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Article

Water Saving and Cost Analysis of Large-Scale

Implementation of Domestic Rain Water Harvesting

in Minor Mediterranean Islands

Alberto Campisano * ID , Giuseppe D’Amico and Carlo Modica

Department of Civil Engineering and Architecture, University of Catania, Viale Andrea Doria, 6,

95125 Catania, Italy; info.giuseppedamico@gmail.com (G.D.-A.); cmodica@dica.unict.it (C.M.)

*Correspondence: acampisa@dica.unict.it; Tel.: +39-095-738-2730

Received: 20 October 2017; Accepted: 22 November 2017; Published: 25 November 2017

Abstract:

This paper describes a novel methodology to evaluate the beneﬁts of large-scale installation

of domestic Rain Water Harvesting (RWH) systems in multi-story buildings. The methodology was

speciﬁcally developed for application to small settlements of the minor Mediterranean islands

characterized by sharp ﬂuctuations in precipitation and water demands between winter and

summer periods. The methodology is based on the combined use of regressive models for water

saving evaluation and of geospatial analysis tools for semi-automatic collection of spatial information

at the building/household level. An application to the old town of Lipari (Aeolian islands) showed

potential for high yearly water savings (between 30% and 50%), with return on investment in less

than 15 years for about 50% of the installed RWH systems.

Keywords:

rainwater harvesting; minor islands; water saving; cost analysis; surrogate models;

geospatial analysis

1. Introduction

Several countries of the Mediterranean are involved in ﬁnding proper solutions to problems

related to the management of scarce water resources under the concurrent increase in water demands.

The growing use of water in urban areas pushes cities and water managers to look for alternative

sources of renewable water.

Problems associated with water scarcity are very sharp in urban contexts of minor islands,

where the water availability is normally limited by the small size of river catchments.

Further difﬁculties in water supply management of minor Mediterranean islands are associated

with the high ﬂuctuations of the population (and water demand) between spring/summer and

autumn/winter seasons due to the incoming/outgoing touristic ﬂuxes.

During the last decades, non-conventional water supply based on the use of water tankers and/or

desalination has contributed in a major way to meet drinking water demands in several archipelagos of

the Mediterranean Sea (including, among others, the Aeolian islands, the Pelagian islands, and Cyprus).

However, because of the high operational costs, the ﬁnancial sustainability of such water sources in

the medium to long term remains highly questionable [1].

In the search for alternative water sources, Rain Water Harvesting (RWH) has been acknowledged

as a valid approach to reduce drinking water consumption in urban areas [

2

–

4

]. In particular,

several literature studies developed in various countries point out the high potential of domestic

RWH systems to meet non-potable water demands in private and public buildings [

4

–

7

]. Typical RWH

systems consider rainwater reclamation from building rooftops, storage within a tank, and use of the

harvested rainwater for a number of non-potable indoor uses (e.g., ﬂushing of toilets, laundry, etc.)

and outdoor uses (e.g., garden irrigation, car washing, etc.) [8–10].

Water 2017,9, 916; doi:10.3390/w9120916 www.mdpi.com/journal/water

Water 2017,9, 916 2 of 14

The quantiﬁcation of water saving beneﬁts associated with the implementation of domestic

RWH has been carried out broadly at the scale of the single-family household [

11

–

14

], but also

with reference to multi-story/multi-family buildings (i.e., the collected rainwater is shared within

the building)

[3,14,15]

. Much emphasis has been placed on the ﬁnancial issues of the system

implementation [

2

,

11

,

15

,

16

]. The ﬁnancial viability of single RWH systems has been assessed using

various approaches, including comparative analysis with other water supply strategies [

17

,

18

].

Methods based on the use of undersized tanks for low water demand conditions have also been

explored to obtain an affordable payback period of the investments [

19

]. However, although

large efforts have been spent over the decades to evaluate the beneﬁts of RWH at the scale of a

single installation, little attention has been paid to exploring the water saving potential derived from

the implementation of RWH at larger spatial scales (e.g., district or city levels). One of the main

reasons for this knowledge gap is probably the lack of reliable and computationally cheap methods for

obtaining general validity for the systematic evaluation of large-scale implementation scenarios.

In fact, such an evaluation requires detailed information on a range of site-speciﬁc parameters

(e.g., building characteristics, type of dwellers, type of water demands, etc.) whose values have

a signiﬁcant impact on the system performance [

8

,

20

]. Early recent examples of methods used to

estimate domestic RWH beneﬁts from the household level include the coupled use of water saving

evaluators and geospatial analysis tools for semi-automatic characterization of building characteristics

in urban areas [

6

,

10

]. However, the available methods have been developed assuming operational

schemes of RWH that are valid for single-family/single-household installations only. In addition,

the sensitivity of rainfall and demand seasonal variations on the system performance has not been

explored exhaustively.

The objective of this paper is to evaluate the potential beneﬁts of large-scale implementation of

domestic RWH as an alternative water supply approach for non-potable use in buildings of urban

settlements of minor islands of the Mediterranean. A novel methodology was developed to evaluate

the water saving performances of multi-story/multi-family RWH systems for toilet ﬂushing use.

The methodology combines the use of non-dimensional surrogate regressive models for water saving

evaluation at the building level and the use of geospatial databases for archiving building/household

information through classiﬁcation routines of high quality imagery in urban contexts. The methodology

was applied to the old town of Lipari (the largest municipality of the homonymous Aeolian island),

which shows intra-annual patterns of precipitation and water demand characterized by sharp

variations between wet and dry seasons.

2. Methods

2.1. Model Framework

Among the design parameters of RWH systems, the size of the rainwater tank is probably the

most important [21].

In the past years, much research effort has been made to identify the optimal size of the

rainwater tank. Available methodologies typically combine the results of models for the evaluation

of the water saving and the cost of the system for a set of tank sizes. Models based on the water

balance simulation of the tank have been used with large success due to their ability to appropriately

describe rainfall and water demand dynamics in the long term [

21

]. The evaluation of large-scale

RWH implementation projects introduces uncertainty due to the spatial variability of buildings and

population-related characteristics, as well as of precipitation and water demand [

22

]. As a consequence,

the application of water balance models for each household/building of the area under study is often

an unpractical option, as it would require very high computational efforts.

A possibility to overcome this problem may be provided by the increase in the

computational resources, for instance through the use of grid/cloud-based computing techniques [

23

].

Alternatively, simpliﬁed approaches have been proposed in the literature by References [

24

,

25

],

Water 2017,9, 916 3 of 14

who performed the simulation of a reduced number of typical demand scenarios and/or selected

“equivalent” buildings representative of all building types in the study area. However, such approaches

do not eliminate the existing uncertainty, thus requiring the provision of a robust sensitivity analysis

of the results [6].

More recently, Reference [

10

] dealt with this issue, and proposed a methodology to overcome the

computational impact associated with the recurrent use of water balance models. The methodology is

based on the development of surrogate models for the accurate evaluation of RWH system performance

based on the results of dimensionless water balance simulations. A similar approach was used in

this paper, through the adaptation of the model by Reference [

10

] to the analysis of RWH schemes

under scenarios characterized by multi-family/multi-story buildings with high seasonal water demand

ﬂuctuation during the year.

The model tracks the dimensionless water balance of the tank using the Yield-After-Spillage (YAS)

algorithm as a tank release rule [10,21], through the numerical solution of the following equations:

vt=vt−1+qt−yt−ot(1)

yt=min(dt

vt−1

(2)

ot=max(vt−1+Rt

R−s

0(3)

where the following dimensionless variables are considered:

vt=Vt

A·R(dimensionless volume in store at time t)

vt−1=Vt−1

A·R(dimensionless volume in store at time t−1)

qt=Qt

A·R(dimensionless tank inﬂow at time t)

yt=Yt

A·R(dimensionless yield from the tank at time t)

ot=Ot

A·R(dimensionless tank overﬂow at time t)

dt=Dt

A·R(dimensionless toilet water demand at time t)

The previous dimensionless variables are obtained by normalization to

A·R

of the volumes in

store

Vt

(m

3

) and

Vt−1

(m

3

), the tank inﬂow

Qt=A·Rt

(m

3

), the yield

Yt

(m

3

), the overﬂow

Ot

(m

3

),

as well as of the demand

Dt

(m

2

), with A(m

3

) being the rooftop catchment area for rainwater collection.

Assuming the daily time scale to be adopted for the water balance analysis [

26

], R(m) is the average

daily rainfall during the whole simulation period (e.g., 30 years).

The daily time scale resolution of Equations (1)–(3) requires the availability of long-term series of

daily precipitation

Rt

and water demands

Dt

for toilet ﬂushing as input information representing the

available rainwater and consumption patterns in the building. While values of

Rt

are usually provided

by local meteorological ofﬁces, the expected daily demands

Dt

for toilet ﬂushing at the building level

can be estimated as [10]:

Dt=nd·nf t·F(4)

where

nd

is the number of dwellers in the building,

nf t

is the number of toilet ﬂushes per capita per

day t, and F(m

3

) is the volume of the toilet cistern (volume of the ﬂush). Values of

nd

can be estimated

as the ratio between the building total ﬂoor surface area and the average per capita living surface

(net surface area in m2attributed to each dweller).

Under the availability of daily patterns of precipitation and water demand for toilet ﬂushing,

the behavior of the rainwater tank can be studied, based on the following two dimensionless

parameters [21]:

s=S

A·R(storage fraction; S[m3]is the rainwater tank capacity)(5)

Water 2017,9, 916 4 of 14

d=D

A·R(demand fraction; D[m3]is the average daily toilet demand in the building)(6)

2.2. Surrogate Regressive Models for Water Saving Evaluation

The described dimensionless approach has the advantage of allowing the analysis of the results

of the tank water balance simulations as a function of the dimensionless parameters of storage (s) and

demand (d) fractions.

The results of the non-dimensional simulations provide the water saving performance of the RWH

system for toilet ﬂushing use in the generic building for each year nof the whole simulation period:

WSn=∑365

t=1yt

∑365

t=1dt

·100 [%](7)

Obtained yearly values of

WSn

can be elaborated to evaluate water saving values corresponding to

preﬁxed yearly reliability levels f(frequency of exceedance) (e.g., f= 0.9 means that the corresponding

value of water saving has been achieved for at least 90% of the years). For all of the simulations,

water saving values were calculated with reference to both the average value in the year and the dry

(April–September) and wet (October–March) seasons, separately.

Simulations were run for scenarios of storage and demand fractions that include the whole range

of variation of building types and of precipitation and water demand characteristics in the selected area.

Simulation results were statistically elaborated in order to set up compact regression relationships that

relate the water saving to s,d, and f. The developed relationships provide surrogate models of the

water balance model used for the simulations, thus enabling quicker evaluation of the water saving

performance of RWH installations at the desired spatial scale. Surrogate models were determined

for the estimation of the average yearly water saving, as well as for the wet (October–March) and

dry seasons, separately, using the following multiple-regression form:

WS =x1·s

x2+s·dx3·fx4[%](8)

where x1,x2,x3, and x4are the calibration coefﬁcients of the regressive models.

2.3. Geospatial Database Setup

Practical advantages of the described approach can be exploited if Equation (8) is used in

combination with a database containing geospatial information concerning detailed building and

population characteristics in the area of implementation.

Accordingly, a geodatabase was built for the systematic collection and archiving of information

including buildings location, building type, height of the construction, number of stories,

type of rooftop, roof collection catchment surface, etc. Data on the building characteristics were

obtained through the combined use of techniques to process high resolution satellite imagery covering

the study area and other internet available software tools (e.g., Google Street View). Surfaces of

the building rooftops were determined by means of automatic extraction methods applied to the

satellite images. Extraction of polygons associated with the identiﬁed rooftops was followed by

post-processing through QGIS software (open source version 3.0), assuming each building polygon

to coincide with the corresponding roof. Using the previous procedure, each identiﬁed rooftop

was univocally associated with a single building. Only residential/private buildings were taken

into account in the analysis, whereas rooftops of public buildings and industrial plants were

excluded from calculations. Additionally, rooftops with extensions smaller than a selected threshold

(e.g., car boxes, small shelters, etc.) were also excluded from the database. Acquired information was

integrated with the data collected during a one-week-long survey in the study area with the objective

of collecting data for buildings in those secondary streets which were not inspected through Google

Street View software (Google Inc., Mountain View, CA, USA). Information on population distribution

Water 2017,9, 916 5 of 14

during the year in the study area was provided by the municipality. Such information was used to

verify the global consistency of calculated values of

nd

for each “building” record in the area during

the whole year.

2.4. Cost-Beneﬁt Analysis

Cost-beneﬁt analysis was carried out for the evaluation of the optimal tank size for each building.

The considered cost items included capital, operational, and maintenance costs.

Capital costs Cc(€) were evaluated as:

Cc=Cs+Ci(9)

where

Cs=α+β·S

(

€

) is the cost of the system equipment (e.g., tank, pump, dual network pipes, etc.),

with

α

(

€

) and

β

(

€

/m

3

) being coefﬁcients usually provided by the tank manufactures, and

Ci

is the

installation cost (€).

Operational costs include the annual cost of drinking water from mains (

€

) used for toilet ﬂushing

when the rainwater tank is empty:

CYWM =CW·1−WS

100 ·D·365 (10)

where

CW

(

€

/m

3

) is the unit cost of drinking water. Operational costs also include the cost of energy

(€) for pumping rainwater from the tank to the toilets in the building:

CYE =CE·WS

100 ·D·365 (11)

where CE(€/m3) is the cost of energy per cubic meter of pumped rainwater.

Maintenance costs (

€

) include pump replacement in case of failure, replacement of ﬁlters,

and eventual costs for chlorination, and are calculated as a percent γof the capital costs:

CM=γ·Cc(12)

The present value of the total costs (capital and recurring costs) was calculated according to the

following relationship [27]:

PV =Cc+(1+r)N−1

r·(1+r)N·[CYWM +CYE +CM](13)

where ris the discount rate and Nis the number of years of the considered period of analysis (normally

coinciding with the average life of the system).

Based on Equation (13), the minimization of PV provides the optimal tank size:

Sopt =A·R·v

u

u

t

[(1+r)N−1]·x1·x2·365·(Cw−CE)

100·β·[(1+r)N·(r+γ)−γ]

·d0.5(1+x3)·f0.5·x4−x2(14)

In addition, for each building, the payback period Tof the investment was calculated as the ratio

between capital costs and savings (from replaced drinking water):

T=Cc

Cw·D·365·CYW M·CYE ·CM

(15)

Water 2017,9, 916 6 of 14

3. Case Study

The described methodology was applied to the old town of Lipari, the largest municipality of the

Aeolian archipelago in southern Italy.

The selected urban area has a total surface of about 0.45 Km

2

, and hosts an average residential

population in the wet season of about 6500 (with a minimum value of about 4000 in January) rising

during spring/summer (average value 9900) up to a peak of about 12,000 in the month of August.

The average population during the whole year is close to 8200. Figure 1shows the monthly pattern of

the population in the area (normalized to the mean yearly value) as extrapolated by the data provided

by the municipality (for residents) and by the census of year 2013. Additional data concerning

the touristic ﬂuxes in the island were provided by the Regional Department for Tourism, Sport,

and Events [28].

Water 2017, 9, 916 6 of 14

Figure 1. Monthly population normalized to mean yearly population for the island of Lipari.

Drinking water distributed in the selected area is supplied through mixing of water from

tankers and desalination at an average cost of = 4.17 (€/m3) throughout the year.

The area is characterized by a dense urban fabric with relatively small streets and buildings

typically made of 1 to 3 stories that usually include commercial/craft activities/shops at the ground

level and 1–6 single-family apartments on the upper floors. A few larger buildings (of more recent

construction) are present in the northern part of the selected area. In many cases, each whole building

belongs to the same owner, thus representing a single household (one single water meter installed) for

the water distribution company. Similar to several urban areas in southern Italy, most (over 95%) of

the existing building covers are terraces with very small longitudinal slope (1–2%), thus highlighting

high potential for the exploitation of catchment surfaces for rainwater collection. Based on the

characteristics of the building and on the described population patterns, the selected urban area of

Lipari evidently represents a good test case for the application of the methodology proposed in this

paper.

Results of the census of year 2013 in combination with data obtained from the direct survey in

the area allowed to estimate the average values of the per capita living surface, thus enabling the

evaluation of the number of dwellers for each building during the year. In absence of direct

measurements of water demand for toilet flushing in the area of Lipari, six flushes per day were

assumed as the per-capita daily pattern of , as suggested by the results of field experiments by

Reference [29] concerning six households in Sicily. Additionally, low consumption toilet cisterns (F =

6 L) were considered in the analysis for a prudent evaluation of the water saving from RWH

implementation in the study area.

Precipitation data was provided by the Regional Agency for Water and Waste [30] and

consisted of a 53-year-long series of daily precipitation recorded at Lipari meteorological station

during the period of 1920–1994. Data showed a distribution of monthly precipitation typical of the

Mediterranean islands (see Figure 2) with a wet semester in the period of October–March and a long

dry summer period (with minimum precipitation values in July–August). The average total annual

precipitation value over the 53 years is about 605 mm.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Monthly popul./ mean yearly popul.

Month

Figure 1. Monthly population normalized to mean yearly population for the island of Lipari.

Drinking water distributed in the selected area is supplied through mixing of water from tankers

and desalination at an average cost of CW= 4.17 (€/m3) throughout the year.

The area is characterized by a dense urban fabric with relatively small streets and buildings

typically made of 1 to 3 stories that usually include commercial/craft activities/shops at the ground

level and 1–6 single-family apartments on the upper ﬂoors. A few larger buildings (of more recent

construction) are present in the northern part of the selected area. In many cases, each whole building

belongs to the same owner, thus representing a single household (one single water meter installed) for

the water distribution company. Similar to several urban areas in southern Italy, most (over 95%) of the

existing building covers are terraces with very small longitudinal slope (1–2%), thus highlighting high

potential for the exploitation of catchment surfaces for rainwater collection. Based on the characteristics

of the building and on the described population patterns, the selected urban area of Lipari evidently

represents a good test case for the application of the methodology proposed in this paper.

Results of the census of year 2013 in combination with data obtained from the direct survey

in the area allowed to estimate the average values of the per capita living surface, thus enabling

the evaluation of the number of dwellers for each building during the year. In absence of direct

measurements of water demand for toilet ﬂushing in the area of Lipari, six ﬂushes per day were

assumed as the per-capita daily pattern of

nf t

, as suggested by the results of ﬁeld experiments by

Reference [

29

] concerning six households in Sicily. Additionally, low consumption toilet cisterns

(F= 6 L) were considered in the analysis for a prudent evaluation of the water saving from RWH

implementation in the study area.

Precipitation data was provided by the Regional Agency for Water and Waste [

30

] and consisted

of a 53-year-long series of daily precipitation recorded at Lipari meteorological station during the

period of 1920–1994. Data showed a distribution of monthly precipitation typical of the Mediterranean

Water 2017,9, 916 7 of 14

islands (see Figure 2) with a wet semester in the period of October–March and a long dry summer

period (with minimum precipitation values in July–August). The average total annual precipitation

value over the 53 years is about 605 mm.

Water 2017, 9, 916 7 of 14

Figure 2. Monthly precipitation pattern. Lipari meteo-station (averages in the period 1920–1994).

4. Results and Discussion

4.1. Water Balance Simulation Scenarios and Surrogate Models for Water Saving Evaluation

The daily scale water balance of the rainwater tank was carried out for a number of scenarios

that include different combinations of values of the storage fraction s and of the demand fraction d.

Combinations were identified based on the ranges of values for practical applications. In total, 25

dimensionless simulations were run that included values of s between 0 and 40, and values of d

between 0.2 and 4.0. The use of such ranges allowed to take into account multi-story/multi-family

buildings with rooftop catchment surfaces between 25 and 500 m2, runoff coefficients between 0.8

and 0.9, and 1–6 apartments per building. Moreover, as for the RWH system characteristics, such

ranges included scenarios with 0.1–15 m3 rainwater tanks, 1–6 dwellers per apartment, 1–2 toilets

with 6–9 liters toilet cisterns per apartment, and 5–8 flushes per capita per day. Simulations

characterized by d > 1 were considered to represent scenarios of high demand conditions (in summer

periods), when the daily demand for toilet flushing in the building usually exceeds the collected

rainfall volume.

The results of the water balance simulations were elaborated in order to evaluate the water

saving performance of the RWH scheme for the whole year, and for the wet and dry seasons

separately. In particular, yearly frequency distributions of water savings were calculated based on

values of WS for each year of the simulation period, with subsequent estimation of WS values

corresponding to yearly reliabilities f of 0.5, 0.75, and 0.9.

The graphs of Figure 3 show, as an example, the results of the simulations for the average yearly

and seasonal water savings for f = 0.9. The graphs point out the typical (non-linear) increasing trend

of the water saving curves as the value of the storage fraction increases (i.e., as the tank capacity

increases and the rainfall volume decreases).

In agreement with previous results from the literature [7,9], the slope of the curves quickly

reduces as s increases, thus showing minor marginal benefits for values of s larger than 20 for the

whole year (Figure 3a) and 10 for the wet season (Figure 3b). Also, the decrease in demand fraction d

provides increasingly larger water saving benefits (minor demand for toilet flushing means

improved possibility to fully satisfy it through rainwater). As expected, the water saving

performance is maximized in the wet period (with values up to 70% for d = 1 and s = 40), while

reduced performances (WS in the order of 30%) are expected with the same value of s in the dry

season, due to reduced rainwater availability in the tank (Figure 3c). Relevantly, this result shows

the impact of the local precipitation pattern (long dry summers with variable rainy winter periods)

on the system performance in the studied area with relatively small values of WS for cases

characterized by d > 1.

0

10

20

30

40

50

60

70

80

90

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Monthly precipitation [mm]

Month

Figure 2. Monthly precipitation pattern. Lipari meteo-station (averages in the period 1920–1994).

4. Results and Discussion

4.1. Water Balance Simulation Scenarios and Surrogate Models for Water Saving Evaluation

The daily scale water balance of the rainwater tank was carried out for a number of scenarios

that include different combinations of values of the storage fraction sand of the demand fraction d.

Combinations were identiﬁed based on the ranges of values for practical applications. In total,

25 dimensionless simulations were run that included values of sbetween 0 and 40, and values of d

between 0.2 and 4.0. The use of such ranges allowed to take into account multi-story/multi-family

buildings with rooftop catchment surfaces between 25 and 500 m

2

, runoff coefﬁcients between 0.8

and 0.9, and 1–6 apartments per building. Moreover, as for the RWH system characteristics, such ranges

included scenarios with 0.1–15 m

3

rainwater tanks, 1–6 dwellers per apartment, 1–2 toilets with

6–9 liters toilet cisterns per apartment, and 5–8 ﬂushes per capita per day. Simulations characterized by

d> 1 were considered to represent scenarios of high demand conditions (in summer periods), when the

daily demand for toilet ﬂushing in the building usually exceeds the collected rainfall volume.

The results of the water balance simulations were elaborated in order to evaluate the water saving

performance of the RWH scheme for the whole year, and for the wet and dry seasons separately.

In particular, yearly frequency distributions of water savings were calculated based on values of WS

for each year of the simulation period, with subsequent estimation of WS values corresponding to

yearly reliabilities fof 0.5, 0.75, and 0.9.

The graphs of Figure 3show, as an example, the results of the simulations for the average yearly

and seasonal water savings for f= 0.9. The graphs point out the typical (non-linear) increasing trend of

the water saving curves as the value of the storage fraction increases (i.e., as the tank capacity increases

and the rainfall volume decreases).

In agreement with previous results from the literature [

7

,

9

], the slope of the curves quickly reduces

as sincreases, thus showing minor marginal beneﬁts for values of slarger than 20 for the whole year

(Figure 3a) and 10 for the wet season (Figure 3b). Also, the decrease in demand fraction dprovides

increasingly larger water saving beneﬁts (minor demand for toilet ﬂushing means improved possibility

to fully satisfy it through rainwater). As expected, the water saving performance is maximized in the

wet period (with values up to 70% for d= 1 and s= 40), while reduced performances (WS in the order

of 30%) are expected with the same value of sin the dry season, due to reduced rainwater availability

in the tank (Figure 3c). Relevantly, this result shows the impact of the local precipitation pattern

Water 2017,9, 916 8 of 14

(long dry summers with variable rainy winter periods) on the system performance in the studied area

with relatively small values of WS for cases characterized by d> 1.

Water 2017, 9, 916 8 of 14

(a) (b)(c)

Figure 3. Water saving performance of the RWH system scheme for frequency of exceedance f = 0.9:

(a) average yearly values; (b) average values for the wet season; (c) average values for the dry season.

The calibration of surrogate models (Equation (8)) provided the results summarized in Table 1.

The table shows the high sensitivity of WS to the storage fraction s (high variability of x1 and x2).

Conversely, the highest sensitivity of WS to the demand fraction d is obtained during the dry season

(the highest value of exponent x3). Importantly, the coefficient of determination (R2) and the mean

absolute error (MAE) show a high degree of fitness, thus assuring reliable and accurate use of the

obtained surrogate models for large-scale evaluation of the RWH system performance.

Table 1. Results of the calibration of the surrogate models.

Validity x1 x2 x3 x4 R2 MAE

Whole year 45.547 3.052 −0.492 −0.264 97.04 3.60

Wet season 61.485 1.729 −0.357 −0.306 92.16 7.34

Dry season 32.461 6.997 −0.715 −0.383 97.44 2.53

4.2. Geodatabase Construction

The geodatabase of the buildings in the area of study was populated with spatial information

concerning the position of the buildings in the catchment, their typology (residential, public,

commercial, etc.), the number of floors, as well as the surface area of the building rooftop.

The spatial resolution of the satellite imagery employed (2 m × 2 m) led to a very accurate

identification of the buildings. Preliminarily, 1301 buildings were identified within the boundaries

of the selected area. A refinement step was carried out to eliminate the buildings with rooftop

polygon surfaces larger than 500 m2. This step allowed to exclude industrial/commercial warehouses

from the dataset, as they are not target buildings for the installation of domestic RWH systems.

Moreover, information obtained through the use of Google Street View and the results of the local

surveys were used to associate isolated rooftops smaller than 25 m2 (shed covers, car box roofs, etc.)

with the closest/adjacent building (about 15% of the rooftops). Finally, a consistency check was

carried out to verify each rooftop catchment to be associated with one building only, and vice versa.

Globally, 984 buildings were archived in the structural geodatabase for the successive calculation of

RWH potential in the selected area. Figure 4 shows a picture of the area under study with all of the

building rooftops that were identified for the analysis.

Information stored in the geodatabase for each “building” record was completed with data

concerning the expected number of dwellers and the estimated water demand for toilet flushing use

in the building.

d=0.2 d=0.5 d=1.0 d=2.0 d=4.0

0

10

20

30

40

50

60

70

80

90

100

0 10203040

WS

[%}

s[-]

0

10

20

30

40

50

60

70

80

90

100

0 10203040

WS

[%}

s[-]

0

10

20

30

40

50

60

70

80

90

100

0 10203040

WS

[%}

s[-]

Figure 3.

Water saving performance of the RWH system scheme for frequency of exceedance f= 0.9:

(

a

) average yearly values; (

b

) average values for the wet season; (

c

) average values for the dry season.

The calibration of surrogate models (Equation (8)) provided the results summarized in Table 1.

The table shows the high sensitivity of WS to the storage fraction s(high variability of x

1

and x

2

).

Conversely, the highest sensitivity of WS to the demand fraction dis obtained during the dry season

(the highest value of exponent x

3

). Importantly, the coefﬁcient of determination (R

2

) and the mean

absolute error (MAE) show a high degree of ﬁtness, thus assuring reliable and accurate use of the

obtained surrogate models for large-scale evaluation of the RWH system performance.

Table 1. Results of the calibration of the surrogate models.

Validity x1x2x3x4R2MAE

Whole year 45.547 3.052 −0.492 −0.264 97.04 3.60

Wet season 61.485 1.729 −0.357 −0.306 92.16 7.34

Dry season 32.461 6.997 −0.715 −0.383 97.44 2.53

4.2. Geodatabase Construction

The geodatabase of the buildings in the area of study was populated with spatial information

concerning the position of the buildings in the catchment, their typology (residential, public,

commercial, etc.), the number of ﬂoors, as well as the surface area of the building rooftop.

The spatial resolution of the satellite imagery employed (2 m

×

2 m) led to a very accurate

identiﬁcation of the buildings. Preliminarily, 1301 buildings were identiﬁed within the boundaries

of the selected area. A reﬁnement step was carried out to eliminate the buildings with rooftop

polygon surfaces larger than 500 m

2

. This step allowed to exclude industrial/commercial warehouses

from the dataset, as they are not target buildings for the installation of domestic RWH systems.

Moreover, information obtained through the use of Google Street View and the results of the local

surveys were used to associate isolated rooftops smaller than 25 m

2

(shed covers, car box roofs, etc.)

with the closest/adjacent building (about 15% of the rooftops). Finally, a consistency check was

carried out to verify each rooftop catchment to be associated with one building only, and vice versa.

Globally, 984 buildings were archived in the structural geodatabase for the successive calculation of

RWH potential in the selected area. Figure 4shows a picture of the area under study with all of the

building rooftops that were identiﬁed for the analysis.

Information stored in the geodatabase for each “building” record was completed with data

concerning the expected number of dwellers and the estimated water demand for toilet ﬂushing use in

the building.

Water 2017,9, 916 9 of 14

Water 2017, 9, 916 9 of 14

Figure 4. Area under study with identification of the buildings for RWH system installation.

Figure 5 shows the spatial variation of the water demand for toilet flushing use in the area for

the wet (Figure 5a) and the dry (Figure 5b) seasons, respectively. The comparison between the

figures shows the increase in water demand during spring/summer due to the growing population

in the area. As expected, the larger water demand was obtained for the larger buildings in the

northern outskirt.

(a) (b)

Figure 5. Spatial distribution of water demand for toilet flushing for: (a) wet season; (b) dry season.

Systematic evaluation of the optimal tank size for each building was carried out using Equation

(14). Calculations were carried out, considering the values of x1, x2, x3, and x4 obtained for the whole

year and considering f = 0.5. Local values of cost parameters = 4.17 (€/m3]), and =0.02 (€/m3)

were also assumed. Moreover, the official regional price list for public and private works was used

to determine the values of =0.02, and β

= 82.0 (€/m3) for all of the installations. The sensitivity of

the results to parameters r and N was analyzed by applying Equation (14) for r = 3% and r = 6% and

for values of N between 15 and 25 years.

As an example of the results, Figure 6 shows the comparison between the distributions of the

optimal tank sizes (r = 3%) for N = 15 and N = 25 years, respectively. Interestingly, a limited

sensitivity of the results to N is highlighted, with a prevalence of tank sizes in the range of 2–5 m3

irrespective of the value of N used for the calculations.

Figure 4. Area under study with identiﬁcation of the buildings for RWH system installation.

Figure 5shows the spatial variation of the water demand for toilet ﬂushing use in the area for

the wet (Figure 5a) and the dry (Figure 5b) seasons, respectively. The comparison between the ﬁgures

shows the increase in water demand during spring/summer due to the growing population in the area.

As expected, the larger water demand was obtained for the larger buildings in the northern outskirt.

Water 2017, 9, 916 9 of 14

Figure 4. Area under study with identification of the buildings for RWH system installation.

Figure 5 shows the spatial variation of the water demand for toilet flushing use in the area for

the wet (Figure 5a) and the dry (Figure 5b) seasons, respectively. The comparison between the

figures shows the increase in water demand during spring/summer due to the growing population

in the area. As expected, the larger water demand was obtained for the larger buildings in the

northern outskirt.

(a) (b)

Figure 5. Spatial distribution of water demand for toilet flushing for: (a) wet season; (b) dry season.

Systematic evaluation of the optimal tank size for each building was carried out using Equation

(14). Calculations were carried out, considering the values of x1, x2, x3, and x4 obtained for the whole

year and considering f = 0.5. Local values of cost parameters = 4.17 (€/m3]), and =0.02 (€/m3)

were also assumed. Moreover, the official regional price list for public and private works was used

to determine the values of =0.02, and β

= 82.0 (€/m3) for all of the installations. The sensitivity of

the results to parameters r and N was analyzed by applying Equation (14) for r = 3% and r = 6% and

for values of N between 15 and 25 years.

As an example of the results, Figure 6 shows the comparison between the distributions of the

optimal tank sizes (r = 3%) for N = 15 and N = 25 years, respectively. Interestingly, a limited

sensitivity of the results to N is highlighted, with a prevalence of tank sizes in the range of 2–5 m3

irrespective of the value of N used for the calculations.

Figure 5. Spatial distribution of water demand for toilet ﬂushing for: (a) wet season; (b) dry season.

Systematic evaluation of the optimal tank size for each building was carried out using

Equation (14). Calculations were carried out, considering the values of x

1

,x

2

,x

3

, and x

4

obtained

for the whole year and considering f= 0.5. Local values of cost parameters

CW

= 4.17 (

€

/m

3

]),

and

CE=

0.02 (

€

/m

3

) were also assumed. Moreover, the ofﬁcial regional price list for public and private

works was used to determine the values of

γ=

0.02, and

β

= 82.0 (

€

/m

3

) for all of the installations.

The sensitivity of the results to parameters rand Nwas analyzed by applying Equation (14) for r= 3%

and r= 6% and for values of Nbetween 15 and 25 years.

As an example of the results, Figure 6shows the comparison between the distributions of the

optimal tank sizes (r= 3%) for N= 15 and N= 25 years, respectively. Interestingly, a limited sensitivity

of the results to Nis highlighted, with a prevalence of tank sizes in the range of 2–5 m

3

irrespective of

the value of Nused for the calculations.

Water 2017,9, 916 10 of 14

Water 2017, 9, 916 10 of 14

(a) (b)

Figure 6. Frequency distribution of optimal tank sizes for r = 3% and for (a) N = 15 years; (b) N = 25

years.

4.3. Water Saving Evaluation for the Area of Interest

The application of the calibrated surrogate models to each record of the geodatabase provided

the systematic (worksheet-based) evaluation of WS for all of the buildings.

Figure 7 reports the results of the application (N = 25, f = 0.5, and r = 3%), including the average

yearly water saving, as well as the values of WS for the wet and dry seasons, respectively.

Interestingly, the analysis of Figure 7a reveals that a very large part of the buildings (94%) in the area

may lead to average WS performance in the year between 30% and 50%. The figure also shows water

saving potential to decrease down to 10–30% for the dry period.

(a) (b)(c)

Figure 7. Obtained values of WS for the large-scale installation of RWH systems in the area under

study (N = 25, f = 0.5, and r = 3%): (a) average in the year; (b) wet season; (c) dry season.

The spatial variation of the obtained water saving for all of the considered buildings in the area

is reported in Figure 8 for the wet and dry seasons, respectively.

(a) (b)

Figure 8. Spatial distribution of water saving for the different buildings (N = 25, f = 0.5, and r = 3%)

for: (a) wet season; (b) dry season.

0

5

10

15

20

25

30

35

123456789101112131415

Number of tanks [%]

Tank storage S[m

3

]

0

5

10

15

20

25

30

35

123456789101112131415

Number of tanks [%]

Tank storage S[m

3

]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings[%]

WS [%]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings [%]

WS [%]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings[%]

WS [%]

Figure 6.

Frequency distribution of optimal tank sizes for r= 3% and for (

a

)N= 15 years;

(b)N= 25 years.

4.3. Water Saving Evaluation for the Area of Interest

The application of the calibrated surrogate models to each record of the geodatabase provided the

systematic (worksheet-based) evaluation of WS for all of the buildings.

Figure 7reports the results of the application (N= 25, f= 0.5, and r= 3%), including the

average yearly water saving, as well as the values of WS for the wet and dry seasons, respectively.

Interestingly, the analysis of Figure 7a reveals that a very large part of the buildings (94%) in the area

may lead to average WS performance in the year between 30% and 50%. The ﬁgure also shows water

saving potential to decrease down to 10–30% for the dry period.

Water 2017, 9, 916 10 of 14

(a) (b)

Figure 6. Frequency distribution of optimal tank sizes for r = 3% and for (a) N = 15 years; (b) N = 25

years.

4.3. Water Saving Evaluation for the Area of Interest

The application of the calibrated surrogate models to each record of the geodatabase provided

the systematic (worksheet-based) evaluation of WS for all of the buildings.

Figure 7 reports the results of the application (N = 25, f = 0.5, and r = 3%), including the average

yearly water saving, as well as the values of WS for the wet and dry seasons, respectively.

Interestingly, the analysis of Figure 7a reveals that a very large part of the buildings (94%) in the area

may lead to average WS performance in the year between 30% and 50%. The figure also shows water

saving potential to decrease down to 10–30% for the dry period.

(a) (b)(c)

Figure 7. Obtained values of WS for the large-scale installation of RWH systems in the area under

study (N = 25, f = 0.5, and r = 3%): (a) average in the year; (b) wet season; (c) dry season.

The spatial variation of the obtained water saving for all of the considered buildings in the area

is reported in Figure 8 for the wet and dry seasons, respectively.

(a) (b)

Figure 8. Spatial distribution of water saving for the different buildings (N = 25, f = 0.5, and r = 3%)

for: (a) wet season; (b) dry season.

0

5

10

15

20

25

30

35

123456789101112131415

Number of tanks [%]

Tank storage S[m

3

]

0

5

10

15

20

25

30

35

123456789101112131415

Number of tanks [%]

Tank storage S[m

3

]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings[%]

WS [%]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings [%]

WS [%]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings[%]

WS [%]

Figure 7.

Obtained values of WS for the large-scale installation of RWH systems in the area under

study (N= 25, f= 0.5, and r= 3%): (a) average in the year; (b) wet season; (c) dry season.

The spatial variation of the obtained water saving for all of the considered buildings in the area is

reported in Figure 8for the wet and dry seasons, respectively.

Water 2017, 9, 916 10 of 14

(a) (b)

Figure 6. Frequency distribution of optimal tank sizes for r = 3% and for (a) N = 15 years; (b) N = 25

years.

4.3. Water Saving Evaluation for the Area of Interest

The application of the calibrated surrogate models to each record of the geodatabase provided

the systematic (worksheet-based) evaluation of WS for all of the buildings.

Figure 7 reports the results of the application (N = 25, f = 0.5, and r = 3%), including the average

yearly water saving, as well as the values of WS for the wet and dry seasons, respectively.

may lead to average WS performance in the year between 30% and 50%. The figure also shows water

saving potential to decrease down to 10–30% for the dry period.

(a) (b)(c)

Figure 7. Obtained values of WS for the large-scale installation of RWH systems in the area under

study (N = 25, f = 0.5, and r = 3%): (a) average in the year; (b) wet season; (c) dry season.

The spatial variation of the obtained water saving for all of the considered buildings in the area

is reported in Figure 8 for the wet and dry seasons, respectively.

(a) (b)

Figure 8. Spatial distribution of water saving for the different buildings (N = 25, f = 0.5, and r = 3%)

for: (a) wet season; (b) dry season.

0

5

10

15

20

25

30

35

123456789101112131415

Number of tanks [%]

Tank storage S[m

3

]

0

5

10

15

20

25

30

35

123456789101112131415

Number of tanks [%]

Tank storage S[m

3

]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings[%]

WS [%]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings [%]

WS[%]

0

10

20

30

40

50

60

70

80

90

100

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

Number of buildings[%]

WS[%]

Figure 8.

Spatial distribution of water saving for the different buildings (N= 25, f= 0.5, and r= 3%) for:

(a) wet season; (b) dry season.

Water 2017,9, 916 11 of 14

The ﬁgure conﬁrms the different value of water saving that can be obtained in the two periods

due to the temporal rainfall pattern and to the different level of water demand associated with

the wet and dry seasons, respectively (see Figure 5). In particular, in agreement with the results

presented in Figure 3, the buildings characterized by lower demands usually provide the larger water

saving performances.

The cost comparison between the RWH implementation scenario and the actual water supply

scenario (tankers and desalination) deserves further discussion. An illustrative example of the

comparison is shown in Figure 9. This ﬁgure points out the curve of PV (

€

) as function of time

for a typical 4-apartment building in Lipari (A= 160 m

2

; average demand for toilet ﬂushing

D= 0.575 m

3

/day; tank size S=3m

3

;f= 0.5; N= 25) and for the two scenarios of discount rate r= 3%

(Figure 9a) and r= 6% (Figure 9b).

Water 2017, 9, 916 11 of 14

The figure confirms the different value of water saving that can be obtained in the two periods

due to the temporal rainfall pattern and to the different level of water demand associated with the

wet and dry seasons, respectively (see Figure 5). In particular, in agreement with the results

presented in Figure 3, the buildings characterized by lower demands usually provide the larger

water saving performances.

The cost comparison between the RWH implementation scenario and the actual water supply

scenario (tankers and desalination) deserves further discussion. An illustrative example of the

comparison is shown in Figure 9. This figure points out the curve of PV (€) as function of time for a

typical 4-apartment building in Lipari (A = 160 m2; average demand for toilet flushing D = 0.575

m3/day; tank size S = 3 m3; f = 0.5; N = 25) and for the two scenarios of discount rate r = 3% (Figure 9a)

and r = 6% (Figure 9b).

(a) (b)

Figure 9. Curve of present value PV for A = 160 m2; 4-apartment building with average total daily

demand for toilet flushing D = 0.575 m3/day; S = 3 m3; f = 0.5; N = 25, and for (a) r = 3%; (b) r = 6%.

As expected, the curve relative to the RWH implementation option (dashed line) shows an

initial step due to the cost of installation. However, yearly cumulative water savings due to the

replacement of drinking water with rainwater for toilet flushing use results in the curve having a

smaller slope in comparison to the curve representing the current scenario of water supply in the

island (solid line). Interestingly, the system return on investment is achieved after about 10 years

and 13 years for r = 3% and r = 6%, respectively, after which the RWH option starts generating

increasing profit up to the end of life of the installation.

Globally, the results of the evaluation of the payback period of the investment through the use

of Equation (15) for each building of the area are summarized in Figure 10 for f = 0.5, N = 25, and r =

3%. The figure points out that more than 85% of the installed systems would show payback periods

smaller than 25 years and that about 50% of the installed systems falls in the range of 0–15 years.

More relevantly, the investment for about one fourth of the installed systems could be repaid in less

than 10 years, thus representing the priority option for the community in the case of batch

installation by successive steps.

Finally, it is underlined that the obtained results do not take into account of all aspects that may

affect the economic impact of a large-scale implementation of RWH systems (e.g., water quality

aspects). Therefore, further research efforts will be devoted in the future to include such aspects

within the developed methodology.

Years

PV

[€*10

3

]

0

2

4

6

8

10

12

14

16

18

20

0 5 10 15 20 25

RWH option

Actual scenario

Years

PV

[€*10

3

]

0

2

4

6

8

10

12

14

16

18

20

0 5 10 15 20 25

RWH option

Actual scenario

Figure 9.

Curve of present value PV for A= 160 m

2

; 4-apartment building with average total daily

demand for toilet ﬂushing D= 0.575 m3/day; S=3m3;f= 0.5; N= 25, and for (a)r= 3%; (b)r= 6%.

As expected, the curve relative to the RWH implementation option (dashed line) shows an initial

step due to the cost of installation. However, yearly cumulative water savings due to the replacement

of drinking water with rainwater for toilet ﬂushing use results in the curve having a smaller slope in

comparison to the curve representing the current scenario of water supply in the island (solid line).

Interestingly, the system return on investment is achieved after about 10 years and 13 years for r= 3%

and r= 6%, respectively, after which the RWH option starts generating increasing proﬁt up to the end

of life of the installation.

Globally, the results of the evaluation of the payback period of the investment through the use of

Equation (15) for each building of the area are summarized in Figure 10 for f= 0.5, N= 25, and r= 3%.

The ﬁgure points out that more than 85% of the installed systems would show payback periods

smaller than 25 years and that about 50% of the installed systems falls in the range of 0–15 years.

More relevantly, the investment for about one fourth of the installed systems could be repaid in less

than 10 years, thus representing the priority option for the community in the case of batch installation

by successive steps.

Finally, it is underlined that the obtained results do not take into account of all aspects that may

affect the economic impact of a large-scale implementation of RWH systems (e.g., water quality aspects).

Therefore, further research efforts will be devoted in the future to include such aspects within the

developed methodology.

Water 2017,9, 916 12 of 14

Water 2017, 9, 916 12 of 14

Figure 10. Distribution of payback periods of RWH installations in the area of interest (results are

relative to f = 0.5, N = 25, and r = 3%).

5. Conclusions

A novel methodology to evaluate the benefits of a large-scale installation of domestic RWH

systems in multi-story urban buildings of minor islands was proposed in this paper.

The methodology was developed for specific application to minor Mediterranean islands that

are characterized by large fluctuations in precipitation and water demands (due to touristic fluxes)

between winter and summer periods. The methodology is based on the development of easy-to-use

regressive models for water saving evaluation and their coupling with geospatial analysis tools for

the collection of detailed building/household information on the urban area of study.

The methodology was applied to the old town of Lipari, the largest municipality of the

homonymous Aeolian island, and showed large potential water saving benefits of RWH

implementation in the area. Globally, the characteristics of 984 buildings were collected and

archived in the geodatabase using information from high resolution satellite imagery which was

integrated by information obtained through the use of Google Street View and the results of local

surveys.

The systematic application of the developed regressive models to all of the buildings in the area

showed average yearly water saving performances between 30% and 50%. The comparison with the

current water supply scenario in the island (tankers and desalination) pointed out the beneficial

impact of RWH installation for toilet flushing use of harvested rainwater in the selected urban area.

The cost-benefit evaluation of the large-scale installation scenario showed that about 50% of the

RWH systems would provide payback periods in the range of 0–15 years, and that about one fourth

of the installed systems could be potentially repaid in less than 10 years.

Author Contributions: All the authors have contributed extensively to the work presented in this paper; A.

Campisano and C. Modica conceived and designed the methodology and the used model; G. D’Amico

collected data and performed the model simulations; all the authors have analyzed the data; A. Campisano

and C. Modica wrote the paper.

Conflicts of Interest: The authors declare no conflict of interest.

References

1. Badiuzzaman, P.; McLaughlin, E.; McCauley, D. Substituting freshwater: Can ocean desalination and

water recycling capacities substitute for groundwater depletion in California? J. Environ. Manag. 2017, 203,

123–135, doi:10.1016/j.jenvman.2017.06.051.

2. Roebuck, R.M.; Oltean-Dumbrava, C.; Tait, S. Whole life cost performance of domestic rainwater

harvesting systems in the United Kingdom. Water Environ. J. 2011, 25, 355–365,

doi:10.1111/j.1747-6593.2010.00230.x.

0

5

10

15

20

25

30

35

0-5 5-10 10-15 15-20 20-2525-30 30-35 35-40 40-45 45-50 50-55 >55

Number of RWH systems [%]

T[years]

Figure 10.

Distribution of payback periods of RWH installations in the area of interest (results are

relative to f= 0.5, N= 25, and r= 3%).

5. Conclusions

A novel methodology to evaluate the beneﬁts of a large-scale installation of domestic RWH

systems in multi-story urban buildings of minor islands was proposed in this paper.

The methodology was developed for speciﬁc application to minor Mediterranean islands that

are characterized by large ﬂuctuations in precipitation and water demands (due to touristic ﬂuxes)

between winter and summer periods. The methodology is based on the development of easy-to-use

regressive models for water saving evaluation and their coupling with geospatial analysis tools for the

collection of detailed building/household information on the urban area of study.

The methodology was applied to the old town of Lipari, the largest municipality of

the homonymous Aeolian island, and showed large potential water saving beneﬁts of RWH

implementation in the area. Globally, the characteristics of 984 buildings were collected and archived

in the geodatabase using information from high resolution satellite imagery which was integrated by

information obtained through the use of Google Street View and the results of local surveys.

The systematic application of the developed regressive models to all of the buildings in the area

showed average yearly water saving performances between 30% and 50%. The comparison with

the current water supply scenario in the island (tankers and desalination) pointed out the beneﬁcial

impact of RWH installation for toilet ﬂushing use of harvested rainwater in the selected urban area.

The cost-beneﬁt evaluation of the large-scale installation scenario showed that about 50% of the RWH

systems would provide payback periods in the range of 0–15 years, and that about one fourth of the

installed systems could be potentially repaid in less than 10 years.

Author Contributions:

All the authors have contributed extensively to the work presented in this paper;

A. Campisano and C. Modica conceived and designed the methodology and the used model; G. D’Amico

collected data and performed the model simulations; all the authors have analyzed the data; A. Campisano and

C. Modica wrote the paper.

Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1.

Badiuzzaman, P.; McLaughlin, E.; McCauley, D. Substituting freshwater: Can ocean desalination and water

recycling capacities substitute for groundwater depletion in California? J. Environ. Manag.

2017

,203, 123–135.

[CrossRef] [PubMed]

2.

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