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How Much Water Does Turf Removal Save? Applying Bayesian Structural Time-Series to California Residential Water Demand

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Abstract and Figures

California water utilities have invested historic amounts of money in turf rebates to incentivize customers to remove their turf grass and replace it with more water efficient landscaping. This study utilizes a data set of 545 unique singlefamily residential turf rebates across 3 California water utilities, totaling 635,713 square feet of converted turf grass to estimate the water savings from turf removal. Monthly water savings are estimated at the household level as the difference between actual usage and a synthetic control and then aggregated using a mixed-effects regression model to investigate the determinants of water savings. Analysis of turf removal at the monthly level is found to be critical for understanding the seasonal behavior inherent in outdoor water use. Mean predicted savings for single-family residential accounts are estimated at 24.6 gallons per square foot per year for the households used in this study.
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How Much Water Does Turf Removal Save? Applying
Bayesian Structural Time-Series to California Residential
Water Demand
Christopher Tull
ctull17@gmail.com
Eric Schmitt
eric.schmitt@protix.eu Patrick Atwater
patrickatwater@gmail.com
California Data Collaborative
418 Bamboo Lane, Los Angeles, CA 90012
1. ABSTRACT
California water utilities have invested historic amounts
of money in turf rebates to incentivize customers to remove
their turf grass and replace it with more water ecient land-
scaping. This study utilizes a data set of 545 unique single-
family residential turf rebates across 3 California water util-
ities, totaling 635,713 square feet of converted turf grass
to estimate the water savings from turf removal. Monthly
water savings are estimated at the household level as the
dierence between actual usage and a synthetic control and
then aggregated using a mixed-eects regression model to
investigate the determinants of water savings. Analysis of
turf removal at the monthly level is found to be critical for
understanding the seasonal behavior inherent in outdoor wa-
ter use. Mean predicted savings for single-family residential
accounts are estimated at 24.6 gallons per square foot per
year for the households used in this study.
2. INTRODUCTION
With outdoor landscaping representing approximately half
of urban water usage, the water community has identified
outdoor water usage in general (Mayer, Lander, and Glenn
2015), and ornamental lawns specifically (CUWCC 2015)
as a key opportunity in the larger eort to increase wa-
ter conservation. Between July 2014 and April 2016, the
Metropolitan Water District (MWD), the regional whole-
saler of Colorado and Bay Delta water for Southern Califor-
nia, paid out $270.7 million directly for turf rebates under its
regional program and another $15.1 million to supplement
member agency spending on turf replacement. Metropoli-
tan indirectly serves 6.1 million residential households across
Southern California (MWD 2016). In addition, millions in
local retailer turf rebate supplements have been paid out
(for example in Los Angeles, Long Beach, San Diego and
Moulton Niguel).
A small number of studies have investigated the impact of
turf removal conservation rebate programs on water usage.
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2016 ACM. ISBN 978-1-4503-2138-9.
DOI: 10.1145/1235
In 2005, the Southern Nevada Water Authority (SNWA)
conducted a turf removal and Xeriscape planting study that
found these rebates led to 55 gallons in water savings per
year per sq. ft. of turf removed (Sovocool, Authority, and
Morgan 2005). These results may not be reflective of the im-
pact of such policies in the climate in Los Angeles and the
absence of a requirement for Xeriscape landscaping. Indeed,
P. Atwater, Schmitt, and Atwater (2015) found a more mod-
est residential reduction of approximately 18 gallons per year
per sq. ft. in the Moulton Niguel Water District in south Or-
ange County, California. Similarly, the Metropolitan water
district conducted a study in 2014 with robust hydrologi-
cal variation that found an average relative water reduction
of 18.2% from participating residential households and 24%
from participating commercial accounts (MWD 2014).
This study builds on previous research and develops a
novel methodology for assessing the impact of water con-
servation actions. Previous work in P. Atwater, Schmitt,
and Atwater (2015) utilized a multilevel quantile regression
model to control for the determinants of water use and iso-
late the average reduction in usage due to turf removal.
However, the authors were unsatisfied with the methodol-
ogy used to control for behavioral elements such as envi-
ronmental attitudes, and structural shifts in usage like Cal-
ifornia’s 2015 mandatory water conservation requirements.
The approach used here instead borrows from the market-
ing context to match residential households based on their
past water usage behavior instead of using static descriptive
attributes like household size and irrigable area. A syn-
thetic control is then created and a dierence-in-dierences
approach is applied to estimate monthly water savings at
the level of individual households. This enables analysis
of the full distribution of water use changes, including sea-
sonal fluctuations in the amount of water saved that high-
light peak summer demand reduction. Finally the individual
estimates are aggregated using a meta-analytic mixed-eects
model to control for moderator variables of interest.
3. METHODOLOGY
3.1 Data
The data used in this study was provided by 3 water utili-
ties: Moulton Niguel Water District (MNWD), Irvine Ranch
Water District (IRWD), and Eastern Municipal Water Dis-
trict (EMWD). Each utility provided two data sources. The
first is a panel data set of monthly billed water usage and
customer characteristics identified by account and service
point (water meter) identifiers for single family households.
The second is a data set detailing participation in water
eciency rebate programs, of which turf removals are the
primary interest for this study.
These two data sets are merged, and any turf rebate in-
stances tied to accounts that appear more than once are
dropped to prevent overcounting. These accounts are then
further restricted to those that have at least two years of
data (24 observations) in the pre-rebate period and one year
of data (12 observations) in the post-rebate period. The
pre- and post-rebate periods are determined relative to the
month that the post-rebate inspection was performed. Fi-
nally, the water districts make use of default values in cases
where the actual value is unknown. Some districts substi-
tute default values for irrigable area when actual values are
not known. Customers with default values were dropped
in cases where this was obvious due to bunching of many
customers at the same value of irrigable area.
The working dataset contains 545 observations of either
traditional or synthetic turf rebates after filtering. The vari-
ables are defined as follows:
Customer ID: unique identifier for each household.
Month and Year: the month and year of the water bill.
HH Size: number of permanent residents at the prop-
erty.
Irr Area Sf and Rebate Quantity: the square feet of
irrigable area and square feet of turf removed during
rebate, respectively.
Rebate Area Ratio: the proportion of turf area re-
moved, calculated as Rebate Quantity
Irr Area Sf .
Evapotranspiration: The reference evapotranspira-
tion, ET0, in inches.
3.2 Time Series Matching and Rebate Impact
Estimation
The estimation of rebate water savings is implemented
using the Rprogramming language. It is done in three steps.
Given N= 545 treatment accounts which participated in a
turf removal rebate and are examined in this study:
1. Each treatment account tri,i21...N which has par-
ticipated in a turf rebate is matched with a set of con-
trol accounts Ci={cj
i},j 21...6 from the same zip
code which did not participate in a turf rebate. These
control accounts are chosen by how similar their his-
torical usage patterns are to the usage patterns of the
treatment account tri, based on a weighted combina-
tion of their Pearson correlation and their warping dis-
tance.
2. After the cihave been chosen, we fit a Bayesian struc-
tural time series model and use it to estimate the
monthly impact of turf removal on water savings. The
structural time series (STS) model uses the water us-
age patterns of the control accounts to create a syn-
thetic control corresponding to the expected water us-
age of triif there had been no turf removal. The
predicted usage in the post-rebate period is then sub-
tracted from observed usage to obtain a monthly water
savings estimate for tri.
3. After a water savings estimate has been calculated for
each treatment account, the last step is to obtain an
overall summary estimate. This is done with a meta-
analytic approach that uses the estimates and vari-
ances from each treatment account as the inputs into
a random eects model.
The first two steps are implemented into a workflow by
the MarketMatching package. 1.
3.3 Choosing Control Accounts
The first step in obtaining an estimate of the turf removal
impact for account triis to find accounts that did not remove
their turf that show similar behavior to tri. Candidate ac-
counts were identified by choosing controls from within the
same zip code as tri. Within each zip code there may still
be thousands of possible controls. These remaining possibil-
ities are ranked by how similar their historical water usage
patterns are to the historical usage of tri.
Account matching is often based on variables like prop-
erty size, property value, or education levels. However, the
importance of environmental attitudes, for example aris-
ing from public awareness actions and social change has
been shown to influence water consumption (Hollis 2016).
The diculty of incorporating these and other dicult-to-
quantify factors driving household water usage, and the fairly
stable water consumption patterns observed by most house-
holds, make matching based on water consumption patterns
attractive. The premise of using historically predictive rela-
tionships between accounts to perform counterfactual anal-
ysis in this fashion has been advocated by cf. Abadie, Dia-
mond, and Hainmueller (2010) and Brodersen et al. (2015).
Let tr and cbe a treatment and control time series with m
observations each for which a similarity ranking is desired.
This similarity ranking is done as a weighted composite of
two other similarity measures. The first is the Pearson cor-
relation:
(tr, c)= Pp
t=1(trt¯
tr)(ct¯c)
pPp
t=1(trt¯
tr)2pPp
t=1(ct¯c)2(1)
The second ranks them according to their dynamic time
warping (DTW) distance from tri. To compute the warp-
ing distance between two time series, we must identify the
warping curve (t)=(tr(t),
c(t)) that has the minimum
warping distance,
D(tr, c)=
p
X
t=1
d(tr(t),
c(t))p(t),(2)
where d(tr(t),
c(t)) is the local of the points at time
tafter they have been remapped by the warping functions
tr(t) and c(t), and m(t) is per-step weight that control
the slope of the warping curve. The calculation of the DTW
distance is done using the dtw package. For details about
the package and about dynamic time warping see Giorgino
and others (2009).
Let the vector r
r
rdenote the similarity scores for Kcandi-
date control accounts ck,k =1,...,K with respect to tri,
1The code was modified and is available at
https://github.com/christophertull/MarketMatching/tree/usability-
improvements
where the kth element of r
r
ris given by:
rk=(1)(tri,c
k)+D(tri,c
k),
with 2[0,1]. Then, the control households corresponding
to the first mvalues of the sorted r
r
rare used as controls
for triin the structural time series model for that series
discussed in the next section.
3.4 Estimating Water Savings
A widely used approach for estimating the causal im-
pact of interventions, like rebate oerings, is dierences-in-
dierences. Taking this approach in the turf removal con-
text, the estimated causal impact of turf removal on water
savings is the dierence between water usage when turf was
removed, and the amount of water that would have been
used if no turf had been removed (Bamezai 1995).
To accurately estimate the reduction in water usage due
to turf removal, a model for the counterfactual case needs to
account for other variables determining water usage. Water
use is determined by a multitude of factors, such as weather,
user size, social perspectives on water usage, and turf re-
moval. Covariates like weather and user size are measured
by agencies and are straightforward to account for with a
model.
This leaves the matter of accounting for dynamic behav-
ioral patterns. Recognizing the need to address this aspect of
water use, Hollis (2016) took variables measuring media fac-
tors, like advertising volume, to explain water use patterns.
The inclusion of media presence explicitly in a usage model
is desirable, but two issues that arise with this approach are
properly quantifying media presence and accounting for the
dierent levels of exposure experienced by water users.
Another way to account for dynamic behavior is to ex-
plicitly model the counterfactual of a time series observed
both before and after the rebate and use the resulting model
to construct a synthetic control (cf. Abadie, Diamond, and
Hainmueller (2010)). The approach of Brodersen et al. (2015)
is to construct a synthetic control by combining three sources
of information using a state-space time-series model, where
one component of state is a linear regression on the contem-
poraneous predictors. The first source of information is the
behavior of the response prior to the turf removal. A second
is to use other time series that were predictive of the target
series before the turf removal. In particular, a relationship
between a time series which removed turf and others that did
not can be used to estimate a synthetic control after the re-
bate. These series allow us to account for unmodeled causes
of variance such as a general decline in water usage due to
media campaigns or mandatory reductions due to drought
restrictions. Thirdly, in a Bayesian framework prior knowl-
edge about the model parameters, from prior studies, for
example, can be used to construct the counterfactual.
We will use static regression coecients in our Bayesian
structural time series model, which assumes that the linear
usage relationship between the controls and the counterfac-
tual expected usage for customers who did remove turf from
their lawn remains fixed even after the turf is removed. Fur-
thermore, we will allow for a local linear trend. For a time
series y
y
y, this model has the form:
yt=µt
|{z}
level
+Zt
|{z}
regression
+"t,(3)
Zt=0x
x
x, (4)
µt+1 =µt+t+µ,t
| {z }
random walk and trend
,(5)
t+1 =t+,t
| {z }
random walk for trend
,(6)
where "tN(0,
2
t), µ,t N(0,
2
µ,t) and ,t N(0,
2
,t ).
The regression component, Ztcaptures the static linear rela-
tionship between the control series and the treatment series,
while the level component µtcaptures local linear trends,
enabling the model to react to unobserved sources of vari-
ability the control and treatment series are exposed to.
By placing a spike-and-slab prior on the set of regression
coecients, and by allowing the model to average over the
set of controls, it is possible to choose from many candi-
date controls (George and Mcculloch 1997). To combine
information about the target time series and the controls,
the posterior distribution of the counterfactual time series
is computed given the value of the target series in the pre-
intervention period, along with the values of the controls
in the post-intervention period. Given a predicted and ob-
served water use ˆytand yt, the dierence ˆytytyields a
semiparametric Bayesian posterior distribution for the wa-
ter savings attributable to the turf removal, which can be
used to obtain credible intervals. We take these estimates
and adjust them to gallons saved per square foot to obtain:
µgpsf: monthly gallons saved per square foot of turf
removed, calculated as
748.052 (yit ˆyit)
rebate instance quantity
where ytand ˆytare the actual and estimated usage in
hundred cubic feet (CCF) of household triat month t.
The structural time series model was fit using the CausalImpact
package provided by Google for estimating the eectiveness
of marketing campaigns (Brodersen et al. 2015). A number
of dierences exist between the Google marketing context
described in Brodersen et al. (2015), for which this approach
was originally proposed, and the turf removal rebate context.
Firstly, Google is able to assess the impact of the marketing
campaign in terms of participation using this method, where
participation is measured in number of clicks, because they
have data on number of clicks prior to the campaign. It
is in their interest to distinguish how many clicks after the
start of the campaign were driven by the campaign, as op-
posed to organic. In contrast, prior to the rebate programs,
the water districts did not track turf removal. The number
of rebate claims before the start of the rebate programs is
zero, and the number of rebates claimed afterwards is best
summarized using simple statistics.
Another dierence is that in the marketing context, the
impact to estimate is the number of clicks generated as a
consequence a marketing campaign, where a marketing cam-
paign is either active or is not. The scale of the marketing
campaign is not addressed. We could stop at estimating an
average eect of turf removal, but this neglects the impor-
tant relationship between how much water use is reduced
Parameter Values
WARPING LIMIT 0, 1
DTW EMPHASIS 0, 0.25, 0.5, 0.75, 1
NUMBER OF MATCHES 6, 12
Table 1: Parameter Values tested in sensitivity anal-
ysis.
and the amount of the turf removed. To account for this,
the estimated savings are divided by the square feet of turf
removed, as calculated by utility stain a post-rebate in-
spection. This allows for a normalized measure of rebate
impact in terms of gallons per square foot of turf removed.
Additionally, variables to quantify the magnitude of the turf
removal are included in the meta-model in the final step.
An added complexity in this study is that in place of a
single treatment cohort, or perhaps a few, hundreds of cus-
tomers participated in the rebate program. The approach
proposed in Brodersen et al. (2015) stops at providing im-
pact estimates on a single time series at a time. To obtain a
broad overview of the impact of turf removal, it is desirable
to aggregate estimates from all of the customers. In the sec-
tion that follows, this issue and the inclusion of the amount
of turf removed in our framework will be addressed using a
meta-analytic approach.
3.4.1 Example
Figure 1 below shows two examples of the process de-
scribed above. Specifically, the output of the matching pro-
cess is shown through charts of water usage over time for
the treatment household and its six closest matches. The
output of the STS model is given by showing the actual
and predicted consumption for the two examples. The ex-
ample households were chosen for their wildly dierent be-
havior patterns in the post-rebate period. One household
appears to cease outdoor watering completely after their
turf removal, causing their usage to stabilize at winter levels
and achieving an estimated 66% reduction in overall water
use. The other example household shows a decrease in usage
relative to its own past behavior, but shows no significant
reduction compared to its similarly-behaving peers. This
eect may be due to increased awareness of the California
drought and the mandatory restrictions put in place in April
2015. Thus the water savings would be attributed to behav-
ioral change among households in the region but not directly
to the removal of turf.
3.5 Parameter selection for the matching and
STS steps
A number of parameters must be chosen when applying
the matching procedure and STS model. We assessed these
in terms of their impact on the mean water savings estimates
obtained from the STS models.
A sensitivity analysis was performed to determine the ef-
fect of parameter choices at the matching stage on final es-
timates of water savings. Specifically, a random sample of
150 accounts that made it through the filtering were rerun
under all combinations of the dierent parameter configu-
rations visible in Table 3. While these are not the only
parameters in the model, they are three of the ones most
likely to impact the water savings estimates because they
directly impact the choice of control accounts.
In the STS model, the value for 2
µ,t in the local linear
trend must also be selected. This is the local level stan-
dard deviation which controls the prior standard deviation
of the local linear trend submodel. The local level term
modifies how adaptable the model is to short term changes,
and its standard deviation is important because it eects the
breadth of the posterior intervals. Brodersen et al. (2015)
recommend that the value of 0.01 can be used when the re-
lationship between the controls and the treatment is strong
enough to obtain an informative model. The authors indi-
cate that this is more likely when many control candidates
are available. The water usage data set contains a large
pool of control candidates, and matching results are typi-
cally strong. The choice of 0.01 results
After calculating savings estimates under each parameter
set, the mean of estimated savings for the sample under each
parameter set was calculated. This gives an idea of how sen-
sitive the matching process is to changes in the parameters.
These estimates are visible below in Figure 2.
Figure 2: The charts display the sensitivity of the
meta-estimate results under various values of the
DTW EMPHASIS parameter. Each chart in turn
uses a dierent warping limit or number of control
account matches.
Table 2 shows the values of the matching procedure pa-
rameters based on the results from the sensitivity analysis,
as well as required minimum observation period lengths and
matching pool sizes.
3.6 Combining the Estimates
Monthly estimated water savings attributable to turf re-
moval are obtained from each of the Bayesian STS models,
yielding a total of 10759 impact estimates for 545 house-
holds. Furthermore, a credible interval can be calculated for
each of these estimates. The aggregation of these estimates
can be seen as a meta-analysis. Before a meta-analysis is
conducted, a robust regression is performed using the same
continuous moderator variables as the meta-model to re-
move large outliers. The robust method, Least Trimmed
Squares is used with default settings as implemented by the
ltsReg function in the robustbase package. After remov-
ing outliers, a random eects model to determine an overall
meta-estimate for water savings is fitted. The meta analysis
is done using the metafor package. Details on the technique
Figure 1: The first row shows the expected and observed usage patterns for two participating rebate accounts,
where the dierence between expected and observed after removal (dashed) is the estimated savings. The
account on the left shows a visible reduction in usage compared to the counterfactual, while the right side
has more ambiguous results. The bottom row shows the raw time series of water usage for the treatment
and corresponding matched controls.
Table 2: Key Parameter choices in the modeling
process.
Parameter Value Description
Min. Months Post-
Period
12 Require at least 12 months
since the rebate took place.
Min. Months Pre-
Period
24 Require at least 24 months
before the rebate for accu-
rate matching.
Zip Sample Size 500 Randomly sample a maxi-
mum of 500 control accounts
within the zip code as possi-
ble matches.
Min. Matching Se-
ries
100 Require a pool of at least 100
possible matches within the
zip code.
Warping Limit 1 The size of the Sakoe-Chiba
band limiting how much the
time series are allowed to
warp.
DTW Emphasis 0.7 Controls the trade-obe-
tween the DTW distance
and Pearson correlation.
Number of Matches 6 The number of control ac-
counts to match with and
pass into the STS model.
and the software are available in Viechtbauer and others
(2010).
4. RESULTS
The meta-analysis we conduct to aggregate the results
from the Bayesian STS model estimates of the water savings
from the ith turf-removing household at time tis a mixed
eects model with the following fixed eects structure:
µgpsfi,t =i+0+1HH Sizei,t
+2Rebate Area Ratioi,t
+3sin(2/12Monthi,t)
+4cos(2/12Monthi,t)
+5sin(4/12Monthi,t)
+6cos(4/12Monthi,t)
+7ln(Rebate Quantityi,t)
+8ln(Irr Area Sfi,t)
+9Evapotranspirationi,t +"i,t.
(7)
where µgpsf is the monthly savings in gallons per square
foot. The trigonometric terms in the model account for sea-
sonality. Month, in this model, is a unique number for each
of the months in the study and runs from 1,...,51.
Table 3 contains the fixed eects estimates of the fitted
model. Examining the eect of household size, we see that
the more people there are in a household, the lower the im-
pact of turf removal per square foot. This is likely because
indoor water usage is larger in larger households, diminish-
ing the potential savings from outdoor water usage relative
to a similarly sized house with fewer inhabitants. Rebate
area ratio has a large negative coecient, meaning that the
larger the share of the household’s irrigable area that is re-
moved, the greater the savings. Three of the trigonometric
eects are significant, and are used by the model to capture
general seasonal trends in water savings. The positive co-
ecient of ln(Rebate Instance Quantity) means that per
foot savings are smaller as the amount of turf removed in-
creases, possibly because watering eciency increases with
larger gardens and lawns. In contrast, the greater the irri-
gable area in total, the larger the savings. This eect can be
similar to the household eect. The larger the irrigable area
of a household, the larger outdoor watering’s share of wa-
ter use, and thus the greater the impact of turf removal on
household water use per square foot of property. Lastly the
evapotranspiration (ET) coecient is negative, indicating
greater savings with increased ET.
Table 3: Fixed eect estimates for the meta-model
of turf removal water savings.
Variable Estimate SE t-stat p-value
Intercept -0.57 0.67 -0.84 0.40
HH Size 0.12 0.03 3.48 0.00
Rebate Area Ratio -3.66 0.57 -6.45 0.00
Month Sin 2 0.43 0.03 15.49 0.00
Month Cos 2 0.24 0.05 4.52 0.00
Month Sin 4 0.13 0.02 5.57 0.00
Month Cos 4 0.03 0.03 1.10 0.27
ln(Rebate Instance Quantity) 2.08 0.22 9.32 0.00
ln(Irr Area Sf) -1.77 0.22 -7.89 0.00
Evapotranspiration -0.08 0.02 -3.61 0.00
The first analysis we conduct is a comparison of average
household savings by year. We do this for the sample in this
study by using the model to predict the household savings
given their moderator variables. Predicted savings are then
grouped by household and year and averaged. The result-
ing savings estimates give an impression of the distributions
of savings outcomes that would be expected by an analyst
or policy-maker on this population. We see that annual
savings were about 20 gallons per square foot. However, by
aggregating the monthly savings to an annual level, we loose
important details about the savings patterns.
A more nuanced approach is to use the model to predict
the monthly savings. Overlaying the predictions are quan-
tiles ranging from 5% to 95%. The savings pattern illus-
trated in Figure 4 is highly intuitive. The highest savings
are in the months of July, August and September, reaching
a monthly average of -2.7 gallons per square square foot less
water use. During the months of January, February, and
March, the reduction is much smaller but still valuable at
-1.5 gallons per square foot.
4.1 Time-Series vs. Traditional Matching
One remaining question of interest is whether time series
matching on historical usage produces comparable results
to traditional matching on static attributes. In order to ad-
dress this question, the mean distance from each treatment
account to its matched control accounts was compared to the
mean distance from each treatment to its potential controls
that were not matched.
Distance was calculated by standardizing the covariates
for household size and irrigable area within each zip code
and customer class. The mean euclidean distance was then
calculated between the treatment and each of the matched
and unmatched groups. The results of this calculation are
Figure 4: Predicted monthly savings for each household in the data set. The dark green line corresponds
to median savings. Seasonal variation leads to swings in average savings from -1.5 to -2.7 gallons per square
foot.
visible in Figure 5. One can see that matching on usage pat-
terns tends to result, on average, in matches that are also
similar in their household size and irrigable area. However,
this was not universally true and manual inspection revealed
a large variation even among the matched control accounts.
This aligns with the intuition that static covariates do not
capture all aspects of water usage, and that dissimilar ac-
counts may have very similar water usage patterns.
5. CONCLUSIONS
This methodology enables estimation of the water sav-
ings associated with turf removal using very minimal data
by requiring only observational water use over time and a
bare minimum of contextual customer attribute data. Many
other approaches rely on extensive lists of covariates that are
at best proxies for water use behavior. This work matches
on observed behavior directly and thereby attempts to incor-
porate the complexities of individual customer conservation
behavior, resulting in estimates of 24.6 gallons saved per year
per square foot of turf removed. Boostrapped standard er-
rors of those predicted water savings are .11 gallons per year
per square foot of turf removed. Those water savings are sta-
ble across district and vary sinusoidally over time highlight-
ing the structural water savings of turf market transforma-
tion for regional and statewide water reliability initiatives.
At $2 paid per square foot turf removed and assuming a hy-
perbolic discount rate of five percent over a landscape con-
version lifespan of thirty years, that translates into a present
value of $1422 plus or minus seven dollars per acre foot of
water saved.
Still, these results should be considered an early data point
measuring the eects of the generational shift away from wa-
ter intensive lawns as the default landscaping. Landscape
conversions can involve up to a two year period for new
drought tolerant plants to establish and thus these results
may need to be reassessed in the future. In addition, the
turf rebate program has some uncertainty regarding the ex-
act timing of turf removal introducing additional variation
into these estimates. Furthermore, this study lacks data on
whether artificial turf or California native or other non-turf
landscaping were implemented after the rebate. Fortunately
the simple data requirements of this method make it easy
to redeploy on regularly updated customer use, rebate, cus-
tomer survey and other creative data sources such as aerial
remote sensing. This is the approach being pioneered by
the California Data Collaborative utilities in this study and
others as they centralize water use data. It enables water
managers to measure the water savings with turf removal
over time and adaptively manage this historic investment in
turf removal.
Measuring savings at the household level also allows wa-
ter managers to target educational materials on ecient wa-
tering practices to customers that have seen dis-savings in
the post rebate period compared to their expected coun-
terfactual water use. Finally, the approach can be used to
evaluate the water savings associated with other conserva-
tion rebates, other customer-level demand management in-
terventions, and potentially other natural resource conser-
vation programs in energy or natural gas. As the old adage
Figure 3: Average yearly savings for each household
over 5 years.
goes, “you cannot manage what you cannot measure” and
such rigorous impact evaluations can help California’s public
managers navigate the uncertain future we face with climate
change.
6. ACKNOWLEDGEMENTS
This research was funded through the California Data Col-
laborative. The authors would like to thank the Moulton
Niguel, Irvine Ranch, Eastern Municipal, Las virgenes Mu-
nicipal, Santa Margarita, and Monte Vista water districts,
along with the Inland Empire Utilities Agency, the East Bay
Municipal Utility District, and the Metropolitan Water Dis-
trict of Southern California for all of their support. The
authors also thank Michael Hollis for his thoughtful com-
ments and peer review.
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Towards California Water Conservation Impact Evaluations by Default: Lessons of a Turf Removal Rebate Study in South Orange County
  • Patrick Atwater
  • Eric Schmitt
  • Drew Atwater
Atwater, Patrick, Eric Schmitt, and Drew Atwater. 2015. "Towards California Water Conservation Impact Evaluations by Default: Lessons of a Turf Removal Rebate Study in South Orange County." Bamezai, Anil. 1995. "Application of Di↵erence-in-Di↵erence Techniques to the Evaluation of Drought-Tainted Water Conservation Programs." Evaluation Review 19 (5). Sage Publications: 559-82.
Conducting Meta-Analyses in R with the Metafor Package
  • Mitchell Morgan
Mitchell Morgan. 2005. "Xeriscape Conversion Study." Final Report. Sout. Viechtbauer, Wolfgang, and others. 2010. "Conducting Meta-Analyses in R with the Metafor Package." Journal of Statistical Software 36 (3): 1-48.
California Friendly Turf Replacement Incentive Program Southern California, Appendix E Water Savings from Turf Replacement, Resource Analyst Unit. The Metropolitan Water District of Southern California. MWD
  • Peter Mayer
  • Paul Lander
  • Diana Glenn
Mayer, Peter, Paul Lander, and Diana Glenn. 2015. Outdoor Water Savings Research Initiative, Phase 1-Analysis of Published Research. 300 W. Adams Street, Suite 601, Chicago, Illinois 60606-5109: Alliance for Water Eciency. MWD. 2014. California Friendly Turf Replacement Incentive Program Southern California, Appendix E Water Savings from Turf Replacement, Resource Analyst Unit. The Metropolitan Water District of Southern California. MWD. 2016. 2015 Urban Water Management Plan, Draft March 2016. The Metropolitan Water District of Southern California.
Southern Nevada Water Authority, and
  • Kent A Sovocool
Sovocool, Kent A, Southern Nevada Water Authority, and