Advances in Water Resources 116 (2018) 167–177

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Advances in Water Resources
journal homepage: www.elsevier.com/locate/advwatres

Evaluating drywells for stormwater management and enhanced aquifer
recharge
Salini Sasidharan a,b,∗, Scott A. Bradford b, Jiří Šimůnek a, Bill DeJong c, Stephen R. Kraemer daDepartment of Environmental Sciences, University of California Riverside, Riverside, CA 92521, United States
United States Department of Agriculture, Agricultural Research Service, U. S. Salinity Laboratory, Riverside, CA 92507, United States
Torrent Resources, 9950 Alder Ave, Bloomington, CA 92316, United Statesd
Systems Exposure Division, National Exposure Research Laboratory, Oﬃce of Research and Development, US Environmental Protection Agency, Los Angeles, CA,
United Statesbca r t i c l e

i n f oa b s t r a c t

Keywords:
Drywell
HYDRUS (2D/3D)
Falling head
Inverse simulation
Hydraulic properties
In situ

Drywells are increasingly used for stormwater management and enhanced aquifer recharge, but only limited
research has quantitatively determined the performance of drywells. Numerical and ﬁeld scale experiments were,
therefore, conducted to improve our understanding and ability to characterize the drywell behavior. In particular,
HYDRUS (2D/3D) was modiﬁed to simulate transient head boundary conditions for the complex geometry of theMaxwell Type IV drywell; i.e., a sediment chamber, an overﬂow pipe, and the variable geometry and storage ofthe drywell system with depth. Falling-head inﬁltration experiments were conducted on drywells located at the
National Training Center in Fort Irwin, California (CA) and a commercial complex in Torrance, CA to determine insitu soil hydraulic properties (the saturated hydraulic conductivity, Ks , and the retention curve shape parameter,𝛼) for an equivalent uniform soil proﬁle by inverse parameter optimization. A good agreement between theobserved and simulated water heights in wells was obtained for both sites as indicated by the coeﬃcient ofdetermination 0.95-0.99–%, unique parameter ﬁts, and small standard errors. Fort Irwin and Torrance drywells
had very distinctive soil hydraulic characteristics. The ﬁtted value of Ks =1.01 × 10−3 m min−1 at the Torrance
drywell was consistent with the sandy soil texture at this site and the default value for sand in the HYDRUS soil
catalog. The drywell with this Ks = 1.01 × 10−3 m min−1 could easily inﬁltrate predicted surface runoﬀ from adesign rain event (∼51.3 m3 ) within 5760 min (4 d). In contrast, the ﬁtted value of Ks =2.25 × 10−6 m min−1 at
Fort Irwin was very low compared to the Torrance drywell and more than an order of magnitude smaller thanthe default value reported in the HYDRUS soil catalog for sandy clay loam at this site, likely due to clogging.
These experiments and simulations provide useful information to characterize eﬀective soil hydraulic properties
in situ, and to improve the design of drywells for enhanced recharge.

1. Introduction

and increasing the volume of surface runoﬀ (Dunne and Leopold, 1978;
Mount, 1995). This can contribute to a decline in natural groundwater recharge, long-term and short-term drought, an increase in waterpollution levels, and ﬂooding caused by surface runoﬀ (Weng, 2001;
Arnold and Gibbons, 1996).
Many arid and semi-arid urban regions of the world face serious
challenges in maintaining water quality and quantity to meet its growing population demand. For example, around 60% of the water supply in Southern California is imported from the Colorado River Basinand Northern California, and the rest is withdrawn from its local
aquifers (Freeman, 2008). Even though Los Angeles (LA) County collects stormwater runoﬀ from its urban surfaces and uses it for artiﬁcial
recharge, more than 680 Mm3 of its urban runoﬀ (equivalent to halfof its imported water) reaches the ocean annually (DWR, 2009). How-

Water has a strong inﬂuence on food and energy production, industrial and agriculture output, and population and economic growth ofnations (Gleick, 1993; Shannon et al., 2008). The availability of highquality water resources is rapidly decreasing, especially in arid andsemi-arid regions of the world, due to the demographic shift and the subsequent increase in demand and overuse of freshwater, and contamination of aquifers and lakes (Shannon et al., 2008; Koehler, 2008). Climate
variability has further increased the vulnerability of water resources andit is estimated that around 5 billion people will be living in countries under water stress by 2025 (Treidel et al., 2011; Vorosmarty et al., 2000;
Arnell, 1999). In addition, urban development with paved and impermeable surfaces has had a profound impact on the hydrologic cycleby altering drainage patterns, reducing inﬁltration and water storage,∗

Corresponding author at: United States Department of Agriculture, Agricultural Research Service, U. S. Salinity Laboratory, Riverside, CA 92507, United States.
E-mail address: salinis@ucr.edu (S. Sasidharan).

https://doi.org/10.1016/j.advwatres.2018.04.003
Received 27 November 2017; Received in revised form 26 March 2018; Accepted 7 April 2018
Available online 14 April 20180309-1708/© 2018 Elsevier Ltd. All rights reserved.

S. Sasidharan et al.

Advances in Water Resources 116 (2018) 167–177

ever, local groundwater basins in LA County have more than 1480 Mm3of unused storage capacity that could be used to store this untappedfreshwater resource. Furthermore, capturing and recharging stormwater into the aquifers will reduce LA County’s dependence on imported
water, energy needs for pumping, and improve its management of ﬂooding and contaminant loads from surface runoﬀ (Dallman and Spongberg, 2012; MWD 2007; Davis and McCuen, 2005). Consequently, implementation of management practices to capture stormwater and enhance its recharge into aquifers can have a large inﬂuence on many
water quality and quantity aspects (LACDPW, 2014).
Engineering systems which manage surface water and inﬁltrate it
into aquifers are classiﬁed as surface, vadose zone, and direct injection
inﬁltration systems (Bouwer, 2002; Edwards et al., 2016). Surface inﬁltration systems (e.g., inﬁltration basins, detention basins, vegetatedswales, and managed aquifer recharge ponds) place stormwater directlyon the ground surface. They require highly permeable sediment anda large surface area for rapid inﬁltration to occur but can lose water
due to evapotranspiration (Bouwer, 2002). Most of the surface areaof vadose zone inﬁltration systems (e.g., recharge trenches and drywells) extends either vertically or horizontally under the ground surface (Bouwer, 2002; Dillon and Pavelic, 1996). Injection wells (e.g.,managed aquifer recharge) are used to transmit water directly into
aquifers (Bouwer, 2002; Dillon, 2005). Both injection and vadose zone
inﬁltration systems have no or only minimal evapotranspiration losses,
do not require large installation area, and may employ large water
ponding depths to facilitate rapid inﬁltration. These various engineering systems also oﬀer diﬀerent potentials for reactive contaminationtransport and attenuation in the vadose zone. Direct injection wells
provide no chance for contaminant attenuation in the vadose zone
(Bouwer, 2002), whereas vadose zone and especially surface inﬁltration
systems provide opportunities of contaminant removal in the vadosezone (Bouwer, 2002; Edwards et al., 2016; Dillon and Pavelic, 1996).
Only vadose zone inﬁltration systems oﬀer potential advantages of minimal evapotranspiration, small installation area, large ponding depths,
and vadose zone treatment.
One vadose zone inﬁltration system that has gained a lot of recent
attention in the United States and around the world is a drywell. Drywells are subsurface storage facilities that receive, temporarily store,
and inﬁltrate stormwater into the vadose zone (Edwards et al., 2016).
Modern drywell designs include a fully permeable well that is locatedabove the water table and may have pretreatment systems such as single/double sedimentation chambers to remove sediments and sponges
to adsorb hydrocarbons in stormwater (Edwards et al., 2016). In addition to the advantages noted above, drywells are also popular because
they have a relatively low installation and maintenance cost in comparison to other engineered inﬁltration systems. However, drywells are
not well suited for areas with low permeability soils, high water tables,
steep slopes, or contaminated soils (landﬁlls and industrial complexes)
due to the potential risks of groundwater pollution (LACDPW, 2014).
Furthermore, drywell failure may occur due to clogging if pretreatments
are inadequate or in situ clays are mobilized. In this case, a complete
reconstruction or a new drywell may be required.
Limited published research has examined the performance of drywells (Edwards et al., 2016; Jurgens et al., 2008; Izuka, 2011).
Snyder et al. (1994) reported on a drywell recharge study in the Portland Basin in Oregon, USA, and found that 5700 drywells in urban areas contributed 38% of the total recharge to groundwater within thebasin. Wilson et al. (1990) analyzed the impact of drywells on recharge,
groundwater pollution, and urban runoﬀ at three sites in Arizona. Results indicated that recharge from drywells created a transmission zone
for water movement with minimal impacts on groundwater quality. In
contrast, ﬁeld and numerical modeling studies in Washington and Arizona, USA demonstrated that pollutant attenuation was related to thesoil particle size, and recommended that drywells be located in soil proﬁles with a clay layer to enhance contaminant adsorption (AdolfsonAssociates, 1995; Bandeen, 1984). The above considerations suggest

that, even if previous results are encouraging, more research is needed
to better understand the impact of drywells on groundwater rechargeand quality.
Knowledge of the soil proﬁle hydraulic properties is essential forthe successful design, execution, and long-term operation of a potential drywell location. Bandeen (1987) simulated the variably-saturated
subsurface ﬂow from a drywell using an axísymmetric ﬂow domain and
the Galerkin ﬁnite element method that was implemented in the Unsat2
program. Water ﬂow from the drywell was represented as a gravel ﬁlled
borehole. The soil hydraulic properties were determined from a constant borehole inﬁltration test and empirical formula (Bandeen, 1984;
Bandeen, 1987). However, existing numerical models have not yet accurately represented the complex geometry of modern drywell designs,
like MaxWell Type IV (Torrent Resources), which includes an upper sedimentation chamber, an overﬂow pipe, and a bottom chamber packed
with gravel that determine the total volume and the maximum head ofthe drywell. Consequently, these models will not accurately simulate
the ﬁlling and falling head cycle within a modern drywell, subsequentinﬁltration into the vadose zone, and determination of the eﬀective soil
hydraulic properties via inverse optimization. To our knowledge, there
is no existing literature or numerical modeling on determining averagesoil hydraulic properties using the inverse optimization of ﬁeld-scalefalling head data in a modern drywell system.
The main objective of this study was to improve our understanding and ability to characterize the drywell behavior by conducting systematic numerical and ﬁeld-scale experiments. The HYDRUS (2D/3D)
computer software was modiﬁed to simulate transient head boundary
conditions for the complex geometry of a modern drywell; i.e., a sediment chamber, an overﬂow pipe, and the variable geometry and storageof the drywell system with depth. Falling-head experiments were conducted at drywells (MaxWell IV model, Torrents Resources, Arizona,USA) located in the National Training Center in Fort Irwin, California
and a commercial complex in Torrance, California. The eﬀective soil hydraulic parameters with the saturated hydraulic conductivity, Ks , andthe retention curve shape parameter, 𝛼, for an equivalent uniform soil
system representative of both sites were determined by inverse parameter optimization of the observed falling head data. Comparison of the
ﬁtted Ks and 𝛼 parameters from these two distinctive sites provide useful
information to characterize and improve the design of the drywells.
2. Materials and methods
2.1. Field sites
The drywell in the ﬁrst study (June 2017) is situated at the Sleepy
Hollow Military Housing within the Fort Irwin National Training Center(NTC) in the Mojave Desert, California (CA) (Fig. 1(A)). The Fort Irwin
base climate is typical of the Mojave Desert with low precipitation, hotsummers, and cool winters (Densmore and Londquist, 1997). It has anannual precipitation of roughly 0–13 mm. Summer monsoon thunderstorms may bring heavy (intensity and depth) rainfall over a small area,
which can cause localized ﬂooding (Higdon, 2004; Hubbard, 2013). The
NTC receives its water from local aquifers, including the Irwin Basin thatis underneath the cantonment area. The Army Net Zero Water program
has a long-term goal to balance water pumping with aquifer recharge. A
drywell was installed on a portion of the cantonment area of Fort Irwin,
adjacent to four softball ﬁelds, referred to as the Four-Plex Site (see Fig.
1(A)) in 2007. Urban runoﬀ generated from housing developments upstream of the Four-Plex site is directed into a detention pond where thedrywell is located.
The drywell in the second study (September 2017) is situated in acommercial complex in Torrance, the Los Angeles County, California(Fig. 1(B)). The tested drywell was part of a four drywell system installed in May 2013 at this site as a Standard Urban Stormwater Mitigation Plan requirement (SUSMP). The SUSMP was developed as partof the municipal stormwater program to address stormwater pollution168

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Advances in Water Resources 116 (2018) 167–177

Fig. 1. (A) The Fort Irwin National Training Center located in California, a portion of the cantonment area within Fort Irwin referred to as the Four-Plex Site, andthe approximate location of the drywell. (B) A commercial complex situated in Torrance, the Los Angeles County in California, and the location of the drywell.

from development projects (LA-Stromwater, 2000). The site drainage
area is about 3.21 acres, with ∼90% impervious surface, and the drywell receives pre-treated (StormFilter, Contech Engineered Solutions)water from subsurface piping (DeJong, 2017).

drocarbon capture pillow, which removes a wide range of hydrocarbons.
Silt, sediment, and debris settle out of the water by gravity inside the upper chamber. Incoming water rises inside the upper sediment chamber
and then enters an overﬂow intake pipe which is connected to a lower
chamber. The overﬂow inlet is equipped with a debris screen, whichblocks the passage of suspended matter and other ﬂoating debris. Water from the overﬂow pipe enters a lower chamber, which is ﬁlled withclean rocks (0.9–3.8 cm). The entire gravel pack is surrounded by a fully
permeable (needle punched) non-woven geotextile (polypropylene orpolyester) fabric sleeve to prevent the migration of ﬁnes into the gravel
pack. Water in the gravel pack inﬁltrates into the vadose zone soil envelope and eventually recharges groundwater aquifers (TorrentResources,
2012).

2.2. Drywell engineering design
The MaxWell IV model (Torrents Resources, Phoenix, Arizona, USA)drywell was installed at Fort Irwin and Torrance. Figs. S1A and S1B show
the engineering designs for the Fort Irwin and Torrance drywells, respectively. The Fort Irwin and Torrance drywells receive inﬂow water into
an upper sediment chamber through a grated opening on top and subsurface detention pipes, respectively. This upper sediment chamber has
an impermeable chamber side, a concrete base seal, and a ﬂoating hy169

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Advances in Water Resources 116 (2018) 167–177

Fig. 2. The geometry of the ﬂow domain, boundary conditions, and initial conditions used during simulations of a homogeneous system representing (A) the Fort
Irwin Drywell and (B) the Torrance Drywell.

(every minute) and a datalogger (Decagon Em50 Series) until the water level in the well reached a steady state. In Phase II (75–98 min), a
constant head test was conducted by reﬁlling the drywell and then maintaining the head by adjusting the inﬂow using a ﬂowmeter connectedto the ﬁre hydrant outlet. In Phase III (99–154 min), the drywell wascompletely reﬁlled and a second falling head test was conducted (Table
S4). During our site visits, we have measured the dimensions of the Fort
Irwin and Torrance drywells and this information is given in Figs. S1Aand S1B.

2.3. Falling head inﬁltration experiment
A ﬁre hydrant was used to ﬂood the detention pond to ﬁll the Fort
Irwin drywell. A vacuum pump truck was used to remove excess waterfrom the detention pond until the water level just reached the top of thegrate covering the entrance to the drywell. The detention pond was partially ﬁlled for 163 min. At this point, a calibrated pressure transducer(Decagon CTD-10 sensor 40683, USA) was lowered into the bottom of
the upper sediment chamber and used to measure the water depth in
the drywell every minute with an accuracy of 0.05% and resolution of
2 mm. This pressure transducer was connected to a data logger (Decagon
Em50 Series) and then to a laptop computer via a USB cable. When thewater level reached the inlet of the overﬂow pipe (at 60 min), the pressure transducer was moved into the overﬂow pipe and lowered to adepth of 10 m from the surface and the water level was monitored for
1098 min. According to the engineering design, the sediment chamber
wall (the bottom section of the concrete liner with a small number ofside perforations) and the base (solid concrete slurry) are impermeable
and should thus allow inﬁltration at a much smaller rate than the bottom of the well. However, we observed that the entire drywell system,
i.e., the sedimentation chamber and the well bottom, acted like a single
system and drained at the same rate. This indicates that the base concrete slurry seal might have broken and allowed water to ﬂow directly
into the bottom chamber. The ﬁnal water level measurement was taken
at 1481 min using a Model 102 Water Level Meter (Solinst Canada Ltd.,
Canada) with a resolution of 1 mm (Solinst, 2017). A small amount ofwater was released into the drywell during the night hours from sprinkler irrigation runoﬀ, and the resultant change in pressure head wasrecorded by the pressure transducer (Table S4).
A calibrated pressure transducer (Decagon CTD-10 sensor 40683,
USA) was placed at the bottom of the Torrance, CA, drywell via theoverﬂow pipe. The experiment was conducted in three phases. In Phase
I (0–74 min), a ﬁre hydrant outlet that was connected directly to theupper sediment chamber was used to ﬁll this shallow drywell system(0–7 min) (only 10.67 m depth) in a controlled manner. Falling headmeasurements (7–74 min) were recorded using the pressure transducer

3. Numerical modeling
The HYDRUS (2D/3D) software package is a ﬁnite-element model for
simulating the two- or three-dimensional movement of water, heat, andmultiple solutes in variably saturated media (Simunek et al., 2016). Water ﬂow is described in HYDRUS (2D/3D) using the Richards equation:
[ (
)]
𝜕𝜃
𝜕
𝜕ℎ
𝐴
=
𝐾 𝐾𝑖𝑗𝐴
+ 𝐾𝑖𝑧
𝜕𝑡
𝜕 𝑥𝑖
𝜕 𝑥𝑗

(1)

where 𝜃 [L3 L−3 , dimensionless, L denotes units of length] is the volumetric water content, h [L] is the pressure head, xi (i = 1, 2) [L] arespatial coordinates, t [T, T is unit of time] is time, 𝐾𝑖𝑗𝐴 are components
of a dimensionless hydraulic conductivity anisotropy tensor KA , and K[L T−1 ] is the unsaturated hydraulic conductivity function given as the
product of the relative (Kr ) and saturated hydraulic conductivity (Ks )[L T−1 ].
The unsaturated soil hydraulic conductivity, K (h), and water retention, 𝜃 (h), functions are needed to solve Eq. (1). The unsaturated soil
hydraulic property models of van Genuchten (1980) and Mualem (1976)
were employed for this purpose:{}𝜃 − 𝜃𝑟𝜃(ℎ) = 𝜃𝑟 + [ 𝑠
ℎ<0
(2)
]
𝑚
1 + |𝛼ℎ|𝑛170

𝜃(ℎ) = 𝜃𝑠 ℎ ≥ 0

(3)

[
(
1∕2 )𝑚 ]2
𝐾(ℎ) = 𝐾𝑠 𝑆𝑒𝑙 1 − 1 − 𝑆𝑒

(4)

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Advances in Water Resources 116 (2018) 167–177

Fig. 3. The geometry of the (A) Fort Irwin and (B) Torrance drywells used in HYDRUS (2D/3D) to simulate inﬁltration from the drywells. The drywells have 6
reservoirs. Their corresponding maximum water levels and volumes (Vol) are represented as hw1 − hw6 and V1 − V6 , respectively. Reservoirs 1–5 are parts of thedrywell design and Reservoir 6 represents the ponding region above the drywell. Qin is the rate of water inﬂow into Reservoir 1 and Qout is the rate of water outﬂow
from Reservoirs 2, 3, 4, 5, and 6. The drywell is subdivided into 2 domains: Domain 1 comprised of V1 , V4 , V5 , and V6 and Domain 2 comprised of V2 , V3 , V4 , V5 , andV6 . Hw1 and Hw2 are the water levels in Domain 1 and 2, respectively. Both drywells are implemented as explained above. However, to account for the impermeable
region surrounding the sediment chamber in the Torrance drywell (Fig. S1B), a zero porosity was assigned to the shaded region of Reservoir 4 and 5 in Fig. (B).

where 𝜃 r and 𝜃 s [L3 L3 ] denotes the residual and saturated water contents, respectively, 𝛼 [L−1 ] is the shape parameter in the soil water retention function, l is the pore connectivity parameter, and n and m = 1–
1/n are empirical parameters related to the pore size distribution. The
eﬀective saturation, Se , is deﬁned as:
𝑆𝑒 =𝜃(ℎ) − 𝜃𝑟𝜃𝑠 − 𝜃𝑟sure head) and which is inactive (with prescribed zero ﬂux) dependingon whether the boundary nodes are saturated or unsaturated, respectively (Šimůnek et al., 2018). Details pertaining to this so-called Reservoir Boundary Condition are provided in the next section.
The water table at the Fort Irwin and Torrance study site is at approximately 61 and 21 m below the soil surface, respectively. Hence,the distance between the bottom of the drywell and the water table isapproximately 34 and 10 m for the Fort Irwin and Torrance drywells,
respectively. Therefore, the soil moisture is uniform throughout the domain, the capillary fringe is well below the bottom of the well, and thesoil domain surrounding the drywell installation is an unsaturated zone.
Hence, the initial condition was speciﬁed in terms of the soil water pressure head h (x,z) and was set to a constant pressure head of −0.5 m forthe entire domain (Fig. 2).
The simulation domain was discretized into two-dimensional triangular ﬁnite element mesh using the MESHGEN tool available within the
HYDRUS (2D/3D). The mesh was reﬁned at the left part of the domain
where inﬁltration from the drywell was simulated. To reduce the massbalance error, the ﬁnite element grid was adjusted such that the size ofelements was smaller (0.05 m) near the well where large water ﬂuxes
were expected. The grid size was gradually increased with a radial distance from the drywell, with a maximum element size of 0.75 m and
0.5 m for the Fort Irwin and Torrance domain, respectively. The qualityof the ﬁnite element mesh was assessed by checking the mass balanceerror reported by HYDRUS (2D/3D) at the end of the simulation. Massbalance errors were always below 1% and these values are generally
considered acceptable (Brunetti et al., 2017).
HYDRUS (2D/3D) includes a parameter estimation procedure basedon the Levenberg–Marquardt nonlinear least-squares optimization
method (Marquardt, 1963). This procedure was employed to estimate(5)

Fig. 2 presents a schematic of the simulation domain, boundary conditions, and initial conditions that were considered for the Fort Irwin
and Torrance drywell sites. A 2-dimensional axisymmetrical ﬂow domain was employed in both cases. The Fort Irwin site geometry had
a 3-m width and a 35-m depth, whereas the Torrance site had a 5-m
width and a 15-m depth. The speciﬁed radial dimensions were chosen
to best capture the inﬁltration radius of the drywell based on the soil
log information and the vertical dimensions were chosen based on thedepth of the drywell. A no-ﬂux boundary condition was assigned to theupper boundary. The nodes representing the right and lower left (theboundary between the drywell and the bottom boundary) sides of theﬂow domain were set to no ﬂux boundaries. The nodes at the bottom
boundary (z = −35 m and z = −15 m for the Fort Irwin and Torrance
drywell simulations, respectively) were assigned a free drainage boundary condition because the groundwater table was deep and below thebottom of the transport domain. Along the left (at z = −2 to −29.4 forFort Irwin and z = −7.36 to −11.2 for Torrance) and bottom boundary,a Reservoir Boundary Condition was speciﬁed to allow for a seepage
face (Šimůnek et al., 2018). For this boundary condition, parts of theboundary that are below and above the water level in the well are internally assigned time-variable pressure head (Dirichlet) and seepage faceboundary conditions, respectively. HYDRUS (2D/3D) calculates whichpart of the seepage face boundary is active (with prescribed zero pres171

172

Note: Default values of the residual soil water content (𝜃 r ), saturated soil water content (𝜃 s ), pore-size distribution parameter in the soil water retention function (n), and the tortuosity parameter in thehydraulic conductivity function (l) in the HYDRUS (2D/3D) Soil Catalog were employed for Sandy Clay Loam and Sand at the Fort Irwin and Torrance sites, respectively. In particular, 𝜃 r = 0.1, 𝜃 s = 0.39,
n = 1.5, and l = 0.5 for Sandy Clay Loam, and 𝜃 r = 0.045, 𝜃 s = 0.43, n = 2.68, and l = 0.5 for Sand.

0.96
0.95
0.96
0.96
0.99
0.97
2.27 × 10−6
3.28 × 10−6
1.18 × 10−3
5.10 × 10−3
1.43 × 10−3
1.02 × 10−3
6.29 × 10−9
1.06 × 10−7
4.8 × 10−6
1.55 × 10−6
1.64 × 10−5
2.59 × 10−6
2.63
2.42
0.52
1.50
2.80
0.85

0.07
0.13
0.04
0.52
0.04
0.001

2.49
2.17
0.45
0.41
2.73
0.84

2.77
2.68
0.59
2.59
2.87
0.85

2.25 × 10−6
3.07 × 10−6
1.17 × 10−3
1.89 × 10−3
1.40 × 10−3
1.01 × 10−3

2.24 × 10−6
2.86 × 10−6
1.16 × 10−3
1.32 × 10−3
1.37 × 10−3
1.01 × 10−3

R2[-]
95% CIKs [UL][m min−1 ]95% CIKs [LL][m min−1 ]S.E. CoefKs
S.E. Coef𝛼

95% CI𝛼 [LL][m−1 ]

95% CI𝛼 [UL][m−1 ]

Ks[m min−1 ]Torrance (Sand)

where Vwi [L3 ] is the volume of water in the reservoir, Ai [L2 ] is the crosssectional area of the reservoir, hwi [L] is the water level in the reservoir,
Qout (t) [L3 T−1 ] is the total water outﬂow to the soil surrounding thedrywell, and the subscript i indicates the reservoir number. In its ﬁnite

Engineering design
Onsite measurements
Phase I
Phase II
Phase III
Phase 1 + II + III(6)

Fort Irwin (Sandy Clay Loam)

𝑑 𝑉𝑤𝑖
𝑑ℎ
= 𝐴𝑖 𝑤𝑖 = 𝑄𝑖𝑛 (𝑡) − 𝑄𝑜𝑢𝑡 (𝑡)𝑑𝑡𝑑𝑡𝛼[m−1 ]The drywell has a very complex geometry with multiple compartments. To accurately determine the ponded water height and water ﬂux
in the drywell during ﬁlling and drainage cycles, the drywell geometry
was subdivided into 6 diﬀerent reservoirs (Fig. 3(A) and (B) for Fort
Irwin and Torrance, respectively). Reservoirs 1–5 mimic the drywell design (Figs. S1A and S1B), whereas Reservoir 6 is used to account for external ponding. Geometry parameters for these reservoirs are read into
the modiﬁed version of HYDRUS from an input ﬁle “Well.in”. Crosssectional areas and volumes for each reservoir are subsequently calculated from this information and are provided in Tables S1, S2, and S3.
It should be mentioned that the design of drywells at Torrance and Fort
Irwin is somewhat diﬀerent. For example, the sedimentation chamber ismuch bigger at Torrance (6.9 m) than at Fort Irwin (4.6 m), and Reservoir 2 is much bigger at Fort Irwin (22.2 m) than at Torrance (3.2 m).
Furthermore, at Torrance, the gravel pack surrounding the upper sediment chamber was replaced by an impermeable concrete slurry (Fig.
S1B) that does not contribute to the volume of Reservoirs 4 and 5 (azero porosity was assigned to this shaded region in Fig. 3(B)). In contrast to the Fort Irwin drywell’s engineering design, we observed thatthe upper sedimentation chamber had ∼1 m thick layer of sediments
and the entire drywell system acted like a single system and drained at
the same rate. Therefore, additional numerical simulations were conducted by employing the well dimensions obtained from site measurements (Tables S2 and S4).
Fig. 3 indicates that the drywell was subdivided into two main domains. Domain 1 comprises Reservoirs 1, 4, 5, and 6, whereas Domain 2
consists of Reservoirs 2, 3, 4, 5, and 6. The drywell ﬁlls from Reservoir
1 to 6. Drainage from Reservoir 1 was not considered since its bottom
is covered with an impermeable concrete slurry. Drainage from Domain
2 occurs in the reverse sequence to ﬁlling (e.g., Reservoirs 6, 5, 4, 3,
and 2). The total water levels (a sum of water levels from reservoirs in
Domains 1 and 2; denoted as Hw1 and Hw2 ) determine which reservoir
is actively ﬁlling or draining. Logical statements in conjunction withwell geometry information were used to determine which reservoir was
active at any given time. The initial values of Hw1 and Hw2 at the beginning of the simulation Hw1 ∗ and Hw2 ∗ , respectively, and the waterinﬂow as a function of time, Qin (t) [L3 T−1 ], are also input parametersin the “Well.in” ﬁle.
The change in the water volume within an active reservoir with respect to time can be calculated using the following mass balance condition:

Phase /numerical experiment3.1. Reservoir boundary condition

Site and soil type

Table 1Fitted values of the saturated hydraulic conductivity (Ks ) and the retention curve shape parameter (𝛼); the standard error (S.E. Coef𝛼 and S.E. CoefKs ), 95% conﬁdence intervals (95% CI𝛼 and 95% CIKs ) forlower (LL) and upper (UL) limits on 𝛼 and Ks ; the coeﬃcient of determination (R2 ), and the mass balance error obtained by the inverse simulation for the Fort Irwin and Torrance Drywell.

several unknown parameters in the unsaturated soil-hydraulic functions
from measurements of the total water height in the drywells over timeby the numerical inversion of the Richards equation. In the absence ofdetailed information on soil proﬁle heterogeneity (e.g., the soil log does
not provide information about the lateral extension of layers), only effective soil hydraulic parameters were obtained for an equivalent uniform soil proﬁle. In particular, the values of 𝛼 and Ks were determined
for the homogeneous soil proﬁle by inverse optimization. Other soil hydraulic parameters (n, l, 𝜃 r , and 𝜃 s ) were set equal to default values fromthe HYDRUS soil catalog for homogeneous Sandy Clay Loam at the Fort
Irwin site and Sand at the Torrance site (Table 1). These soil texturalclasses were chosen based on available soil log information from thesesites. Additional research is needed to fully characterize hysteretic hydraulic properties for the heterogeneous soil proﬁles, but this is beyond
the scope of this study.

0.0098
0.01
0.31
0.02
0.007
0.05

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Mass balance error[%]

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4. Results and discussion
4.1. Dynamics of a drywell
The functioning of the newly developed drywell reservoir boundary
condition module and its coupling with the HYDRUS (2D/3D) model
during one complete ﬁlling and drainage cycle was evaluated using theFort Irwin drywell geometry. A time-variable boundary condition was
employed in the simulation as follows: hypothetical water inﬂow was
0.23 m3 min−1 for 0 to 100 min and 0 m3 min−1 for 100–10,000 min(Table S5). Fig. 4(A) shows the change in Hw1 and Hw2 . Fig. 4(B) shows
the sequence of ﬁlling and draining of Reservoirs 1–6 and the corresponding change in the total water volume in the drywell system (Vwell ).
These ﬁgures show that Vwell increased as the injection continued at aslow pace until Reservoirs 1 through 6 were full. At the beginning of
the simulation, the water volume in Reservoir 1 (V1 ) (as well as thecorresponding value of Hw1 ) increased until it reached the maximum
volume of 3.185 m3 . During the next stage, water overﬂowed throughthe overﬂow pipe (Fig. S1) and ﬁlled sequentially Reservoirs 2 and 3(V2 and V3 ) while Hw1 stayed constant and Hw2 increased. In the next
phase, Reservoirs 4, 5, and 6 (V4 , V5 , and V6 ) were ﬁlled and the corresponding water levels Hw1 and Hw2 increased. Once all 6 reservoirs
were full, they started draining in the order of V6 , V5 , V4 , V3 , and V2(Fig. 4(B)) and the corresponding water levels Hw1 and Hw2 decreased
with time (Fig. 4(A)). Note that we do not consider draining of V1 since
the sedimentary chamber is surrounded by an impermeable wall and abase seal concrete slurry. Fig. 4 shows that Vwell , Hw1 , and Hw2 increased
and reached the maximum during the ﬁlling cycle and then started decreasing to a constant value of V1 and Hw1 , whereas Hw2 went down tozero during the draining cycle. Figures S2A (in a normal scale) and S2B(in a log scale) present the change in the well water volume, cumulative inﬁltration, and the mass balance for the well water volume during
one complete ﬁlling (injection) and emptying (inﬁltration/falling head)cycle for the entire drywell.

Fig. 4. The Fort Irwin drywell experiment. (A) The water level in Domain 1(Hw1 ) and Domain 2 (Hw2 ) and (B) the volume of water in each Reservoir (V1 –V6 ) and the total volume of water in the whole drywell system (Vwell = V1 +V2 + V3 + V4 + V5 + V6 ) during a complete injection (ﬁlling) and inﬁltration (emptying) cycle over 100,000 min simulated with ﬁtted soil hydraulic parameters(Table 1) using the drywell Reservoir Boundary Condition in HYDRUS (2D/3D).

4.2. Inverse estimation of soil hydraulic properties
Fig. 5 shows the observed and inversely simulated values of Hw2 overtime at the Torrance site. Fig. 5(A)–(D) consider Phases I (initial ﬁllingof the entire drywell and then draining of reservoirs 5, 4, 3, and 2), II(reﬁlling of the drywell and the constant head test), III (reﬁlling of theentire drywell, and then draining of reservoirs 5, 4, 3, and 2), and theentire experiment (Phases I, II, and III), respectively. A sudden decreasein Hw2 occurs during draining of Reservoir 4 during Phases I and III because it holds a very small volume of water (0.1 m3 ) compared to theother reservoirs (Fig. 3(B) and Table S1). Table 1 shows the corresponding ﬁtted values of Ks and 𝛼, the standard error coeﬃcient, 95% upperand lower conﬁdence interval limits, the coeﬃcient of determination(R2 ), and the mass balance errors for each simulation period.
Fig. 5 and the Table 1 show a very good agreement between theobserved and simulated values, with R2 values of 0.96–0.99% and themass balance error less than 0.3%. Based on these simulations, thevalue of 𝛼 ranged from 0.52–2.80 m−1 and the value of Ks ranged from1.01 × 10−3 –1.89 × 10−3 m min−1 . Table 1 shows that all ﬁtted parameters were unique with a very small standard error and a narrow 95%
conﬁdence interval. During the simulation for each phase, the initialcondition was imported from the last time step of the previous phase,and the observed diﬀerence in the ﬁtted Ks and 𝛼 value can be suggestedby the variations in the initial condition (pressure head). This resultshows that, conducting multiple ﬁlling and drainage cycle experimentwill help for the accurate estimation of eﬀective hydraulic properties. In
addition, we acknowledge that there is a slight deviation in the slope atPhase I and Phase III during emptying of Reservoir 2. We have tried ourbest to incorporate the engineering design and onsite measurements todescribe the drywell’s geometry. However, we believe that this discrepancy in the simulated value of Hw2 is due to variations in the volume of

diﬀerence discretization Eq. (6) becomes:𝑗ℎ𝑗+1
𝑤𝑖 = ℎ𝑤𝑖 +)Δ𝑡 (𝑄 − 𝑄𝑜𝑢𝑡
𝐴𝑖 𝑖𝑛

(7)

𝑗
where Δt [T] is the time step, and ℎ𝑗+1
𝑤𝑖 [L] and ℎ𝑤𝑖 [L] are water levels in
the reservoir at current and previous time steps, respectively. Eq. (7) isused to determine the water level in an active reservoir at the current
time step. The values of Hw1 and Hw2 are updated based on this information and the boundary conditions in the HYDRUS domain are dynamically adjusted. Values of Qout (used for the next time step) and thetotal drywell water volume are subsequently calculated in HYDRUS andprinted to new output text ﬁles “Well.out” and “WellVolume.out”, respectively. The value of Hw1 ∗ and Hw2 ∗ and the time-variable boundary
conditions used in the inverse simulation for the Fort Irwin and Torrance
drywells are given in Table S4.
In addition, numerical experiments were conducted with hypothetical conditions. The ﬁrst experiments were conducted to understandthe dynamics of the drywell and the functioning of the new Reservoir
Boundary Condition. In this case, simulations for a complete ﬁlling anddrainage cycle were conducted for the Fort Irwin drywell geometry (anengineering design) over 100,000 min (Figs. 4 and S2, Table S5). Other
simulations were conducted to compare the cumulative inﬁltration volume between the Fort Irwin and Torrance drywells over 1100 min for
a complete drainage cycle and multiple ﬁlling and drainage cycles, respectively (Fig. 8). The time-variable boundary conditions used in theseexperiments are given in Table S5.

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Fig. 5. The Torrance dry well experiment. Observed (black circle markers) and inversely simulated (solid red line) values of pressure heads (Hw2 ) in (A) PhaseI (initial ﬁlling of the entire drywell, and then draining of Reservoirs 5, 4, 3, and 2), (B) Phase II (reﬁlling of the drywell and the constant head test), and (C)Phase III (reﬁlling of the entire drywell and then draining of Reservoirs 5, 4, 3, and 2). Simulations for each phase were conducted after ﬁrst importing the initialconditions from the ﬁnal time step in the previous Phase. (D) The observed and inversely simulated Hw2 values for the entire inﬁltration experiment (Phases I, II,
and III = 154 min). Table 1 shows the ﬁtted parameters and Table S4 shows the time-variable boundary conditions. (For interpretation of the references to color inthis ﬁgure legend, the reader is referred to the web version of this article.)

small value of Ks = 2.25 × 10−6 m min−1 was obtained. The ﬁtted value
for 𝛼 was 2.63 m−1 . Fig. 6(B) compares the measured and simulated(based on the drywell dimensions from onsite measurement) values of
Hw2 with respect to time at the Fort Irwin drywell site. Very good agreement between the observed and simulated falling head data was observed with a total mass balance error of 0.01% and a high R2 value of
0.95%. Table 1 presents the ﬁtted parameters. The ﬁtted values of Ks(3.07 × 10−6 m min−1 ) and the value of 𝛼 (2.42 m−1 ) were very close
to the Ks and 𝛼 values obtained by the previous (engineering design
dimensions) simulation. Based on the soil log information, this site contains ∼90% sandy clay, which has a reported Ks value in the range from2 × 10−5 to 8.46 × 10−5 m min−1 (van Genuchten MT and Yates, 1991;
Carsel and Parrish, 1988; Pachepsky and Park, 2015). This observationsuggests that other factors (e.g., clogging) may have contributed to theﬁtted Ks value for this site, which is more than an order of magnitude
lower than the default value for sandy clay loam in the HYDRUS soil catalog. Soil log information collected from the Fort Irwin site showed thepresence of a sandy clay layer in a depth of 0–5.2 m, a sandy loam layerin 5.2–7.6 m, a sandy clay layer 7.6–20.7 m, a sand layer in 20.7–21.6 m,
and a sandy clay layer in 21.6–33 m. However, the lateral extension ofthese highly permeable sand and sandy loam layers is not known and
we assume that it must be very small based on the observed falling headdata and the ﬁtted Ks value. The ﬁtted value of 𝛼 falls in the range ofreported values for sandy clay loam (2.7 m−1 ) (van Genuchten MT and
Yates, 1991; Carsel and Parrish, 1988). Therefore, the increased exposure of the Fort Irwin drywell to more sandy clay layers explains thevalue of 𝛼. Our results show that the new Reservoir Boundary Condition
implemented in the HYDRUS (2D/3D) model is very ﬂexible and canaccount for various drywell designs.

the actual well (e.g., slight variations in the well geometry and/or theporosity of the packing material).
Soil log information collected from 4 drywells at the Torrance site
showed the presence of a silty clay layer in a depth of 0–7 m, a silty sandlayer in 4.87–9.7 m, a sand layer in 5.4–11.6 m, and a silty sand layerin 10.1–14.93 m. However, the lateral continuity of these layers is notknown. Fitted values of high Ks were consistent with previously reported
values for sandy soils (van Genuchten MT and Yates, 1991; Carsel andParrish, 1988; Rawls et al., 1982), and were therefore assumed to becontrolled by coarse-textured soils at this site. However, ﬁtted values of
𝛼 were much smaller than the values reported for sandy soils. Indeed,
the ﬁtted value of 𝛼=0.85 m−1 was much closer to the value reported
for silt and clay (van Genuchten MT and Yates, 1991; Carsel and Parrish, 1988; Rawls et al., 1982), and was, therefore, assumed to be controlled by ﬁne-textured silt and clay layers at this site. In comparison toKs , the ﬁtted value of 𝛼 exhibited much greater standard error coeﬃcients (Table 1). This uncertainty likely reﬂects diﬀerences in the actualand simulated initial conditions, soil heterogeneity, and/or the inﬂuenceof hysteresis on hydraulic properties, which were not accounted for thissimulation.
Fig. 6(A) compares the measured and simulated (based on the drywell dimensions from the engineering design) values of Hw2 with respect to time at the Fort Irwin drywell site (Table S4). In comparison
to the Torrance drywell results, Fort Irwin falling head data show avery slow decrease in Hw2 with time during the experiment. Very good
agreement between the observed and simulated falling head data wasobserved, including the change in Hw2 due to irrigation runoﬀ (821–919 min). The total mass balance error was very small (0.0098%) anda high R2 value of 0.96% was obtained between observed and simulated values. Table 1 presents the ﬁtted hydraulic parameters. A very174

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head) was employed at the Fort Irwin site due to its much lower valueof Ks (Tables 1 and S5).
Fig. 8 indicates that the Torrance drywell manages to inﬁltrate∼53.2 m3 of water within 1100 min. The Los Angeles County Low Impact Development standard for drywells indicates that there should be
no standing water within 5760 min (4 days) of a design rain event(LACDPW, 2014). A design rain event of 1.9 cm h−1 for one hour (an average recurrence interval of 5 years) (Atlas, 2018) on 3.21 acres (with a
90% impervious surface) at the Torrance site will contribute ∼205 m3 ofwater to 4 drywells present at this site, or ∼51.3 m3 /drywell (LACDPW,
2014). Thus, the Torrance drywell site can easily inﬁltrate the expected
incoming volume of water from a design rain event within the requiredtime.
The Fort Irwin drywell can only inﬁltrate ∼12.6 m3 of water within
1100 min (Fig. 8). This inﬁltrated water volume is much smaller thanthat for the Torrance site, even though the inﬁltration area and a ponding depth for the Fort Irwin drywell are much larger (Fig. S1). A designrain event of 2 cm h−1 (an average recurrence interval of 10 years)(Atlas, 2018) on 56.9 acres (with a 44% impervious surface) will contribute ∼3643.1 m3 to the Fort Irwin drywell. A simple calculation using
the simulated inﬁltration volume, i.e., ∼12.6 m3 /1100 min from Fig. 8,
indicates that it will take ∼220 days to inﬁltrate this design rain event
volume (3643.1m3 ). Not surprisingly, maintenance contractors at Fort
Irwin have reported ponding at the drywell site for several months after
a storm event (personal communication). This observation conﬁrms thatthe drywell site has a very small hydraulic conductivity and the well islikely clogged and damaged.
A number of factors provide convincing evidence that clogging has
signiﬁcantly contributed to the low saturated hydraulic conductivity at
the Fort Irwin site. For example, sediment and ﬂoating objects such as
plastics and leaves entered the sediment chamber via the lid and passed
through the open overﬂow pipe into the bottom well during the inﬁltration experiment, which mimics a natural storm event. The debris shieldthat goes on top of the overﬂow pipe was found on the upper sedimentchamber ﬂoor at the start of the inﬁltration experiment (Fig. S4). Finally, a comparison of the drywell dimensions using engineering designand our direct measurements shows that there is at least 1–2 m of sediment at the bottom of the upper sediment chamber (Reservoir 1) andthe well (Reservoir 2).

Fig. 6. The Fort Irwin drywell inﬁltration study. Observed (black circles markers) and inversely simulated (a solid red line) pressure head (Hw2 ) changes overtime for the inverse simulation conducted by employing (A) Torrent Resources’engineering design dimensions during the installation time (2007) and (B) drywell dimensions measured during the site visit (2017). Table 1 shows the ﬁtted
parameters, Table S2 shows the well dimensions, and Table S4 shows the timevariable boundary conditions. (For interpretation of the references to color inthis ﬁgure legend, the reader is referred to the web version of this article.)

5. Summary and conclusions

4.3. Predicted h and 𝜽 distributions

Field-scale falling head and inﬁltration experiments were conductedat the Torrance and Fort Irwin drywell locations. These two drywells encompass very diﬀerent characteristics. The Torrance drywell was muchsmaller in depth, the soil was highly permeable, the drywell was situated in an urban setting with mainly impervious surfaces covering thesoil, incoming water was pretreated via a stormwater ﬁlter and large
sediment chamber, and the drywell was routinely maintained and functioning according to design standards. In contrast, the Fort Irwin drywell
was much larger, the soil had low permeability and was likely clogged,
the drywell was situated in a rural setting, and it received little or no
maintenance and was not functioning within design speciﬁcations.
The HYDRUS (2D/3D) model was modiﬁed to simulate the complex
geometry of the drywell, and a new Reservoir Boundary Condition was
implemented to account for time-variable pressure heads and seepage
faces in the drywell that are coupled with the vadose zone. The modiﬁed HYDRUS (2D/3D) code was run in an inverse mode to estimate
the soil hydraulic properties for an equivalent uniform soil proﬁle atthe Torrance and Fort Irwin drywell sites and to successfully simulate
temporal changes in the inﬁltration behavior of two completely diﬀerent drywells. Furthermore, model simulations predicted the spatial andtemporal behavior of subsurface water ﬂow at these drywell sites.
Additional information regarding diﬀerent drywell sizes, depths, locations, soil heterogeneity, engineering designs, and various contaminant and sediment loadings during individual storms and for a long-Figs. 7 and S3 show simulated spatial h and 𝜃 distributions, respectively, in the model domain for the Torrance and Fort Irwin drywell sites
after 154 and 1481 min, respectively. A very signiﬁcant change in h and𝜃 occurs over a large area in the model domain for the Torrance drywellsite after 154 min. In particular, the wetting front reached 3.3 m in thevertical direction and 15 m in the lateral direction (i.e., the maximum
vertical and lateral extensions of the domain from the bottom point of
the well). In contrast, spatial changes in h and 𝜃 occur over a muchsmaller region at the Fort Irwin drywell site after 1481 min. The wetting front reached a depth of 1 m in the vertical direction and 1.19 m
in the lateral direction.

4.4. Cumulative inﬁltration behavior at drywell sites
Additional simulations were conducted to better understand the cumulative inﬁltration behavior at drywell sites. Fig. 8 shows cumulative
inﬁltration at the Torrance and Fort Irwin drywell sites when using
calibrated unsaturated soil hydraulic parameters (Table 1) and timevariable input boundary conditions from Table S5. The total simulation
time was 1100 min. Multiple ﬁlling and draining cycles using a hypothetical water inﬂow rate of 1.6 m3 min−1 were considered at the Torrance site (5 of them) (Table S5), whereas only a single cycle (falling
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Fig. 7. The soil water pressure head (h) proﬁle during the falling head inﬁltration test at (A) t = 164, 690, 954, and 1481 min at the Fort Irwin drywell and (B)t = 7, 74, 78, 98, and 154 min at the Torrance drywell.

that pose a risk of clogging (a potential reduction in the inﬁltration capacity) and a reduction in overall drywell performance, and designs that
improve the long-term management and sustainability of drywell injection.
Competing ﬁnancial interest
None.
Acknowledgment
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
the authors and do not necessarily represent the views or policies of the
U.S. Environmental Protection Agency. Mention of commercial products
does not constitute an endorsement. The data supporting the conclusions
can be obtained in the Tables and the Figures available in the supporting
information.

Fig. 8. A comparison of the cumulative inﬁltration volume for the Torranceand Fort Irwin sites when using ﬁtted hydraulic parameters from Table 1 andthe time-variable boundary conditions from Table S5.

term (multiple years) data series are important design considerations toevaluate the eﬀectiveness of drywells in comparison other inﬁltration
systems. Future research activities will, therefore, focus on inﬁltrationfrom a drywell in heterogeneous soil systems, changes in water quality during transport through the vadose zone, the impact of inﬁltrated
water on groundwater quantity and quality, identiﬁcation of conditions

Supplementary materials
Supplementary material associated with this article can be found, inthe online version, at doi:10.1016/j.advwatres.2018.04.003.
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