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Ecosystem services from rainwater harvesting in IndiaDaniel Trevor Stouta, Thomas C. Walsha & Steven J. Buriana
a Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT,USAPublished online: 19 Jun 2015.
To cite this article: Daniel Trevor Stout, Thomas C. Walsh & Steven J. Burian (2015): Ecosystem services from rainwaterharvesting in India, Urban Water Journal, DOI: 10.1080/1573062X.2015.1049280
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RESEARCH ARTICLE
Ecosystem services from rainwater harvesting in India
Daniel Trevor Stout*, Thomas C. Walsh and Steven J. Burian
Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT, USA
(Received 10 May 2014; accepted 17 April 2015)
Availability of a safe and reliable water supply is an issue in developing nations, including India. Rainwater harvesting(RWH) is a site-specific source control used to satisfy human, agricultural, and safety demands for water. This studyanalyzed the effects of capturing rainwater for a 12.5 year period (Jan 1999–Jun 2011) to provide three ecosystem services:water supplementation for indoor use, water supplementation for food production and groundwater recharge (GWR).A hydrologic analysis was completed using satellite rainfall data and a water balance approach. Two demand scenarios,indoor and outdoor, were considered, with water in excess of demand and storage directed to recharge groundwater.An economic analysis quantified RWH system net present value. The results indicated significant ecosystem servicesbenefits were possible from RWH in India. RWH for the purpose of providing irrigation to a small garden and allowingoverflow to a drywell for GWR was concluded to be an approach to maximize benefits. This scenario provided the greatestnet present value (21,764–38,851 INR), fastest payback period (0.30–0.98 years), and recharge to groundwater of morethan 40% of onsite rainfall. The benefit of the outdoor vegetable irrigation was determined and the results showed that thecaloric demands of the typical Indian household (2.75 kg of tomatoes and 1.05 kg of lettuce) could be met with a 20m2
garden, and excess food could be sold to offset the capital cost of the system and later for economic gain.
Keywords: rainwater harvesting; ecosystem services; India
1. Introduction
India is in a water crisis. While 89% of the Indian
population has access to improved water sources, it is
generally intermittent with regional disparities in avail-
ability (UNICEF, 2008). In the early 1980s, residents of
Bangalore had nearly twenty hours per day (hr/day) of
access and Chennai had between 10–15 hr/day; however,
these values dropped to 2.5 and 1.5 hr, respectively as of
2006 (World Bank, 2006). A widely accepted measure of
water stability, the Falkenmark Indicator (Brown &
Matlock, 2011), provides four levels of water scarcity,
including: no stress, stress, scarcity and absolute scarcity.
For India, which withdrew 627m3/yr per person in 2010
(The Encyclopedia of Earth, 2012), the Falkenmark
reveals a level of scarcity. This water supply crisis has
been found to be autonomous of annual precipitation, as an
investigation in Chennai revealed shortages despite an
annual rainfall depth of 1300mm (Jency, 2009).
Urbanization in India, as in other countries, has
resulted in numerous water quality and quantity issues
(Kumar, 2005; Lee & Heaney, 2003). Despite major
investments in infrastructure over the past century, India’s
existing water infrastructure cannot sufficiently provide
sustainable or reliable water to its citizens, with the overall
system capable of storing approximately 200 cubic meters
per person (m3/person), far below the desired 1000m3/
person for countries with similar climate (Briscoe, 2006).
For example, developed nations, like the United States,
can store up to 5000m3/person and middle income
countries (e.g. Mexico and China) can store up to 1000m3/
person. The 200m3/person in India is equivalent to
approximately 30 days of rainfall, whereas major river
basins in arid areas of developed nations can store up to
900 days of rainfall. To further complicate matters,
precipitation in India is highly seasonal, with 90 percent
occurring over the period of June–September (Briscoe,
2006).
Beyond supply, the rapid development of urban
centers has further degraded water quality. One direct
impact is the rise of endemic rates of diarrheal disease,
which is not seasonally dependent (Dasgupta, 2004). For
instance, citizens often resort to polluted sources of water
when rations are insufficient during dry months. Alter-
natively, increased urban drainage (e.g. stormwater runoff,
untreated urban domestic sewage, industrial effluent)
during wetter months contributes to contamination of
waterways and groundwater stores with fecal coliforms
(Buecheler, 2005; Dasgupta, 2004). As such, citizens often
resort to groundwater for personal consumptive uses. For
example, to improve the reliability of existing water,
homeowners are increasingly reliant upon personal
tubewells (Shah & Patnaik, 2007). Such measures have
q 2015 Taylor & Francis
*Corresponding author. Email: dan.stout@utah.edu
Urban Water Journal, 2015
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resulted in rapidly decreasing groundwater tables. A study
by Rodell (2009) measured falling rates of 4 ^ 1 cm/yr in
northwest India over only six years (2002–2008). This
resulted in a total volumetric loss of 109 million m3
(Rodell, 2009).
With such a wide array of negative impacts of urban
development, improvements to the sustainability of urban
water resources should consider a broad range of factors
and include the valuation of potential ecosystem services
(Bolund & Hunhammar, 1999; Pataki et al., 2011).
Ecosystem services, as defined by Costanza et al. (1997),
include direct and indirect benefits derived by humans
from ecosystem functions, including stormwater
reduction, potable water demand supplementation, and
food (e.g. crop) procurement. These services also carry
both social and economic implications, including
improved house prices, reduced household bills, ecologi-
cal conservation with improved biodiversity of native
species, and improved physical and mental well-being
(Tratalos et al., 2007). Community gardens for example
have been shown to directly improve the social,
ecological, and economic sectors of urban areas (Barthel
et al., 2010).
Increasing efforts are being made to harvest rainwater
to combat water scarcity in India (Kumar et al., 2006).
Tankas, or johads, are examples of common agricultural/
rural runoff collection and storage practices employing
earthen check dams or tanks in India (Kumar et al., 2006).
For urban areas, where space is often limited, RWH offers
a small footprint (i.e. high retrofit-ability) and relatively
low cost solution to water supply and management issues.
Direct ecosystem service benefits of RWH include (1)
infiltration and recharge of the depleted groundwater
(Sakthivadivel, 2007) and (2) provision of direct water
supply for human needs (Grover, 2010). Additional
ecosystem service benefits of urban RWH include
stormwater runoff reduction, groundwater recharge
(GWR), indoor demand supplementation, and irrigation
for vegetation (e.g. small personal gardens). Small
personal gardens also address the low consumption of
vegetables (i.e. micronutrients) resulting from non-
availability (NSSO, 2007). These benefits are in sync
with the provisioning and regulating services highlighted
in healthy urban ecosystems.
The widespread implementation of RWH has been
shown to mitigate the catchment- and site-scale impacts of
both stormwater runoff volumes (Steffen et al., 2013;
Walsh et al., 2014) and pollution loading to both surface
and groundwater sources (Kinkade-Levario, 2007).
A study of RWH in Paris found that despite limited
large event mitigation, prevention of sewer overflows for
smaller, more frequent rainfall events can be improved
(Petrucci, 2011). In a study looking at RWH in the
Southeast US, it was found that increasing visibility and
implementation of RWH also aids in educating the public
about negative impacts from urbanization, resulting in
smarter water use (Jones & Hunt, 2010).
When aquifer recharge rates are exceeded by
groundwater pumping rates, RWH for GWR has been
targeted, such as in a case study in Bangalore (Suresh,
2001). A vast majority of RWH studies in India target the
benefits of GWR, since nearly 85 percent of drinking water
is supplied by groundwater (World Bank, 2010). While
RWH for GWR has had positive impacts on the
subsurface, a direct benefit for immediate water supply
does not exist (Jency, 2009). For instance, while Chennai
was the first city to mandate RWH for new developments,
GWR is the sole target, providing no benefit to piped water
supply (Srinivasan, 2010).
Meanwhile, studies targeting the potential benefit of
RWH for indoor uses found 77 percent of households
surveyed were dissatisfied with the duration of city
supplied water (Singh & Turkiya, 2013). However, 86
percent of these households were unaware of RWH
technologies targeting the acquisition of water for potable
uses (Singh & Turkiya, 2013). Another study found RWH
for indoor use to be largely un-exploited (Grover, 2010),
since RWH in India often refers to “enhanced aquifer
recharge; rather than collection of rainwater in cisterns for
(indoor use)” (Srinivasan, 2010). As such, exploration of
other benefits stemming from RWH, including the
ecosystem services of stormwater runoff mitigation, food
production, and potable water supplementation, is
warranted.
This study presents the potential for RWH to
supplement indoor, recharge of groundwater, and garden
irrigation demands throughout India. Six case study cities
were established, representing the range of climatic and
geographic characteristics, including Delhi, Hyderabad,
Kolkata, Srinagar, Mumbai, and Bangalore. Hydrologic
analysis was completed using processed satellite rainfall
data, at three-hour temporal scales, for a total of twelve
and a half years (Jan 1999–Jun 2011). Potential
evapotranspiration rates established the irrigation
demands, while growing seasons dictated the supply (i.e.
in season or out of season) of water. The addition of long-
term costs for purchasing, operation and maintenance
(O&M), municipally-supplied water, and produce (i.e.
vegetables) sales provided a net present value (NPV) for
each regional scenario. Ultimately, the potential for water
supply with RWH was translated into the following
ecosystem services: (i) a reduction in monthly and annual
water bills, (ii) a reduction in the stormwater runoff, and
(iii) a caloric potential for a garden plot (i.e. one-to-one
ratio of catchment area to irrigated plot area). The results
provide a spatial and temporal analysis of the ecosystem
services’ potential of RWH throughout India and suggest
that RWH for the purpose of providing irrigation to a small
garden and allowing overflow to flow to a drywell for
GWR is the best option to maximize benefits.
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2. Methodology
2.1. Regional selection
Case study cities were selected based on geospatial
characteristics, including differences in population, climatic
patterns, and geographic location. Climatic zones were
delineated using the Koppen classification system (Mapsof,
2012). A minimum population of one million was
established for geographically-distinct climatic region
cities. This resulted in a total of six cities, including
Kolkata, Mumbai, Hyderabad, Delhi, Bangalore, and
Srinigar as can be seen in Figure 1, which also provides
the populations and average annual rainfall (Government of
India, 2011). GIS datasets were obtained from Global
Administrative Areas (http://www.gadm.org/).
2.2. Rainfall data source
Precipitation data was obtained from the publicly-
available NASA Tropical Rainfall Measuring Mission
(TRMM) (NASA, 2013). TRMM is a joint data-gathering
mission between NASA and the Japanese Aerospace
Exploration Agency. The benefit of TRMM data for RWH
analysis in areas without easily accessible data is in its
relatively fine temporal resolution and wide spatial
distribution. This facilitates regional analysis of RWH
and its effectiveness. This study used the three-hour
temporal resolution precipitation intensity data, officially
named “TRMM 3-Hourly 0.25 deg. TRMM and Other-
GPI Calibration Rainfall Data,” with the short name
“TRMM_3B42” (NASA, 2013). The period of analysis
was 1 January 1999–30 June 2011. Quality assessment
ensured the processed datasets were accurate by compar-
ing with ground-based measurements at stations in the
regions of analysis (Stout, 2013).
2.2.1. Rainfall distributions by city
An analysis of the continuous, long-term precipitation
datasets found that the annual average precipitation event
Figure 1. Republic of India, with study cities delineated. Population and average annual rainfall depths are indicated.
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intensities ranged from 2.9–10.4mm/event (Srinagar to
Kolkata). The annual average inter-event time ranged from
8 days to 30 days (Srinagar to Hyderabad). Average annual
rainfall depths extracted from TRMM datasets totaled
492mm (Srinagar), 658mm (Delhi), 819mm (Hyder-
abad), 991mm (Bangalore), 1379mm (Kolkata), and
1605mm (Mumbai). Seasons were distinguished accord-
ing to climate patterns following Rao et al. (2012),
including winter (Jan–Feb), summer (Mar–May), south-
west monsoon (June–Sept), and northeast monsoon (Oct–
Dec). Results for seasonal average event intensity
(Table 1) and inter-event duration (Table 2) highlight the
regional differences in precipitation characteristics.
2.3. Household characteristics
Despite vast geographic differences, uniform application
of a typical middle class housing unit was deemed
acceptable. This is based on an analysis by Stout with the
goal of providing the most widely applicable results, as the
vast majority of the country’s residents live in such
housing (2013). A typical middle class housing unit, in this
case, was defined as a four storey (13.2 meters) apartment
building housing two five-member families per storey
(family unit floor plan area of 84-m2). Case study
households were representative of a typical apartment
layout, which are multi-unit dwellings. This information
was obtained through conversations with Mr. Satish
Kumar Vedulla, General Manager and founder of Harit
Solutions (www.haritsolutions.com), a water engineering
and consulting company headquartered in Visakhapatnam,
India (Vedulla, 2013, personal communication) and
checked using Google Earth. Using ArcGIS, a typical
rooftop area for the multi-unit dwellings indicated by Mr.
Vedulla was quantified (167-m2). This value was then
subdivided by the number of apartment units, yielding a
“typical Indian household” catchment area of 21-m2. This
approach yields applicable catchment areas for many parts
of India according to analysis of Google Earth image files
of the living situations in each of the cities of analysis and
consultation with Mr. Vedulla. Background research on
RWH capacities and catchment areas was completed by
Stout (2013) and targeted the greatest efficiency for the
typical family of five. The greatest efficiency was defined
as matching most benefit (% water captured relative to
demand) to lowest size cistern readily available in market
(to keep material cost low). The analysis used precipitation
patterns for each area, along with varying cistern sizes to
compare the different benefits. At the point where the
incremental increase in benefit relative to increasing
cistern size began to decrease, the tank was considered
most efficient. This resulted in the 757 L capacity
providing the highest Water Saving Efficiency (WSE)
for all cities except Mumbai, which maximized WSE with
a 1893 L capacity (Stout, 2013).
2.4. Hydrologic analysis components
2.4.1. Storage volume calculation
Several methods exist for determining the volume stored
in a rainwater cistern. For this analysis, the mass balance
method was chosen. The mass balance method applies a
water budget, accounting for the volume of the roof runoff
(inflow), demand (outflow), and remaining water (storage)
at each time step (Panu & Rebneris, 1997). The mass
balance method produces the RWH volume necessary to
supplement centralized water demand and allow for
spillage at each time step. Inflow was calculated by
multiplying the average catchment area by the rainfall
volume and applying a runoff coefficient (Rc) value of 0.9.
The Rc of 0.9 represents a ten percent loss due to
abstractions (e.g. evaporation, depression storage) and
inefficiencies in collection.
2.4.2. Storage and water demand
The volume of water stored was calculated based on the
user-defined volume of the cistern, combined with the
inflow volume and demand. Cistern spillage, equivalent to
the overflow volume that occurs when storage capacity is
met, can be estimated with either the yield before spillage
(YBS) or yield after spillage (YAS) algorithms. This study
chose YAS since it provides a more conservative estimate
for supply (Schiller, 1987). Last, water demand (i.e.
Table 1. Seasonal average precipitation event intensity (mm/event) for case study cities.
City Winter SummerSouthwestMonsoon
NortheastMonsoon
Bangalore 5.3 7.6 10.9 4.5Delhi 3.7 7.2 11.8 2.4Hyderabad 2.8 6.0 10.9 2.7Kolkata 3.4 11.2 16.1 6.7Srinagar 3.5 3.2 3.2 1.6Mumbai 0.6 10.3 19.3 1.3
Table 2. Seasonal inter-event time (IET) (days) for case studycities.
City Winter SummerSouthwestMonsoon
NortheastMonsoon
Bangalore 47 15 10 35Delhi 33 22 17 44Hyderabad 58 22 11 43Kolkata 46 17 9 40Srinagar 8 7 7 10Mumbai 43 45 12 27
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outflow) was estimated based on survey data collected in
the city of Visakhapatnam (i.e. population over 2.0 million
persons), near Hyderabad. This survey found an average
daily indoor water demand of 135 liters/person. This value
fit within the range estimated by other studies (Falkenmark
& Widstrand, 1992; Nandgaonkar, 2005; UNDP, 2006).
Combined with the average household size of five persons
(Census of India, 2001), the average daily household
demand was estimated to be 675 liters.
Two different demand scenarios were analyzed to
evaluate RWH’s effectiveness in meeting variable
demands, including indoor potable and non-potable uses
(IPnP) and outdoor vegetable irrigation (OVI). The
volume of water available for GWR (cistern spillage)
was also recorded for the long-term analysis (Jan 1999–
Jun 2011) for each demand scenario. Constant indoor
demands totaled 675 liters per day while irrigation
demands varied seasonally as a function of evapotran-
spiration (ET) rates. The ET rates for each city were
determined for four defined seasons by Rao et al. (2012)
using the Penman-Monteith approach of the Food and
Agriculture Organization of the United Nations described
in Smith et al. (1991) (Table 3). The garden space being
irrigated were assumed to have half of the area (10-m2)
planted with lettuce and the other half (10-m2) planted
with tomatoes, resulting in a 20m2 garden plot per
household. Using a crop coefficient of 1.05 for both
vegetables, the daily water demand was calculated. Two 4-
month growing seasons were assumed for all cities except
Kolkata, which was assumed to have three.
2.4.3. Water saving efficiency (WSE)
To determine the regional effectiveness of RWH in
providing the household-scale ecosystem services of
GWR, indoor demand supplementation, and irrigation
potential, the Water Savings Efficiency (WSE) was
calculated (Equation (1)).
ET ¼PT
t YtPT
t Dt
*100 ð1Þ
where Yt is the yield (i.e. demand satisfied) and Dt is the
household demand for a specified time period. This
percentage, ET, is well-known throughout RWH literature
and provides a useful tool to analyze the temporal
distribution of the value of the harvesting system (Fewkes,
1999).
2.4.4. RWH performance metrics
A MATLAB code was written to analyze the performance
of the different RWH configurations. The mass balance
method described above was used to calculate the volume
of water that was available for meeting the demand, the
volume of water stored after the demand was subtracted
and the overflow. The only inputs for the program were the
discussed TRMM precipitation data and the cistern
volume. The analysis was performed on a daily time step
given the demand data acquired was on a daily basis,
despite having finer scale precipitation data (3 hour). The
TRMM data was downloaded on a 3-hour increment as the
demand was anticipated having an hourly pattern. New
precipitation data was not downloaded once the demand
data returned on a daily basis, as the TRMM data
download proved to be cumbersome, and the summed 3-
hour data would provide the same level of accuracy as the
daily. Comparison of values (volumetric reductions, WSE)
was compiled at the annual scale for each city. A value of
0% signifies that none of the demand was met throughout
the course of the year, while a value of 100% signifies full
supplementation with RWH. The volumes available for
GWR were divided by the total volume of precipitation for
the year and multiplied by 100. For GWR results, a value
of 0% signifies no GWR, while a value of 100% signifies
all the rainfall was infiltrated via GWR.
2.5. Cost analysis
A regional cost analysis for this study was based on
individual municipal water costs for each city, the capital
and operation and maintenance (O&M) costs for RWH
systems, and the vegetation supplementation potential.
Water rates (Table 4) were obtained through the respective
agencies, including the Delhi Jal Board (DJB), Municipal
Corporation of Greater Mumbai (MCGM), Bangalore
Water Supply and Sewerage Board (BWSSB), Kolkata
Municipal Corporation (KMC), and Hyderabad Metropo-
litan Water Supply and Sewerage Board (HMWSSB). For
Srinagar, which resides in the state of Jammu and
Kashmir, water usage charges could not be found and,
therefore, were not included in the study.
These values were used in tandem with the demand
supplied by municipal sources to calculate monthly cost
savings for each typical apartment unit per region when
RWH is implemented. To obtain the adjusted municipal
Table 3. Seasonal ET rates (mm/day) for the case study cities(Rao et al., 2012).
City Winter SummerSouthwestMonsoon
NortheastMonsoon
Bangalore 4.5 5.8 4.3 3.5Delhi 2.2 5.4 4.8 2.4Hyderabad 4.1 6.7 4.7 3.5Kolkata 2.7 4.7 3.7 2.7Mumbai 3.5 4.8 3.1 3.3Srinagar 2.1 4.6 3.8 3.6
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volume supplied, the daily cumulative water supplemen-
tation was subtracted from the cumulative daily demand
without RWH. Individual RWH tank costs were applied
based on research of available plastic tanks in Hindustan,
Jindal (1.8 INR/liter capacity) and Storex, Ganga (2.75
INR/liter capacity) (Rainwaterharvesting, 2014). These
costs assumed inclusion of conveyance and basic filtration
components. Recurring annual O&M costs were estimated
to be 50 INR, accounting for filtration and conveyance
cleaning and potential parts replacement. The eleven year
net present value (NPV) was estimated with capital costs,
O&M costs, and annual bill savings for each city assuming
a five percent interest rate. Produce costs were estimated
with regional data acquired from Numbeo (2014). Since
tomatoes and lettuce were targeted for crop production,
these vegetables were used to perform the RWH
ecosystem services’ benefit analysis (Table 5).
2.6. Food yield and consumption
The food yield was determined based on the previous
assumption of 20-m2 gardens consisting of half lettuce and
half tomatoes. Values for crop yield were assumed to be
the same for each city, and were estimated based on
information presented in Ackerman (2011). The yields
used for lettuce and tomatoes were 2.44 kg/m2 and 2.93 kg/
m2, respectively. These unit yields were multiplied by the
irrigated area of 10-m2 (for both lettuce and tomatoes) to
produce the seasonal yields of 29.3 kg/season (tomato) and
24.4 kg/season (lettuce) for each garden.
Based on NSSO (2007) data, consumption patterns for
targeted crops (i.e. tomato, lettuce) were extracted. This
yielded a thirty day average per capita consumption of
0.55 kg and 0.21 kg for tomatoes and lettuce, respectively.
For a family of five, this equated to a monthly household
consumption of 2.75 kg of tomatoes and 1.05 kg of lettuce.
These values established the monthly caloric needs per
household and provided the basis upon which food yield
would meet or exceed this demand.
3. Results and discussion
3.1. Water savings efficiency for OVI and IPnP
3.1.1. Annual results
The annual WSE (recall WSE is water savings efficiency)
was calculated and plotted in Figure 2. Both of the demand
scenarios, IPnP (recall IPnP is indoor potable and non-
potable uses) and OVI (recall OVI is outdoor vegetable
irrigation), are shown to vary considerably. The OVI
scenario showed a regional discrepancy in WSE, with less
efficiency for Delhi, Hyderabad, and Kolkata (approxi-
mately 30–60%). Alternatively, the cities Bangalore,
Mumbai, and Srinagar had the greatest WSE (approxi-
mately 60–90%). This is a function of the higher annual
Table 4. Water usage charges relative to the cities analyzed (INR ¼ Indian rupee).
City Municipality Tier Unit (kL) per month Monthly Rate (INR) per Consumption (kL)
Delhi DJB 1 0–10 2.66/kL2 10–20 3.99/kL3 20–30 19.97/kL4 .30 33.28/kL
Mumbai MCGM 1 0–22.5 50 (flat)2 22.5–30 4.75/kL, þ50 flat3 .30 29/kL, þ50 flat, þ(4.75/kL for tier 2)
Bangalore BWSSB 1 0–8 6/kL2 8–25 9/kL3 25–50 15/kL4 50–75 30/kL5 75–100 36/kL6 .100 36/kL
Kolkata KMC 1 Domestic flat rate 12/kLMulti-storey
2a 0–30 9/kL2b .30 12/kL
Hyderabad HMWSSB 1 Multi-storey flat rate 9/kLSrinagar N/A N/A N/A N/A
Table 5. Regional costs of targeted vegetables (INR/kg).
Vegetable Bangalore Delhi Hyderabad Kolkata Mumbai Srinagar
Tomato 27 36 26 33 36 35Lettuce 25 34 24 24 29 20
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precipitation volumes characteristic of Bangalore and
Mumbai’s climate, and the less seasonal (non-monsoonal)
variance in Srinagar’s climate. Compared with OVI uses,
IPnP has a much lower range of efficiency (4–12% across
regions), this being a result of higher demands in IPnP than
OVI. Kolkata and Mumbai possessed the highest IPnP
WSE, ranging between 9–11% and 7–15%, respectively,
while Srinagar provided the least (range 3–7%). Despite
the regional dichotomy between WSE for OVI, the overall
RWH efficiency outperformed that of IPnP for the long-
term analysis. The efficiency of benefits result from the
targeted demand, with smaller volumes (i.e., OVI) being
realized in regions with annually reliable rainfall
characteristics.
3.1.2. Seasonal results
The seasonal WSE was calculated from average supply
and demand (Figure 3). As with the annual analysis, both
of the demand scenarios, IPnP and OVI, are shown to vary
considerably. It should be noted that 0% for OVI indicates
no seasonal demand. This was due to poor growing
conditions (i.e. out of season). All regions perform best
during the southwest monsoon (maximized WSE of 63–
97%), resulting from enhanced precipitation magnitude.
Winter yields the least efficiency for all regions, resulting
from poor precipitation magnitude. Similar to the annual
results, IPnP efficiencies are lower than OVI across
regions for the same reason of higher demands. Maximum
IPnP efficiency is realized during the southwest monsoon
(6–31%), though this is substantially lower than the OVI
results (between 68% and 90% less). Similar to OVI,
summer and northeast monsoon seasons have lower
efficiencies for IPnP ranging from 1–7% and 0.6–7%,
respectively. Winter values are all approximately 1%.
3.2. Groundwater recharge
3.2.1. Annual results
The percentage of total available precipitation (inflow)
directed to GWR (recall GWR is groundwater recharge)
Figure 2. Annual regional results showing the WSE (water savings efficiency) and the GWR (groundwater recharge) potentials (Jan1999 – Jun 2011) for the two demand scenarios.
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was calculated on an annual basis and plotted (Figure 2).
Similar to the WSE results, the OVI scenario had much
higher GWR potential compared with the IPnP scenario.
GWR rates for IPnP ranged from 0–20% across all
regions, with Srinagar simulating the least potential (0–
8%) and Mumbai with the greatest potential (2–25%).
This is a function of the annual precipitation volumes in
Mumbai being the highest in the study and occurring
almost exclusively during the monsoon, resulting in many
times of cistern overflow; inversely Srinagar has the
lowest annual precipitation volumes in the study, and
fairly evenly distributed precipitation patterns across the
year. OVI results could be separated into three categories
of efficiency, based on regional results. The least efficient
city was Srinagar (34–66%), the mid-efficient cities
included Bangalore, Delhi, Hyderabad, and Kolkata
(approximately 40–70%), and the most efficient city was
Mumbai (75–88%). This hierarchy indicates the regions
where the volume of GWR approaches the volume of
precipitation (i.e. input). This highlights the potential for
GWR to mimic the natural, pre-developed conditions (i.e.
infiltration). Again, OVI outperformed IPnP for potential
GWR rates for the long-term study due to the ability of
captured rainfall to quickly satisfy OVI volumetric
demands and, thus, supplement a greater proportion of
the GWR demands.
3.2.2. Seasonal results
The percentage of the average available precipitation
directed to GWR on a seasonal basis was calculated and
plotted (Figure 3). Similar to the WSE results, the OVI
scenario had a greater GWR potential compared with IPnP
as a result of lower OVI demands being quickly satisfied
and resulting in cistern overflow. The IPnP GWR rates
Figure 3. Seasonal results for the WSE (water savings efficiency) and GWR (groundwater recharge) potentials (Jan 1999–Jun 2011) ofIPnP (indoor potable and non-potable) and OVI (outdoor vegetable irrigation).
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were greatest during the southwest monsoon across all
regions (2–18%) with Kolkata, Delhi and Mumbai having
the highest results at 18%, 17% and 16%, respectively.
IPnP GWR rates were high during the monsoon as a result
of the high precipitation volumes in short periods of time,
characteristic of monsoons, quickly filling the cistern and
overflowing. Srinagar provided the lowest seasonal IPnP
GWR rates as a result of non-monsoonal precipitation
characteristics. Less GWR was experienced during the
summer and northeast monsoon seasons, ranging between
5–12% and 2–19%, respectively. Again, winter values
were less than 1% for all cities, except in Mumbai (21%)
and Srinagar (5%), characteristic of months with very low
precipitation. GWR rates when targeting OVI were
appreciably higher, maximizing during the southwest
monsoon (39–85%). All regions were capable of
infiltrating over 50% of the precipitation, with the
exception of Srinagar (39%), resulting from high
precipitation volumes during the monsoon in all areas
except Srinagar. Infiltration rates during the summer and
northeast monsoon seasons were, again, less despite
having reasonably high GWR values (20–48%). All cities,
except Mumbai, infiltrated greater than 25% (11–68%).
Winter infiltration ranged from 4–90%.
Comparison of monthly inter-event time (days) and
precipitation event intensity (mm/event) (Figure 4) yielded
a distinction in household IPnP reduction potential for
months with values less than 30 days and greater than
5mm, respectively. Similarly, seasonal plots (Figure 5)
highlight a greater distinction in the reduction potential.
Seasons exceeding the threshold values provide negligible
IPnP benefits to the household. Srinagar is the only city in
which this relationship does not hold true, which is a
function of the precipitation trends (i.e. less intense, longer
Figure 4. Monthly Average Inter-Event Time (days), X-Axis, versus Monthly Average Precipitation Event Intensity (mm/event), Y-Axis, for cities. Size of points, Z-Axis, indicates the average monthly volumetric reductions in household IPnP (indoor potable and non-potable) demand with the implementation of RWH.
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duration precipitation events). Logarithmic relationships
between these variables are also extracted, with R-
coefficients ranging from 0.48–0.86.
3.3. Cost-benefit analysis
3.3.1. IPnP
Based on average daily consumption for a typical
household of five, RWH was found to reduce annual
water bills up to 1550 INR per year (Delhi), including
annual O&M. No benefit was simulated for Mumbai,
highlighting the importance of the water rate structure (due
to usage less than 22.5 kL/mo being charged a flat rate).
Monthly variations in IPnP reductions, represented as
reductions in monthly bills, indicate the intra-annual
periods when RWH is more beneficial both regionally and
temporally (Figure 6).
Seasonal analysis of RWH effectiveness highlights the
greatest reductions in bills occurring during the southwest
monsoon, followed by the summer, northeast monsoon,
and winter. The winter is least effective, due to the lack of
precipitation. Figure 7 indicates the annual reductions as a
Figure 5. Seasonal Average Inter-Event Time (days), X-Axis, versus Seasonal Average Precipitation Event Intensity (mm/event), Y-Axis. Size of points, Z-Axis, indicates the average seasonal volumetric reductions in household IPnP (indoor potable and non-potable)demand with the implementation of RWH.
Figure 6. Average monthly water bill reductions with RWH.
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percent of the season, with inset values representing the
total seasonal household bill reductions (INR).
The capital cost of each 757-liter RWH unit was 1363
INR, while the 1863-liter RWH unit for Mumbai was 2082
INR (e.g. based on 1.8 INR/L). Combined with the annual
savings and O&M costs for the eleven years of analysis,
the NPV for each city was calculated (Table 6). Table 6
also presents the regional average volumetric reductions
for IPnP. Ranks indicate the best (value of one) locations,
in terms of NPV and the long-term average volumetric
reduction, for the implementation of RWH. These results
highlight the difference between the seasonal and
consistent annual potential of RWH for IPnP demand.
Dividing the initial capital costs by the annual indoor
potable water savings, which were adjusted for recurring
O&M, the simple payback periods were estimated at: one
(Delhi), six (Kolkata), ten (Hyderabad), and twelve
(Bangalore) years. Mumbai never achieved payback due
to the rate structure resulting in zero annual savings.
3.3.2. OVI and caloric potential
Cost-benefit analysis of OVI with RWH found a slight
reduction in the average annual bill savings across regions.
This was the result of supplementation from municipal
sources to irrigate vegetation during growing seasons. The
incorporation of vegetable consumption supplementation
dramatically increased the NPV of all scenarios. The
average annual production potential for tomatoes and
lettuce was 58.6 kg and 48.8 kg, respectively, for all cities
except Kolkata (87.9 kg, 73.2 kg). This exceeded the
average annual household consumption of 33 kg and
12.6 kg for tomatoes and lettuce, resulting in the potential
for profit by selling based on market prices (Table 5).
Regarding annual consumption of vegetables, household
savings ranged from 1172 INR to 1601 INR as a result of
crop production. When leftover produce was sold,
households profited between 1548 INR and 3261 INR
annually. Total cost savings per region from targeting OVI
ranged between 2605–4522 INR once capital costs were
recouped after one year.
Normalizing total annual profits to the increase in
annual bills, due to supplementation of OVI demands,
yielded a long-term average annual savings between 6 INR
(Delhi) and 82 INR (Bangalore). Significant improve-
ments in RWH NPV were made by combining the
potential profits with annual O&M, capital costs of
purchasing, and bill reductions for all regional scenarios
(21,764–38,851 INR). Thereby reducing all simple
payback periods to within one year.
4. Conclusion
This study highlighted the potential for RWH as a
decentralized method of reducing stormwater runoff,
providing individual households with profits and caloric
benefits, and recharging groundwater. The results provided
a spatial and temporal analysis of the ecosystem services’
potential of RWH in India.
For vegetable irrigation, greater than 50% of the
annual demand was supplemented by RWH across all
geographic regions. Benefits were maximized during the
southwest monsoon season. Supplementing outdoor
demand with municipal water only reduced monthly bill
savings by a small amount, but provided a significant
increase in the net present value of the RWH project as a
result of consumption supplementation and produce sales.
When outdoor irrigation was targeted with captured
rainwater, payback periods were reduced by 66% (Delhi,
0.3 years), 95% (Hyderabad, 0.5 years; Kolkata, 0.3
years), 96% (Bangalore, 0.5 years) and 100% (Mumbai,
1.0 years) compared with the indoor water demand
scenario. This was driven by household profits stemming
from consumptive supplementation and excess produce
sales, which ranged from 2721 INR to 4647 INR
Figure 7. Average seasonal bill reductions per city. Inset datavalues represent the average bill reduction (INR) per season.
Table 6. NPV and long-term average volumetric reduction percity for IPnP (indoor potable and non-potable) with theimplementation of RWH.
City NPV (INR) Rank
Long-term averagevolumetric reduction
for IPnP Rank
Bangalore 115 4 7.6% 3Delhi 12,105 1 4.7% 5Hyderabad 219 3 6.2% 4Kolkata 1,128 2 9.7% 2Mumbai 23,245 5 11.6% 1Srinagar N/A N/A 4.0% 6
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regionally. Despite initial losses due to capital investment,
results showed that households will quickly recoup costs
through profits from vegetable sales.
Seasonal analysis of precipitation characteristics
relative to indoor water demand supplementation high-
lighted the reduced efficiency of RWH for regions where
less intense, more frequent very small events were
analyzed (Srinagar). Cost-benefits were a function of the
rate structure established by the municipality, where
supplementation was maximized with Delhi’s rates and
completely negated by Mumbai’s. This highlights the
importance of water rates and public policy when indoor
water demand supplementation benefits are targeted.
Less than 20% of the average annual indoor demand
was met with RWH, though individual seasons (southwest
monsoon, 6–26%) were shown to have improved
supplementation. The southwest monsoon season yielded
the greatest reductions in household water bills in India
(average savings of 19–54 INR). Alternatively, RWH
efficiencies were minimized during the winter (average
savings of 1–5 INR). Seasonal reductions in household
indoor water demand are greatest for all cities, except
Srinagar, when inter-event dry time is less than 30 days
and precipitation event intensities exceed 5mm. For
outdoor water use scenario, the overflow, or excess water
not used for vegetation irrigation, was equivalent to
greater than 40% of the annual precipitation (i.e. input) for
all regions.
RWH for the purpose of providing irrigation to a small
garden and allowing overflow to a drywell for ground-
water recharge was found to be the most effective
approach to maximize benefits. This scenario provided the
greatest net present value (21,764–38,851 INR), fastest
payback period (0.30–0.98 years), and an average annual
groundwater recharge of 40% of onsite precipitation. This
is important in the urbanized centers of developing
nations, where density often restricts retrofitability for
meeting such ecosystem services.
Acknowledgement
This research was partially supported by the NASA PrecipitationMeasurement Mission (PMM) Program.
Disclosure statement
No potential conflict of interest was reported by the authors.
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