Abstract

Intermittent water distribution systems are a stark reality in developing countries. Puerto Ayora, the centre of tourism of the Galápagos Archipelago, is not an exception, with its population and tourists suffering from scarce water resources. However, the extreme per capita consumptions, well above those in 24/7 supply situations in many other countries, contradict the (sense of) scarcity. In this study, 18 water meters were installed and a water-appliance diary was carried out in 15 randomly selected households in Puerto Ayora. The aim was to determine and analyse domestic water consumption, as well as the diurnal patterns, also by verifying per capita figures ranging from 40 to 380 litres per capita per day from a previous study. Also, the aim was to find a correlation between the consumption and schedules of distribution conducted by the municipality. The paper also elaborates on typical household appliances used, and gives an insight about a wide range of domestic demands in a predominantly water-scarce area. The conclusions indicate no influence of the specific intermittency patterns to the specific demand, but rather a wide range of different lifestyles, the possible presence of informal accommodations and excessive wastage.

INTRODUCTION

Limited water resources are considered one of the most sensitive issues in arid and semi-arid areas (Singh & Turkiya 2013). Urban settlements located in these areas experience water deficits, still showing no improvement after interventions have been made. Due to the population growth trends, as well as the specific water demands, the condition is expected to worsen. Therefore, rationing the distribution of water has been one of the most used strategies to reduce water demand. Until a few decades ago, water supply infrastructures were designed with the assumption of a continuous and unlimited supply, which nowadays hardly takes place, specifically in developing countries (Vairavamoorthy et al. 2008). That is mainly due to the fact that the intermittency is rarely foreseen by water utilities. The intermittent regime is therefore applied due to necessity, rather than being designed. According to Vairavamoorthy & Elango (2002), this intermittency often results in insufficient pressure in the network, increase in water contamination, as well as in inequity and limitation in the distribution of available water.

Due to the population growth trends, water demand has exceeded water supply capacity in many places around the world. Therefore, the water demand needs to be accurately assessed in order to ensure the resource for the coming years. This also includes the demand patterns and diurnal peak factors, which will provide useful inputs for the design of networks, reservoirs, and pumping stations (Trifunović 2008). In order to ensure a reliable infrastructure expansion strategy, reliable estimates of water consumption and use are required, especially while assessing peak demands (Bougadis et al. 2005).

Accurate demand quantification will also result in the calculation of the real range of consumption, making a basis for an optimal water tariff structure. According to Arbués et al. (2003), the price of water is the main tool that helps to control the demand. Moreover, Rogers et al. (2002) stated that low water prices cause consumers to forget important economic and environmental issues. In addition, according to OECD (2009), the lowering of water tariffs contributes to higher input volumes of water, its pollution, and discourages the efficient use of the resource. Therefore, an optimal water tariff is considered a step towards full-cost pricing of environmentally harmful activities and may lead to the development of policies which internalize social and environmental costs (Dinar 1998).

It is also imperative to understand the users' habits and behaviour within households regarding water consumption. This will help authorities to develop policies which address the preservation of the resource, as well as consumers' satisfaction (Fidar et al. 2010). For instance, water appliance analysis is a useful tool to understand which demand management measures are more suitable, such as installing water-saving devices.

CASE STUDY AREA DESCRIPTION

With more than 60% of the total population, Puerto Ayora is the main urban settlement and tourist centre of the Galápagos Archipelago, located on Santa Cruz Island. This settlement has been confronted by an exponential increase of local population and tourism over the last decades (INEC 2010), while the local authorities have not been able to cope with this growth; in turn, this has caused a significant impact on the water supply.

The water in Puerto Ayora is supplied in an intermittent regime, with the aim to preserve scarce water resources. However, this intermittency is also caused by the lack of proper management, additionally obstructed by sensitive political issues (Reyes et al. 2015a). Due to this situation, the local population perceives the system as unreliable. The municipal water is supplied 3 hours per day on average, depending on the location of the neighbourhood. According to the municipality, the system was initially designed as a continuous supply. However, the lack of individual water meters and fixed tariff structure inflicted water wastages in households forcing the municipality to operate intermittently. In addition, the system has been expanded in an improvised way, adapting to the fast population increase. As a result, the municipality divided the town into five distribution zones with different schedules of supply. To mitigate the intermittency, the premises in Puerto Ayora have storage such as cisterns or elevated tanks. Due to the lack of monitoring/data and various forms of illegal extractions (Reyes et al. 2015a), the overall water demand in Puerto Ayora has been very difficult to estimate.

An additional problem to the reduced quantity is the poor water quality. The water supplied by the municipal system is brackish. During the operation of the system in the 2000s, the brackish water was treated with chlorine-gas to minimize the contamination of the source. However, this treatment was stopped, based on the recommendation of international technicians. In addition, there is evidence of contamination with Escherichia coli (Liu & d'Ozouville 2013), due to the proximity of septic tanks to the source and the filtration of untreated wastewater. The untreated brackish water from several sources is, therefore, considered unsuitable for human consumption (Liu 2011; Liu & d'Ozouville 2013). The only water considered safe for drinking is desalinated-bottled water sold by local vendors in different sized containers.

Reyes et al. (2015b) made an attempt to categorize and assess the water demand in Santa Cruz Island, which was derived from nearly 400 surveys carried out in domestic premises, hotels, laundries and restaurants. This study reported that average per capita water demand (from municipal supply only) ranged in Puerto Ayora from 40 litres per capita per day (lpcpd) to 380 lpcpd. In the same study, the total average domestic consumption was estimated at 163 lpcpd, considering only the municipal supply, adding up to a total average of 177 lpcpd (considering other sources such as bottled drinking water) with standard deviation of ±60 lpcpd. Another study, carried out by d'Ozouville (2009), estimated the specific demand to be as high as 1,500 lpcpd, suggesting informal/illegal accommodation. Also, the same study calculated specific demand based on the total supply records from the Municipality of Santa Cruz at approximately 500 lpcpd.

WMI, a non-governmental organization from Germany, installed approximately 300 water meters in the period 2013 to 2015, in three small and different pilot zones with the aim of monitoring water demand as well as making an attempt to quantify water losses. 115 water meters were installed in pilot zone 1 (PZ 1), located in the northern part of the town. This zone was chosen due to the prevalence of domestic premises. An additional 140 water meters were installed in pilot zone 2 (PZ 2), located in the south-western part of the town. Lastly, pilot zone 3 (PZ 3) was designed to cover the two main avenues of the town where most tourist facilities are located (Av. Baltra and Av. Charles Darwin). The readings in this zone were taken from 54 domestic water meters. All of these metering devices were placed in the distribution network before the individual storage facilities, accounting also for domestic spillage and wastage. The raw data obtained in all three zones were processed as a part of this research, and the results suggest a specific consumption ranging from 20 lpcpd to as high as 4,500 lpcpd. The latter figure is explained by Lupo (2015) as a likely result of illegal tourist accommodation. By all means, this extreme range questions the reason for intermittent supply in Puerto Ayora.

This study aimed to establish the range of domestic consumption in Puerto Ayora, by comparing it with the findings of the survey conducted on water demand by Reyes et al. (2015b), as well as to establish the domestic demand patterns using more accurate measurements. It further analyses the lack of equity by assessing the correlation between the schedules of intermittent distribution applied by the municipality and the water consumption in different zones, trying to find any influence of the network's pressure on the household consumption. For that purpose, an analysis of household water appliances was done with the aim of understanding the end uses of water in the domestic category.

METHODOLOGY

In order to verify the previously obtained and published data, fieldwork was carried out from June to August 2015. In collaboration with the municipality, 18 water meters (1/2″ Flodis-single jet turbine device) were installed in private premises based on the ease of accessibility (Figure 1). The meters were installed on the pipe after the individual storage, thus not accounting for spillage of tanks or cisterns. The meters were installed for 30 days and then dismantled. The hourly readings took place from 6:00 until 20:00 hours and then were completed on the following day at 6:00 hours, in order to register cumulative demand overnight. The readings were taken for each household during 2 working days and 1 weekend day (in the same week), to compare habits with respect to water use.

Figure 1

Installation of water meters.

Figure 1

Installation of water meters.

Table 1 shows the records of 18 water meters over a period of one month. The average specific demand refers to the weighted average of the readings corresponding to the 3 days (Equation (1)), and the monthly specific demand refers to the per capita average from the 30 days the meter was installed.  
formula
(1)
where refers to consumption on the working day 1; refers to consumption on the working day 2; and refers to the consumption on the selected day of the weekend.
Table 1

Consumption registered in 18 installed water meters

Water meter Consumption working day 1 (lpcpd) Consumption working day 2 (lpcpd) Consumption weekend (lpcpd) Weekly specific demanda (lpcpd) Monthly specific demandb (lpcpd) 
M1 151 117 208 153 158 
M2 101 82 94 91 150 
M3 132 78 143 115 140 
M4 48 187 216 144 172 
M5 257 169 90 176 172 
M6 176 114 74 124 288 
M7 62 25 76 52 52 
M8 247 117 116 162 223 
M9 238 238 269 244 275 
M10 370 215 97 235 410 
M11 140 175 241 179 250 
M12 105 71 129 99 86 
M13 56 90 80 74 96 
M14 80 151 116 114 126 
M15 90 54 80 73 71 
M16 79 147 51 94 127 
M17 78 46 90 69 80 
M18 113 50 52 72 75 
Average 140 118 123 126 164 
Standard deviation ±87 ±62 ±66 ±56 ±94 
Water meter Consumption working day 1 (lpcpd) Consumption working day 2 (lpcpd) Consumption weekend (lpcpd) Weekly specific demanda (lpcpd) Monthly specific demandb (lpcpd) 
M1 151 117 208 153 158 
M2 101 82 94 91 150 
M3 132 78 143 115 140 
M4 48 187 216 144 172 
M5 257 169 90 176 172 
M6 176 114 74 124 288 
M7 62 25 76 52 52 
M8 247 117 116 162 223 
M9 238 238 269 244 275 
M10 370 215 97 235 410 
M11 140 175 241 179 250 
M12 105 71 129 99 86 
M13 56 90 80 74 96 
M14 80 151 116 114 126 
M15 90 54 80 73 71 
M16 79 147 51 94 127 
M17 78 46 90 69 80 
M18 113 50 52 72 75 
Average 140 118 123 126 164 
Standard deviation ±87 ±62 ±66 ±56 ±94 

aThis value refers to the weighted average of specific demand of the 3 days of measurement.

bRefers to the average specific demand based on the monthly measurement taken when the water meters were uninstalled after 30 days and considering the number of inhabitants.

In addition, a diary of domestic water appliance analysis was made for 15 premises, one per neighbourhood. The tenants were asked to register the frequency of use of each appliance (toilet, bathroom basin, kitchen basin and shower) during 7 days. Also, hand counters were installed in toilets to register every flushing. The consumption was then calculated based on the time registered and the flow of each appliance, which was previously measured. For the toilets, the size of the flush cisterns was previously annotated.

Finally, the information from the water cadastres belonging to the pilot zones was thoroughly assessed for each zone. The results were analysed in order to find some relation between the schedules of distribution and the wide ranges of consumption.

RESULTS AND DISCUSSION

Results based on installed water meters

Water consumption

The average specific demands for the measured week (weekly average specific demand) and the entire month (monthly specific demand) vary significantly. Moreover, most of the average monthly demands are higher than the average weekly demands, suggesting different daily routines of the users. Furthermore, the results show surprisingly high (up to 410 lpcpd), as well as low demands (46 lpcpd). The measurements were further assessed from the perspective of the water meter location in a particular distribution zone and its specific schedule of supply, as well as by anticipating the standard of living in the metered premises.

The comparisons could not give any correlations between the demands and specific zones, i.e., the schedule of intermittency. At first, it was hypothesized that the households located the furthest from the sources of supply could experience lower network pressures and, consequently, the lower consumptions. However, this hypothesis could not been proven. In contrast, the meter with the highest specific demand is located in the distribution zone with supply of only 2 hours per day. Hence, these figures may possibly be explained by significant differences in lifestyles and/or volume of individual storage, as well as more negligence in some households than in the others, and/or an indication of some sort of informal/illegal tourist business, as the literature suggests. Furthermore, the inspection of water appliances in some houses, such as in toilets, revealed additional water losses.

Consumption classification

Using the average specific demands, the consumption classification was made as in Table 2.

Table 2

Consumption classification of metered households

Description (lpcpd) Consumption classificationa No. of households Percentage (%) Average consumption (lpcpd) 
<100 Low 33 76 
100–180 Medium 39 149 
>180 High 28 289 
Description (lpcpd) Consumption classificationa No. of households Percentage (%) Average consumption (lpcpd) 
<100 Low 33 76 
100–180 Medium 39 149 
>180 High 28 289 

aClassification is based on Ecuadorian standards where average per capita supply should be 150 lpcpd.

Table 2 shows that the majority of the observed population consumes above 100 lpcpd, suggesting reasonable access to water measured by Ecuadorian and international standards. Moreover, the measurements showed that around 50% of the metered households consume more than 150 lpcpd, which contradicts widespread perception on the island that the water availability is restricted.

Water demand patterns

For practical reasons, the daily demand patterns were composed from the hourly measurements during daytime only. According to the schedule of intermittency, there is no supply between 18:00 hours and 6:00 hours; hence, the water in this period is consumed exclusively from the individual storage volume.

Figure 2 shows the diurnal patterns based on the registrations of the 18 water meters done every hour, representing total demand variation of all meters for every day the readings were carried out. The highest peak is observed at the weekend (Saturday), due to the fact that cleaning, laundry and gardening are usually performed then. Also, the lowest peak is observed at the weekend, portraying the habits of spending some time outside home during the weekend. Furthermore, the three peaks (morning, midday and night) are more evident during the working days. These peaks occur in the morning between 6:00 and 7:00 hours, at midday, and in the evening between 17:00 and 20:00 hours. This is explained by the common practice of the local population to have a lunch break at home, due to the short distances in the small town.

Figure 2

Diurnal patterns in Puerto Ayora registered from 18 installed water meters.

Figure 2

Diurnal patterns in Puerto Ayora registered from 18 installed water meters.

Water appliances household analysis

This analysis yielded the results of 15 households who kept diaries on their own water use over a week. This was distributed between the toilet, bathroom basin, kitchen basin and shower. Not surprisingly, the toilets and showers are the appliances that discharge the most of the domestic water, as stated by EPA (2014). Other water appliances were not included since the selected premises did not have gardens or any outdoor water activity. Table 3 shows the average quantities, considering the number of inhabitants per household.

Table 3

Water use of household appliances per person

Water appliance Average demand (l/person/day) Standard deviation (l/person/day) 
Toilet 28 13 
Toilet sink 
Shower 30 13 
Kitchen sink 25 29 
Total 90 11 
Water appliance Average demand (l/person/day) Standard deviation (l/person/day) 
Toilet 28 13 
Toilet sink 
Shower 30 13 
Kitchen sink 25 29 
Total 90 11 

Comparison of the demand from water meters and water appliance analysis

In the next stage, the consumptions observed from 18 water meters were compared with the quantities registered in 15 water diaries. This relation was drawn based on relative proximity of the households. Yet, the correlation (R2) found between the specific demand and the location was extremely low (0.06). Figure 3 shows a map with the average specific demand from water meters and from the water appliance analysis.

Figure 3

Map with average specific demand from installed water meters and water appliance analysis (lpcpd).

Figure 3

Map with average specific demand from installed water meters and water appliance analysis (lpcpd).

This reconfirms the conclusion that the zone of distribution, as well as the schedules, do not influence the specific domestic demand. This may suggest either different lifestyles and/or likely presence of informal (possibly also illegal) tourist businesses, resulting in unreasonably high specific demands calculated by neglecting a ‘hidden’ tourist population.

Analysis of water demand in pilot zones

The information from the water cadastres of the municipality were thoroughly organized and analysed. Table 4 summarizes the water consumption of the three pilot zones.

Table 4

Summary of water demand for three metered pilot zones in Puerto Ayora for 2013, 2014 and 2015

Pilot zone Year Total average consumption per month (m3Average consumption per premise (m3Specific demand (lpcpd) Standard deviation (lpcpd) 
PZ 1 2013 2,689 23 156 ±19 
2014 1,039 29 191 ±39 
2015 3,449 30 200 ±36 
Average 2,393 27 182 ±31 
PZ 2 2013 – – – – 
2014 2,175 24 158 ±37 
2015 4,877 35 232 ±93 
Average 3,526 26 195 ±80 
PZ 3 2013 2,197 33 217 ±32 
2014 4,041 75 499 ±88 
2015 4,599 85 568 ±92 
Average 3,612 64 428 ±70 
Pilot zone Year Total average consumption per month (m3Average consumption per premise (m3Specific demand (lpcpd) Standard deviation (lpcpd) 
PZ 1 2013 2,689 23 156 ±19 
2014 1,039 29 191 ±39 
2015 3,449 30 200 ±36 
Average 2,393 27 182 ±31 
PZ 2 2013 – – – – 
2014 2,175 24 158 ±37 
2015 4,877 35 232 ±93 
Average 3,526 26 195 ±80 
PZ 3 2013 2,197 33 217 ±32 
2014 4,041 75 499 ±88 
2015 4,599 85 568 ±92 
Average 3,612 64 428 ±70 

Source: Water cadastres from the Municipality of Santa Cruz.

Table 4 shows a comparable situation in PZ 1 and PZ 2 that have similar averages of specific demand and standard deviations, showing a wide range of consumption for different households. However, the range of consumption is wider when compared to the results obtained from the surveys done by Reyes et al. (2015a). The highest average values of specific demand are shown in PZ 3. Most of the tourist facilities are also located in this zone, meaning the accumulation of different consumption categories.

The measurements done in PZ 1 and PZ 2 showed the specific demands as high as 1,600 lpcpd and as low as 50 lpcpd. No other assumption could have been made, other than that the highest values indicate excessive wastage in the form of spillage from elevated tanks or cisterns and/or informal tourist businesses. According to a survey done by WMI-GIZ (2013), some of the metered premises were confirmed to be informal tourist accommodation. Moreover, in PZ 3, there were premises observed consuming as high as 4,500 lpcpd, leading to a conclusion that these households are actually tourist facilities. Furthermore, most of the peaks observed were in the months of March, April and May, corresponding to the warmest months of the year. Also, the lowest consumptions are observed in August, September and October, the coldest months of the year, suggesting some influence regarding the season.

Consumption classification in the pilot zones

With the information obtained from the water cadastres, it was possible to classify the consumers into low, moderate and high, depending on their average specific demand for the entire period each pilot zone was metered.

As observed in Table 5, water demand in some households is very high, especially for a place where water resources have been considered to be scarce and the inhabitants' perception is that there is a lack of water. The results suggest that there might be a lack of equity in the distribution of water, since houses that consume higher quantities leave less amount of water for the rest of the households in the same distribution zone.

Table 5

Consumption classification for three pilot zones

Description (lpcpd) Consumption classification No. of households Percentage (%) Average consumption (lpcpd) 
Pilot zone 1 
 <100 Low 18 17% 77 
 100–180 Medium 29 27% 144 
 >180 High 62 57% 262 
Pilot zone 2 
 <100 Low 18 13% 78 
 100–180 Medium 47 34% 148 
 >180 High 72 53% 293 
Pilot zone 3 
 <100 Low 18 53% 75 
 100–180 Medium 24% 164 
 >180 High 24% 637 
Description (lpcpd) Consumption classification No. of households Percentage (%) Average consumption (lpcpd) 
Pilot zone 1 
 <100 Low 18 17% 77 
 100–180 Medium 29 27% 144 
 >180 High 62 57% 262 
Pilot zone 2 
 <100 Low 18 13% 78 
 100–180 Medium 47 34% 148 
 >180 High 72 53% 293 
Pilot zone 3 
 <100 Low 18 53% 75 
 100–180 Medium 24% 164 
 >180 High 24% 637 

Analysis of water scarcity in Puerto Ayora

Table 6 shows the comparison of the average supply per day obtained from the municipal records, against the estimated average water demand from the three metered pilot zones and the 18 installed water meters (referred to as scenarios). The total water demand is based on the average specific demand and the local population per year (INEC 2010). The leakage was taken as a half of the non-revenue water estimated at ±35% by Reyes et al. (2015a). The remaining water calculated (m3/day) refers to the difference between supply and demand, suggesting that for most scenarios there is enough water to supply average per capita consumption.

Table 6

Estimation of water remaining based on different scenarios of consumption

  2013
 
2014
 
2015
 
Source of info Average specific demand 2013 (lpcpd) Total demand 2013 (m3/day) Total average supply 2013 (m3/day) Leakagea (m3/day) Remaining (m3/day) Average specific demand 2014 (lpcpd) Total demand 2014 (m3/day) Total average supply 2014 (m3/day) Leakagea (m3/day) Remaining (m3/day) Average specific demand 2015 (lpcpd) Total demand 2015 (m3/day) Total average supply 2015 (m3/day) Leakagea (m3/day) Remaining (m3/day) 
Pilot zone 1 156 2,026 3,224 564 634 191 2,480 4,305 753 1,071 200 2,598 4,303 753 952 
Pilot zone 2 – – – – – 158 2,051 4,305 753 1,500 232 3,020 4,303 753 530 
Pilot zone 3 217 2,819 3,224 564 159 499 6,483 4,305 753 −2,932 568 7,378 4,303 753 −3,828 
Water meters – – – – – – – – – – 164 2,130b 4,303 753 1,420 
  2013
 
2014
 
2015
 
Source of info Average specific demand 2013 (lpcpd) Total demand 2013 (m3/day) Total average supply 2013 (m3/day) Leakagea (m3/day) Remaining (m3/day) Average specific demand 2014 (lpcpd) Total demand 2014 (m3/day) Total average supply 2014 (m3/day) Leakagea (m3/day) Remaining (m3/day) Average specific demand 2015 (lpcpd) Total demand 2015 (m3/day) Total average supply 2015 (m3/day) Leakagea (m3/day) Remaining (m3/day) 
Pilot zone 1 156 2,026 3,224 564 634 191 2,480 4,305 753 1,071 200 2,598 4,303 753 952 
Pilot zone 2 – – – – – 158 2,051 4,305 753 1,500 232 3,020 4,303 753 530 
Pilot zone 3 217 2,819 3,224 564 159 499 6,483 4,305 753 −2,932 568 7,378 4,303 753 −3,828 
Water meters – – – – – – – – – – 164 2,130b 4,303 753 1,420 

aLeakage was assumed to be 50% of previously calculated NRW (35%) by Reyes et al. (2015a) since there is no other estimation of leakage in the literature and it represents an average leakage figure. It assumes only leakage along the distribution system.

bCalculated based on the projection of population for 2015 (INEC 2010).

The comparisons show that there is mostly enough water to satisfy the average specific demand. Only for the pilot zone 3 scenario (2014 and 2015), the average amount of supplied water would not suffice. This also indicates that the perception of ‘scarce’ water is not accurate. Yet, potentially high numbers of unreported tourists lower the extreme values of specific demand significantly. Furthermore, an interesting observation is that over the period 2014–2016, the amount of water extracted increased by 50%, while the schedule of intermittency did not change.

The discussion of the results therefore points to an evident lack of management regarding the water distribution system. According to the results, the quantity of water does not seem to be the key issue; it is more the quality. As observed from specific demand figures, some premises registered as domestic, seem to be offering services to tourists, especially in PZ 3. This refers also to the lack of cross-checking of information among institutions. Even though there are only around 40 premises registered as hotels in the water cadastre of the Municipality of Santa Cruz, the Ministry of Tourism affirms there are around 150 tourist accommodations.

Extremely high specific consumption in some households suggests that there is enough brackish water, for at least more hours than currently supplied. Due to the negligence allowing the overflow of individual tanks, this form of storage may not be aiding the intermittency, as originally considered, but actually amplifying it, as well as causing the inequity in consumption; the bigger the storage facilities, the less the availability of water for the rest of the consumers in each zone of distribution. For this reason, the installation of individual water metering is imperative in order to monitor more accurately the domestic consumption. Also, the tariff structures should be changed to a volumetric system.

Finally, monitoring and registering of tank overflows and the development of policies involving penalties should be put into place, with the objective of creating awareness within the population of the excessive wastage of water.

CONCLUSIONS

The results of the measurements in this study indicate that the extreme range of specific consumptions can be most likely attributed to three different factors: (1) diverse domestic habits within the local population; (2) lack of awareness and consequent wastage of water within premises, which could also be a direct consequence of fixed tariff structure; and (3) inequity in the distribution. The spillage from elevated tanks and other storage facilities has been identified previously as a major problem, which can be further supported by the figures in this research. Furthermore, the high specific demands create the suspicion of informal accommodations/any other type of tourist business, which may be drastically inflating the specific consumption figures. For these reasons, the municipality needs to develop strict monitoring programmes, as well as specific policies that will incentivize the population to reduce water demand and reduce wastage in households. When it is known that every single visitor needs permission to enter the island and is registered upon arrival and departure, pinpointing illegal tourist facilities should not be too difficult; the results in this research could actually serve as complementary information to that survey.

Furthermore, the wide range of consumptions are surprising for an island where water resources are perceived as scarce. This study suggests that the average per capita demand is even higher than the study made previously by Reyes et al. (2017). The obtained figures suggest that there is enough water available, leading to a reduction of intermittency. Unfortunately, no correlation could be found between zone of distribution of supply and measured consumptions. On the other hand, based on the water appliance diary, if toilets were to be replaced by more efficient ones (current ones have tanks of approximately 12 litres), it is believed that the domestic demand could be reduced by approximately 25%. This would also contribute to the improvement of the supply system, allowing more hours of service as well.

Finally, this study has shown that the need for storage facilities should also be reassessed. These facilities do not seem to be helping the local population as has been thought over the last two decades, but may be limiting the performance of the supply system. Finally, the leakage levels need to be further investigated, since this is an important figure when calculating the amount of total actual supply to consumers.

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