EPA is an integrated hydraulic and hydrological modelling tool which is broadly used to assess the efficiency of low-impact development measures in urban environments ( Zhang and Chui, 2018 ). SWMM is widely used for the analysis and design of urban drainage systems (e.g., Elliott and Trowsdale, 2007 ; Jayasooriya and Ng, 2014 ; Zhang and Chui, 2020 ).…”

Design measures to mitigate the impact of shallow groundwater on hydrologic performance of permeable pavements

“…Furthermore, shallow groundwater, that is, groundwater table shallower than 1–3 m below the ground surface, can be another major obstacle in implementing infiltration‐based GI. With a shorter distance from the groundwater table, GI's hydrologic performance can be affected (Locatelli et al, 2015; Jackisch & Weiler, 2017; Zhang, Chui, & Yang, 2018), and there is also a higher risk to contaminate the groundwater (Fischer, Charles, & Baehr, 2003; Datry, Malard, & Gilbert, 2004; Newman et al, 2004; Chui & Trinh, 2016; Zhang & Chui, 2017, 2018, 2019).…”