Journal of Hydrology 583 (2020) 124569

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Journal of Hydrology
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Research papers

Groundwater recharge from drywells under constant head conditions
Salini Sasidharan
a
b
c

a,b,⁎

, Scott A. Bradford , Jiří Šimůnek , Stephen R. Kraemer
b

a

T

c

Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USA
United States Department of Agriculture, Agricultural Research Service, U. S. Salinity Laboratory, Riverside, CA 92507, USA
U.S. Environmental Protection Agency, Oﬃce of Research and Development, Los Angeles, CA 90017, USA

A R T I C LE I N FO

A B S T R A C T

This manuscript was handled by Corrado
Corradini, Editor-in-Chief, with the assistanceof Dongmei Han, Associate Editor

Drywells are widely used as managed aquifer recharge devices to capture stormwater runoﬀ and recharge
groundwater, but little research has examined the role of subsurface heterogeneity in hydraulic properties on
drywell recharge eﬃciency. Numerical experiments were therefore conducted on a 2D‐axisymmetric domainusing the HYDRUS (2D/3D) software to systematically study the inﬂuence of various homogenous soil types andsubsurface heterogeneity on recharge from drywells under constant head conditions. The mean cumulative inﬁltration (μI) and recharge (μR) volumes increased with an increase in the saturated hydraulic conductivity (Ks )for various homogeneous soils. Subsurface heterogeneity was described by generating ten stochastic realizationsof soil hydraulic properties with selected standard deviation (σ), and horizontal (X) and vertical (Z) correlation
lengths. After 365 days, values of μI, μR, and the radius of the recharge area increased with σ and X but decreased
with Z. The value of μR was always smaller for a homogeneous than a heterogeneous domain. This indicates thatrecharge for a heterogeneous proﬁle cannot be estimated with an equivalent homogeneous proﬁle. The value of
μR was always smaller than μI and correlations were highly non-linear due to vadose zone storage. Knowledge of
only inﬁltration volume can, therefore, lead to misinterpretation of recharge eﬃciency, especially at earlier
times. The arrival time of the wetting front at the bottom boundary (60 m) ranged from 21 to 317 days, withearlier times occurring for increasing σ and Z. The corresponding ﬁrst arrival location can be 0.1–44 m awayfrom the bottom releasing point of a drywell in the horizontal direction, with greater distances occurring forincreasing σ and X. This knowledge is important to accurately assess drywell recharged performance, water
quantity, and water quality.

Keywords:
DrywellInﬁltration
Recharge
Arrival time
Arrival location
HYDRUS (2D/3D)

1. Introduction
The World Economic Forum lists freshwater scarcity as the largest
global risk to the sustainable development of human society due to theincreasing demand of the world population and expansion of irrigatedagriculture (Ercin and Hoekstra, 2014; Mekonnen and Hoekstra, 2016;
Vorosmarty et al., 2000). Spatial and temporal variations in water demand lead to water scarcity in several parts of the world during speciﬁctimes of the year (Mekonnen and Hoekstra, 2016; Postel et al., 1996;
Savenije, 2000). Water resources can be divided into renewable (e.g.,surface water and groundwater) and non-renewable (deep aquifers,
which do not have a signiﬁcant replenishment rate on the human timescale) (Mancosu et al., 2015). Groundwater is a vital source of freshwater, (Zektser and Lorne, 2004), however, overexploitation ofgroundwater resources has resulted in groundwater depletion, increased pollution, land subsidence, soil salinization, and seawater intrusion in parts of the world (Feng et al., 2013). Urban development⁎with impermeable and paved surfaces leads to reduced inﬁltration of
runoﬀ water and extreme ﬂooding events, but also oﬀers opportunities
to capture and store excess runoﬀ and recharge groundwater. Increased
ﬂooding from climate extremes also provides opportunities for collecting, storing, and injecting stormwater into an aquifer for use during
droughts (Scanlon et al., 2016).
Sustainable utilization of water resources can be achieved by reducing the demand for groundwater through increased water use eﬃciency, conjunctive use of alternative water sources, or increasing recharge (Dillon, 2005). Stormwater capture, inﬁltration, and recharge
technologies such as inﬁltration basins, vadose zone inﬁltration devices
like drywells (Edwards et al., 2016; Sasidharan et al., 2018a;
Sasidharan et al., 2019), inﬁltration trenches, stormwater wells, anddirect injection wells (Dillon, 2005; Gale, 2005; Richard and Peter,
2017) have been developed over the last 60 years. The main focuses of
all these managed aquifer recharge strategies are to prevent ﬂooding
and surface and groundwater pollution, enhance drought resilience

Corresponding author at: USDA, ARS, Salinity Laboratory, Riverside, CA 92507, USA.
E-mail address: salinis@ucr.edu (S. Sasidharan).

https://doi.org/10.1016/j.jhydrol.2020.124569
Received 5 September 2019; Received in revised form 19 November 2019; Accepted 9 January 2020
Available online 11 January 20200022-1694/ © 2020 Elsevier B.V. All rights reserved.

Journal of Hydrology 583 (2020) 124569

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However, many previous studies using microbial contaminants likeviruses (Sasidharan et al., 2018b; Sasidharan et al., 2017a; Schijven and
Hassanizadeh, 2000) and bacteria (Camesano and Logan, 1998;
Johnson et al., 1995; Sasidharan et al., 2017b) demonstrated that microbial retention and inactivation is a function of the ﬂow velocity andresidence time. Soil proﬁles with high permeability or preferential ﬂow
paths (Arnaud et al., 2015; Bradford et al., 2017) can lead to the earlyarrival of pathogenic microbes at the water table. In addition, monitoring wells to characterize recharge water quality are usually installed
within a few meters of a drywell (Hamad et al., 2016; Izuka, 2011). Thisimplicitly assumes that the arrival of recharge water occurs directly
below the bottom of the drywell. In many cases, drinking water supply
wells have been contaminated or threatened by water inﬁltrated fromdrywells (i.e., stormwater drainage wells) (Haney et al., 1989). Varying
degrees contamination of groundwater by stormwater drainage wells
has been reported across the US (Cadmus, 1991, 1996, 1999; Haney
et al., 1989; Michael, 1997; Orr, 1993; USEPA, 1997; Wilde, 1994).
Therefore, an understanding of the inﬂuence of subsurface heterogeneity on the arrival time and location of recharge during the shortterm operation of a drywell is essential when assessing water quality.
The objective of this study is to investigate the inﬂuence of subsurface heterogeneity on groundwater recharge from a drywell, especially the short-term condition when recharge does not yet equal inﬁltration. The HYDRUS (2D/3D) software was used to directly simulate
cumulative inﬁltration and recharge volumes from a drywell for different homogenous and heterogeneous soils. Constant head conditions
were considered in the drywell to facilitate the determination of subsurface soil properties on the upper limit for recharge (and contaminanttransport) and the lower limit on arrival time. Subsurface heterogeneity
was described in this model by generating stochastic realizations of soil
hydraulic properties with selected standard deviation, and vertical andhorizontal correlation lengths. The inﬂuence of stochastic subsurface
heterogeneity parameters on cumulative inﬁltration (I) and recharge(R) volumes, the radius of recharge (rx), early (EAT) and late (LAT)
arrival times, and early (EAP) and late (LAP) arrival points were determined to understand the drywell inﬁltration and recharge behavior
in the deep vadose zone (60 m).

with short-term coping strategies and long-term adaptive capacity, andincrease groundwater recharge (Dillon et al., 2019; Scanlon et al., 2016;
Washington State Department of Ecology, 2006). A few major cities in
California and Washington achieve nearly 70–100 percent of their
groundwater recharge through stormwater drainage wells or drywellinﬁltration (Cadmus, 1999; EPA, 1999). Similarly, rapidly developing
urban areas, in Arizona, Florida, and Texas, are planning to continue tobuild stormwater wells as a cost-eﬀective way to manage runoﬀ and torecharge groundwater (Cadmus, 1999; EPA, 1999).
Accurate estimation of groundwater recharge remains a challenge inthe ﬁeld of hydrology due to its nonlinear nature, which varies withspace and time (Wang et al., 2016). Traditionally, both physical (waterbalance, Darcy ﬂux, root-zone drainage) and chemical (tracers) approaches have been used to estimate recharge in arid and semi-arid
areas (Allison et al., 1994). However, these techniques are expensive,
not useful for short-term measurement, and exhibit large errors(Hendrickx and Walker, 2017; Huang et al., 2017). In addition, spatial
variability in local topography, soil texture, and structure produce
heterogeneity in groundwater recharge that requires a large number ofsampling locations (Allison et al., 1994). Limited ﬁeld measurements
are increasingly used in combination with vadose zone numericalmodeling as a time and cost-eﬀective method to estimate groundwater
recharge (Min et al., 2015; Ries et al., 2015; Small, 2005; Turkeltaub
et al., 2015; Wang et al., 2016). However, most of these studies only
considered root zone soil moisture, deep vadose zone soil water content, and matric pressure data to determine the average soil hydraulicparameters for the vadose zone to a maximum depth of 1–20 m. Furthermore, these investigations have shown large diﬀerences ingroundwater recharge and simulated data due to uncertainty in model
parameters.
Soil heterogeneity such as the presence of high and low permeablesoil layers, their horizontal and vertical distribution, and connectivity
signiﬁcantly impacts water ﬂow in the subsurface (Mantoglou andGelhar, 1987; Schilling et al., 2017; Xie et al., 2014; Yeh et al.,1985a,b). Inadequate characterization of subsurface heterogeneity canlead to uncertainty in the prediction of water ﬂow through the vadosezone, contaminant migration, and estimation of recharge. Deterministic
and stochastic models can be used to study water ﬂow in the vadosezone and groundwater recharge from a drywell. The Miller and Miller(1956) similitude scaling approach is a stochastic model that has been
widely used to analyze ﬂow and transport processes in heterogeneous
unsaturated soil systems (Hammel and Roth, 1998; Roth, 1995; Rothand Hammel, 1996; Tseng and Jury, 1994; Vereecken et al., 2007). Our
previous study investigated the inﬂuence of stochastic vadose zone
heterogeneity on drywell inﬁltration behavior and determined the effective unsaturated soil hydraulic properties (Sasidharan et al., 2019).
Most previous inﬁltration and recharge studies have looked at the longterm steady-state conditions when inﬁltration (below the root zone)
equals recharge (Gray and Norum, 1967; Mantoglou and Gelhar, 1987;
Yeh et al., 1985a,b). However, the installation of a new drywell thatreceives episodic water input will create transient conditions in thevadose zone that will change inﬁltration, recharge, and storage. In thiscase, inﬁltration will be greater than recharge until a new steady-state
condition is achieved. Transient recharge behavior is important fordrywells because during their typical operational lifespan (e.g., a
decade) they may not ever achieve steady-state conditions. However,no literature has investigated the eﬀect of subsurface heterogeneity on
groundwater recharge from a drywell under constant head conditions,which is an upper bound (best-case scenario) for recharge and worstcase scenario for contaminant transport.
One of the major selling points for drywells compared to direct injection wells is the opportunity for vadose zone treatment of contaminants (Edwards et al., 2016). Current regulatory standards fordrywells only require 1.5–13 m separation distance between the bottomof the drywell and the local groundwater table (City of Portland, 2008,2015; EPA, 1999; Washington State Department of Ecology, 2006).

2. Materials and methods
2.1. Mathematical modelThis research explores the role of subsurface heterogeneities on
recharge from a drywell using numerical experiments. Input parameters, initial conditions, and boundary conditions that were employedin these numerical experiments were generally based on results fromdrywells located in Fort Irwin, CA that are discussed in the supporting
information (SI). The HYDRUS (2D/3D) software package version 3(Šejna et al., 2018; Šimůnek et al., 2018, 2016) was used to simulate
inﬁltration from the drywell, redistribution in the vadose zone, andrecharge into the groundwater. HYDRUS (2D/3D) numerically solves
the 2-dimensional axisymmetric form of Richards equation using thevan Genuchten (1980) and Mualem (1976) unsaturated soil hydraulicfunctions. Unless otherwise stated default values of hydraulic parameters (e.g., the saturated soil hydraulic conductivity, Ks; the shapeparameter in the soil water retention function, α; the residual soil watercontent, θr; the saturated soil water content, θs; the pore-size distribution parameter in the soil water retention function, n; and thetortuosity parameter in the hydraulic conductivity function, l) for various soil textural classes were taken from the HYDRUS (2D/3D) Soil
Catalog (Carsel and Parrish, 1988; Šejna et al., 2014).
Fig. 1 shows a schematic of an example simulation ﬂow domain,
drywell geometry, initial condition, boundary conditions, and scale ofthe domain. The drywell geometry employed in this study was adaptedfrom a MaxWell IV model (Torrents Resources, Phoenix, Arizona, USA)drywell (DW2) installed at the Fort Irwin study site (see Section 1 in SI).
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S. Sasidharan, et al.

mesh was adjusted such that the size of elements was smaller(0.05–0.1 m) near the drywell and the grid size was gradually increased
with radial distance from the drywell, with a maximum element size of
0.5–0.75 m. The quality of the ﬁnite element mesh was assessed bychecking the mass balance error reported by HYDRUS (2D/3D) at theend of the simulation. Mass balance errors were always below 1%, andthese values are generally considered acceptable (Brunetti et al., 2017).
A no-ﬂux boundary condition was assigned to the upper boundary.
The nodes representing the right and lower left (the boundary between
the drywell and the bottom boundary) sides of the ﬂow domain were set
to no ﬂux boundaries. The nodes at the bottom boundary (z = −60 m)
were assigned a free drainage boundary condition (i.e., the water table
was assumed to be far below this point). Note that the numerical experiments in this study do not account for the eﬀect of the water tableand future site-speciﬁc analysis and modeling studies are therefore
warranted. Justiﬁcation for the constant head condition is given in
Section 1 of the SI. To represent a continuously full drywell, the totalhead (H = h + z) at the left boundary (at z = −8 to −38) was constant and equal to 0 m, i.e., the pressure head decreased linearly withdepth from 38 m at the bottom boundary of the drywell to 8 at the top(Fig. 1). The initial condition (IC) in the ﬂow domain was speciﬁed interms of the soil water pressure head h (x, z) and was set to a constantpressure head of −0.5 m (Fig. 1). This initial condition is justiﬁed
because the bottom of the drywell at Fort Irwin is approximately 25 mfrom the water table. In addition, the water retention curve for the Fort
Irwin soil (Fig. S4) shows that at −50 cm pressure head the corresponding water content (0.06) is close to the residual water content(0.043, Table 1) of the Fort Irwin soil. Therefore, we choose this relatively low ICs to make the change in storage smaller and to estimate
recharge accurately within the simulation time.

Fig. 1. The drywell geometry, dimensions, initial condition, the boundary
conditions, and X and Z correlation length scale for the 2D-axisymmetrical ﬂow
domain. The detailed drywell geometry and the water ﬂow dynamics were
presented in our previous study (Sasidharan et al., 2018a).

3. Numerical experiments

The complex geometry of the drywell and the dynamics of the waterﬂow from the drywell is described in detail by Sasidharan et al.
(2018a). The drywell has a size of 36 m (Height, H) × 1.8 m (Width,
W). A 60 m (H) × 50 m (W) simulation ﬂow domain was chosen to best
capture the inﬁltration radius of the drywell, to account for the drywelldepth, and to accurately estimate the arrival time of the recharge waterand rate (Fig. 1). The simulation domain was discretized using a twodimensional triangular ﬁnite element mesh with the MESHGEN tool
available within HYDRUS (2D/3D) (Šejna et al., 2014). The mesh was
reﬁned at the left part of the domain where inﬁltration from the drywell
was simulated. To reduce the mass balance error, the ﬁnite element

3.1. Homogeneous domain
Numerical experiments (Experiment I, Table 1) were conducted todetermine the values of I, R, and rx under the Sand (S), Loamy Sand
(LS), Sandy Loam (SL), Loam (L), and Silt (Si) soil materials for365 days (1 year). A homogeneous ﬂow domain was considered in thesesimulations and the hydraulic parameters for various soil textures weretaken from the HYDRUS (2D/3D) Soil Catalog (Table 1). The value of I
was collected over time during the simulation as the total volume ofwater inﬁltrated from the full drywell (i.e., the constant total head

Table 1
The residual soil water content (θr ), the saturated soil water content (θs ), the shape parameter (α ), the pore-size distribution parameter (n), the saturated isotropic
hydraulic conductivity (Ks ) , and the tortuosity parameter (l) for diﬀerent soil materials used in the numerical experiments.
Experiments

Soil

DomainHeterogeneity

Soil Hydraulic Parametersθr [−]¥

I
Soil Catalog

II
Stochastic Heterogeneity (Fort Irwin Soil)

Sand (S)Loamy Sand [LS] ¥Sandy Loam [SL] ¥Loam [L] ¥Silt [Si] ¥Fort Irwin Soil §HomogeneousNA

Heterogeneous

X = 10; Z = 0.1; σ = 1
X = 1; Z = 0.1; σ = 1
X = 0.1; Z = 0.1; σ = 1
X = 1; Z = 1; σ = 1
X = 1; Z = 2; σ = 1
X = 1; Z = 0.1; σ = 0.5
X = 1; Z = 0.1; σ = 0.25

¥

0.045
0.057
0.065
0.078
0.034
0.043

θs [−]
0.43
0.41
0.41
0.43
0.46
0.39

α [m−1]
14.5
12.4
7.5
3.6
1.6
9.17

n [−]
2.68
2.28
1.89
1.56
1.37
2.76

K s [m min−1]−34.95 × 10
2.43 × 10−3
7.37 × 10−4
1.73 × 10−4
4.17 × 10−5
3.20 × 10−5

l [−]
0.5
0.5
0.5
0.5
0.5
0.5

The values were adapted from the HYDRUS (2D/3D) Soil Catalog (Šejna et al., 2018).
The value of Ks and α was obtained by the inverse optimization of the falling head experiment conducted at the Fort Irwin drywell using HYDRUS (2D/3D) as
explained in our previous research (Sasidharan et al., 2018a). The values of θr , θs , and n were determined by the particle size distribution analysis using laser
diﬀractometry device (LA930: Horiba LTD, Japan) (Segal et al., 2009) and applying the sand-silt-clay faction in the Rosetta pedotransfer function model (Schaap
et al., 2001).
§

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S. Sasidharan, et al.

stochastic parameters were employed to determine mean values of I (μI)
and R (μR) and associated standard deviations. The computational timeand cost to conduct these recharge simulations were large. To minimize
these factors only ten realizations were considered for each stochastic
heterogeneity parameter combination. The justiﬁcation for using 10
realizations is given in our previous research (Sasidharan et al., 2019).
In particular, these authors found that the mean and standard deviationof cumulative inﬁltration volume in heterogeneous soil proﬁles stabilized after around ten realizations for stochastic parameters consideredin the present study. In this study, the following hypothetical combinations of the scaling factors with variable σ (σ = 0.25, 0.5, 1: when
X = 1 m and Z = 0.1 m), X (X = 0.1, 1, 10 m when σ = 1 and
Z= 0.1 m), and Z (Z = 0.1, 1, 2 m when σ = 1 and X = 1) were used.
Similar parameter ranges have been measured in the ﬁeld (Freeze,
1975; Sudicky and MacQuarrie, 1989) and employed in previous
modeling studies (Sasidharan et al., 2019). In addition, a simulation fora homogenous domain with the Fort Irwin soil hydraulic parameters
was conducted to compare the μI and μR between homogeneous andheterogeneous domains.
The times when the ﬁrst (i.e., early arrival time, EAT) and last (i.e.,late arrival time, LAT) bottom boundary nodes got saturated for the ﬁrst
and last times were extracted from the output data ﬁle with nodal watercontents from each HYDRUS (2D/3D) simulation. Similarly, the ﬁrst
(i.e., the early arrival point, EAP) and last (i.e., the late arrival point,
LAp) bottom boundary nodes that were saturated were also calculated.
A mean radius of recharge (rx) was calculated as the distance in the xdirection between the ﬁrst and last nodes at the bottom boundary thatwere fully saturated at the end of the 365 days simulation. Meanparameter values and 95%-Conﬁdence Intervals (CI) were calculated
for all parameters from ten simulations.

boundary). Similarly, the value of R was calculated as the volume ofwater drained through the bottom free drainage boundary. The radiusof recharge was calculated from the saturated nodes and its corresponding distance in the x-direction at the bottom boundary of the ﬂow
domain. Note that all this information was available in output ﬁles ofthe HYDRUS (2D/3D) software.
3.2. Heterogeneous domain
The previous experiment considered idealized homogeneous soil
systems. However, most ﬁeld-scale soil proﬁles are highly heterogeneous as shown in the Fort Irwin case study (Section 1 in SI), and thehydraulic parameters may change over short distances. Natural geologic processes tend to produce layers and lenses in soil texture thatform parallel to the soil surface (Phillips and Lorz, 2008). In addition,
preferential water ﬂow pathways can occur in the vertical direction as aresult of: unstable ﬂow behavior; dynamic capillary properties; macropores from decaying plant roots, burrowing earthworms, and animals; spatial variations in soil structure; and cracks in clayey soils; andfractured rocks (Flury et al., 1994; Hencher et al., 2011; Hendrickx andFlury, 2001; Phillips and Lorz, 2008; Sidle et al., 2000; Smith et al.,2008; Uchida et al., 2003; USDA, 2016). This study did not explicitly
investigate the eﬀect of preferential ﬂow pathways and macropores,
which has been extensively explored in previous research (Kung, 1990;
Larsbo et al., 2005; Nativ et al., 1995; Šimůnek and Bradford, 2008;
Wood et al., 1997).
Several approaches such as univariate, multivariate, and multiplepoint geostatistical techniques can be used to represent geometricalfeatures and quantify soil variability (Elkateb et al., 2003; Jackson andCaldwell, 1993; Meerschman et al., 2013). In this study, high permeable horizontal and vertical soil layers/lenses are represented using theMiller-Miller similitude geostatistical approach, which has been widelyused in previous research (Feyen et al., 1998; Roth, 1995; Roth andHammel, 1996; Sasidharan et al., 2019; Vereecken et al., 2007). The
original Miller-Miller concept of scaling is based on using the scaling
factor to adjust the degree of magniﬁcation or reduction of the Ks and hrelative to a standard (reference) value (Šimůnek et al., 2012). The
HYDRUS (2D/3D) computer software has an option to generate stochastic distributions of hydraulic conductivity (αK) and pressure head(αh) scaling factors using the Miller-Miller similitude approximation(Miller and Miller, 1956), which links these two scaling factors as follows: αK = αh−2. In this study lognormal distribution for the scaling
factors were employed (Šimůnek et al., 2007). Three input parameters:
the standard deviation (σ ) of log10(αK), and its correlation length in thelateral (the X-correlation length, X) and vertical (the Z-correlation
length, Z) directions, were used to generate the stochastic ﬁeld. The
value of σ determines the extent of variations in the scaling factors,
with higher values leading to higher variations in scaling factors. The
correlation length is a measure of how similar the scaling factors are ina speciﬁc direction and to what distance. A high value of the X-correlation length means that the scaling factor maintains similar values for agreater horizontal distance. The same applies to the Z-correlation
length in the vertical direction (Rassam et al., 2003). Procedures forgenerating autocorrelated or uncorrelated scaling factor ﬁelds and theoriginal algorithm used in HYDRUS have been described in detail in theliterature (El-Kadi, 1986; Freeze, 1975; Mejía and Rodríguez-Iturbe,1974).
Soil hydraulic parameters (Ks, α, θr, θs, and n) for stochastic simulations were obtained from experiments conducted at the Fort Irwin site
(Experiment II, Table 1). In particular, inverse optimization of fallinghead experimental data was used to obtain Ks and α (Sasidharan et al.,2018a), whereas other parameters (θr, θs, and n) were obtained fromthe measured particle size distribution (silt-clay-fraction) and the Rosetta software (Schaap et al., 2001; Segal et al., 2009).
Constant head drywell simulations in heterogeneous domains wereused to determine I and R after 365 days. Multiple realizations of the

4. Result and discussions
4.1. Homogeneous soil proﬁlesNumerical experiments were conducted to better understand thetransient relationship between the cumulative inﬁltration and recharge
volumes in homogeneous soil domains. Simulations considered higher
permeability soils (e.g., S, LS, SL, L, and Si) that are generally used forvadose zone inﬁltration devices. Fig. 2A and B show simulated watercontent (θ) proﬁles in various soils (e.g., S, LS, SL, L, and Si) after 0.73
and 365 days, respectively. Fig. 3 presents corresponding information
about the inﬁltration and recharge behavior in these same simulations.
The width and depth of the cone of inﬁltration after 0.73 days (Fig. 2A)
decreases with a decrease in the soil Ks in the order S, LS, SL, L, and Si(Table 1). Similarly, the value of I as a function of time increases withthe soil Ks (Fig. 3A).
The recharge at a speciﬁc time depends on changes in the soil hydraulic properties with θ (van Genuchten et al., 1991) and the radius of
the cone of inﬁltration at the bottom boundary (rx). Yeh et al. (1985a)
and Yeh et al. (1985b) demonstrated that the variability of capillarypressure or moisture content increases when the mean capillary pressure increases, and the anisotropy ratio (vertical/horizontal) of the effective unsaturated Ks increases when the mean capillary pressure increases or mean moisture content decreases. Coarser soils (e.g., sand)with larger pore sizes have lower θ and K(θ) (hydraulic conductivity atvarying water saturation) values before the wetting front arrives (Fig.
S5A). Consequently, the background θ (Fig. 2) and the initial recharge
rate (Fig. 3B) is lower in coarser than ﬁner textured soils. However,coarser soils also have higher values of K(θ) when θ approaches saturated conditions (Fig. S5B). The wetting front, therefore, arrives at thebottom boundary faster in coarser than ﬁner textured soils (in 0.8 days
for sand compared to 16.7 days for silt) and this is associated with adramatic increase in R (Fig. 3B) and rx (Fig. 3C). At the end of thesimulation (365 days), the cones of inﬁltration (Fig. 2B) are similar andrx = 21.5 m (Fig. 3C) for various soil types and the ﬁnal value of R
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Journal of Hydrology 583 (2020) 124569

S. Sasidharan, et al.

Fig. 2. The cone of inﬁltration for sand, loamy sand, sandy loam, loam, and silt with hydraulic properties obtained from the HYDRUS (2D/3D) soil catalog during a
constant head simulation presented as water content (θ) proﬁles at 0.73 (A) and 365 (B) days of simulation.

increased when I = 9526 m3 for sand compared to I = 1896 m3 for silt.
This occurs because inﬁltrating water needs to ﬁll more of the porespace in coarser (lower initial θ) than in ﬁner textured soils before thecone of inﬁltration can reach the bottom boundary. These simulation
results will be sensitive to the model's initial conditions. In this work,
we have assumed an initial head of −0.5 m throughout the domain.

increases with the soil Ks (Fig. 3B).
Fig. 3D shows plots of I versus R at 365 day for the simulations invarious soils. Note that a large volume of water needs to inﬁltrate into
the soil before recharge from the drywell rapidly increases. Furthermore, this delay in recharge increases for coarser textured soils thathave lower background values of θ. For example, recharge rapidly

Fig. 3. The cumulative inﬁltration volume (I) as a function of time (normal scale) (log scale is presented in Fig. S8) (A), the cumulative recharge volume (R) as afunction of time (log scale) (B), the radius of recharge (rx) vs time of arrival at the bottom boundary (C), and I vs R (log scale) at the same time (D) during 365 days
constant head simulation for the Sand (S), Loamy Sand (LS), Sandy Loam (SL), Loam (L), and Silt (Si) soils with hydraulic parameters obtained from the HYDRUS(2D/3D) Soil Catalog.
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Journal of Hydrology 583 (2020) 124569

S. Sasidharan, et al.

Fig. 4. Pressure head (h) proﬁles for homogenous (A) and heterogeneous (X = 10, Z = 0.1, σ = 1 (horizontal lens) (B), X = 1, Z = 0.1, σ = 1 (C), X = 0.1, Z = 0.1,
σ = 1 (D), X = 1, Z = 1, σ = 1 (E), X = 1, Z = 2, σ = 1 (thick vertical lens) (F), X = 1, Z = 0.1, σ = 0.5 (G), and X = 1, Z = 0.1, σ = 0.25 (H)) Fort Irwin soil ﬂow
domains after the 1-year constant head simulation.

Note that the simulated I = 1896–9526 m3 can have a resemblance toan actual arid- or semi-arid site like Fort Irwin, which received 515 m3
input water from irrigation runoﬀ during 200 days monitoring period(summer).

results for homogeneous and heterogeneous simulations reveals thatboth inﬁltration and recharge were higher for heterogeneous domains.
Consequently, simulations that employ a homogenous domain will always underestimate the actual inﬁltration and recharge eﬃciency of adrywell. The heterogeneous simulations indicate that the value of μR
will always be smaller than μI during the short-term experiment. Thisrevealed that over the 365 days simulation period, not all inﬁltrated
water will recharge groundwater because some of the water will be
trapped in isolated pockets or high permeable regions surrounded by
low permeable layers (Figs. 4 and 5).
The distribution and volume of trapped water in the ﬂow domain
will be highly dependent on the subsurface heterogeneity. Fig. 6 showsthat μI and μR tended to increase with σ (Fig. 6A) and X (Fig. 6B) but
decreased with Z (Fig. 6C) due to diﬀerences in the water distribution(Figs. 4 and 5). Fig. 7 shows a similar dependence of the mean value ofrx at the bottom boundary on σ (Fig. 7A), X (Fig. 7B), and Z (Fig. 7C). An
increase in σ leads to the formation of more permeable regions, which
enhance inﬁltration (Fig. 6A), recharge (Fig. 6A), and spreading(Fig. 7A). Similarly, an increase in X produces larger lateral lenses withhigh permeability that enhance the rapid movement of the wetting
front in the horizontal direction (Fig. 5D, C, and B) and facilitate theinﬁltration and recharge of water over a larger area (Figs. 6B and 7B).
Conversely, an increase in Z creates larger vertical lenses with highpermeability, which facilitates the rapid vertical movement of waterover a relatively small area (Fig. 5E, F, 6C, and 7C). The 95% CIs for μI
and μR were sometimes very large in Fig. 6, especially for the highest
values of X and Z in Fig. 6B and C, respectively. This uncertainty reﬂects
diﬀerences in the continuity of the high permeability regions and thearea of recharge (Fig. 7). Several other studies have reported extensive
lateral (horizontal) moisture movement but a limited vertical movement in diﬀerent systems (Mantoglou and Gelhar, 1987; Routson et al.,
1979; Sinai et al., 1974; Yeh et al., 1985a,b). However, these generalﬁndings only give information regarding the long-term trend of inﬁltration and recharge, and do not consider the presence of highpermeable horizontally and vertically correlated soil layers. Therefore,
it is important to understand the inﬁltration and recharge behavior, thearrival time and arrival location during short-term drywell operation toestablish a site-speciﬁc monitoring system.

4.2. Heterogeneous soil proﬁles
4.2.1. Cumulative inﬁltration and recharge
The above simulations were highly idealized because they considered homogeneous soil proﬁles. In contrast, natural subsurface isinherently heterogeneous. However, previous studies (Hencher et al.,2011; Phillips and Lorz, 2008; Sasidharan et al., 2019; Sidle et al., 2000;
Smith et al., 2008; Uchida et al., 2003; USDA, 2016) and the case study
presented in the SI demonstrates that layered soil structures are very
common. Illustrative simulations were therefore conducted to better
understand the eﬀects of stochastic heterogeneity parameters (X, Z, andσ) for Ks on the cumulative inﬁltration and recharge volumes from adrywell. Figs. 4 and 5 show representative h and θ distributions, respectively, after 365 days constant head simulation in homogeneous(Figs. 2A and 3A) and stochastic (Figs. 2B–H and 3B–H) domains. The
Fort Irwin soil hydraulic parameters (Experiment II, Table 1) were
employed in these simulations, and stochastic parameters equaled
X = 10 m, Z = 0.1 m, and σ = 1 in Fig. 4B and 5B; X = 1 m, Z = 0.1 m,and σ = 1 in Figs. 4C and 5C; X = 0.1 m, Z = 0.1 m, and σ = 1 inFigs. 4D and 5D; X = 1 m, Z = 1 m, and σ = 1 in Figs. 4E and 5E;
X = 1 m, Z = 2 m, and σ = 1 in Figs. 4F and 5F; X = 1 m, Z = 0.1 m,and σ = 0.5 in Figs. 4G and 5G; and X = 1 m, Z = 0.1 m, and σ = 0.25
in Figs. 4H and 5H. Heterogeneous domains display wider distributions(in the lateral direction) of the saturated area and the presence of unsaturated pockets within the cone of inﬁltration compared to a homogeneous domain (Figs. 4A and 5A).
Fig. 6 shows values of μI and μR, and their 95%-CI after 365 days as
a function of σ (Fig. 6A), X (Fig. 6B), and Z (Fig. 6C). Speciﬁc stochastic
parameters that were employed in these simulations included:
σ = 0.25, 0.5, and 1 when X = 1 m and Z = 0.1 m in Fig. 6A; X = 0.1,
1, 10 m when Z = 0.1 m and σ = 1 in Fig. 6B; and Z = 0.1, 1, 2 m when
X = 1 m and σ = 1 in Fig. 6C. Fig. 6 also includes values of I and R after
365 days for the corresponding homogeneous domain. A comparison of6

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Fig. 5. Water content (θ) proﬁles for homogenous (A) and heterogeneous (X = 10, Z = 0.1, σ = 1 (horizontal lens) (B), X = 1, Z = 0.1, σ = 1 (C), X = 0.1, Z = 0.1,
σ = 1 (D), X = 1, Z = 1, σ = 1 (E), X = 1, Z = 2, σ = 1 (thick vertical lens) (F), X = 1, Z = 0.1, σ = 0.5 (G), and X = 1, Z = 0.1, σ = 0.25 (H)) Fort Irwin soil ﬂow
domains after the 1-year constant head simulation.

distance to the early and late arrival points. Interestingly, all 4 parameters (EAT, LAT, EAP, LAP) showed a large 95%-CIs due to diﬀerencesin the predicted water distributions.
Fig. 9 provides summary information about recharge with time andlocation for stochastic simulations when Z = 0.1 m, σ = 1, andX = 0.1, 1, 10 m. Fig. 9A–D show values of EAT, LAT, EAP, and LAP,
respectively, and their corresponding 95%-CIs that increase with the Xcorrelation length. These trends reﬂect increasing water spreading with
X due to larger lenses in the lateral direction with high permeability.
Fig. 9E and F show values of μR as a function of time during early(i.e., < 75 days) and late (i.e., < 365 days) stages, respectively. Early
stages of recharge show a decrease in μR with an increase in X because
recharge is delayed by increasing lateral spreading. This trend is consistent with the results displayed in Fig. 9A–D. Conversely, μR was
greatest when X = 10 m at later stages of recharge (Fig. 9F). Thissuggests that vertical migration and recharge was enhanced once larger
horizontal lenses with higher permeability were ﬁlled. In this case,higher values of θ and K(θ) were achieved in high permeability lenses
that enhanced recharge.
Previous researchers have similarly reported that the vertical effective Ks is small and the lateral eﬀective Ks is large in the wetting frontof stratiﬁed soil (Mantoglou and Gelhar, 1987), which increased thelateral movement of contaminants in the unsaturated zone below various waste disposal facilities (Crosby III et al., 1968; Crosby III et al.,
1971; Palmquist and Johnson, 1962; Price et al., 1979; Prill, 1977).
Enhanced lateral movement can be attributed to the fact that at hightension the hydraulic conductivity of ﬁne-textured materials is relatively high, and water may prefer to spread laterally in a ﬁne bed thanto move vertically through coarser ones (Routson et al., 1979). In
natural environments, layered soil proﬁles with high permeable sandlayers followed by low permeable clay lenses with extensions andthicknesses of several tens to hundreds of meters can be present(Phillips and Lorz, 2008). Trautwein et al. (1983) investigated wastewater movement from an evaporation pond in the stratiﬁed unsaturated
zone and observed that the contamination pool extended laterally to a
distance of 2000 m due to the formation of a saturated perched waterzone above the water table. The presence of such geological formations(e.g., clay layer at Fort Irwin, SI) could delay groundwater recharge

4.2.2. Time and location of recharge
The drywell regulatory guidelines require a mandatory separation
distance of only 1.5–13 m between the drywell and the local groundwater table to provide a contaminant treatment zone (City of Portland,
2008; City of Portland, 2015; EPA, 1999; Washington State Departmentof Ecology, 2006). However, soils with highly permeable vertical lenses
or fractures could minimize the contaminant treatment time and can
produce an early arrival of contaminants such as pathogenic microbes
to groundwater (Bradford et al., 2017; Mohanty et al., 2016; Wanget al., 2014a,b). Figs. 4–7 examined inﬁltration and recharge from adrywell in heterogeneous and homogeneous soils after 365 days underconstant head conditions. Values of the mean ﬁnal recharge rate (μr)
and mean rx in Fig. 7 can be used to estimate the average yearly recharge ﬂux to groundwater from a drywell (i.e., μr divided by πrx2) for aconstant head condition (Fig. S6). This information is vital for determining the average yearly Darcy velocity during recharge for conservatively transported contaminants during recharge. However, it does
not address variability in the recharge behavior with time or the location on the bottom boundary. Simulation results were therefore further
evaluated to address these issues. This information has important implications for properly determining times and locations for assessing thepotential migration of contaminants through the vadose zone to thegroundwater table.
Fig. 8 provides summary information about recharge with time andlocation for stochastic simulations when X = 1 m, Z = 0.1 m, andσ = 0.25, 0.5, and 1. The early (EAT) and late (LAT) mean arrival times
decreased with σ in Fig. 8A and B, respectively. However, the mean
location for early (EAP) and late (LAP) arrival point increased with σ(Fig. 8C and D). Observed diﬀerences in the early and late arrival times
and points can be explained by analyzing the μR data with time (Fig. 8E
and F). There was a signiﬁcant delay in recharge when σ = 0.25
(compared to higher σs) (Fig. 8E) and the value of μR was very close tothat for the homogeneous soil at 365 days (Fig. 8). A small value of σleads to a more uniform and lower permeability, and a cone of inﬁltration that takes longer to ﬁll before the wetting front arrives at thebottom boundary. An increase in σ creates more high permeability
zones that decrease the early and late arrival times. It also increases the
spreading of water (Figs. 4H, G, D, and 7A) and thereby increases the7

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Fig. 6. The mean cumulative inﬁltration volume (μI) and the mean cumulative
recharge volume (μR) (from 10 simulations) and the corresponding 95%
Conﬁdence Intervals as a function of σ (σ = 0.25, 0.5, 1) when X = 1 and
Z = 0.1 (A), X (X = 0.1, 1, 10 m) when σ = 1 and Z = 0.1 (B), and Z (Z = 0.1,
1, 2 m) when σ = 1 and X = 1 (C) for a heterogenous Fort Irwin soil ﬂow
domain after 365 days constant head simulation. The I (Homo-I, solid greenline) and R (Homo-R, dashed orange line) values after 365 days simulation for ahomogeneous domain are also presented. (For interpretation of the references
to colour in this ﬁgure legend, the reader is referred to the web version of thisarticle.)Fig. 7. The mean radius of recharge (rx) (from 10 simulations) and the corresponding 95% Conﬁdence Intervals as a function of σ (σ = 0.25, 0.5, 1) when
X = 1 and Z = 0.1 (A), X ( X = 0.1, 1, 10 m) when σ = 1 and Z = 0.1 (B), andZ (Z = 0.1, 1, 2 m) when σ = 1 and X = 1 (C) for a heterogeneous Fort Irwin
soil ﬂow domain after 365 days constant head simulation.

Z = 0.1 m and lowest when Z = 2 m (Fig. 10F). In this case, thepresence of a thick vertical lens (Fig. 10F) when Z = 2 m reduced theoverall width of the cone of inﬁltration (Figs. 4 and 5) and the recharge
area (Fig. 7), and this decreased the long-term value of μR. All 4parameters showed a large 95%-CIs for simulations shown in Fig. 10
and this reﬂects the variability in water distributions and ﬂow rates.
Groundwater monitoring wells are commonly installed a few meters
away from a drywell (Hamad et al., 2016; Izuka, 2011). Previous stochastic theory studies (Mantoglou and Gelhar, 1987; Yeh et al.,1985a,b) predicts that a contamination plume tends to spread laterally
in dry vadose zone soils, while vertical movement is slow. Contaminants may, therefore, arrive at the water table much later than ispredicted by many classic one-dimensional models. However, this study
demonstrated that the arrival time could range from 33 to 317 days andthe arrival location could be 0.1–44 m away from the bottom releasing
point of a drywell depending on the heterogeneity. If the location of amonitoring well and the sampling timing are not properly selected, thenthe recharge water quality can lead to a misinterpretation of thefrom a drywell from 1 to 100+ years. Therefore, the presence of a highpermeable soil around the drywell inﬁltration area may not contributeto groundwater recharge for many years.
Fig. 10 provides summary information about recharge with timeand location for stochastic simulations when X = 1 m, σ = 1, and
Z = 0.1, 1, and 2 m. Fig. 10A and B show that the values of EAT and
LAT, respectively, decreased with Z. Conversely, values of EAP and LAP
increased (Fig. 10C) and decreased (Fig. 10D) with Z. Fig. 10E showsthat values of μR during the early stage (< 75 days) increased with Z.
Among all stochastic parameter combinations, the X = 1, Z = 2, σ = 1
simulation had the earliest arrival time (34 ± 12 days). The presenceof highly permeable vertical lenses facilitates the faster downward
movement of water and thus an early arrival time (Fig. 10A). However,the value of μR during the late stage (< 365 days) was greatest when8

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S. Sasidharan, et al.

Fig. 8. The early arrival time (EAT) (A), the late arrival time (LAT) (B), the early arrival point (EAP) (C), the late arrival point (LAP) (D), mean cumulative recharge(μR) at an early stage (E), and at a late stage (F) as a function of σ (σ = 0.25, 0.5, 1) when X = 1 and Z = 0.1 for a heterogeneous Fort Irwin soil domain during
365 days constant head simulation.

5. Conclusions and future implications

anticipated transport and risks from contaminants. Consequently, theuse of model projections for estimating the magnitude of groundwater
recharge and transport of contaminants from drywells in heterogeneous
subsurface conditions will likely have a high degree of uncertainty. One
should, therefore, be cautious when determining the risks from drywells
during the short-term operation at a site that is not thoroughly characterized.
Based on the modeling realizations, the value of μR was always
smaller for a homogeneous than a heterogeneous domain (Figs. 8F, 9F,
and 10F). Previously, Sasidharan et al. (2018a) found that an equivalent homogeneous soil domain could be used to accurately predict μI fora heterogeneous soil domain. The sensitivity of a modeled recharge
volume to various physical conﬁgurations and connectivity of soil hydraulic properties implies that measurement of the soil heterogeneity
below the drywell is an important consideration when evaluating thepotential for groundwater recharge. As shown in Fig. S7, the correlation
between drywell inﬁltration and the recharge volume can be highly
non-linear for heterogeneous soil domains. Thus, model estimates ofshort- or long-term recharge eﬃciencies may not be reliable whenconstrained solely by the inferred volume of inﬁltration based on empirical measurements within a drywell system.

Drywells are widely used to capture stormwater and convey it to thevadose zone for subsequent groundwater recharge. However, assessing
the performance of a drywell and its recharge eﬃciency over the shortor long-term is very tedious, time-consuming, expensive, and complex.
To successfully install a drywell, conduct groundwater monitoring, andcollect accurate groundwater quality information requires a careful
design of ﬁeld experiments and a knowledge of the subsurface heterogeneity in soil hydraulic properties.
Numerical experiments were conducted to understand the relationship between the cumulative inﬁltration and recharge volumes in
the homogeneous (S, LS, SL, L, and Si) soil domains. The cumulative
inﬁltration and recharge volumes increased with Ks. During the initialstages of inﬁltration, the coarser textured sandy soil had a lower
background θ and, therefore, a large volume of water had to inﬁltrate
before the recharge rate from the drywell rapidly increased. The wetting front arrived at the bottom boundary faster in coarser than ﬁnertextured soils. However, all soil types had a similar value of the recharge area radius at the end of the simulation. Note that this may notoccur when the model is run with a more representative transientloading condition. Groundwater monitoring well installations placed at9

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Fig. 9. The early arrival time (EAT) (A), the late arrival time (LAT) (B), the early arrival point (EAP) (C), the late arrival point (LAP) (D), mean cumulative recharge(μR) at an early stage (E), and at a late stage (F) as a function of X ( X = 0.1, 1, 10 m) when Z = 0.1 and σ = 1 for a heterogeneous Fort Irwin soil domain during
365 days constant head simulation.

creates more high permeability and connectivity zones and increases
the spreading of water. The values of both early and late arrival times
and locations increased with the X-correlation length. The presence oflarger, highly permeable lateral lenses increases the spreading of water.
At the same time, values of early and late arrival times decreased with
the Z-correlation length. In contrast, early and late arrival locations
increased and decreased with Z, respectively. The presence of highly
permeable vertical lenses facilitates the faster downward moment ofwater but reduces the overall width of the cone of ﬁltration and therecharge area.
This research points out that the assumption of a highly permeable
homogenous soil around the drywell may result in unrealistic model
estimates of recharge to groundwater. Some understanding of the hydraulic properties, subsurface heterogeneity, and its continuity between
the drywell and the groundwater table is essential for making an informed decision on recharge and contaminant transport. The current
drywell inﬁltration tests performed by contractors are very simple andfail to capture accurately the hydraulic behavior of a site. Therefore,
carefully planned falling head and constant head (multiple cycles) experiment should be conducted in a newly installed drywell and eﬀective
hydraulic properties can be obtained by the inverse optimization of theobserved data (Sasidharan et al., 2018a, 2019). In the absence ofthe outer radius of the recharge area may therefore not be the best
choice.
Homogeneous soil proﬁles are highly idealized conditions comparedto natural subsurface soils that are highly heterogeneous. Therefore,
additional numerical experiments were conducted to better understandthe inﬂuence of stochastic heterogeneity in soil hydraulic properties on
drywell inﬁltration and recharge behavior. The cumulative inﬁltration
and recharge volume after a 365 days constant head simulation werehigher for heterogeneous domains than for the homogeneous domain.
However, the cumulative recharge volume was smaller than the cumulative inﬁltration volume, and their correlations were highly nonlinear. This result indicates that a fraction of inﬁltrated water can betrapped in isolated pockets or highly permeable regions in the vadosezone and the knowledge of the inﬁltration volume under varying
stormwater ﬂow events may not be a reliable predictor of potential
short-term recharge. Both cumulative inﬁltration and recharge increased with increasing σ and the lateral correlation length but decreased with an increasing vertical correlation length. The radius ofrecharge at the bottom boundary showed a similar dependency on
stochastic parameters.
The mean early and late arrival times decreased with σ, whereas themean early and late arrival points increased with σ. An increase in σ
10

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S. Sasidharan, et al.

Fig. 10. The early arrival time (EAT) (A), the late arrival time (LAT) (B), the early arrival point (EAP) (C), the late arrival point (LAP) (D), mean cumulative recharge(μR) at an early stage (E), and at a late stage (F) as a function of Z (Z = 0.1, 1, 2) when X = 1 m and σ = 1 for a heterogeneous Fort Irwin soil domain during
365 days constant head simulation.

water content probes and solution sampling ports at several depths totrack water content proﬁles and contaminant migration, respectively(Dahan et al., 2009). Such information can help to quantify the inﬂuence of subsurface heterogeneity on recharge and serve as an early
warning system of contaminant migration in the vadose zone (Dahanet al., 2009). In addition, a variety of near-surface geophysical techniques can provide valuable information on site structure, hydrology andhydrogeology, and contamination plume information (Annan, 2005;
Binley and Kemna, 2005; Cassiani et al., 2014; Everett and Meju, 2005;
Revil et al., 2006). However, these sophisticated academic methods andtechniques are often not feasible given real-world limits on time, cost,and data availability, especially for risk and uncertainty analyses.
Therefore, academics and practitioners should collaborate to develop
qualitative methods for drywell optimization and risk assessment (Bodeet al., 2018). If site-speciﬁc data are not available for a given subsurface, literature values of similar systems can in principle be used for an
initial assessment (Schilling et al., 2017).
The Fort Irwin drywell case study demonstrated that urban andrural areas in the arid or semi-arid environment may still receive regular input of water from urban runoﬀ and can be used as an alternative
source of inﬁltration water. Such a scenario will lead to a constant headboundary condition within the drywell and will act as an upper boundextensive knowledge about the subsurface heterogeneity, an equivalenthomogeneous domain with inversely optimized stochastic parameters
can be used to estimate the cumulative inﬁltration (Sasidharan et al.,2019), even if such an analysis may not provide an accurate estimationof recharge under transient conditions.
This research demonstrated the importance of subsurface heterogeneity on recharge from drywells. However, it is important to recognize the limitations and assumptions employed in this study. The
consideration of heterogeneous subsurface and subsequent data analysis can be time-consuming and adds a lot more complexity. For example, a maximum X-correlation length of 10 m was considered in thisstudy due to the limitation in the size of the model simulation domain
size and the associated simulation time and cost. The stochastic input
parameters used in the numerical experiments were hypothetical (sincesite-speciﬁc data is not available) and initial and boundary conditions
were chosen based on the limited knowledge available from Fort Irwin's
study site.
It is recommended that hydrogeologists, drywell practitioners, andregulators implement new strategies during drywell installation tocollect detailed site-speciﬁc subsurface heterogeneity data for future
validation studies. For example, drywell performance could be assessedusing single or multiple slanted boreholes that are instrumented with
11

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S. Sasidharan, et al.

for groundwater recharge and as a worst-case scenario for the contaminant transport. Therefore, this study only considered constant headboundary conditions to understand the eﬀect of stochastic parameters
on cumulative inﬁltration and recharge volumes, the area of recharge,
arrival time, and arrival location of inﬁltrated water from a drywell.
Additional research is needed to study the inﬂuence of natural weather
conditions such as rain frequency and intensity at a drywell site.
Episodic rainfall events create transient boundary conditions in thedrywell that will add more complexity to recharge estimates and risk
assessment. This topic will be addressed in a future paper.

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Larsbo, M., Roulier, S., Stenemo, F., Kasteel, R., Jarvis, N., 2005. An improved dualpermeability model of water ﬂow and solute transport in the vadose zone. VadoseCredit Authorship Contribution Statement
Salini Sasidharan: Investigation, Methodology, Software, Formal
analysis, Validation, Data curation, Writing - original draft,
Visualization. Scott A. Bradford: Conceptualization, Software,
Resources, Writing - review & editing, Supervision, Project administration. Jiří Šimůnek: Conceptualization, Software, Resources, Writing- review & editing, Supervision, Project administration. Stephen R.
Kraemer: Writing - review & editing, Project administration, Funding
acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing ﬁnancial
interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.
Acknowledgments
Funding for this research was provided by the U.S. Environmental
Protection Agency (US EPA) through an interagency agreement with
the United States Department of Agriculture (EPA DW-012-92465401;
ARS 60-2022-7-002). The views expressed in this article are those of theauthors and do not necessarily represent the views or policies of the
U.S. Environmental Protection Agency. The mention of commercial
products does not constitute an endorsement.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jhydrol.2020.124569.
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