Received: 23 January 2024

Accepted: 10 March 2024

DOI: 10.1002/vzj2.20335
Vadose Zone Journal

ORIGINAL ARTICLE
S p e c i a l S e c t i o n : Tr i b u t e t o R i e n v a n G e n u c h t e n , R e c i p i e n t o f t h e 2 0 2 3 Wo l f P r i z e f o r A g r i c u l t u r e

Measuring and modeling soil moisture and runoff at solar farms
using a disconnected impervious surface approach
David Mulla1

Jake Galzki1

1 Department of Soil, Water, and Climate,

University of Minnesota, Saint Paul,
Minnesota, USA
2 Institute on Environment, University of

Minnesota, Saint Paul, Minnesota, USA
3 Department of Environmental Sciences,

University of California, Riverside,
Riverside, California, USA
CorrespondenceDavid Mulla, Department of Soil, Water,and Climate, 1991 Upper Buford Circle,
University of Minnesota, Saint Paul, MN
55108, USA.
Email: mulla003@umn.edu
Assigned to Associate Editor Jan Hopmans
Funding information
Solar Energy Technologies Office,Grant/Award Number: DE-AC3608GO28308

Aaron Hanson2

Jirka Simunek3

Abstract
Ground-mounted photovoltaic sites are often treated as impervious surfaces instormwater permits. This ignores the pervious soils beneath and between solar arrays
and leads to an overestimation of runoff. Our objective was to improve solar farm
stormwater hydrology models by explicitly considering the disconnected impervious nature of solar design and site characteristics. Experimental sites establishedon utility scale solar farms in Colorado, Georgia, Minnesota, New York, and Oregon had perennial vegetative plantings with mean precipitation ranging from 40.6 to124.5 cm, and soil texture ranging from loamy sand to clay. Soil moisture measurements were collected beneath arrays, under drip edges, and in the vegetated area
between arrays at each site. Hydrus-3D models for soil moisture and stormwaterhydrology were developed that accounted for precipitation falling on solar panels,
drip edge redistribution of rainfall, infiltration, and runoff in the pervious areas
between solar arrays and beneath panels. Drip edge runoff averaged 3- to 10-times
incident precipitation at the New York and Minnesota sites, respectively. Root meansquare error values between measured sub-hourly soil moisture and predicted moisture for large measured single storm events averaged 0.029 across all five sites.
Predicted runoff depths were strongly affected by precipitation depth, soil texture,soil profile depth, and soil bulk density. Runoff depths across the five experimentalsites averaged 13%, 25%, and 45% of the 2-, 10-, and 100-year design storm depths,
clearly showing that these solar farms do not behave like impervious surfaces, butrather as disconnected impervious surfaces with substantial infiltration of runoff inthe vegetated areas between and beneath solar arrays.

1

Abbreviations: NOAA, National Oceanic and Atmospheric
Administration; NRCS, Natural Resource Conservation Services; PV,photovoltaic; PV-SMaRT, photovoltaic stormwater management researchand testing; RCN, runoff curve number; RMSE, root mean square error;
SWMM, storm water management model.

INTRODUCTION

Climate change poses an existential threat to ecosystems andtheir human, animal, insect, and plant life as a result of
increasing temperatures and changes in precipitation patterns.
As a result, there is a significant shift away from nonrenewable to renewable energy. At the end of 2021, over 120 or
670 GW in utility-scale ground-mounted solar photovoltaic

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© 2024 The Authors. Vadose Zone Journal published by Wiley Periodicals LLC on behalf of Soil Science Society of America.
Vadose Zone J. 2024;e20335.
https://doi.org/10.1002/vzj2.20335

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1 of 13MULLA ET AL.

Vadose Zone Journal(PV) capacity were either installed or in the “interconnection queue” across the United States, respectively (Rand et al.,2022). If developed, approximately 2 million ha of land would
be needed (based on 2.4–3.2 ha/MW produced) to construct
the solar capacity in the interconnection queue. By the endof 2050, the U.S. Department of Energy projects that roughly
4.2 million ha could be needed to deploy solar energy to meet
U.S. climate and electrification goals (Heath et al., 2022).
Utility scale ground-mounted solar farms typically focus
on maximizing production of renewable energy, rather thanon reducing runoff and erosion or providing pollinator habitat (Hernandez et al., 2014). Government jurisdictions having
permitting authority accordingly often treat ground-mounted
PV facilities as predominantly impervious surfaces in local
stormwater and water quality permitting (GPI, 2021; Yavari
et al., 2022). Most hydrologic models used to estimate
stormwater runoff do not explicitly assess the impacts onhydrology of the disconnected impervious arrays at solar
farms, including an inability to account for arrays that intercept precipitation and reroute it to the drip edges of solar
panels, or to account for areas directly beneath solar panels
that can infiltrate runoff. Inaccurate estimates of stormwater runoff at solar farms result in the need to install costly
structural practices such as on-site infiltration basins, and a
resulting decrease in the area on the project site available for
production of solar energy.
Only a handful of studies have been conducted on thehydrology of utility-scale ground-mounted solar PV sites(Yavari et al., 2022). A simple hydrologic model for the
impact of disconnected impervious solar PV sites on stormwater runoff was developed by Cook and McCuen (2013). They
used soil hydrologic group to estimate runoff curve numbers (RCNs) and Manning’s n values to estimate velocity ofrunoff in response to rainfall hyetographs representing 2-, 25, and 100-year return frequencies. The most important factors
affecting runoff were (in decreasing order) storm characteristic (return frequency and duration), ground cover (gravel vs.
turf grass), and soil hydrologic group (C vs. B). Factors that
had little impact on runoff included the landscape slope andpanel tilt angle. Surprisingly, Cook and McCuen (2013) concluded that there were no differences in stormwater runoff
between simulations in the presence or absence of solar panels. This was perhaps due to the presence of grass plantings
beneath and between the solar arrays that enhanced infiltration. However, no experimental measurements were collected
to verify model performance in the Cook and McCuen (2013)
study, and the impacts of concentrated drip edge runoff onstormwater runoff were not considered.
A majority of runoff modeling efforts at ground-mounted
solar sites make no attempt to directly consider either theirdisconnected impervious nature or how detailed combinations of soil factors affect infiltration (e.g., texture, depth,bulk density, saturated hydraulic conductivity, etc.). An exam-

Core Ideas∙ Hydrus-3D accurately estimated soil moisture andinfiltration beneath and between solar arrays.
∙ Solar panel drip edge runoff averaged from 3X to
10X precipitation depth falling on panels.
∙ Design storm depth, soil texture and depth, andsoil bulk density had a large impact on solar farm
runoff.
∙ Solar farm runoff on coarse-textured, deep soils
was much less than runoff from fine-textured,
shallow soils.
∙ Solar farm hydrology is accurately represented
with a disconnected impervious modeling framework.

ple is the application of the widely used FLO-2D hydrologic
model to simulate infiltration and runoff and route surface
flows across utility-scale solar PV sites (Barnard et al., 2017).
Model inputs include precipitation, elevation, land cover, andsoil hydrologic group, which are used to estimate RCN and
Manning’s n roughness coefficients. Model outputs are generated by assuming an absence of solar array surfaces, and these
outputs are subsequently empirically adjusted for the percent
impervious cover.
Recently, there have been attempts to simulate infiltration under varying rainfall inputs with consideration for solar
panel orientation and angle impacts on drip edge runoff andon sheltering of soil from rain using sophisticated soil hydrologic models such as Hydrus-3D (Elamri et al., 2018). Dripedge runoff in Elamri et al. (2018) varied from five times incident rainfall when panels were oriented facing the wind to
two times incident rainfall when panels were oriented awayfrom the wind. These differences caused large increases innear-surface soil moisture content in areas directly beneaththe drip edge as compared with areas directly beneath panels,
which were much drier, or in the area between panels, which
had intermediate soil moisture levels. Simulated soil moisture
contents were apparently in reasonable agreement with moisture contents measured using neutron probes installed beneathpanels, under the drip edge, or between arrays (thoughdetailed accuracy statistics were not provided). No estimatesof stormwater runoff were provided by Elamri et al. (2018).
Urban stormwater management has long been plagued by
the relentless expansion of impervious surfaces such as roads,
parking lots, and building roof tops. To counter the adverse
impacts of these surfaces on stormwater runoff, there has been
an increasing interest in urban green infrastructure, including vegetated roof tops (Hilten et al., 2008; Vergroesen &Joshi, 2010) Similarly, in the solar industry, it is standard

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2 of 13

practice to cover pervious ground surfaces at solar farms withgravel or to plant low-growing turf grass (Walston et al.,2021). More recently, many states have been promoting theestablishment of native prairie or pollinator habitat at solar
farms (Moore-O’Leary et al., 2017) in order to enhance pollinator habitat, improve soil moisture retention (Hassanpour
Adeh et al., 2018), and reduce stormwater runoff and soil loss
(Walston et al., 2021). Yet, the impact of native prairie or pollinator habitat vegetation on soil moisture, infiltration, andstormwater runoff at solar farms is rarely studied.
Given this background information, there is a criticalneed to develop better approaches for modeling impactsof perennial vegetation practices such as native prairieor pollinator habitat at solar farms. Furthermore, there isa critical need to develop models that can comprehensively evaluate impacts on soil moisture and stormwaterrunoff of factors such as climate, soil type, soil depth, soil
bulk density, drip edge runoff, and solar array design factors. The Photovoltaic Stormwater Management Research
and Testing (PV-SMaRT; https://www.nrel.gov/solar/marketresearch-analysis/pv-smart.html) experimental and modeling study described here was designed to estimate soil
moisture and stormwater runoff at large-scale solar farms
having perennial vegetation plantings in five states acrossthe conterminous United States. In addition, soil moistureand stormwater runoff were modeled using Hydrus-1D and
Hydrus-3D (Šimůnek et al., 2008) as affected by soil characteristics (texture, bulk density, and saturated hydraulicconductivity), perennial vegetative cover, solar panel drip
edge runoff, and solar array design factors (array spacing
and orientation). To the authors knowledge, this is the firsteffort ever attempted to collect experimental data to calibrate and validate a sophisticated numerical soil hydrology
model that simulates both soil moisture and stormwater runoff
across a wide range of ground-mounted solar PV sites having
perennial vegetated cover.

2
2.1

MATERIALS AND METHODS
Experimental sites

Experimental sites consisted of five ground-mounted solar PV
installations, generating a combined nameplate and facility
capacity between 1 and 18 MW, located in Colorado, Georgia, Minnesota, New York, and Oregon (Table 1). To protect
confidentiality of project partners, the exact locations of eachexperimental site are not provided. Installations in Minnesota
and New York had south-facing fixed arrays, whereas tracking arrays facing east (morning) or west (afternoon) were atsites in Colorado, Georgia, and Oregon. Array spacing varied
between 5.6 m at the Colorado site and 7.6 m at the Minnesota
site. Individual solar panel dimensions were 3 m × 1 m. Lines

Vadose Zone Journal

3 of 13of arrays were generally oriented along flat (Georgia) to moderate (Colorado) slope topographic contours and consisted ofeither single (1 portrait in Table 1) or double (2 portrait in
Table 1) panels in portrait orientation. Mean annual precipitation ranged from a high of 124.5 cm in Georgia and New Yorkto a low of 40.6 cm in Colorado and Oregon. Perennial groundcover between and beneath solar arrays was well establishedand dense at all sites, ranging from mowed cover crops at
Georgia to native pollinator, grass, or prairie vegetation at theother sites.
Soils varied considerably across the five experimental sites.
Colorado was a Renohill clay (US Taxonomic classification:
Fine, smectitic, mesic Ustic Haplargids), while Georgia was a
deep Greenville sandy clay (Fine, kaolinitic, thermic Rhodic
Kandiudults), Minnesota was a deep Hubbard sandy loam
(Sandy, mixed, frigid Entic Hapludolls). New York was a shallow Erie channery silty clay loam (Fine-loamy, mixed, active,
mesic Aeric Fragiaquepts), while Oregon was a Phoenix clay
(Very fine, smectitic, mesic Xeric Epiaquerts).

2.2

Site sampling and monitoring

All soil samples and infiltrometer measurements described
below were taken underneath the panel, at the drip edge, andin the full sun area between panels in order to provide groundtruth data for model results beneath the panels, at the drip
edge, or in the full sun area. Soil samples were collected fromtwo different solar array row locations at each site to a depthof 30 cm to estimate soil textural class based on percent sand,silt, and clay (Bouyoucos, 1962). At the same two array locations, surface soil samples were collected to a depth of 20 cm
using an Uhland sampler to estimate soil bulk density (Doran& Mielke, 1984). Field saturated soil hydraulic conductivity
was estimated in surface soils at the same six locations foreach site using a Cornell sprinkle infiltrometer, with corrections for soil textural class (van Es & Schindelbeck, 2006). All
samples correspond to locations for soil moisture monitoring
described below.
Figure 1 illustrates the in situ precipitation and soil moisture monitoring strategy at each experimental site. Incident
daily precipitation was measured from July 2020 to October
2022 using the Meter ECRN rain gauges in the full sun area.
Another rain gauge was installed beneath the drip edge of
fixed orientation panels at Minnesota and New York. TEROS
10 soil moisture sensors (Meter Group, Inc.) were installedat each site in July 2020 at two depths (15 and 30 cm) in
the full sun area and beneath the drip edge, and at one depth(15 cm) beneath the array. Soil moisture measurements wererecorded at 15 min intervals using a Meter ZL6 datalogger andaveraged over hourly intervals for analysis. Replicate measurements using this monitoring setup were taken at each site
in a different solar panel row.

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MULLA ET AL.

TA B L E 1

Site

MULLA ET AL.

Vadose Zone Journal

Solar installation design specifications and site characteristics.

Capacity(MW)

Area (ha)Landscapeslope (%)

Array type

Panel No. andarrangement

Arrayspacing(m)

Mean annual
precipitation(cm)

Ground cover

CO

1.0

2.4

5

Tracking

1 portrait

5.6

40.6

Native mix

GA

1.3

3.2

1.5

Tracking

1 portrait

6.2

124.5

Mowed cover crops

MN

4.5

11.7

2.5

Fixed

2 portrait

7.6

94.0

Prairie mix

NY

18.0

43.7

3

Fixed

2 portrait

7.0

124.5

Tall grass

OR

13.0

18.6

3

Tracking

2 portrait

6.4

40.6

Native pollinators

F I G U R E 1 Monitoring layout for fixed array photovoltaic sites at
Minnesota and New York. At Colorado, New York, and Oregon, thesame layout was used without a rain gauge under drip edge.

2.3
Soil moisture and stormwater runoff
modeling
Hydrus numerical soil hydrologic models were used tosimulate infiltration, redistribution, evapotranspiration, soil
moisture, and surface runoff at each of the five experimentalsites (Simunek et al., 2008). Hydrus-1D was used to simulate
one-dimensional vertical variably saturated soil water flow in
the area between solar panels using measured precipitation,
while Hydrus-3D, driven either by the largest storm in the precipitation monitoring data or by 2-, 10-, and 100-year design
storms, was used to simulate two-dimensional water flowin the cross-sectional area extending across three successive
continuous solar array segments composed of solar panels,
panel drip edges, and a full sun vegetated area (Figure S1).
This cross-section represents the complex hydrologic characteristic of the disconnected impervious solar farms studied inthis project.
Hydrus models numerically solve the nonlinear Richards
partial differential equation for variably-saturated water flowin the vadose zone, representing soil hydraulic functions using
van Genuchten models for the soil moisture characteristicand hydraulic conductivity curves (van Genuchten & Nielsen,1985). Stormwater runoff is generated when incoming intensity of rainfall (plus solar panel drip edge runoff) exceeds soil
infiltration rate. Runoff occurs primarily when soils are saturated, rainfall intensity is high, and soils are relatively low inpermeability.
Initially, a standard Hydrus-1D model (v4.xx) was used
for the purposes of calibrating van Genuchten model parameters in Hydrus-1D using at least 3 months of continuously
monitored precipitation and 1-h soil moisture measurements
versus predicted soil moisture contents for vertical soil profiles in the full sun area between solar arrays (Figure 1, Table
S1). Outputs of Hydrus-1D included volumetric soil moisture contents at various depths and surface runoff. Initial
estimates for saturated and field capacity moisture contents
were based on histograms of longer term soil moisture content measurements (generally 1 year of data), following themethod of Chandler et al. (2017). Initial estimates for van
Genuchten soil hydraulic properties were based on neural network interpolators embedded in Hydrus (Schaap et al., 2001)
using measured soil texture, saturated moisture content, bulk
density, and saturated hydraulic conductivity. Certain Hydrus
model parameters applied at the upper boundary condition
were left at default values. These include parameters that control model convergence (HCrit = 10,000 cm) and initiationof runoff (h0 = 0 cm). However, because Hydrus-1D simulations could not account for horizontal hydrologic connections
between the shaded area beneath panels, the area beneath thepanel drip edge, and the full sun area, the upper boundary condition for Hydrus-1D simulations in the full sun area during
rainy days was estimated by adding incident daily precipitation to the effective normalized drip edge runoff factor. This
normalized runoff factor was estimated based on the ratio ofimpervious width (based on panel dimensions) to total spacing width between arrays. Normalized drip panel runoff istreated as an additional source of water input spread out acrossthe entire vegetated area between arrays in Hydrus-1D simulations. This increases the runoff potential because drip edge
runoff is added to incoming precipitation in the full sun areas
between arrays. Calibrated estimates of soil hydraulic properties were set based on minimizing the root mean square

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4 of 13error (RMSE) between predicted Hydrus-1D soil moistureand measured soil moisture contents from data at sensors
located only in the full sun area between arrays.
Following calibration of the vertical Hydrus-1D model inthe full sun area, a Hydrus-3D model (v3.x) with an overland flow module was used for short-term (2-day) simulationsof soil moisture and runoff in response to measured storms(generally the largest measured storms with a duration of<1 day) at 15 min time steps for each site. The Hydrus-3D
domain considered was a 2-D cross-section extending acrossand beneath three successive continuous areas with solar panels, below the panel drip edge, and in the full sun vegetatedarea (Figure S1). Following slight calibration of Hydrus-3Dwith 2-day simulations based on the largest measured storm
events, additional Hydrus-3D simulations were generated
involving 24-h design storms at 2-, 10-, and 100-year return
frequencies determined from National Oceanic and Atmospheric Administration (NOAA) Atlas 14 tables appropriate
for each of the five experimental sites. These simulations
were motivated by an interest in representing runoff from24-h design storms of varying return frequency that are ofinterest to regulatory agencies that issue solar farm construction permits that consider storm event impacts on stormwaterrunoff.
Hydrus-3D software code was modified to represent upperboundary conditions for disconnected impervious surfaces
typical of solar farms (Figure S1) having three successivelines of solar arrays that accounted for (a) interception of rainfall by impervious solar panels with subsequent drip edge
runoff, (b) incident precipitation in the full sun pervious area
downslope of arrays, (c) surface runoff and sub-surface lateralsoil moisture migration from the full sun area to the pervious area beneath the next downslope panel, and (d) resulting
overland runoff from the three impervious upslope solar
arrays and their three downslope pervious areas. Simulationof soil moisture contents during 2-day periods experiencing
large magnitude measured storm events was initiated with anantecedent moisture content of field capacity (Table S1) estimated using long-term experimental data from each site using
the method of Chandler et al. (2017). A no flux lower boundary condition was imposed to accurately estimate temporalvariations in soil moisture beneath and between solar arrays.
Directly beneath solar arrays, an upper boundary condition
involving no precipitation was imposed. Beneath the solar
panel drip edge, an experimentally measured upper boundary condition involving a drip edge multiplier for incident
rainfall was imposed across a ground surface width ranging
from 22.5 to 35.5 cm below the actual drip edge; the width
relates to the drip edge multiplier used at each site to maintain water balance within the system. In the full sun area, anupper boundary condition was imposed for ambient daily precipitation. Spacing of numerical finite difference grids in theHydrus model was approximately 5 cm vertically and between

Vadose Zone Journal

5 of 13

5 and 50 cm for horizontal nodes. Higher nodal densities
were used around the transitions between the three segmentswith differing upper boundary conditions (no precipitation
beneath the panel, increased incident precipitation beneathpanel drip edge, and incident precipitation in area between
the arrays). Runoff from the uppermost drip edge and full sun
area was routed by the Hydrus-3D model downslope, where
it could infiltrate under the panel in the next downslope array.
This process was repeated for the second and third lines ofarrays. Cumulative runoff was predicted from the full sun area
downslope of the third line of arrays.
Root water uptake in daily Hydrus modeling was simulated using the Feddes’ et al. (2001) algorithm without
considering compensation. Root distribution with depth was
assumed constant with a rooting depth of 50 cm. Feddes’
parameters represented perennial vegetation at each experimental site using “pasture” in the Hydrus default database.
Hourly solar radiation, temperature, and humidity data weretaken from the nearest solar radiation database location in the
National Solar Radiation Database (https://nsrdb.nrel.gov/),
and evapotranspiration rates were estimated by Hydrus basedon these measurements using a Penman–Monteith energy balance approach accounting for vapor pressure deficit recordedat the site. Evapotranspiration plays a small role during thetime when larger experimentally measured storms are occurring when compared to evapotranspiration over long-termseasonal data that includes long, dry periods between storms.
A daily average evapotranspiration rate of 3 mm/day was
imposed in the full sun area between arrays, corresponding
well to the average observed drawdown of soil moisture in thefull sun moisture probe in 48-h simulations. No evapotranspiration was assumed in areas beneath panels due to minimal
solar radiation reaching these areas during storm events. In
addition, different soil hydraulic properties were applied in
the full sun area and the area beneath panels during model
calibration to reflect differences in soil moisture arising fromdifferences in infiltration, soil bulk density, and impacts ofsurface crusting.
Initial parameters for the Hydrus-3D modeling were basedon calibrated Hydrus-1D parameters. Hydrus-3D parameters
were adjusted slightly to account for soil crusts observed inshaded areas beneath arrays, to account for soil horizonation at the Georgia and New York sites, and to account forfocused runoff directly beneath the drip edges that had notbeen accounted for in Hydrus-1D modeling. Final Hydrus-3Dmodel parameters were set based on minimizing the RMSE
during 48-h simulations between predicted and measured soil
moisture contents from soil moisture sensors installed in thefull sun, under panel, and beneath drip edge locations shownin Figure 1. RMSE was minimized primarily by varying van
Genuchten parameters α symbol and n and to a much lesser
extent, saturated soil hydraulic conductivity and soil bulk
density during model calibration.

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MULLA ET AL.

MULLA ET AL.

Vadose Zone Journal

dler et al., 2017) indicated that field capacity ranged from 0.19
at the sandy loam site in Minnesota to 0.43 at the silty clay
loam site in New York (Table S1). Saturated hydraulic conductivity measurements ranged from a low of 2.9 cm/h at thesilty clay loam site in New York to a high of 25.3 cm/h at the
sandy loam site in Minnesota.

3.2

Measured drip edge runoff

Daily drip edge runoff values (mm/day) from fixed orientationarrays in Minnesota and New York (Figure S3) were divided
by incident daily precipitation depth (X) to estimate the drip
edge runoff multiplier factor. At the Minnesota site, drip edge
multiplier values averaged 10.6X and ranged between 2X and
23X, but had no significant relationship with daily precipitation. The low values in Minnesota were associated withsituations where wind was blowing from the north, drivingmuch of the precipitation under solar arrays. The highest values were likely associated with situations where wind was
blowing from the south, driving most of the precipitation
directly against solar arrays. At the New York site, drip edge
multiplier values averaged 3X and ranged between 1X and 5X.
This narrower range of scatter at the New York site relative
to the Minnesota site is likely due to prevailing winds thatgenerally blew parallel to array orientation (E–W). Drip edge
multiplier used in Hydrus modeling represents an averageover dozens of storm events, primarily during the dominantrainy season.

3.3
Hydrus-1D Predictions of Soil Moisture
Content
F I G U R E 2 Minnesota (MN) (top), New York (NY) (middle), andOregon (OR) (bottom) histograms for volumetric soil moisture (x-axis)
versus count of observed soil moisture occurrences in each soil
moisture bin (y-axis) for full sun sensors at a depth of 15 cm. Based on
the Chandler et al. (2017) method, field capacity water content is 0.19,
0.43, and 0.4 for the MN, NY, and OR sites, respectively.

3
3.1

RESULTS AND DISCUSSION

Hydrus-1D predictions of soil moisture content were calibrated against 3–4 months of measured soil moisture at depthsof 15–30 cm from full sun areas between arrays (Table 2).
Because Hydrus-1D simulations could not simultaneously
represent infiltration in the full sun area as well as beneaththe panel drip edges, the upper boundary condition for thefull sun area during rainy days was estimated by adding aneffective drip edge runoff factor to incident daily precipitation. This was determined based on the ratio of impervious
panel width to the total spacing from panel to panel.

Soil characteristics

Soils at the five experimental sites (Table S1) ranged in texture
from sandy loam (Minnesota) to clay (Colorado and Oregon).
Soil depths varied from shallow (0–46 cm) at New York to
deep (0–150 cm) at Georgia and Minnesota. Distinct A and B
horizons were present at Georgia and New York, while soils
at the other three sites had relatively homogeneous sub-soils.
Histograms of long-term soil moisture content measurements(e.g., Figure 2 and Figure S2, following the method of Chan-

3.4
Hydrus-1D saturated hydraulicconductivity values
Construction of solar facilities occurred in 2019, 2020, 2019,2019, and 2018, respectively, for the Colorado, Georgia, Minnesota, New York, and Oregon sites. Saturated hydraulicconductivity measurements were estimated after 2–3 yearsof vegetation establishment. Measured Saturated hydraulic15391663, 0, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20335, Wiley Online Library on [16/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

6 of 13

Vadose Zone Journal

7 of 13T A B L E 2 Hydrus-1D calibrated van Genuchten hydraulic properties and root mean square error (RMSE) between measured and Hydrus-1D
predicted soil moisture values in full sun area.

nSaturatedhydraulicconductivity(cm/h)

RMSE

0.02

1.4

5.0

0.041

0.024

1.4

2.0

0.027

Site

Soil moisture sensor
location/ depth(s)(cm)

Precipitation
measurement period for
Hydrus-1D simulations

Saturatedvolumetricwater content

Residual
volumetricwater content

αCO

Full sun/15, 30

Mar. 20–July 21, 2021

0.49

0.11

GA

Full sun/15, 30

Sept. 10–Dec. 31, 2020

0.44

0.09

MN

Full sun/15, 30

July 16–Oct. 31, 2020

0.44

0.06

0.027

1.6

20.0

0.017

NY

Full sun/15, 30

July 4–Oct. 31, 2020

0.48

0.09

0.005

1.3

2.5

0.027

OR

Full sun/15, 30

Jan. 1–Mar. 31, 2021

0.50

0.11

0.04

1.3

0.5

0.021

conductivity values reflect impacts of solar farm construction activities as well as mitigating impacts from growth of
perennial vegetation. Hydrus-1D calibrated van Genuchten
saturated hydraulic conductivity values (Table 2) matchedexperimentally measured values (Table S1) reasonably well
for all sites except Georgia and Oregon. At Georgia, Hydrus1D calibrated saturated hydraulic conductivity (2.0 cm/h)
was smaller than the measured value (7.9 cm/h). The calibrated value for Hydrus-1D is likely influenced by the needto account for impacts of a change to finer texture in the
Georgia sub-soil below a depth of 20 cm. At Oregon, Hydrus1D calibrated saturated hydraulic conductivity (0.5 cm/h)
was smaller than the measured value (3.8 cm/h), which
was obtained during summer when the clay soil exhibitedlarge desiccation cracks. The calibrated value for Hydrus-1D
likely better represents the slower soil infiltration processes
that occur during rainfall events in the early spring, whenantecedent moisture content is relatively large, and desiccation cracks are absent. Calibrated saturated volumetric watercontent values (Table 2) were not estimated using measured
soil bulk density results at Colorado, New York, or Oregon
because of the impacts of desiccation cracks in very dry soilat Colorado and Oregon, or dense rooting masses in soil at
New York.

3.5
Hydrus-3D modeling of storm-event
based drip edge runoff
Measured average drip edge multipliers of 10.6X and 3X
were used at the fixed-panel Minnesota and New York sites,
respectively (Table 3). The tracking array sites of Georgia andColorado had panel widths that were half that at the Minnesota
site. At these sites, a multiplier of 5X (half of the Minnesota
multiplier) was used to simulate a worst-case scenario wherewind did not redirect any rainfall around the panel. In addition,
because the Georgia measured storm event occurred at night
when panels were held parallel to the ground, the 5X multiplier for the Georgia site was split and applied to each edgeof the panel. Oregon panel width was the same as Minnesota,
however during the measured storm event used for Hydrus3D modeling, winds drove precipitation under the panel fromthe west; therefore, a 5X multiplier was used to simulate Oregon’s panel interception rate. In addition, half the precipitation
was applied underneath half of the wind-blown side of theOregon panel. For all Hydrus-3D simulations, drip edge multipliers were applied to the upper boundary condition at thepanel edge; the width of ground surface area where the multiplier was applied was calculated to preserve water balancein the system. This ground surface area where panel runoff
was infiltrated varied between 22.5 and 35.5 cm, which was
based on observations of soil scour (rather than assuming avery narrow scour band directly beneath the drip edge).

3.6
Hydrus model accuracy at predicting
soil moisture content
3.6.1

Hydrus-1D model accuracy

RMSE values (Table 2) for calibrated Hydrus-1D simulationsof soil moisture content at the five experimental sites ranged
from 0.017 to 0.041, indicating very good accuracy. RMSE
values were lowest in the coarsest textured soil (Minnesota)
and highest in one of the finest textured soils (Colorado).
Example comparisons between daily measured andHydrus-1D predicted soil moisture at the 15-cm depth in thefull sun region between arrays are shown for the New Yorksite in Figure 3 (top). Soil moisture values were accurately
predicted at the fixed solar arrays in New York (RMSE of
0.027), but there was a tendency to slightly underpredict
peak values of soil moisture following the onset of individual
storms at the tracking arrays in Oregon and Georgia. The
steep decreases in modeled water content in July and August
correspond to dry periods without significant rainfall. The
underprediction is reasonable, given that Hydrus-1D isunable to explicitly account for concentrated runoff at panel
drip edges that would travel downslope into the full sun

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T A B L E 3 Hydrus-3D van Genuchten model parameters and root mean square error (RMSE) for predicted versus measured soil moisture driven
by selected measured storm events.

nSaturated/crust
hydraulicconductivity (cm/h)

RMSE

0.01

1.3

7.5/0.75

0.019

0.09

0.03

1.4

8.0/0.1

0.023

0.40

0.08

0.025 1.25 3.0/NA

Soil moisture sensor
location/ depth(s)(cm)

Saturated
Measured storm
event depth/drip edge volumetricwater content
multiplier (cm)Residual
volumetricwater content

αCO

Full sun/15
Under panel/15
East and west drip
edges/15

3.0/5X

0.49

0.10

GA A
HorizonFull sun/15
Under panel/15
East and west drip
edges/15

2.0/5X

0.38

Site

GA B
Horizon
MN

Full sun/15, 30
Under panel/15
South drip edge/30

9.0/10.6X

0.42

0.06

0.10

2.0

25.0/2.0

0.033

NY A
HorizonFull sun/15,30
Under panel/15
South drip edge/30

4.3/3X

0.50

0.09

0.005 1.3

0.5/NA

0.028

0.50

0.09

0.01

1.5

3.0/NA

0.50

0.11

0.04

1.4

2.0/0.2

NY B
HorizonOR

Full sun/15
Under panel/15
East and west drip
edges/15

4.1/5X

0.038

Note: NA indicates no soil crust.

area during a given storm. In Hydrus-1D, the drip edge
runoff is averaged over the entire full sun area and thenadded to incident rainfall for the location in the middle ofthe full sun area. In general, the timing of increases in soil
moisture is closely tied with, but lags slightly, the onset of a
precipitation event. The slight lag is due to the way drip edge
runoff is redistributed into the full sun area with Hydrus-1Dmodeling. In Hydrus-3D modeling (Figure 3, bottom), thelag increases because of the distance between the drip edge
and the full sun area sensors. Predicted soil drying curves atthe end of a precipitation event follow experimental drying
curves reasonably well at Oregon (RMSE of 0.021), but are
somewhat faster to decline than experimental drying curves
at the wetter Georgia (RMSE of 0.027) site (results notshown).

3.6.2

Hydrus-3D model accuracy

Two-day simulations with Hydrus-3D of soil moisture content
for selected measured storm events (exemplified by Figure 3,
bottom) required slight modifications in Hydrus-1D calibrated surface layer saturated hydraulic conductivity values(Table 3) to account for soil crusting beneath solar arrays at
the Colorado, Georgia, Minnesota, and Oregon sites where
vegetation tended to be less dense than in the full sun area.
Crust hydraulic conductivity values ranged between 0.1 and
2.0 cm/h at the latter sites for the ground surface node in theHydrus-3D domain. No crusting was observed at the New
York site. In addition, Hydrus-3D sub-soil hydraulic conductivity values at Georgia and New York were modified toaccount for sub-soils with markedly different texture than surface soils, bringing modeled (Table 3) and measured (Table
S1) saturated hydraulic conductivity values at Georgia andOregon into close agreement.
Comparison between Hydrus-3D predicted and measured
soil moisture contents at the New York site (Figure 3[bottom], Figure S4) showed that the magnitude of increases
in soil moisture from beginning to end of the storm events
agreed closely with each other. The timing of predicted soil
moisture content increases lagged measured increases somewhat at Oregon, but was relatively well-timed at all other sites.
RMSE values (Table 3) for 2-day simulations of soil moisture
content with Hydrus-3D at the five experimental sites averaged over all sensor locations within a site ranged from 0.019
to 0.038, indicating very good accuracy.

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8 of 13

Vadose Zone Journal

9 of 13F I G U R E 3 (Top) Measured versus predicted soil moisture over a 3.5-month monitoring period using full sun 15-cm depth sensors at New
York with Hydrus-1D modeling and (bottom) with Hydrus-3D modeling over a 2-day period that includes a large measured storm event.

3.7
Hydrus-3D estimates of stormwaterrunoff across a range of design storms
The calibrated and validated Hydrus-3D model was subsequently used to simulate soil moisture contents and runoff
from soils using 24-h 2-, 10-, and 100-year return frequency
design storms from NOAA Atlas 14 tables appropriate foreach of the five experimental sites. We did not directly monitor runoff at any of the sites. However, we noted clear signsof runoff at all sites except Minnesota based on soil erosion
and scour. Figure S5 illustrates the evolution of soil moisture content for the sandy loam and silty clay loam soils
at Minnesota and New York. The wetting front reaches thebottom of soil profile after an extended period of infiltration, as shown in Figure S5. The no flux lower boundary
condition promotes soil saturation at shallower depths in finetextured soils, leading to runoff. No runoff was generated in
the coarse-textured Minnesota soil with the 24-h 100-year
return frequency design storm, whereas there was significant
runoff generated in the fine-textured, shallow New York soil.
The Minnesota soil exhibited pronounced vertical infiltration
patterns, particularly directly underneath the array drip edges.
Surface soils in the full sun area and beneath the solar arrays of
the Minnesota soil remained unsaturated throughout the duration of the design storm, allowing for enhanced infiltrationthat prevented runoff. In contrast, the New York soil exhib-

ited rapid onset of saturated soil starting beneath the array drip
edges, leading to runoff after only 6 h of rainfall. After 18 h ofrainfall, the New York soil was uniformly saturated in the nearsurface region. This comparison illustrates the large impactthat soil texture has on stormwater runoff at solar farms. Oneof the primary strategies solar developers should use for mitigating runoff is to select sites for development that have goodpotential for infiltration, avoiding development on sites withlow permeability or shallow soils.
Figure 4 illustrates the magnitudes of 24-h 2-, 10-, and100-year return frequency design storms and associatedHydrus-3D simulated stormwater runoff depths at the fiveexperimental sites. 100-year design storms varied from about
23.8 cm at Georgia to 14.2 cm at Colorado. Despite a 24-h,
100-year return frequency design storm depth of 18 cm at
the sandy Minnesota site, this site generated no runoff due
to coarse textured deep soil profile with a large saturated
hydraulic conductivity value. The 100-year design storms
generated 6.1, 13.3, 0, 12.5, and 6.5 cm of runoff during a 24-h
period at Colorado, Georgia, Minnesota, New York, and Oregon, respectively. High runoff at Georgia was due primarily
to high intensity precipitation, while high runoff at New York(81.9% of the 100-year design storm depth) was primarily
due to very shallow soil depth and limited capacity for storageof infiltrated water. Moderate runoff on the low-permeability
clay soils in Colorado and Oregon was due to a combination

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MULLA ET AL.

F I G U R E 4 Two-, 10-, and 100-year design storms (cm) at the five experimental study sites and corresponding Hydrus-3D stormwater runoff(cm) in comparison with corresponding Natural Resources Conservation Service (NRCS) runoff curve number stormwater runoff (cm).

of relatively low-intensity rainfall combined with low soil
bulk densities associated with deep-rooted native and pollinator vegetation. Hydrus-3D runoff averaged 12.9%, 24.8%,
and 44.8% of design storm depth for the 2-, 10-, and 100-year
24-h design storms, respectively, for the Colorado, Georgia,
Minnesota, New York, and Oregon sites. Clearly, runoff fromthese sites is much less than runoff that would be generated
from completely impervious surfaces, which would haverunoff depths that are equal to (100% of) design storm depths.

3.8
Comparisons of stormwater runoff for
Hydrus versus NRCS runoff curve number
methods
Most engineers attempting to predict solar farm runoff rely on
Natural Resource Conservation Service (NRCS) RCN tied to
specific soil types at each site. Post-construction NRCS RCN
values based on soil hydrologic group and perennial groundcover of pasture were estimated to be 85, 72, 58, 81, and
85 at Colorado, Georgia, Minnesota, New York, and Oregon,
respectively. These RCN values generated stormwater runoff
depths for the 24-h 100-year design storm of 10.0, 15.1, 6.3,
9.9, and 4.2 cm at Colorado, Georgia, Minnesota, New York,and Oregon, respectively (Figure 4). These depths are 3.9, 1.8,
6.3, 13.6, and 4.3 cm larger than the corresponding runoff
depths generated by Hydrus-3D for the 100-year storm atColorado, Georgia, Minnesota, and Oregon, respectively. The
RCN runoff depth for the 100-year storm at New York generated 2.6 cm less runoff than Hydrus-3D, this difference was a
result of increases in runoff caused by shallow soil depth at the
New York site that were properly accounted for in Hydrus-3D
simulations, but not in RCN-based estimates. Averaged acrossthe 2-, 10-, and 100-year design storms at all sites, NRCS
RCN runoff estimates were 58% larger than Hydrus-3D runoff
estimates, even though the NRCS RCN method did not explicitly consider presence of the solar arrays. NRCS RCN runoff
estimates were considerably larger than Hydrus-3D runoff

estimates at the Minnesota site, where Hydrus-3D predicted
no runoff for any of the design storms. Runoff depths with
the Hydrus-3D approach are not surprisingly more accurate
than depths generated using the RCN-based runoff approach
because Hydrus-3D was explicitly calibrated for site-specificcharacteristics such as soil depth, saturated hydraulic conductivity, and vegetative cover that are only dealt with in an
average sense using the RCN method. Stormwater runoff predictions with Hydrus-3D across the five experimental sites in
the presence of solar arrays were on average 14% larger thanrunoff in the absence of solar panels. Thus, the disconnected
impervious nature of solar arrays, along with the presenceof perennial vegetation, can mitigate much of the runoff thatwould otherwise be expected by installing impervious solar
arrays.

3.9Comparisons between Hydrus-3Dmodeling of solar farm runoff and conventionalmodeling
Recently, two other studies have conceptually explored modeling of stormwater runoff from solar farms using the conceptof disconnected impervious surfaces (Gullotta et al., 2023;
Nair et al., 2023). Both studies conducted runoff modeling with US Environmental Protection Agency’s storm water
management model (SWMM). Neither study evaluated model
accuracy, because experimental data on soil moisture, infiltration, and runoff were not measured in either study. SWMM isa one-dimensional rainfall excess runoff model that simulates
infiltration using a Green–Ampt approach that depends on soil
texture and simulates runoff using a Manning’s n roughness
coefficient that is sensitive to vegetative cover. In contrast,
the present study used experimental data to evaluate accuracyof the Hydrus-3D model, which is explicitly capable of considering impacts of soil texture, soil depth, and bulk density,
along with vegetative cover impacts on soil moisture content,
infiltration, and runoff.

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10 of 13

F I G U R E 5 Runoff (cm) from 100-year design storms at
Colorado (CO), Georgia (GA), New York (NY), and Oregon (OR)experimental sites in response to 25% increases or decreases in soil
bulk density relative to runoff under baseline soil bulk density values.

Hydrus-3D uses a Manning’s roughness coefficient (n),which was kept at the minimum default value of 0.01. Generally, all sites studied had relatively smooth surfaces, as soils
were never tilled. Surface roughness was controlled largely
by vegetation effects. In this respect, the impacts of surface
roughness and vegetation on infiltration and runoff in thepresent study were experimentally accounted for by incorporating the sprinkler infiltrometer measurements of fieldsaturated hydraulic conductivity into Hydrus models. Initiation of runoff is controlled by a parameter h0 setting the
maximum pressure head for surface ponding before onset of
runoff, which was set at the default value of 0 cm.
Hydrus-3D results with and without solar arrays at each of
the five experimental sites showed that runoff increased by
14% with arrays present. While a similar result was obtainedby Nair et al. (2023) for solar arrays installed on a site with a
23% slope, Gullotta et al. (2023) stated that there were no differences in runoff between sites with or without solar arrays.
Their SWMM modeling indicated that changes in soil texture
had a larger impact on runoff than changes in vegetative cover(as controlled by the Manning’s n factor). The latter results are
consistent with those from the present study, though they arise
from completely different modeling approaches. Neither the
Nair et al. (2023) nor the Gullotta et al. (2023) modeling studies were able to account for impacts of changes in soil depth
or soil bulk density on runoff. In the present study, a comparison of runoff from 100-year storms of similar magnitude at
New York and Oregon (Figure 4) showed that the shallow silty
clay loam soils at New York had roughly double the runoff ofthe clay soils at Oregon, indicating that soil depth has a large
impact on solar farm runoff. This difference is critical, since
the simple act of selecting sites with deeper soils for solar farmdevelopment can greatly reduce the potential for stormwater runoff, just as managing sites with post-construction
tillage to avoid soil compaction and/or perennial vegetation to promote infiltration can greatly reduce stormwaterrunoff.
The present study also indicates that soil bulk density has
a significant impact on runoff (Figure 5). Hydrus-3D simula-

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tions were conducted to estimate runoff with 100-year storms
at the four sites (Colorado, Georgia, New York, and Oregon)that generated runoff by varying soil bulk density by plus orminus 25% of baseline bulk density values. Increasing bulk
density by 25% caused increases in runoff averaging 17%
across the four sites, while decreasing bulk density by 25%
caused an average 2.5-fold decrease in runoff across the foursites. These results show that management practices that prevent soil compaction and promote low soil bulk density during
and after construction of solar facilities can result in significant reductions in solar farm runoff. These practices to reduce
soil bulk density could include avoiding heavy machinery
traffic during construction when soils are wet, ripping soils
after construction ends (if soils have become compacted), andplanting deep-rooted vegetation after solar panels have been
installed.
The SWMM model explicitly allows for considerationof runoff routing across large areas with many rows ofsolar arrays, whereas the finite difference algorithms usedin Hydrus-3D have run-time limitations that only allow consideration of relatively small areas with homogeneous soil
extending across no more than three rows of solar arrays. Nevertheless, the Hydrus-3D model is very useful at illustrating(within these small areas) the relative importance on runoff
of factors such as design storm depth, soil texture, soil depth,
soil bulk density, vegetation, slope steepness, and presence orabsence of solar arrays and their spacing, if present. Nair et al.
(2023) pointed out that concentrated runoff is likely to occur
when more than three or four rows of solar arrays are considered. They addressed this issue by routing SWMM-generated
runoff for sub-areas having four rows of arrays to specified
channels, diversion areas, and structural holding basins. Suchrouting of concentrated runoff was not used in the present
study, which seems reasonable based on simulation of only
three rows of solar arrays, which Nair et al. (2023) also treatedas having sheet flow.

4

CONCLUSIONS

Ground-mounted PV sites are often treated as impervious surfaces (GPI, 2021), which our research results show is notcorrect. Ignoring the disconnected pervious soils beneath andbetween solar arrays leads to an overestimation of runoff.
Based on experimental measurements of precipitation and soilhydrology at ground-mounted PV sites in Colorado, Georgia,
Minnesota, New York, and Oregon with vegetative plantingsof perennial vegetation, we adapted a numerical Hydrus-3Dmodel for infiltration, soil moisture, and runoff at each site.
The model was very accurate at representing soil moisture forlarge storms occurring within a 2-day period at all sites. This
study improves on estimation of stormwater runoff at solar
farms by considering experimentally measured factors such

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MULLA ET AL.

Vadose Zone Journal

as rainfall hitting solar panels, generating concentrated runoff
at a drip edge, and the subsequent infiltration of this water
downslope in a pervious sunlit area having a wide range of
surface conditions, as well as in a pervious area beneath theadjacent downslope row of solar arrays that are themselves
impervious to rainfall. Many of these factors are currently
ignored by other stormwater models used at ground mounted
solar facilities. Runoff across the five experimental sites estimated using Hydrus-3D was, on average, considerably lower
than runoff estimated using the standard NRCS RCN method,primarily because of increased capacity for infiltration of
runoff beneath solar panels that are shielded from rainfall aswell as infiltration in the vegetated areas between solar arrays.
These improved runoff estimates using Hydrus-3D for solar
PV sites with perennial vegetation could significantly reduce
the need to install expensive stormwater mitigation practices
at ground-mounted solar PV sites. Further, the model provides useful insight into variations in runoff at solar farms
due to the complex interplay between disconnected impervious surfaces, drip edge runoff, and variations in soil texture,soil depth, soil bulk density, and vegetative cover.
AU T H O R C O N T R I B U T I O N SDavid Mulla: Conceptualization; funding acquisition;
methodology; project administration; supervision; writing—original draft. Jake Galzki: Data curation; formal analysis;
methodology; validation; visualization; writing—review andediting. Aaron Hanson: Data curation; investigation. Jirka
Simunek: Software.
AC K N OW L E D G M E N T S
This research was supported by the US Department of Energy
Office of Energy Efficiency and Renewable Energy under
the Solar Energy Technologies Office Award Number DEAC36- 08GO28308. We acknowledge helpful suggestions on
research direction by the PV-SMaRT Water Quality Task
Force Advisory Board members. Assistance with installationof monitoring equipment at research sites during the COVID
pandemic is acknowledged for Dr. Tim Green (CO), Dr. Craig
Kvien (GA), Dr. Scott McArt (NY), and Maggie Graham(OR).
C O N F L I C T O F I N T E R E ST STAT E M E N T
The authors declare no conflicts of interest.
ORCID
David Mulla https://orcid.org/0000-0001-7040-5888
Jirka Simunek https://orcid.org/0000-0002-0166-6563
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S U P P O RT I N G I N F O R M AT I O N
Additional supporting information can be found online in the
Supporting Information section at the end of this article.

How to cite this article: Mulla, D., Galzki, J.,
Hanson, A., & Simunek, J. (2024). Measuring andmodeling soil moisture and runoff at solar farms using
a disconnected impervious surface approach. Vadose
Zone Journal, e20335.
https://doi.org/10.1002/vzj2.20335

15391663, 0, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20335, Wiley Online Library on [16/04/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

MULLA ET AL.