Evapotranspiration Potential of Green Infrastructure Vegetation

A Thesis Submitted

to the

Faculty of

Drexel University

by

Stephanie Marie Miller

in partial fulfillment of the

requirements for the degree

of

Master of Science in Environmental Engineering

May 2014

© Copyright 2014

Stephanie M. Miller. All Rights Reserved.

  ii  

TABLE OF CONTENTS

LIST OF TABLES iii

LIST OF FIGURES iv

ABSTRACT v

LITERATURE REVIEW 1

METHODS 7

Study Site 7

Plants 7

Lysimeters 8

Daily ET 10

Cumulative ET 10

Crop Coefficients 10

RESULTS

Plants 14

Daily ET 14

Cumulative ET 16

Crop Coefficients 19

DISCUSSION 23

CONCLUSION 27

LIST OF REFERENCES 29

  iii  

LIST OF TABLES

Table 1: Daily biomass change in grams (N/A, not available) 14

 

Table 2: Daily ET in mm/day (N/A not available) 14

 

Table 3: Cumulative ET in mm (N/A not available) 16

 

Table 4: Total biomass accumulation of C. lurida replicates between June 26 and September 11, 2013 18

 

Table 5: Adjust Cumulative ET rates for A. incarnata, L. muscari, C. lurida- where "A" represents the slower growing replicates,"B" indicates the faster growing replicates, and E. purpurea replicate 3 was not included due to senescence (N/A not available) 18

 

Table 6: Seasonal crop coefficients. 19

 

Table 7: Comparison of cumulative ETk and the average cumulative (unadjusted) ET values. 19

 

Table 8: Comparison of 72-hour ETk to total precipitation (9.9 mm) 20

 

Table 9: Comparison of 72-hour ETk to the total volume of rain falling on the bioswale 22

   

  iv  

LIST OF FIGURES

Figure 1: 10'x5' right-of-way bioswale design according to NYC DEP’s “Standards for Green Infrastructure” (2012b). 12

 

Figure 2: Daily ET results measured from changes in lysimeter mass 15

 

Figure 3: Cumulative ET of A. incarnate, L. muscari, C. lurida, and E. purpurea over a 77-day period (June 26-September 11, 2013). 17

 

Figure 4: Graph of cumulative ET over a 72-hour period following a 3-hour rainstorm in New York. 21

 

 

  v  

ABSTRACT Evapotranspiration Potential of Green Infrastructure Vegetation

Stephanie Miller Franco Montalto, PhD

To better understand the evapotranspiration potential of urban vegetation, daily

evapotranspiration (ET) of four species commonly found in green infrastructure in

New York City and Philadelphia (A. incaranta, L. muscari, C. lurida, and E.

purpurea) was measured using microlysimeters. Plants were grown in a

greenhouse and provided with ample water supply to ensure any differences in ET

were due to plant characteristics alone. Values ranged from 1.35 mm/day (A.

incaranta) to 1.98 mm/day (E. purpurea) and were statistically different (p=.018).

Cumulative ET over the measurement period was also statistically different

between the four species (p=.046). Crop coefficients were then developed and

used to predict each species’ ability to evapotranspire rainfall under well-watered

conditions. After exposure to a 9.9 mm storm, 72-hour ET amounted to 3.17 mm

for A. incaranta, 3.40 mm for L. muscari, 4.07 mm for C. lurida, and 4.30 mm for

E. purpurea. The range of ET/P is 32-43% for these four species, with E.

purpurea being capable of evapotranspiring the most rainfall. However, when ET

is adjusted to actual planting densities in an example 10’X5’ bioswale, E.

purpurea inhabits only 16.5% of the green infrastructure (GI) and can only

remove 6% of the total rainfall volume. Ultimately, C. lurida’s lower planting

density and greater total area allow the plant to manage more total water, 12.5%,

than any other species. This research serves as a starting point to better quantify

ET of urban GI species and improve the accuracy of ET modeling.

  1  

LITERATURE REVIEW Green infrastructure (GI) refers to landscape management techniques that

provide both human and ecosystem benefits (Keeley et al., 2012). Urban

stormwater management efforts refer to GI more specifically as any system that

infiltrates, retains, detains, and evapotranspires stormwater to reduce runoff

loading of sewers (NYC DEP, 2012a; NYC DEP, 2012b; PWD, 2011). Plant-soil

interactions in bioswales, bioretention systems, rain gardens, green roofs, and the

urban tree canopy help to maximize stormwater capture. To optimize GI designs,

more attention must be paid to the water requirements and evapotranspiration

(ET) potential of terrestrial vegetation (Nouri et al 2012).

ET is the combined processes of transpiration and evaporation from soil

and canopy surfaces. Evaporation is the phase change from liquid to gas. In the

context of GI, evaporation refers to any water loss from soil or canopy surfaces to

the atmosphere; the process is determined by solar radiation, air temperature, air

humidity, wind speed, and soil moisture content (Allen et al., 1998). Similarly,

transpiration is the loss of water through plants’ stomata during photosynthesis

(Allen et al., 1998). Overall only 90% of the water taken up by plants is lost by

transpiration (Campbell et al., 2008). Like evaporation, transpiration is affected

by weather conditions, but plant characteristics also have a significant influence.

When ET is broken down into it’s two parts, transpiration accounts for around

52% of ET worldwide (Lawrence et al., 2007). In terrestrial landscapes,

transpiration accounts for 60 to 80% of all ET (Shlesinger and Jasechko, 2014).

Through transpiration’s role in ET, terrestrial vegetation is a key driver of the

hydrologic cycle (Voyde et al., 2010).

  2  

While there have been efforts to quantify ET in forests and agriculture,

there is little research on differentiating the ET capabilities of urban GI species. A

common method for comparing ET from different species is through crop

coefficients (Wright, 1983; Allen et al., 1998; Tyagi et al., 2000; Liu et al., 2002;

Kang et al., 2003; Yuan et al., 2009). Crop coefficients (k) relate reference ET

(ETo) to the observed ET of the species under study (Doorenbos and Pruitt, 1975;

Allen et al., 1998; Romero and Dukes, 2007). ETo is the upper bound to ET

determined by meteorological conditions for a well-water vegetated surface,

usually a uniform field of grass or alfalfa (Allen et al., 1998). It represents the

evaporative demand of the atmosphere and ignores the role plant characteristics

have on ET. Crop coefficients serve as an adjustment for ETo and capture any

effects on ET due to plant type (Allen et al., 1998). k must be determined

empirically. Together, ETo and k predict the upper bound of ET for a surface of

interest.

Traditionally, research in the area of crop coefficients has focused almost

exclusively on agricultural plants (Wright, 1983; Allen et al., 1998; Liu et al.,

2002; Rana and Katerji, 2000; Shaoo et al., 2009; Beziat et al., 2013). It wasn’t

until 1994 that scientists realized detailed research was needed in the field of

urban ET (Costello and Jones, 1994). Along this vein research has focused on

determining adjustment factors suitable for the urban landscape, including

landscape coefficients (KL) or plant factors (PF) (UC Cooperative Extension,

2000; Staats and Klett, 2004; Romero and Dukes, 2007; St. Hilaire et al., 2008;

DiGiovanni et al., 2010; Pannkuk et al., 2010; Sun et al., 2012; DiGiovanni et al.,

  3  

2013; Nouri et al., 2013). KL is the multiple of the species composition, density,

and microclimate factors of an area (Costello and Jones, 1994; UC Cooperative

Extension, 2000). The species composition (ks) factor ranges from 0.1-0.9,

regardless of vegetation type, and is based on water use studies, such as the Water

Use Classifications of Landscape Species (WUCOLS) (Costello and Jones, 1994;

UC Cooperative Extension, 2000; Garcia-Navarro et al., 2004; Romero and

Dukes, 2007; Pannkuk et al., 2010; Nouri et al., 2013). The plant density (kd)

factor has a range of 0.5-1.3 and is based on the percent coverage, with 70-100%

groundcover representing average conditions (kd=1), while the microclimate

factor (kmc) is based on average ETo for the region, ranging from 0.5-1.4, with an

open, cool, non-windy field representing average conditions (kmc=1) (UC

Cooperative Extension, 2000). Similarly, plant factors are an adjustment based on

plant appearance. PF values modify ETo to represent the minimum ET that a plant

can experience while maintaining a certain level of aesthetics (St. Hilaire et al.,

2008). In practice the product of ETo and PF represents the plant’s ET under

water-limited conditions and the minimum amount of water that must be

reapplied (UC Cooperative Extension, 2000; St. Hilaire et al., 2008; Sun et al.,

2012).

As with the k values, both KL and PF facilitate ET estimation. However,

landscape coefficients include no empirical ET measurements while plant factors

only enable ET comparisons between species under dry conditions (Costello and

Jones, 1994; UC Cooperative Extension, 2000; St. Hilaire et al., 2008; Nouri et

al., 2013). Additionally, whereas crop coefficients help determine water

  4  

requirements to maximize production and yield, KL and PF are more concerned

with identifying water conservative species. Both values help determine species

that will require minimal irrigation to sustain acceptable aesthetics (UC

Cooperative Extension, 2000; Romero and Dukes, 2007; St. Hilaire et al., 2008;

Pannkuk et al., 2010). Since maximizing plant growth, and correspondingly ET, is

not generally a consideration in urban settings, there is still not a good

understanding of how landscape ET varies under water surplus conditions, like

one would expect to see in GI systems.

Within the body of research already completed on urban vegetation, much

attention has been given to mixed-species conditions, where competition between

plants can hide an individual species’ actual water use (Garcia-Navarro et al.,

2004; Sun et al., 2012). Because such results cannot be narrowed down to

individual plants, they are only applicable to the species composition as presented

in the study and cannot help stakeholders in designing novel plant compositions.

To this end there has been very little work done to determine k, KL, or PF vales

for individual species. What little work has been done has concentrated on urban

trees, shrubs, or turfgrasses at the expense of ornamental, herbaceous species

(Aronson et al., 1987; Levit et al., 1995; St. Hilaire et al., 2008; Yuan et al., 2009;

Irmak et al., 2013). While these analyses are valuable, the bulk of GI installations

are planted with ornamental grasses and forbs, herbaceous flowering plants (PHS

and PWD, 2008; NYC DEP 2012a). Peters et al. (2011) determined that the

fractional cover of species is equally important when estimating ET from a

  5  

catchment, giving credence to the study of the smaller but more abundant species

in GI.

Of the recent studies into grasses and herbaceous landscape species, most

have focused on water use efficiency in dry landscapes, particularly the American

West (Levitt et al., 1995; St. Hilaire et al., 2008; Pannkuk et al., 2010; Nouri et

al., 2013). In contrast, very little work has been done on wetland species or

ornamentals native to cooler, wetter environments, such as the Northeast, USA.

Since municipalities such as New York City and Philadelphia are making an

effort to use these native plants in their GI construction, there exists a disconnect

between stakeholder needs and the current knowledge. Additionally, these cities

need vegetation to help manage excess stormwater, so their needs differ from

previous works aimed at water conservation. For municipalities where plants

serve a vital role in GI stormwater management, it is important to understand the

ET capabilities of ornamental landscape species and use this knowledge to

maximize urban ET through species selection to help reduce GI water volumes

quickly after rain events.

The purpose of this study was to 1) determine the average daily and

cumulative ET of four landscape plants native to the Mid-Atlantic region, 2)

develop crop coefficients for each species, and 3) quantify how plant selection

affects GI performance. ET was measured using weighing lysimeters. Crop

coefficients were developed according to the Food and Agricultural

Organization’s methodology with the ASCE Standardized Reference

Evapotranspiration equation used to calculate reference ET (ETo) (Allen et al.,

  6  

1998; ASCE, 2005). DiGiovani et al. (2013) examined ET from green roofs and

determined that the ASCE Standardize Reference Evapotranspiration Equation is

the best model for estimating ET in the urban setting.  Finally, crop coefficients

were used to estimate how GI performance varied between species after a

rainstorm.  

  7  

METHODS Study Site Data collection occurred in Drexel University’s rooftop greenhouse (65

m2) atop Stratton Hall, 13 m above sea level. The greenhouse was not climate-

controlled and experienced an average daily temperature of 28.4°C with an

average relative humidity of 60.1% over the study period, June 26 through

September 11, 2013.Windows around the edge of the greenhouse and a large

exhaust fan provided air circulation. The greenhouse and experimental setup were

exposed to full-sun. A weather station positioned in the greenhouse adjacent to

the experiment measured atmospheric conditions every five minutes.

Plants This experiment focused on common grasses and forbs used in GI designs

in Philadelphia and New York City- Liriope muscari, Carex lurida, Asclepias

incarnata, and Echinacea purpurea. Grasses and forbs are two of the most

common plant types used in GI (UC Cooperative Extension, 2000; PHS and

PWD, 2008; NYC DEP, 2012a). These two plant groups are suitable to a range of

sediment and growth conditions, making them easier to place in urban landscapes

than trees or shrubs. Also, the size and growth patterns of shrubs and trees were

deemed unsuitable for this single-season, micro-lysimeter experiment.

The species used in this study were chosen based on their popularity in

bioswale and greenstreet designs. The NYC DEP’s Standards for Green

Infrastructure and Interagency Bioswale Planting Lists (2012a), as well as the

Philadelphia Stormwater Planter Design Showcase (2008), were consulted to

generate a list of the species most often recommended for use in GI in New York

City and Philadelphia. From this list the two most common grasses- Carex lurida

  8  

and Liriope muscari- and two common forbs-Asclepias incarnata and Echinacea

purpurea- were selected.

Seedlings were started at the New York Native Plant Center before being

transferred to Drexel University; four replicates were used for each species.

Before being potted in weighing lysimeters, each plant was rinsed with water to

remove any residual dirt and allowed to dry. Plants were then weighed to establish

a starting biomass. Replicates were then potted in weighing lysimeters.

At the end of the experiment, plants were carefully removed from the

lysimeters, rinsed, dried, and weighed to determine a final biomass. The daily

biomass change for each species was calculated using equation 1

∆𝐵 =𝑏! − 𝑏!𝑛

Eq. (1)

where bf is the final biomass, bi is the initial biomass, and n is the number of days

in between.

Lysimeters The lysimeters used in this experiment were cylindrical units 36.8 cm deep

and 33 cm in diameter. Each lysimeter was loaded with 30.5 cm of substrate

engineered to match the New York City’s standard for GI soil: 50-65% sand, 10-

35% silt, 5-15% clay, and 9-12% organic matter (NYC DEP, 2012b). Five

centimeters of red mulch were added to mimic the professional landscaping

typical of GI installations. Lysimeters were weighed once a day using a load cell

accurate up to 1 g capable of measuring changes in water depth up to .01 mm.

Daily weight changes were recorded for each replicate in the late afternoon after

the bulk of ET had ceased.

  9  

There were a total of 18 lysimeters- 4 for each C. lurida, L. muscari, A.

incarnata, and E. purpurea- as well as two control units with no plants. The

control lysimeters were used as a standard of plant health- if a replicate’s daily ET

showed no significant difference with the control pots, then it was considered

dead or senesced and no longer used in calculating species averages. One A.

incarnata died two weeks into the experiment and was not used in the final

analysis. Also, one E. purpurea replicate senesced before the end of the

experiment and so was not used in any cumulative calculations.

Lysimeter soil moisture was completely replenished every three days. A

three-day irrigation cycle was chosen because New York City experiences a

rainfall event approximately every three days (NWS 2013). Since GI installations

receive runoff from the surrounding neighborhoods, in addition to rainfall, it was

assumed that soil moisture is regularly replenished at these sites. To determine the

volume of water needed for replenishment, lysimeters with dry soil were subject

to a variety of irrigation amounts before the experiment began. 500mL was the

maximum volume the systems could contain without any drainage. In practice soil

moisture was not completely depleted during the three days between watering so

some water was lost each time to percolation and drainage through holes at the

bottom of each lysimeter. To account for any mass changes due to irrigation and

drainage, lysimeters were weighed both before watering and after gravitational

drainage had ceased.

  10  

Daily ET Any change in lysimeter mass over a 24-hour period represented the

system’s combined losses due to evaporation and transpiration. Daily ET, then,

was calculated using equation 2.

𝐸𝑇 =(𝑚! −𝑚!!!)

𝜌𝐴

Eq. (2)

where m is the weight of the lysimeter, ρ is the density of water, and A is the area

of the lysimeter. The average daily ET and standard deviation was then calculated

for each species. The Kruskal-Wallis test was used to see if the differences

between species’ average daily ET were statistically significant.

Cumulative ET Cumulative ET was calculated as the sum of all daily ET values for each

replicate (equation 3).

𝐶𝑢𝑚𝑢𝑎𝑡𝑙𝑖𝑣𝑒  𝐸𝑇 = 𝐸𝑇!

!

!!!

Eq. (3)

Average cumulative ET and standard deviation was then calculated for each

species. The Kruskal-Wallis test was used to see if the differences between

species’ cumulative ET were statistically significant.

Crop coefficients Crop coefficients (k) are used to adjust reference ET (ETo) to better

predict species-specific ET. For this experiment ETo was determined using

weather data collected for the greenhouse and the ASCE Standardized Reference

Evapotranspiration Equation for short reference surfaces (ASCE, 2005) (equation

4).

  11  

ETo = !.!"#∆ !!!! !!   !!

!!!"#!!(!!!!!)

∆!!  (!!!!!!)

Eq. (4)

Where Rn is the net radiation at the plant surface (MJ m2 d-1), G the soil heat flux

(MJ m-2 d-1), T the average air temperature (˚C), u2 the mean daily windspeed

(m s-1), (es-ea) the vapor pressure deficit (kPa), ∆ the slope of the vapor pressure

temperature curve (kPA ˚C-1), γ the psychorometric constant (kPA ˚C-1), Cd is the

constant .34, and Cn is the constant 900. DiGiovanie et al. (2013) determined the

ASCE Standardized Reference Evapotranspiration Equation was the best model

for predicting ET in an urban setting. Calculation of ETo was performed using the

REF-ET software. Seasonal crop coefficients (k) were then derived for each

species using the formula described in Allen et al. (1998) (equation 5).

𝑘 =1𝑛

𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒  𝐸𝑇!𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒  𝐸𝑇!

!

!!!

Eq. (5)

where n = the number of replicates. Daily ET was then estimated for each species

using the formula described in Allen et al. (1998) (equation 6).

ETk=k*ETo Eq. (6)

Daily ETk were then summed to validate k by comparing cumulative ETk to

measured cumulative ET for each replicate.

Once validated, crop coefficients were used to predict plant performance

after a storm event. 2012 climate data from one of Drexel University’s

Sustainable Water Resource Engineering (SWRE) lab’s GI monitoring sites-

located at the corner of 116th street and Nashville Blvd in Cambria Heights, New

York- was used to calculate hourly ETo. For this analysis, an isolated storm on

  12  

June 29, 2012 was chosen as the example event. Using hourly ETo and the

appropriate crop coefficient, each species’ ETk was determined for the first 72-

hours after rainfall ceased. 72-hours was chosen as the cutoff since all GI is

designed to drain within 72-hours (NYC DEP and NYC DOB, 2012; PWD,

2014). The total ETk was then compared to the depth of rainfall for each species.

 

Figure 1: 10'x5' right-of-way bioswale design according to NYC DEP’s “Standards for Green Infrastructure” (2012b).

Results were then scaled to an actual GI design to determine the

percentage of rainfall on a 20’x5’ bioswale managed by each species under wet-

weather conditions. New York City and Philadelphia have both published plant

pallets for their GI designs; one example of a 50 ft2 bioswale is shown in Figure 1.

In this design 14.5 ft2 has been allotted for L. muscari, with another 8.25 ft2 for E.

purpurea (Figure 1). Based on the planting densities recommended by the

“Stormwater Design Planter Showcase,” this bioswale could contain 8 L. muscari

  13  

and 4 E. purpurea (PHS and PWD, 2008). This planting configuration was used

to compute the percentage of rainfall captured by L. muscari and E. purpurea in

the 72-hours after a small rainstorm.

Since C. lurida and L. muscari are both grasses and occupy similar

ecology niches, a sister design to Figure 1 might replace all L. muscari with C.

lurida. C. lurida has a significantly lower planting density so this bioswale

arrangement could fit more grass- up to 18 plants comfortably- in the same area

(PHS and PWD, 2008). Similarly, E. purpurea and A. incarnata are both

ornamental flowering perennials and could easily swap places in the bioswale

design, in which case 5 A. incarnata would occupy 8.25 ft2 of the bioswale (PHS

and PWD, 2008). This second configuration was also analyzed to determine the

percentage of rainfall removed by C. lurida and A. incarnata from a 20’x5’

bioswale in the 72-hours after a small rainstorm. The two configurations were

then compared to determine the best combination of flowering perennials and

grasses in the bioswale to maximize ET.

  14  

RESULTS Plants Daily biomass accumulation was negligible for each species. All A.

incarnata, L. muscari, C. lurida, and E. purpurea replicates had a daily biomass

change of less than 1 gram (Table 1). Since this is below the sensitivity of the

load cell, ∆B was not accounted for when determining daily ET from lysimeter

mass changes.

 

 

Table 1: Daily biomass change in grams (N/A not available).

Replicate A. incarnata L. muscari C. lurida E. purpurea 1 0.04 0.74 0.91 0.01 2 0.06 0.60 0.85 0.00 3 N/A 0.43 0.42 0.01 4 0.01 0.41 0.91 0.00

Daily ET Rates

Table 2: Daily ET in mm/day (N/A not available).

Replicate 1

Replicate 2

Replicate 3

Replicate 4

Average Daily ET (mm)

STD DEV

A. incaranta 1.24 1.36 N/A 1.43 1.35 0.10 L. muscari 1.69 1.33 1.33 1.30 1.41 0.19 C. lurida 1.93 2.00 1.42 1.76 1.78 0.26

E. purpurea 2.23 1.62 2.03 1.69 1.98 0.31

  15  

Table 1 shows the daily ET rates calculated over a 77-day period. A.

incarnata and L. muscari were the least productive in terms of daily ET,

averaging only 1.35 (±.10) mm/day and 1.41 (±.19) mm/day, respectively (Table

2). C. lurida averaged 1.78 (±.26) mm/day while E. purpurea average 1.98 (±.31)

mm/day (Table 2). C. lurida and E. purpurea, although the two biggest consumers

of water, were also the species with the most variability (Figure 2). Among the

four species daily ET values were non-homogenous. Non-parametric statistics

determined that differences between species are statistically significant (Kruskal-

Wallis test, p=.018).

 

 

Figure 2: Daily ET results measured from changes in lysimeter mass.  

       

  16  

Cumulative ET

Table 3: Cumulative ET in mm (N/A not available).

Replicate 1

Replicate 2

Replicate 3

Replicate 4

Average Cumulative ET (mm)

STD DEV

A. incaranta 102.4 106.0 N/A 107.7 105.4 2.7 L. muscari 124.3 110.2 110.4 106.9 113.0 7.7 C. lurida 151.1 149.7 109.0 122.2 133.0 20.8

E. purpurea 159.7 N/A 152.2 123.8 145.2 18.9

Figure 3 shows the cumulative ET trends mirror those of the daily ET

results. C. lurida and E. purpurea remained the two most productive species in

terms of their ability to remove water from the system, with cumulative ET rates

of 133.9 mm and 145.2 mm, respectively (Table 3). In contrast, on average A.

incarnata and L. muscari replicates evapotranspired only 105.4 mm and 113.0

mm, respectively, over the 77 day period (Table 3). Once again C. lurida and E.

purpurea also showed the most intra-species variability, with standard deviations

of ±20.8 mm and ±18.9 mm, respectively (Table 3). Also again, values were non-

homogenous and non-parametric statistics showed that differences between

species were statistically significant (Kruskal-Wallis test, p=.046).

  17  

 

Figure 3: Cumulative ET of A. incarnate, L. muscari, C. lurida, and E. purpurea over a 77-day period (June 26-September 11, 2013).

In order to better understand some of the intra-species variability, results

were adjusted based on plant growth patterns. C. lurida replicates had inconsistent

growth rates. Of the four C. lurida plant, two showed immediate growth upon

being transplanted to the lysimeter. Two others, however, stalled in their growth

for a month before beginning to flourish. At the end of the experiment C. lurida

replicates 1 and 2 had accumulated significantly more biomass than C. lurida 3

and 4 (Table 4). When the C. lurida plants are considered with their growth

contemporaries, cumulative ET rates adjust to 150.4 (±.99) mm for the larger

plants and 115.6 (±9.39) mm for the slower developing plants (Table 5). These

standard deviations are more similar to those presented by A. incaranta and L.

muscari.

  18  

Table 4: Total biomass accumulation of C. lurida replicates between June 16 and September 11, 2013.

Initial Biomass (g) Final Biomass (g) Δ Biomass (g) Carex 1 18 132 114 Carex 2 16 122 106 Carex 3 14 48 34 Carex 4 8 78 70

Table 5: Adjusted cumulative ET rates for A. incarnata, L. muscari, C. lurida- where "A" represents the slower growing replicates,"B" indicates the faster growing replicates, and E.

purpurea replicate 3 was not included due to senescence (N/A, Not Applicable)

Replicate 1

Replicate 2

Replicate 3

Replicate 4

Avg. Cumulative

ET

Std Dev

A. incarnata 102.4 106.0 107.7 N/A 105.4 ±2.7 L. muscari 124.3 110.2 110.4 106.9 113.0 ±7.7

C. lurida "A" N/A N/A 109.0 122.2 115.6 ±9.3 C. lurida "B" 151.1 149.7 N/A N/A 150.4 ±1.0 E. purpurea 159.7 152.2 123.8* N/A 156.0 ±5.3

*Replicate not included due to senesce.

Differences in E. purpurea cumulative ET rates were affected by

differences in the timing of senescence. While one replicate was removed from

cumulative calculations due to its complete senescence before the end of the

experiment, replicate 3 also began to lose leaves before the end of measurements.

Despite consistently evapotranspiring at levels statistically different from the two

control pots, individual performance fell below that of the remaining E. purpurea.

When this replicate is removed from calculations, cumulative ET is adjusted to

become 156.0 (±5.3) mm (Table 5). This standard deviation is more similar to

those recorded for A. incarnata and L. muscari. When all adjustments are

  19  

considered, the overall ranking of water use for the four species does not change,

but intra-species variability is reduced (Table 5).

Crop coefficients

Table 6: Seasonal crop coefficients.

A. incarnata L. muscari C. lurida E. purpurea k 0.81 0.87 1.04 1.10

Std. Dev. 0.02 (n=3)

0.06 (n=4)

0.16 (n=4)

0.15 (n=3)

Seasonal crop coefficients mirror daily and cumulative ET trends (Table

6). C. lurida and E. purpurea again have both the highest values, 1.04 (±.16) and

1.10 (±.15), and the greatest variability (Table 6). A. incaranta and L. muscari

both have k values below 1.00. Monthly crop coefficients were also calculated for

each species but showed no statistically significant difference with seasonal k

values (p=.52). During the validation process, testing also showed no statistically

significant differences between cumulative ETk and measured cumulative ET (p

value=.96). In fact cumulative ETk values are well within the standard deviations

of measured cumulative ET (Table 7).

Table 7: Comparison of cumulative ETk and the average cumulative (unadjusted) ET values.

Estimated Cumulative ETk

Avg. Measured Cumulative ET

C. lurida 135.3 133.0 L. muscari 113.1 113.0

A. incarnata 105.4 105.4 E. purpurea 143.1 145.2

  20  

Once validated, crop coefficients were used to calculate hourly ETk for the

first 72-hours after a rain event. The example storm occurred over 3 hours on the

morning of June 29, 2012; total precipitation was 9.9 mm with a peak rainfall

intensity of 4.9 mm/hr (Figure 4). ETk results show E. purpurea, the species with

the most ET potential, could evapotranspire an estimated 4.30 mm in the days

immediately following the storm, with a maximum rate of .35 mm/hr (Table 8). A.

incarnata, L. muscari, and C. lurida could only evapotranspire 3.20 mm, 3.40

mm, and 4.10 mm, with maximum rates of .26 mm/hr .28 mm/hr, and .33 mm/hr,

respectively (Table 8). Ultimately the plants could return between 32% and 43%

of the storm back to the atmosphere over a three-day period (Table 8). In

comparison, the reference ET for this time period was 3.90 mm, or 39% of the

total precipitation (Table 8).

Table 8: Comparison of 72-hour ETk to total precipitation (9.9 mm).

Total ETk after 72 Hours (mm) ET/P (Percentage)

A. incaranta 3.17 32% L. muscari 3.40 34% C. lurida 4.07 41%

E. purpurea 4.30 43% ET0 3.90 39%

  21  

 

Figure 4: Graph of cumulative ET over a 72-hour period following a 3-hour rainstorm in New York.

To illustrate how these differences would appear in the field, results were

scaled to a typical GI system and natural plant densities (Table 9). Given the areas

and planting conditions of each species, the bioretention sections devoted to L.

muscari and E. purpurea could evapotranspire .16 ft3 and .10 ft3 of water from the

system (Table 9). Compared to the 1.62 ft3 of all water falling on the entire

bioswale, these plants could remove between 10% and 6%, respectively, of the

total rainfall volume within 72-hours (Table 9). Combined, L. muscari and E.

purpurea would occupy 45.5% of the GI system and evapotranspire 16% of the

total rainfall (Table 9).

Whereas 8 L. muscari could evapotranspire 10% of the total storm, 18 C.

lurida in a similar bioswale could remove up to 12.5% (Table 9). A bioswale that

replaced E. purpurea with A. incarnata would see a decrease in ET, with A.

0

1

2

3

4

5

6 0

1

2

3

4

5

6/28/12 0:00

6/29/12 12:00

6/30/12 0:00

6/30/12 12:00

7/1/12 0:00

7/1/12 12:00

7/2/12 0:00

Rai

nfal

l Int

ensi

ty (m

m/h

r)

Cum

ulat

ive

ET

(mm

)

Date and Time

ET After a Rainstorm

A. incarnata L. muscari C. lurida E. purpurea

  22  

incarnata plants only evapotranspiring 5% of the total rainfall. Taken together A.

incarnata and C. lurida could remove up to 17.5% of all rainfall from the system

in 72-hours (Table 9). Based on these values the most efficient GI would combine

C. lurida and E. purpurea and remove up to 18.5% of the total rainfall volume

falling on the bioswale (Table 9).

Table 9: Comparison of 72-hour ETk to the total volume of rain falling on the bioswale (1.62 ft3).

72-hour ETk (in)

Number of Replicates

Area per Plant (ft2)

Volume of ET (ft3)

ET/P (%)

A. incarnata 0.12 5 1.6 0.08 5.0 L. muscari 0.13 8 1.77 0.16 10.0 C. lurida 0.16 18 0.85 0.20 12.5

E. purpurea 0.17 4 1.8 0.10 6.0

  23  

DISCUSSION Daily ET values determined in this experiment are below the levels

reported by Denich et al. (2010) or Culberston and Hutchinson (2004), whom

report rates between 3-8 mm/day. However, Denich et al. (2010) and Culberston

and Hutchinson (2004) focused on average daily ET from entire bioretention

facilities under mixed species conditions, including tree and shrub contributions.

In contrast this experiment only looked at the ET of individual small herbaceous

plants. Additionally, air circulation within the greenhouse was minimal and

relative humidity was often above 50%, limiting the atmosphere’s ability to

remove water and the potential for ET. This experiment also does not consider the

impact competition between plants, shading, and a more variable climate,

including inconsistent rainfall patterns, would have on daily ET. Despite daily ET

calculations failing to illustrate field ET, they do show how variances in species

performance are detectable at even small time scales.

Cumulative ET calculations also differ from what one would see in the

field during a growing season. This experiment ran from June 26 through

September 11, a total of 77 days. However, the growing season is considerably

longer (90+ days), meaning seasonal ET would be significantly higher than the

values reported here (Allen et al., 1998; PWD and PHS 2008). Additionally, the

growing season varies by species. A. incaranta and E. purpurea both bud in the

early spring, flower between June and August, and senesce quickly near the end

of summer, going completely dormant before fall (PWD and PHS 2008). L.

muscari and C. lurida, however do not senesce after blooming (August through

September and May through June, respectively), maintaining their leaves at least

  24  

through the end of October (PWD and PHS 2008). This provides them with a few

extra weeks for evaporative processes to continue. If the entire growing season

were considered, then, one might expect C. lurida and L. muscari to have higher

cumulative ET values than E. purpurea and A. incarnate.

Based on the daily and cumulative ET results, it appears a combined

planting of E. purpurea and C. lurida would provide maximum benefits. These

are the two most productive species in terms of ET, occupy different ecological

niches, and have staggered bloom times that would help beautify the city for

much of the spring and summer. Additionally, C. lurida has a lower planting

density than the other species in this experiment, meaning municipalities can

receive more ET in less space (PWD and PHS 2008). Interestingly, although C.

lurida and E. purpurea are both heavy water users, neither Philadelphia nor New

York City has a GI design that includes both plants, suggesting current GI designs

are not necessarily reaching their full ET potential which would maximize their

impact on stormwater management (PHS and PWD, 2008; NYC DEP, 2012a). It

could be that bioretention facilities are designed to maximize other criteria

instead, such as aesthetics, interception, nutrient use, pollutant removal, or

resiliency. Also, this experiment only looked at four species commonly found in

GI; in reality, there are hundreds of native plants available for stormwater

management with unknown ET potential.

While daily and cumulative ET results can help in determining, generally,

differences in species water use, the calculated crop coefficients provide a more

exact means of modeling urban ET. These values can be used to estimate the

  25  

effect of ET and specific species on stormwater management. A common problem

for implementing urban GI is poor soil and geology. New York City requires all

open-bottom GI to pass a soil boring and permeability test, proving the system has

sufficient permeation rates (NYC 2012). Additionally, the bottom of GI surfaces

must be at least .91 m above the groundwater table to prevent systems from

infiltrating into the sewers (NYC 2012). One option to expand the implementation

of GI is to consider lining systems in areas of the city that do not meet these

requirements. However, analysis of a storm event suggests that ET rates of

herbaceous plants are not high enough to remove stormwater from bioretention

facilities on their own, at least not within the required 72-hours (Figure 4).

Additionally, the storm event used in this experiment occurred near the middle of

the growing season and early in the morning, giving plants the maximum

opportunity to evapotranspire since they would be in full bloom and have full use

of daylight hours. Any precipitation falling at night reduces the system’s ability to

respond quickly since ET is significantly reduced after dusk, as do any storms

occurring at the tail ends of the growing season when plants have less surface area

available for ET. Additionally, if storms come in rapid succession, lined GI

systems run the risk of flooding without an additional means of removing

stormwater. Since city policy does not currently allow any GI to have an outlet to

sewers, this severely constrains GI implementation in certain sectors of the city

(NYC 2012).

Ultimately, additional research is needed to quantify performance from

more herbaceous GI species and generate new system designs. The techniques

  26  

applied in this experiment should be expanded to more plants to develop a

complete water-use analysis of GI vegetation, similar to WUCOL efforts in

California (Costello and Jones, 1994). A more thorough investigation of

herbaceous plants and their crop coefficients will also help determine if other

species might be more suited for the rapid removal of stormwater from

bioretention systems than A. incaranta, L. muscari, C. lurida, or E. purpurea.

Future efforts should also consider the impact of climate change on urban ET,

particularly how extensive drought or repeated flooding might affect long-term

plant performance. Such experiments are already underway in Drexel University’s

SWRE Laboratory.

  27  

CONCLUSION Daily and cumulative ET was measured for four species commonly found

in GI within the urban Northeast: A. incarnata, L. muscari, C. lurida, and E.

purpurea. This is the first study to quantify ET of herbaceous urban GI plants.

Results show that A. incarnata and L. muscari consistently evapotranspire at rates

below that of E. purpurea or C. lurida, although the later plants show more

intraspecies variability. Crop coefficients were also developed for these same

species, with values ranging from 0.81 for A. incaranta to 1.10 for E. purpurea.

The use of these coefficients was then illustrated by predicting each species’

ability to evapotranspire stormwater after a precipitation event, using climate data

from one of the SWRE lab’s GI monitoring site in New York City. Crop

coefficients predict that after 72-hours the plant-soil systems could evapotranspire

between 32 and 43% of the total rainfall.

The results of this experiment have real implications for practitioners of

New York City and Philadelphia GI stormwater management efforts, as well as

those of other cities in the urban Northeast. Cumulative and daily ET analysis

shows statistically significant differences between plant species, indicating that all

herbaceous GI vegetation is not created equal. These differences are magnified

when considering actual planting densities in GI designs. Just as researchers have

devoted resources to determining the minimal water needs of plants, especially in

water-scarce regions like the western USA, a similar effort is needed in water-rich

regions like the northeast, where stakeholders are interested in maximizing ET

rather than minimizing irrigation. Having a better understanding of which species

are best able to evapotranspire water will allow managers to create GI designs that

  28  

can rely heavily on ET to maximize the beneficial uses of stormwater.

Specifically, developing crop coefficients for urban landscape species will

improve GI modeling efforts and help to better illustrate the effect of ET on

stormwater management.

  29  

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