3D hydrogeophysical characterization of managed aquifer recharge basins

Abstract

Aquifers are increasingly stressed. Managed aquifer recharge provides a potential solution to mitigate this stress and provide sustainable groundwater resources. Subsurface properties are known to have a strong control on the infiltration rates that can be achieved. However, these properties are often highly heterogeneous and difficult to assess with conventional probing techniques. Here, we show the application of 3D geophysical imaging to assess the recharge potential and its variation across several basins used for managed aquifer recharge. We link in-situ measurements of saturated hydraulic conductivity with the electrical resistivity of the subsurface to establish petrophysical relationships and use those relationships to estimate the distribution of hydraulic conductivity throughout the five recharge basins. Our results show a considerable variability in the hydraulic properties, i.e., soil texture and saturated hydraulic conductivity, that have a direct impact on potential infiltration rates. We use the 3D hydraulic property distributions to model groundwater recharge and provide estimates for infiltration rates and volumes, and use this approach to assess the impact of management activities on groundwater recharge performance. Having such data not only enables us to predict infiltration rates, but also provides means for optimizing such water infrastructure.

Prolonged Drought in a Northern California Coastal Region Suppresses Wildfire Impacts on Hydrology

“…Conditions contributing to hydrological resilience and return to steady state conditions is an area of inquiry informed by many prior works (Ebel & Mirus, 2014; Lloret & Zedler, 2009; McLauchlan et al., 2020; McWethy et al., 2019; Spence et al., 2020; Van Meerbeek et al., 2021). A paucity of baseline data means wildfire research that includes new rapid‐response field campaigns (Newcomer, Stavros, et al., 2021), novel geophysical field techniques for monitoring hydrological change (Bera et al., 2022; Uhlemann et al., 2022), robust integration of remote sensing characterization of vegetation functional traits (Enguehard et al., 2022; Wainwright et al., 2022), and machine learning assisted data‐streams that can be used to answer short‐term and long term wildfire related questions (Ebel & Mirus, 2014) may all contribute to improved understanding of wildfire effects on hydrology. Additionally, the use of in‐stream signals as indicators of changing watershed mechanisms is a novel area of inquiry to attribute stage of disturbance recovery and resilience to potential hydrological mechanisms (e.g., Arora et al., 2020; Godsey et al., 2009; Newcomer, Bouskill, et al., 2021) Our analysis provides insight into watershed hydrological responses to wildfire across the gradient of burn area and severity and may provide guidance to forest management practices aiming to use prescribed wildfires as a water maintenance and landscape health strategy by identifying the landscape wide thresholds of hydrologic change (Hahn et al., 2019).…”