Thesis

Groundwater recharge sensitivity to low impact development design and future climate variability

Groundwater sustainability is at the forefront of resource management. In light of climate change and growing populations, meeting future water needs must be met with planning and innovation. This is particularly challenging in cities where recharge is often limited by impervious surfaces and runoff is contaminated by urban pollutants. Low impact development (LID) is a design strategy that mimics the natural hydrologic cycle and is usually implemented as an alternative to the traditional stormwater system. Examples of LID best management practices (BMPs) include rain gardens, bioswales, infiltration trenches, rooftop gardens, and permeable pavement. LID BMPs delay and decrease peak runoff flows and improve water quality, and there is a growing number of studies investigating LID’s effect on groundwater. Understanding potential recharge under LID BMPs and identifying the design features influencing recharge can serve an important role in the move toward groundwater sustainability and management. In this study, I used HYDRUS-1D to model five LID BMPs (two rain gardens, two bioswales, one infiltration trench) from 1948-2099 with observed historic climate data and 9 global climate models (GCMs) at representative concentration pathways (RCP) of 4.5 and 8.5. Mean recharge ranged from 1725-3458 mm/yr under the LID BMPs, with the highest recharge rates occurring under the infiltration trench. Though simulated recharge from historic, 4.5 and 8.5 RCP showed no statistically significant changes in recharge over time, runoff is predicted to increase significantly, indicating that current LID BMPs should be redesigned to store increased inflow expected from climate change. Recharge efficiency during heavy rainfall events such as El Nino can be improved by increasing the loading ratio of a BMP. Results of a one-at a time (OAT) method sensitivity analysis showed that the hydraulic conductivity of the soil underlying a LID BMP has the most influence on recharge and suggested that location is critical for optimizing or minimizing recharge.

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