ORIGINAL RESEARCH article
Volume 10 - 2022 | https://doi.org/10.3389/feart.2022.942737
3D hydrogeophysical characterization of managed aquifer recharge basins
- 1Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- 2Ventura County Public Works Agency, Water and Sanitation Department, Moorpark, CA, United States
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.
In many parts of the world aquifers are stressed, resulting in falling groundwater levels and diminishing groundwater quality due to a rapid increase in groundwater extraction and changes in precipitation pattern, which is leading to unsustainable conditions (Scanlon et al., 2012; Rodell et al., 2018). Groundwater recharge is the most important parameter to assess groundwater sustainability, particularly in arid and semi-arid regions like California, yet it is almost impossible to measure directly (Kinzelbach et al., 2003). Many indirect measurements of groundwater recharge exist, such as tracer techniques, estimations from groundwater fluctuations, or numerical models, but these are often difficult to calibrate or require considerable field effort (Singh et al., 2019). Hence, there is a need to further develop approaches that can assess potential infiltration and recharge rates through integrated approaches that include data-informed modelling.
Due to the shortage of this valuable resource, water users are facing increasing constraints in the amount of groundwater they can withdraw. With climate change exacerbating the duration of droughts, and shifting intensity and duration of precipitation events during wet periods (Füssel et al., 2012; Arias et al., 2021), water managers are seeking ways of capturing a greater fraction of precipitation to increase aquifer recharge. In California, recent regulations require that all Groundwater Sustainability Agencies become compliant with their newly formulated groundwater sustainability plans (SWRCB, 2014). One of the methods to achieve this sustainability is managed aquifer recharge (MAR). In California, local water districts and other entities are working together to identify methods to capture elevated flood flows before they reach the Pacific Ocean and to use the captured water for aquifer recharge. Ventura County Waterworks District No. 1 (VCWWD) intend to utilize the existing percolation ponds at the Moorpark Water Reclamation Facility to capture some of the yearly flood flows of the Arroyo Las Posas for managed aquifer recharge. VCWWD estimates the project could potentially recharge up to 3.7 Mm3 of captured runoff per year. However, the realization of that potential depends on how much water can be infiltrated quickly during large storm events, making the performance of infiltration ponds a key factor in overall benefit.
Hence, this study aims at developing a stream-lined workflow that provides water managers with an estimate of spatially resolved infiltration rates. We combine geophysical measurements, i.e., electrical resistivity tomography and electromagnetic induction, which are both measuring the electrical properties of the subsurface, with measurements of the saturated hydraulic conductivity of the materials forming the recharge ponds. At the site, where the groundwater level is known to be at
These relationships between geophysical and hydrological properties have been exploited in many applications (Binley et al., 2015), and have also been used to map hydraulic properties for groundwater recharge problems. Mawer et al. (2016) used fiber optics based distributed temperature sensing to estimate infiltration rates and relate those values with electrical resistivities of the shallow subsurface. They showed that increasing resistivity (or decreasing electrical conductivity) relates to increasing infiltration rates. While this is developed on a single infiltration pond, Knight et al. (2018) made use of similar relationships to map aquifer systems for optimal recharge conditions using Airborne Electromagnetic measurements. This approach has recently been extended by Kang et al. (2021) to ease interpretation of such data over large domains. However, these small and large scale investigations are often disconnected, which is mainly due to missing instrumentation that can acquire data rapidly over different scales. Novel electromagnetic systems have been developed to more efficiently gather data at high resolution across relatively large recharge facilities (i.e., hectares to km2, Behroozmand et al., 2019) and the data of those is starting to close this gap. Using these novel, towed time-domain electromagnetic (TEM) methods data can be acquired quickly at high spatial resolution (i.e., meter), yet the vertical resolution is often limited to the upper 1 or 2 m, which may not be sufficient to fully describe recharge pathways. Hence, often frequency-domain electromagnetic (FDEM) instruments are used that can sense the shallow subsurface, and can also be used to acquire data comparably quickly (Sendrós et al., 2020). Latest FDEM instrumentation make use of multiple frequencies or coil spacing, sensing different parts of the subsurface, allowing for detailed subsurface imaging, without the need for galvanic coupling to the ground.
Mawer et al. (2016) and Sendrós et al. (2020) have shown that electrical and electromagnetic geophysical methods can be used to assess spatially varying infiltration rates of recharge facilities. Here, we want to go a step forward and provide volumetric estimates of hydraulic conductivities that can be used to parameterize hydraulic models to assess the recharge performance of infiltration ponds, that in turn can be used to optimize their management. We show that using this approach, management activities can be designed that can increase the recharge efficacy considerably. Here, we show that a single survey can be used to characterize a recharge facility, but the same approach could also be used repeatably to control and enhance the performance of recharge facilities. While developed on a single site, the workflow can be considered transferable, and should be applicable to many different sites.
2.1 Study site
The study site is located at the Ventura County Waterworks Moorpark Reclamation Facility in Moorpark, CA. It is located along the Arroyo Las Posas River in the Little Simi Valley. The facility contains about 32 percolation ponds of which six ponds (ponds 8 to 13, Figure 1) were selected to be evaluated in this study. The site is located within the South Las Posas Basin, which has a surface area of about 41 km2, and belongs to the Calleguas creek watershed. Geologically, the site is located at the western margin of the Transverse Ranges of Southern California, which is characterized by east-west oriented mountain ranges that are separated by valleys, faults, and basins. The valley fills consist of unconsolidated and semi-consolidated sediments of considerable thickness (
FIGURE 1. Map of the study site showing the investigated infiltration ponds (Ponds 8 to 13 in which EMI data were acquired), the locations of ERT electrodes, infiltration and DTP probes, ambient seismic noise sensors, and measurements of saturated hydraulic conductivity. Aerial photograph obtained from NOAA. Coordinates in NAD83 UTM Zone 11N.
2.2 Electrical resistivity tomography
Electrical Resistivity Tomography (ERT) is a geophysical technique that images the subsurface electrical properties in two, three, or four dimensions (including time). The electrical resistivity of the subsurface is predominantly a function of the lithology, grain size/porosity, water saturation, and pore water chemistry (Archie, 1942). At the study site, groundwater levels were lower than 10 m below ground level (bgl), and hence variations in lithology and grain size are expected to dominate the signal.
ERT data were acquired in 3 of the studied ponds (Uhlemann and Ulrich, 2022). Within ponds 9 and 10, we acquired 3D ERT data, and in pond 11 we acquired a set of 3 parallel ERT profiles. We used an electrode spacing of 1.5 m, and 42 electrodes per profile. For Ponds 9 and 10, 3D data were acquired using a set of 11 and 12 parallel lines, respectively, spaced at 3 m. We acquired dipole-dipole data with dipole lengths a of 1.5, 3.0, 4.5, 6.0, and 7.5 m, and dipole spacing n of 1 to 8a. A full set of reciprocal data were acquired for each profile to assess the data error (Tso et al., 2017). Each electrode location was surveyed using RTK-GPS, with an accuracy of
The measured resistances need to be inverted to obtain a tomographic resistivity model of the subsurface. Before the inversion, data with reciprocal errors of
FIGURE 2. Error distribution of the ERT data for each pond, and the linear error models (values shown in the lower left corner of each panel) that were used in the data inversion. Errors are usually small compared to the measured resistances.
2.3 Electromagnetic induction mapping
To rapidly characterize the shallow subsurface properties, an electromagnetic induction (EMI) system, the GF Instruments MiniExplorer 6 L (GF Instruments s.r.o., Brno, Czech Republic), was used. This system weighs only 2 kg, and can easily be walked across a large area sampling the ground at high resolution. The system consists of a transmitter coil and six receiver coils at distances of 0.2, 0.33, 0.5, 0.72, 1.03, and 1.50 m. A primary electromagnetic field at 24.7 kHz created within the transmitter loop, in the presence of conductive material, induces eddy currents in the subsurface, which create a secondary magnetic field that is sensed in the receiver coils and reflects the subsurface electrical properties. To locate measurements, we attached a DGPS (Topcon HIPERV, Livermore, CA, United States) to the logging unit, and recorded both the EMI ground response and the GPS location at 1s intervals. The system was operated using a horizontal coplanar coil orientation, maximizing the effective penetration depth. Data were acquired across 6 ponds (Uhlemann and Ulrich, 2022), Ponds 8 to 13 of the Moorpark Water Reclamation Facility, and data acquisition took about 3 h.
By repeating measurements at the same location at various times, the drift of the system was recorded, but found to be negligible. Data processing included filtering of outliers (data with apparent conductivities ECa
To calibrate the EMI response [which is known to improve the performance of data inversion, McLachlan et al. (2021)], we extracted the ERT resistivity depth-profile at 231 random locations of pond 9, and calculated the EM forward response of those 1D resistivity models. We compared this with the measured EMI data, and used a linear relationship for each coil spacing to calibrate the data (McLachlan et al., 2021). The EM data were then inverted using a cumulative sensitivity approach (McNeill, 1980), which showed comparable results to more exact solutions making use of Maxwell’s equation, but is computationally more efficient. Each EMI sounding was inverted by discretizing the subsurface into 8 layers, with defined thicknesses of 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.5, and an infinite halfspace below. These thicknesses were fixed and we inverted for the resistivity distribution only. We applied an L2 smoothness constraint with a vertical regularization of α = 0.01.
2.4 Saturated hydraulic conductivity measurements
Saturated hydraulic conductivity (Ksat) measurements were made in the top 45–50 cm below the surface of the ponds with a SOILMOISTURE Equipment Corp. Guelph Permeameter. This system is an in-hole constant-head permeameter that can be used to measure the steady-state rate of water infiltration into unsaturated soil in a cylindrical hole, following the Marriotte principle. The measurement is made by filling a cylindrical borehole with a known height of water, maintaining a constant head of water in the borehole, and recording the steady-state rate of water infiltration. By knowing the diameter of the borehole and the height of water, the field saturated hydraulic conductivity can be estimated; note that this represents only the vertical component, i.e., Ksat,z, which we will refer to as Ksat in the following. Within each pond, three to four measurements were conducted, with the locations being guided by the EMI data (Uhlemann and Ulrich, 2022). At each location a 45–50 cm deep, 3” hole was augered, and the test performed with variable heads of 5 and 10 cm. We tried to target locations covering the range of measured electrical properties, however, in areas of particularly sandy material, the shape of the hole could not be sustained throughout the infiltration test, and locations had to be moved. Ksat was calculated for each test following the theory described in Elrick and Reynolds, (2012).
2.5 Groundwater flow modelling
To eventually estimate infiltration rates, we discretize each pond into 1 m × 1 m cells, and calculate a 1D vertical groundwater flow model for each cell, resulting in 2,405 to 3,468 1D flow models per pond. We use Hydrus1D (Šimůnek and Genuchten, 2008), and the python package Phydrus (Collenteur et al., 2021) to automate the model parameterization and simulation. Hydrus1D is solving the Richard’s equation for saturated and non-saturated water flow, and hence provides an adequate choice for the modelling of the vadose zone processes. Unsaturated flow is governed by unsaturated soil hydraulic properties, which are described by van Genuchten, (1980). The model is parameterized in 2.5 cm thick layers to a depth of 250 cm, with Ksat being estimated using a relationship between the measured Ksat and the inverted electrical properties obtained from the calibrated EMI data. Other parameters important for the van Genuchten equation, such as residual soil water content Θr = 0.045, saturated soil water content Θs = 0.43, soil water retention parameters α = 0.145 1/cm and n = 2.68, and the tortuosity parameter l = 0.5, were taken from literature values for sandy soils. The initial pressure head was set to −500 cm, and the model had a time discretization with a minimum and maximum time step of 10 s and 3 min, respectively. We prescribed a varying pressure head at the surface, resembling a 12 h infiltration period with a head of 50 cm, and defined the lower boundary to be freely draining. This defines a zero pressure head gradient at the lower boundary, which is a valid assumption given the deep groundwater level. The simulation was run for 72 h, and we extracted the infiltration rate at the surface boundary 10 h after start of the infiltration, representing saturated conditions. This model was run for each 1 m × 1 m cell, estimating the infiltration rates for the spatially varying Ksat field of each pond. Note that this is a simplified model that accounts for vertical water flow only, and is not accounting for evapotranspiration or complex subsurface flow patterns.
3.1 Electrical resistivity tomography
The resistivity distribution of ponds 9 to 11 show considerable horizontal and vertical variation (Figure 3). In pond 9, a clear boundary can be seen between the low resistivity towards the northern end of the pond compared to the high resistivities of the southern part of the pond. Based on the borehole observations, these changes relate to more clayey and sandy soils that were observed in the northern and southern parts, respectively. The vertical extent of the low resistivity, clayey-dominated fine-grained soils, appears to be limited to 1–2 m depth. This layer is underlain by higher resistivity soils (
FIGURE 3. Electrical resistivity models obtained from the ERT data inversion. Shown are slices through the 3D models for ponds 9 and 10, and the 2D profiles that were acquired in pond 11. For ponds 9 and 10, resistivities
The resistivity data of pond 11 shows a resistive (
3.2 Electromagnetic induction mapping
Apparent conductivities recorded using the EMI instruments show that soil electrical conductivities vary spatially and with depth in each pond (Figure 4). The apparent electrical conductivities (ECa) range from close to zero to 50 mS/m. Very low conductivity (
FIGURE 4. Apparent conductivity at 0.3–2.3 m depth measured using EMI. Note the differences in pond 8 and 9 where low and high conductivities are clearly separated. Shown are also the saturated hydraulic conductivities measured with the Guelph permeameter in the top left image (ECa at 0.3 m depth).
FIGURE 5. Misfit between modelled and measured electrical conductivity after inversion for the uncalibrated (A) and ERT-calibrated EMI data (B). Note the much smaller misfit for all coil spacings in the calibrated case.
The inverted EMI resistivity models (Figure 6) show the same observations for ponds 9 to 11 as presented for the ERT data. For pond 8, shallow depths are characterized by a conductive northern part and a resistive southern part. This distinct characteristic continues throughout the depth of investigation of the EMI (≈2.5 m), with slightly decreasing resistivities with depth. In contrast, for ponds 12 and 13, a shallow (
FIGURE 6. Inverted resistivity models. (A) 3D view of Ponds 8 to 13., (B) horizontal slice at 0.1 m depth, (C) horizontal slice at 1.0 m depth. (B) and (C) capture the lateral and vertical soil heterogeneity and identify bounds of the fines dominated, low hydraulic conductivity (blue) and coarser, high hydraulic conductivity (yellow-red) soils.
From those data, and relating high resistivity with high hydraulic conductivity and vice versa, we expect pond 10 to have the highest recharge potential, as this pond shows the highest resistivities throughout (mean resistivity of 152.3 Ωm). Even though ponds 8 and 9 have a shallow low resistivity layer in the northern part, which is likely of low hydraulic conductivity, this layer is thin, and below resistivities are comparably high, resulting in comparably high average resistivities (75.2 and 79.9 Ωm, respectively). Similarly, pond 11 shows moderate resistivities throughout (average of 91.4 Ωm). Hence, we expect ponds 8, 9, and 11 to have moderate recharge potential. Ponds 12 and 13, even though characterized by a thin resistive soil layer, which is likely high in sand content, have likely low recharge potential because of the low resistivity layer that underlays this thin top layer (average resistivities of 66.9 and 50.0 Ωm, respectively). This indicates a trend of decreasing electrical resistivity towards the western side of the facility.
3.3 Saturated hydraulic conductivities
Similar to the ERT and EMI data, the measured saturated hydraulic conductivities (Figure 7) show variable values for each pond, with pond 9 showing the biggest range with Ksat varying between 6.49 ⋅ 10–7 m/s and 9.9 ⋅ 10–5 m/s (average of Ksat = 3.8 ⋅ 10–5 m/s). The largest average Ksat values were observed for ponds 11 and 8 (1.33 ⋅ 10–4 m/s and 7.04 ⋅ 10–5 m/s, respectively), followed by pond 10 (3.19 ⋅ 10–5 m/s). Ponds 12 and 13 showed the smallest average Ksat (6.61 ⋅ 10–6 m/s and 4.23 ⋅ 10–6 m/s, respectively). Since the average of each pond is defined by 3–4 sampling points only, the measured values and distributions may not represent the true heterogeneity of each pond, but these values provide some insight into their distribution. Nevertheless, the comparably small variability of pond 10 highlights again the relatively homogeneous conditions of this pond, and the rather poor potential recharge performance of ponds 12 and 13, where the lowest Ksat were measured. Similarly to the electrical resistivities, this indicates a trend of decreasing hydraulic conductivity towards the western ponds.
FIGURE 7. (A) Distributions of saturated hydraulic conductivity for each pond. (B) Linear relationship linking electrical conductivity to hydraulic conductivity.
To provide an estimate of the spatial heterogeneity of Ksat throughout the ponds, we established a relationship between measured Ksat and the electrical conductivity obtained from EMI. This is a valid approach since the electrical conductivity of soils is known to be a function of grain size (Banton et al., 1997; Uhlemann et al., 2017), which in turn relates to the hydraulic conductivity (Rosas et al., 2014). A linear relationship between conductivity and Ksat was found to fit the data reasonably well (R2 = 0.58, Figure 7). Ksat can be estimated from the electrical conductivity (σ) through:
Using this relationship, we translated the measured 3D models of electrical conductivity into Ksat (Figure 8). At 0.1 m depth, throughout all ponds Ksat is relatively high with values mostly
FIGURE 8. Horizontal slices through the derived 3D hydraulic conductivity model at (A) 0.1 m depth and (B) 1.0 m depth.
FIGURE 9. Measured and estimated saturated hydraulic conductivity. The estimated data usually falls within the same order of magnitude than the measured data and has a R2 = 0.63.
3.4 Estimated infiltration rates
To assess potential infiltration rates, we used the derived spatial distribution of hydraulic conductivities (Figure 8) to parameterize a simple 1D vertical groundwater flow model. Each pond, we subdivided into 1 m × 1 m grid cells, and we extracted Ksat for each of those cells at 0.5 m intervals from 0 to 2.5 m depth. Recharge was simulated by applying a 0.5 m head to this area over a period of 12 h. The model itself was run for a duration of 72 h. We extracted the infiltration rate at each surface cell 10 h after start of the infiltration to ensure saturated conditions.
Similar to the Ksat observations, ponds 8 and 9 show a clear division in potential infiltration rates (Figure 10), with the northern parts showing small values (close to zero cm/h), and the southern part having larger values (
FIGURE 10. Infiltration rate estimated using 1D vertical groundwater flow models for 1 m × 1 m cells parameterized based on the hydraulic conductivity estimated from the EMI data (Figure 8). Note, grey zones within the ponds indicate areas of estimated infiltration rates
Our results show the benefit of using geophysical investigations to rapidly assess the hydrogeological conditions of recharge ponds. The resistivity distributions of ERT and EMI show low values in the shallow subsurface of the northern part of ponds 8 and 9, and decreasing values with depth for ponds 12 and 13. Measurements of Ksat show similar variability, with pond 10 and 11 showing the largest values, while ponds 12 and 13 show the smallest hydraulic conductivities. Using simple 1D groundwater flow simulations, we can relate the distribution of hydraulic conductivities to potential infiltration rates, and show that the ponds that are characterized by higher resistivity, and thus higher hydraulic conductivity, also have higher infiltration rates. Here, infiltration rates vary from
Although we could not directly measure the infiltration rate of these ponds, the presented approach shows considerable variability throughout the facility, which is in agreement with observations of recharge activities. Here, data acquisition of the ERT, EMI, and hydraulic conductivity took less than 4 days for a crew of two people. With this effort, we could provide a quantitative assessment of infiltration rates for six different ponds, and propose detailed management activities that could increase the infiltration volume of a particular pond by 284%.
Here, we translate the inverted 3D resistivity models into 3D distributions of vertical saturated hydraulic conductivity, based on a linear relationship between the electrical conductivity (i.e., the inverse of the resistivity) and Ksat. While the linear fit is acceptable, additional measurements of Ksat at different locations and depths would certainly help in providing a more robust relationship. In this analysis, we are neglecting the effects of anisotropic hydraulic conductivity, and hence only consider vertical water movements. If the horizontal hydraulic conductivity would be considerably larger than the vertical, complex groundwater pathways may also contribute to recharge. Additionally, the Ksat data may not necessarily truly represent the range of hydraulic conductivities of the ponds, as measurements could not be made in the most sandy locations, as the boreholes were collapsing and prevented measurements. Using field determined petrophysical relationships to translate imaged electrical properties to hydraulic conductivity assumes that there is a direct relationship between these values. While both electrical and hydraulic conductivity are governed by the soil texture (Durner, 1994; Domsch and Giebel, 2004), other factors such as the clay content may affect the electrical conductivity, leading to non-linear and more complex relationships between the two values. While this is not the case at this study site, it may be a limiting factor at other sites. Additionally, here we assume that the inversion recovers the true resistivity distribution throughout the model space. Yet, the inversion makes use of smoothness constraints, and particularly with increasing depth, the sensitivity of the measurements decreases, resulting in a smooth representation of the resistivity distribution. This may affect the translation of the inverted resistivity model, which is most likely underestimating the true resistivity distribution, into Ksat (Day-Lewis et al., 2005), and we are hence likely underestimating Ksat at depth. Since we only consider the upper 2.5 m for the 1D groundwater flow models, the impact of that should be limited.
While the presented approach should be applicable to characterize and estimate hydrological properties of other recharge facilities, the linear relationship (Eq. 1) between the electrical conductivity and Ksat is likely site-specific, and should be obtained for each site. Nevertheless, as shown here, such an integrated approach of geophysical and hydrological measurements can provide valuable data for managers of such facilities, and may increase performance, while at the same time lowering maintenance costs through detailed information to design improvement strategies.
Managed aquifer recharge is a technology that can enhance groundwater recharge, thereby reducing the stress that is put on aquifers due to increased groundwater extraction and changing precipitation patterns. However, we are lacking approaches that can quantify recharge contributions, which can directly aid in the management of these facilities. Here, we introduce a workflow that combines electrical and electromagnetic geophysics with direct measurements of saturated hydraulic conductivity. Through establishing a linear relationship between hydraulic conductivity and electrical properties of the ground, we can use geophysical imaging methods to map the 3D distribution of hydraulic conductivity and use the distributed values to parameterize hydrological models to estimate infiltration rates. We show that the electrical and hydraulic properties vary considerably throughout the investigated recharge facility, showing infiltration rates of
The presented approach is cost-efficient, and transferable to other sites, and can provide a workflow to rapidly assess potential infiltration rates and volumes for recharge facilities. We show that this approach can also be used to assess the impact of management activities, and by repeating the presented approach over time, it could also be used to monitor the performance and design detailed management activities. To gain detailed information about the actual recharge processes, full-scale recharge experiments were conducted in ponds 9 and 10 and monitored using a range of sensing techniques. The results of those experiments highlight the importance of preferential flow paths that were identified in this characterization study, and will be the focus of a follow-on paper.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://www.hydroshare.org/resource/74650ddf75d946fdae57956045490a1c.
SU, CU, PF, and JK contributed to conception and design of the study. CU, JK, and JP managed the project, as well as coordinated on COVID-19 safety requirements. SU and CU acquired and analyzed the geophysical and hydraulic data. SU wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
Funding for this project was provided through the Metropolitan Water District of Southern California’s Future Supply Actions Funding Program under Agreement Number 188682.
The authors would like to acknowledge our partners at the Metropolitan Water District, Calleguas Municipal Water District and VCWWD for their efforts in making this project successful despite the challenges encountered during the COVID-19 Pandemic.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., et al. (2021). “Technical summary,” in Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Editors V. Masson-Delmotte, P. Zhai, and A. Pirani (Cambridge University Press).
Banton, O., Cimon, M.-A., and Seguin, M.-K. (1997). Mapping field-scale physical properties of soil with electrical resistivity. Soil Sci. Soc. Am. J. 61, 1010–1017. doi:10.2136/sssaj1997.03615995006100040003x
Binley, A., Hubbard, S. S., Huisman, J. A., Revil, A., Robinson, D. A., Singha, K., et al. (2015). The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales. Water Resour. Res. 51, 3837–3866. doi:10.1002/2015WR017016
Blanchy, G., Saneiyan, S., Boyd, J., McLachlan, P., and Binley, A. (2020). ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling. Comput. Geosciences 137, 104423. doi:10.1016/j.cageo.2020.104423
Day-Lewis, F. D., Singha, K., and Binley, A. M. (2005). Applying petrophysical models to radar travel time and electrical resistivity tomograms: Resolution-dependent limitations. J. Geophys. Res. 110, B08206. doi:10.1029/2004JB003569
Elrick, D. E., and Reynolds, W. D. (2012). Infiltration from constant-head well permeameters and infiltrometers. Adv. Meas. Soil Phys. Prop. Bringing Theory into Pract. 30, 1–24. doi:10.2136/sssaspecpub30.c1
Füssel, H. H.-M., Jol, A., Kurnik, B., Hemming, D., and Hildén, M. (2012). Climate change, impacts and vulnerability in europe 2012: An indicator-based report, 12. Copenhagen: EEA - European Environment Agency.
Hanson, R., Martin, P., and Koczot, K. (2003). Simulation of ground-water/surface-water flow in the Santa Clara-Calleguas ground-water basin, Ventura County, California. Tech. rep. Sacramento, CA: USGS.
Johnson, T. C., Versteeg, R. J., Ward, A., Day-Lewis, F. D., and Revil, A. (2010). Improved hydrogeophysical characterization and monitoring through parallel modeling and inversion of time-domain resistivity and induced-polarization data. Geophysics 75, WA27–WA41. doi:10.1190/1.3475513
Kang, S., Knight, R., Greene, T., Buck, C., and Fogg, G. (2021). Exploring the model space of Airborne electromagnetic data to delineate large-scale structure and heterogeneity within an aquifer system. Water Resour. Res. 57, 1–31. doi:10.1029/2021WR029699
Knight, R., Smith, R., Asch, T., Abraham, J., Cannia, J., Viezzoli, A., et al. (2018). Mapping aquifer systems with Airborne electromagnetics in the central valley of California. Groundwater 56, 893–908. doi:10.1111/gwat.12656
McLachlan, P., Blanchy, G., and Binley, A. (2021). EMagPy: Open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data. Comput. Geosci. 146, 104561–104632. doi:10.1016/j.cageo.2020.104561
Nouveau, M., Grandjean, G., Leroy, P., Philippe, M., Hedri, E., and Boukcim, H. (2016). Electrical and thermal behavior of unsaturated soils: Experimental results. J. Appl. Geophys. 128, 115–122. doi:10.1016/j.jappgeo.2016.03.019
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H. K., Landerer, F. W., et al. (2018). Emerging trends in global freshwater availability. Nature 557, 651–659. doi:10.1038/s41586-018-0123-1
Rosas, J., Lopez, O., Missimer, T. M., Coulibaly, K. M., Dehwah, A. H., Sesler, K., et al. (2014). Determination of hydraulic conductivity from grain-size distribution for different depositional environments. Groundwater 52, 399–413. doi:10.1111/gwat.12078
Scanlon, B. R., Faunt, C. C., Longuevergne, L., Reedy, R. C., Alley, W. M., McGuire, V. L., et al. (2012). Groundwater depletion and sustainability of irrigation in the US high plains and central valley. Proc. Natl. Acad. Sci. U. S. A. 109, 9320–9325. doi:10.1073/pnas.1200311109
Sendrós, A., Himi, M., Lovera, R., Rivero, L., Garcia-Artigas, R., Urruela, A., et al. (2020). Geophysical characterization of hydraulic properties around a managed aquifer recharge system over the llobregat river alluvial aquifer (barcelona metropolitan area). Water 12, 3455. doi:10.3390/w12123455
Singh, A., Panda, S. N., Uzokwe, V. N., and Krause, P. (2019). An assessment of groundwater recharge estimation techniques for sustainable resource management. Groundw. Sustain. Dev. 9, 100218. doi:10.1016/j.gsd.2019.100218
Tso, C.-h. M., Kuras, O., Wilkinson, P. B., Uhlemann, S., Chambers, J. E., Meldrum, P. I., et al. (2017). Improved characterisation and modelling of measurement errors in electrical resistivity tomography (ERT) surveys. J. Appl. Geophys. 146, 103–119. doi:10.1016/j.jappgeo.2017.09.009
Uhlemann, S., Kuras, O., Richards, L. A., Naden, E., and Polya, D. A. (2017). Electrical resistivity tomography determines the spatial distribution of clay layer thickness and aquifer vulnerability, Kandal Province, Cambodia. J. Asian Earth Sci. 147, 402–414. doi:10.1016/j.jseaes.2017.07.043
[Dataset] Uhlemann, S., and Ulrich, C. (2022). Hydrogeophysical characterization data of the Moorpark reclamation facility. Available at: http://www.hydroshare.org/resource/74650ddf75d946fdae57956045490a1c.
Keywords: managed aquifer recharge, geophysics, recharge potential, property translation, subsurface characterization
Citation: Uhlemann S, Ulrich C, Newcomer M, Fiske P, Kim J and Pope J (2022) 3D hydrogeophysical characterization of managed aquifer recharge basins. Front. Earth Sci. 10:942737. doi: 10.3389/feart.2022.942737
Received: 12 May 2022; Accepted: 01 August 2022;
Published: 24 August 2022.
Edited by:Feng Li, Institute of Subtropical Agriculture (CAS), China
Reviewed by:Surinaidu Lagudu, National Geophysical Research Institute (CSIR), India
Venkateswara Rao Bekkam, Jawaharlal Nehru Technological University, Hyderabad, India
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