User water demand can be met in intermittent water supply systems, and normally it is met if they have sufficient capacity to store water in their homes to be used in the hours when it is not supplied from the public pipe network. The situation in many intermittent water supply systems is that they have enough water to cover the demand of the users but are supplying it intermittently, and the challenge is to achieve a transfer from intermittent to continuous supply. If the delivery of water is continuous, which supplies drinking water 24/7, it covers not only the basic needs of the user, but all the needs for water they may have. The present study was carried out using smart domestic water meters to obtain consumption information in an intermittently supplied pilot sector. The electromagnetic meters record the water flow through pulses and generate consumption information, which gives us the consumption pattern in volume of water and in the time it is used. During the study, multivariate statistical methods were applied to obtain information from the domestic meters and to identify, as realistically as possible, the consumption of drinking water and the relationship between consumption and specific characteristics of the users and thus anticipate future demand. The results showed a close relationship between consumption and the size of the residence. The most influential factors were the number of bathrooms and the number of occupants. A direct relationship between the pressure and the volume supplied was presumed at the beginning of the study but the opposite was found. Equipment was installed to measure the pressure in the network at the time of establishing the continuous supply of drinking water. The multivariate analysis provides a selection of the most important variables that influence and explain the behavior of the water use and consumption pattern, with the operation determined by the operating agency.

  • Users' water consumption and demand.

  • Intermittent water supply systems.

  • Water distribution network with changes of pressure.

  • Smart domestic water meter to obtain consumption information.

  • Multivariate analysis to select the most important variables.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Research on the consumption of a sector helps us to understand the habits of the users and to establish the best way to supply drinking water to their homes. However, the investigation involves difficulties since the supply of drinking water varies from one place to another.

In some areas of Mexico, the water supply service follows a continuous delivery scheme, 24 hours a day, 7 days a week. In other zones, it is scheduled for certain hours of the day by the water utility in charge and catalogued as ‘intermittent supply’ (Andey & Kelkar 2009). Countries with arid and semi-arid climate opt for intermittent supply (IWS) to manage consumption and preserve their water reserves. Many cities with exponential population growth turn to intermittent supply to solve the imbalance of supply and demand. However, such drastic measures often fail, as they do not contemplate the negative effects of intermittent supply in piped networks and the resulting water losses (Christodoulou & Agathokleous 2012). In an intermittent supply, the water required depends on user needs and the amount of water collected by the users depends on system pressure. In this type of supply, the transit time in the pipes is reduced, so the operator may be forced to increase the pressure to boost the flow and take advantage of the few hours of supply.

It is important to monitor water consumption and changes in the current supply to anticipate future demand, especially in areas of controlled population where no present or future growth is anticipated. On the other hand, the know-how serves as a reference for application in similar sectors. Consumption characteristics help to improve management and operation, including meeting the demand of users according to their activities. In addition, the water utility in charge will be able to determine balances as a tool for flow recovery (Serrano et al. 2018).

There is currently no study that analyzes the consumption of drinking water in intermittent supply mode. This is the case in Mexico and, consequently, in the state of Chihuahua as well. In literature isolated efforts exist. They are of short duration, poor sampling and are basically focused on continuous delivery (Alcocer et al. 2012). In the study described in this report, an effective and objective tool was used to design the sampling tests. The domestic water meter generates measurements autonomously which avoids human error, it also generates a pulse when it detects water flow in the service connection pipe and creates a numerical value that is sent and processed to quantify the volume and is recognized by any current or future receiving system.

The definition of steady and unsteady flow is key to the investigation. This is achieved by looking closely at the time interval of permanence, the pressure and the type of flow in the network. Water networks have been designed, throughout history, by assuming a steady flow that maintains constant pressure and flow characteristics (Bon 2017). Generally, the flow rate in continuous supply networks is not permanent due to variations in demand. Neither is the flow rate in intermittent supply networks; the filling of pipes and evacuation of air within them result in changes of both pressure and velocity. After air evacuation and network filling, modeling standards (Nagarajan et al. 2003) are applied to measure the permanent flow: water in liquid phase with no gas mixture (air). Few field studies present evidence of the process, which depends on ‘how’ the water flow transits in the network, by recording velocity and pressure (Nelson & Erickson 2017). Domestic water consumption in intermittent supply systems (IWS) has been calculated using retrospective surveys, structured observations, storage inventories, metered (limited) data and flowmeters. The items can be used individually or in tandem (Guragai et al. 2018). This methodology is also used in particular locations to link objective measurement of equipment with the user's and the operator's perception.

The target of this research is to analyze the drinking water consumption of domestic users employing smart domestic water meters in an area with intermittent supply. A consumption relationship must be established with data collected in surveys, such as the number of bathrooms, size of the dwelling and number of tenants. Then, based on the information obtained, the transition to continuous delivery can be made.

Site description

The State of Chihuahua is one of 32 states in the Mexican territory. It has a population of 3,741,869 inhabitants, 3.0% of the country's population (INEGI 2019). The Municipality of Chihuahua is located at latitude north 28° 38′, longitude west 106°04′, altitude 1,455 masl (metres above sea level). It has an area of 9219 km2 (3.4% of the state's total area). Chihuahua City is the state capital (IMPLAN 2017). The neighborhood of Colonia Riberas de Sacramento is a low-quality housing complex made up of 1,500 families. It is located to the north of the city of Chihuahua (Figure 1) and covers an area of approximately 1.1 km2 with an average altitude of 1,500 masl. It has an annual dry-temperate climate of 18.5°C.

Figure 1

Study area: Riberas de Sacramento.

Figure 1

Study area: Riberas de Sacramento.

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Riberas de Sacramento is a high priority area for the government of Chihuahua because of its low levels of schooling and social structure. Most of the community members are young and have long work schedules, so they come home to rest. Such a routine is opposed to the recording of potable water consumption. The high consumption reflected in the government agency records do not match; the activity and type of residents suggest low consumption. The area was chosen for study based on this discrepancy.

Representative study points

In a previous analysis of this research (Mendoza & Navarro 2020), the general consumption of users in the Riberas de Sacramento sector was examined. The research identified important elements related to consumption, such as topography, typology of the existing network, economic capacity of the users and the type of existing supply. This led to the division of the sector into areas with homogeneous technical and social characteristics and also to locate future measurement points for the individual study of the consumption of representative users in the sector's areas according to the population size. This also allowed us to determine a sample size based on the inspection level of the MIL-STD 105E sampling (Montgomery 2004), where the sample varies according to its total population by levels (Table 1).

Table 1

Sampling quantities obtained (Mendoza & Navarro 2020)

SectorClassificationPopulation sizeInspection levelSample size
(0–15 m3456 
(16–200 m328 
(0–15 m3502 13 
(16–200 m338 
(0–15 m31,339 13 
(16–200 m3178 
(0–15 m3680 13 
(16–200 m398 
SectorClassificationPopulation sizeInspection levelSample size
(0–15 m3456 
(16–200 m328 
(0–15 m3502 13 
(16–200 m338 
(0–15 m31,339 13 
(16–200 m3178 
(0–15 m3680 13 
(16–200 m398 

Note: Abbreviations in the level inspection column signify a particular classification based on population size.

To identify the main guiding characteristics of the sector's consumption pattern, a survey was performed among the different users studied (Figure 2), at the same time as the pressure and flow data were read. Although the survey was developed by the research group, it was based on standardized surveys used by the authorities to ascertain and evaluate the user's perception regarding service and fees (Dorantes 2016).

Figure 2

User characteristics survey.

Figure 2

User characteristics survey.

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Data compilation and data sampling

The objective of statistical interpretation is to obtain information on one or more of the characteristic parameters of the inhabitants. To achieve this, a population measurement of the analyzed sector must be performed and conclusions must be drawn based on the values resulting from the number of samples tested (Devore 2008). The method of analysis was performed using the Military Standard 105E (MIL-STD-105E) sampling technique (Montgomery 2004). This procedure has a significant statistical value of 80%, according to the characteristics surveyed and analyzed in the sector.

The measurement points detected in a previous analysis of this research were inspected by installing Contazara CZ4000 flow domestic water meters (CONTAZARA 2022) and Multilog 2 pressure recorders (HWM 2022) to obtain information on the water consumption of each user per week, and their subsequent analysis on the specific kind of consumption and its relationship with the existing drinking water supply in the sector. According to the sample size defined in the stratified experimental design, a methodology for the measurement installation and to obtain the survey monitoring point in a synchronized manner was carried out. The Contazara CZ4000 domestic water meter (Figure 3) and the Multilog pressure recorder are intelligent instruments that detect maximum and minimum flow, operating ranges and automatic consumption peaks.

Figure 3

Contazara CZ4000 meter installation.

Figure 3

Contazara CZ4000 meter installation.

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The consumption measurement took 3 months from April to June 2020, with stays of one week per household but not the same week for all; there were only five meters, so they took turns on installation. Fifty-five samplings were performed in consumption households. It required one week to measure the pressure per zone according to the classification of zones previously detected (Mendoza & Navarro 2020) so five samplings were performed. Measurements were made in the same spring season, so there was no problem in the analysis with respect to having readings from different seasons of the year. Recorded for study were: date, address, meter ID, number of bathrooms, number of tenants, number of storeys, customer category (domestic or commercial), meter brand, consumption and pressure. Cumulative flow volumes were taken per week and at the time of meter installation. Users were asked about the number of tenants and characteristics of the residence to determine the real water endowment of the area under study. A multivariate analysis was performed with Minitab software (Minitab 2022). This software offers statistical analysis of variables and correlations. This tool was chosen because it provides an objective evaluation of the variables obtained independently and establishes the relationship between variables and factors numerically or visually, making it possible to identify the most important variables and the predominant relationships in the problem analyzed.

Data analysis

Water distribution network models are widely used by water companies. Consumer demands are one of the main uncertainties in these models, but their calibration is not feasible due to the low number of sensors available in most real networks. However, individual demand behavior can also be gauged if background information is available (Sanza & Perez 2014). From the data obtained in a previous stratigraphic analysis, the consumption demand can be inferred by calculating the estimated water endowment of the sector from the relationship between average consumption and population typology.

Consumption readings were taken with Contazara CZ4000 meters. These smart devices recorded data ranging from 0 m3 to 8.84 m3 of consumption per week. Through the process it was possible to establish the time of highest consumption and determine the flow passing through the meter. Consumption was classified by group, volume of accumulated consumption and independent consumption by meter used, in order to compare the heterogeneity of the data and its independence with respect to the similarity of meter operation. This also allowed us to establish the actual water supply existing at the site and led to a future forecast calculation to make a demand prediction in Riberas de Sacramento, Chihuahua. The analysis of these data was performed statistically and with a multivariate support that reflects the relationship between the variables of the analysis such as pressure, flow, topography, time and user characteristics. A statistical model was formulated to differentiate the effects of the factors (Wong et al. 2010).

Much more attention needs to be given to probabilistic forecasting methods if water utilities are to make decisions that reflect the level of uncertainty in future demand forecasts. Reliable urban water demand forecasting facilitates operational, tactical and strategic decisions that are critical for effective drinking water supply (Donkor et al. 2014).

Consumers and homes: features and consumption

Data on the particular characteristics of each network user were classified as shown in Figure 4. The chart showing type of meter reflects that most of the users do not have a meter at all, and those that do have a meter have very different brands. The type of use is mostly domestic, with only five measurements in commercial premises. The average number of people living in each home is three, ranging from a minimum of one to a maximum of six people. Almost all of the houses are single storey and there are a few two storey houses. The vast majority of the houses have only one bathroom, although there are a few with two or three bathrooms. Finally, the registered consumption per week is around the average of 3 m3, with maximums of 9 m3 and minimums of 1 m3. Several brands of meters were registered in the homes surveyed: Arad, Sensus, Dorot, Invensys and Cicasa. However, the vast majority of the surveyed users did not have a meter, but connected to the network with a tap hose. The total data from the surveys carried out is shown in Table 2.

Table 2

Riberas de Sacramento measurement data

DateAddressMeter IDStart readingEnd readingBathroomsPeopleNumber of storeysWater UseMeter typeConsumption (m3)
May 13, 2020 C. Rio Volga #23928 P18VA426876 26.84782 35.695 Domestic Arad 
April 8, 2020 C. Rio Missouri #23124 P18VA426864 7.41216 15.80156 Commercial Arad 
April 22, 2020 C. Rio de la Plata #23910 P18VA426842 33.0887 40.896 Domestic None 
April 22, 2020 C. Rio Mosela # 2222 P18VA426864 19.49136 27.0193 Domestic None 
April 1, 2020 C. Rio Columbia #1840 P18VA426876 10.89898 17.90348 Commercial None 
April 8, 2020 C. Rio Amur #2202 P18VA426876 17.90348 24.44186 Domestic None 
April 8, 2020 C. Rio Elba #1413 P18VA426852 11.46338 17.8234 Domestic Invensys 
April 1, 2020 C. Rio Rhin #2416 P18VA426842 22.87054 28.92836 Domestic Dorot 
April 22, 2020 C. Rio Lorenzo #23324 P18VA426876 27.27098 32.89292 Domestic None 
April 1, 2020 C. Rio Madeira #1841 P18VA426852 5.88214 11.46338 Domestic None 
June 3, 2020 C. Rio Meta #1818 P18VA426849 45.00964 50.3927 Domestic Sensus 
June 3, 2020 C. Rio Marañon #1409 P18VA426864 35.67138 40.15034 Domestic Dorot 
May 6, 2020 C. Rio Missouri #23522 P18VA426849 35.695 39.9546 Domestic Arad 
April 29, 2020 C. Rio Obi #2034 P18VA426852 22.3848 26.391 Domestic None 
April 15, 2020 C. Rio Negro #2220 P18VA426849 26.57582 30.5554 Domestic Cicasa 
May 6, 2020 C. Rio Amur #1626 P18VA426842 42.1728 46.0518 Domestic Invensys 
April 15, 2020 C. Rio Tigris #2434 P18VA426864 15.80156 19.49136 Domestic None 
April 1, 2020 C. Rio Mississippi #2203 P18VA426849 19.44754 23.0997 Domestic Sensus 
May 27, 2020 C. Rio Missouri #23114 P18VA426852 27.7685 31.38972 Domestic Dorot 
May 20, 2020 C. Rio Obi #1608 P18VA426849 40.897 44.48428 Domestic None 
May 20, 2020 C. Rio Indo #1408 P18VA426876 26.84782 30.39876 Domestic Sensus 
April 8, 2020 C. Rio Lena #2222 P18VA426849 23.0997 26.57582 Domestic Arad 
April 22, 2020 C. Rio Parana #2400 P18VA426852 19.08756 22.3848 Domestic None 
June 10, 2020 C. Rio Rojo #1841 P18VA426852 32.11138 35.33296 Domestic None 
May 13, 2020 C. Rio Murray #1600 P18VA426842 46.0518 49.237 Domestic Sensus 
June 10, 2020 C. Rio Cauca #1800 P18VA426842 53.17556 56.29048 Domestic Sensus 
June 10, 2020 C. Rio Cauca #1600 P18VA426864 40.15034 43.1789 Domestic Arad 
May 27, 2020 C. Rio Ventuari #23106 P18VA426842 49.33734 52.30882 Domestic Arad 
April 29, 2020 C. Rio Rhin #2213 P18VA426849 32.776 35.695 Domestic None 
April 1, 2020 C. Rio Arno #1405 P18VA426864 7.41216 10.31966 Domestic None 
May 27, 2020 C. Rio Yojoa #23103 P18VA426864 32.8125 35.67138 Domestic None 
April 15, 2020 C. Rio Zambeze #23718 P18VA426876 24.44186 27.27098 Domestic Sensus 
June 3, 2020 C. Rio Marañon #1800 P18VA426876 31.16744 33.9712 Domestic Sensus 
April 15, 2020 C. Rio Obi #2219 P18VA426842 30.5082 33.0887 Domestic Sensus 
May 6, 2020 C. Rio Pardo #2003 P18VA426852 26.391 28.96428 Commercial None 
June 10, 2020 C. Rio San Fco #22500 P18VA426876 33.9712 36.52192 Domestic None 
May 20, 2020 C. Rio Yojoa #23108 P18VA426864 30.3384 32.8125 Domestic Dorot 
April 22, 2020 C. Rio Amur #2040 P18VA426849 30.5554 32.776 Domestic None 
May 13, 2020 C. Rio Ventuari #23532 P18VA426852 26.7912 28.96428 Domestic Sensus 
May 6, 2020 C. Rio Mayo #1810 P18VA426864 27.643 29.8004 Domestic Arad 
April 29, 2020 C. Rio Congo #2008 P18VA426876 32.89292 34.669 Domestic None 
April 8, 2020 C. Rio Obi #1800 P18VA426842 28.92836 30.5082 Domestic None 
April 29, 2020 C. Rio Mayo #2037 P18VA426842 40.896 42.1728 Domestic None 
April 15, 2020 C. Rio Yang Tse Kiang #2420 P18VA426852 17.8234 19.08756 Domestic Cicasa 
May 20, 2020 C. Rio Beni #1614 P18VA426852 26.7912 27.7685 Domestic Dorot 
May 13, 2020 C. Rio Murray #1626 P18VA426849 39.9546 40.897 Commercial Sensus 
June 3, 2020 C. Rio Arauca #1801 P18VA426842 52.30882 53.17556 Domestic Sensus 
May 27, 2020 C. Rio Mosa #23104 P18VA426876 30.39876 31.16744 Domestic Dorot 
May 6, 2020 C. Rio Obi #1838 P18VA426876 34.669 35.4366 Commercial Atron 
June 3, 2020 C. Rio Meta #1437 P18VA426852 31.38972 32.11138 Domestic Dorot 
April 29, 2020 C. Rio Missouri #23322 P18VA426864 27.0193 27.643 Domestic None 
May 13, 2020 C. Rio Congo #2207 P18VA426864 29.8004 30.3384 Domestic Arad 
May 27, 2020 C. Rio Columbia #1432 P18VA426849 44.48428 45.00964 Domestic Dorot 
May 20, 2020 C. Rio Niger #1410 P18VA426842 49.237 49.33734 Domestic Dorot 
June 10, 2020 C. Rio Rojo #1403 P18VA426849 50.3927 50.39284 Domestic None 
Mean Domestic None 
Max Domestic None 
Min Commercial Atron 
DateAddressMeter IDStart readingEnd readingBathroomsPeopleNumber of storeysWater UseMeter typeConsumption (m3)
May 13, 2020 C. Rio Volga #23928 P18VA426876 26.84782 35.695 Domestic Arad 
April 8, 2020 C. Rio Missouri #23124 P18VA426864 7.41216 15.80156 Commercial Arad 
April 22, 2020 C. Rio de la Plata #23910 P18VA426842 33.0887 40.896 Domestic None 
April 22, 2020 C. Rio Mosela # 2222 P18VA426864 19.49136 27.0193 Domestic None 
April 1, 2020 C. Rio Columbia #1840 P18VA426876 10.89898 17.90348 Commercial None 
April 8, 2020 C. Rio Amur #2202 P18VA426876 17.90348 24.44186 Domestic None 
April 8, 2020 C. Rio Elba #1413 P18VA426852 11.46338 17.8234 Domestic Invensys 
April 1, 2020 C. Rio Rhin #2416 P18VA426842 22.87054 28.92836 Domestic Dorot 
April 22, 2020 C. Rio Lorenzo #23324 P18VA426876 27.27098 32.89292 Domestic None 
April 1, 2020 C. Rio Madeira #1841 P18VA426852 5.88214 11.46338 Domestic None 
June 3, 2020 C. Rio Meta #1818 P18VA426849 45.00964 50.3927 Domestic Sensus 
June 3, 2020 C. Rio Marañon #1409 P18VA426864 35.67138 40.15034 Domestic Dorot 
May 6, 2020 C. Rio Missouri #23522 P18VA426849 35.695 39.9546 Domestic Arad 
April 29, 2020 C. Rio Obi #2034 P18VA426852 22.3848 26.391 Domestic None 
April 15, 2020 C. Rio Negro #2220 P18VA426849 26.57582 30.5554 Domestic Cicasa 
May 6, 2020 C. Rio Amur #1626 P18VA426842 42.1728 46.0518 Domestic Invensys 
April 15, 2020 C. Rio Tigris #2434 P18VA426864 15.80156 19.49136 Domestic None 
April 1, 2020 C. Rio Mississippi #2203 P18VA426849 19.44754 23.0997 Domestic Sensus 
May 27, 2020 C. Rio Missouri #23114 P18VA426852 27.7685 31.38972 Domestic Dorot 
May 20, 2020 C. Rio Obi #1608 P18VA426849 40.897 44.48428 Domestic None 
May 20, 2020 C. Rio Indo #1408 P18VA426876 26.84782 30.39876 Domestic Sensus 
April 8, 2020 C. Rio Lena #2222 P18VA426849 23.0997 26.57582 Domestic Arad 
April 22, 2020 C. Rio Parana #2400 P18VA426852 19.08756 22.3848 Domestic None 
June 10, 2020 C. Rio Rojo #1841 P18VA426852 32.11138 35.33296 Domestic None 
May 13, 2020 C. Rio Murray #1600 P18VA426842 46.0518 49.237 Domestic Sensus 
June 10, 2020 C. Rio Cauca #1800 P18VA426842 53.17556 56.29048 Domestic Sensus 
June 10, 2020 C. Rio Cauca #1600 P18VA426864 40.15034 43.1789 Domestic Arad 
May 27, 2020 C. Rio Ventuari #23106 P18VA426842 49.33734 52.30882 Domestic Arad 
April 29, 2020 C. Rio Rhin #2213 P18VA426849 32.776 35.695 Domestic None 
April 1, 2020 C. Rio Arno #1405 P18VA426864 7.41216 10.31966 Domestic None 
May 27, 2020 C. Rio Yojoa #23103 P18VA426864 32.8125 35.67138 Domestic None 
April 15, 2020 C. Rio Zambeze #23718 P18VA426876 24.44186 27.27098 Domestic Sensus 
June 3, 2020 C. Rio Marañon #1800 P18VA426876 31.16744 33.9712 Domestic Sensus 
April 15, 2020 C. Rio Obi #2219 P18VA426842 30.5082 33.0887 Domestic Sensus 
May 6, 2020 C. Rio Pardo #2003 P18VA426852 26.391 28.96428 Commercial None 
June 10, 2020 C. Rio San Fco #22500 P18VA426876 33.9712 36.52192 Domestic None 
May 20, 2020 C. Rio Yojoa #23108 P18VA426864 30.3384 32.8125 Domestic Dorot 
April 22, 2020 C. Rio Amur #2040 P18VA426849 30.5554 32.776 Domestic None 
May 13, 2020 C. Rio Ventuari #23532 P18VA426852 26.7912 28.96428 Domestic Sensus 
May 6, 2020 C. Rio Mayo #1810 P18VA426864 27.643 29.8004 Domestic Arad 
April 29, 2020 C. Rio Congo #2008 P18VA426876 32.89292 34.669 Domestic None 
April 8, 2020 C. Rio Obi #1800 P18VA426842 28.92836 30.5082 Domestic None 
April 29, 2020 C. Rio Mayo #2037 P18VA426842 40.896 42.1728 Domestic None 
April 15, 2020 C. Rio Yang Tse Kiang #2420 P18VA426852 17.8234 19.08756 Domestic Cicasa 
May 20, 2020 C. Rio Beni #1614 P18VA426852 26.7912 27.7685 Domestic Dorot 
May 13, 2020 C. Rio Murray #1626 P18VA426849 39.9546 40.897 Commercial Sensus 
June 3, 2020 C. Rio Arauca #1801 P18VA426842 52.30882 53.17556 Domestic Sensus 
May 27, 2020 C. Rio Mosa #23104 P18VA426876 30.39876 31.16744 Domestic Dorot 
May 6, 2020 C. Rio Obi #1838 P18VA426876 34.669 35.4366 Commercial Atron 
June 3, 2020 C. Rio Meta #1437 P18VA426852 31.38972 32.11138 Domestic Dorot 
April 29, 2020 C. Rio Missouri #23322 P18VA426864 27.0193 27.643 Domestic None 
May 13, 2020 C. Rio Congo #2207 P18VA426864 29.8004 30.3384 Domestic Arad 
May 27, 2020 C. Rio Columbia #1432 P18VA426849 44.48428 45.00964 Domestic Dorot 
May 20, 2020 C. Rio Niger #1410 P18VA426842 49.237 49.33734 Domestic Dorot 
June 10, 2020 C. Rio Rojo #1403 P18VA426849 50.3927 50.39284 Domestic None 
Mean Domestic None 
Max Domestic None 
Min Commercial Atron 
Figure 4

Summary of factors influencing water consumption in Riberas de Sacramento.

Figure 4

Summary of factors influencing water consumption in Riberas de Sacramento.

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Multivariate analysis of consumption

Comparative graphs of maximum consumption flow (Figure 5) and hourly rate of highest consumption (Figure 6) were obtained directly from Contazara software which provides this advantage of instant analysis through its algorithm based on the data collected. In both graphs, consumption represents the total amount of water accounted in m3 for the time in which the smart water meters were in place (a week), within the ranges specified on the other axis (flow rate and time range). This information confirmed the intermittency of supply in the sector due to its relationship with the visible increase in measurements during the opening hours of supply in the sector, which were from 4:00 am to 8:00 am and from 4:00 pm to 8:00 pm. The increase in consumption is reflected in the high flow rates, mostly 500 to 1,000 liters/hour, due to the effect of the refilling of containers in homes. When the supply valves are opened in the morning and evening hours, users in the area store water in tanks, cisterns and other containers, causing a peak in consumption that is reflected in readings of 500–1,000 L/hr (liters per hour). These are the consumption characteristics of the average user. The existing supply in the sector is also evident.

Figure 5

Maximum consumption flow in Riberas de Sacramento.

Figure 5

Maximum consumption flow in Riberas de Sacramento.

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Figure 6

Hourly range of highest consumption.

Figure 6

Hourly range of highest consumption.

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By comparing these two graphs (Figures 5 and 6), it is possible to immediately detect that the water supply is intermittent. The saturation of the network indicates the beginning of semi-continuous supply and a schedule in which consumption increases. As consumption stabilizes, users resume regular consumption at a stable pressure. A new study can be proposed at this point under the premise that the network can be analyzed as a continuous system.

The consumption classification in ascending order was also obtained (Figure 7). A wide variety of private consumption is observed, ranging from 8.84 m3 to 0 m3 per week, although it registers a parity between 2 m3 and 4 m3 per week. This is shown in the consumption classification graph (Figure 7) and means that the peak in consumption is due to the water storage tank in the house (Arreguin et al. 2010). The tank is constantly refilled because when the supply is cut off, the user consumes the stored water. The deficit of 1 m3 to 2 m3 is maintained since that is the standard storage capacity of the installed reservoirs. At the end of the tank filling, the water flow is stabilized and continuous flow begins. However, the supply ends soon. Valves controlling intermittent service must be closed. Using Minitab software, it was possible to compare the range of values for each consumption category by means of a box plot (Figure 8). It can be observed that four ranges are registered outside the average. The range with no record is the highest.

Figure 7

Consumption classification in ascending order.

Figure 7

Consumption classification in ascending order.

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Figure 8

Box plot for consumption ranges.

Figure 8

Box plot for consumption ranges.

Close modal

In addition to the graphs of the overall flow rate of the meters, a comparative graph of the readings of the different meters is elaborated in Figure 9, which shows a comparison of the measurements taken by each of the five meters available for sampling. In this graph it can be seen that the measurement points were evenly distributed among the different meters installed (Pau et al. 2013) using similar trend lines in each of the representative lines per registered meter.

Figure 9

Comparison of meter readings.

Figure 9

Comparison of meter readings.

Close modal

A comparative graph of the information generated by the pressure gauges (Figure 10) was generated to corroborate the drinking water supply schedule in the sector, which is consistent with the information from the government agency that controls the operation of the hydraulic infrastructure and establishes a current service schedule from 4:00 to 8:00 and from 16:00 to 20:00. A variety of pressures is also observed in the sector, ranging from 10 mwc (meter water column) to 21 mwc, confirming that all the previously defined homogeneous areas are suitably representative.

Figure 10

Comparison of pressures by representative points of zones.

Figure 10

Comparison of pressures by representative points of zones.

Close modal

Subsequently, by means of a multivariate analysis of the principal components, the sedimentation (Figure 11) and components (Figure 12) plots were obtained. Principal component analysis is a method that evaluates each variable independently, defining the associations with other variables and their impact in terms of the magnitude of the eigenvector (Bartholomew 2010). In this way, different conjectures could be corroborated, such as that pressure and consumption have a similar behavior, although they behave in the opposite direction in the component graph. On the other hand, the sedimentation graph shows that there is a variable with greater weight in determining the general behavior. In fact, four variables are the most important to achieve results in the research.

Figure 11

Multivariate sedimentation analysis.

Figure 11

Multivariate sedimentation analysis.

Close modal
Figure 12

Components of multivariate analysis.

Figure 12

Components of multivariate analysis.

Close modal

In the component graph (Figure 12), many items are highlighted, including pressure and date, which appear on the negative side of the graph but mean a relevant but opposite importance from the consumption data and user characteristics of bathrooms and floors. It establishes a relationship between both variables, since the weather generates a behavior of higher or lower pressure on water consumption. There are three analysis subsets that do not seem to influence the results notably (Meter ID, Meter brand and type), but should be considered because of their trend line. Bathrooms, consumption and floors are closely related components and their values reflect high correspondence.

The analysis of value and eigenvector is used to evaluate how the response means of the different subsets differ between the levels of the different terms in the model. Emphasis is placed on the eigenvectors whose coefficients or magnitude correspond to high eigenvalues. The values of the eigenvector coefficient components (Table 3), which are data obtained from the multivariate factor analysis in Minitab, give us the relationship of the most important components (the highest), which are also highlighted in Figure 12 (bathrooms, storeys and consumption).

Table 3

Values of coefficient components

VariablePC 1
Date −0.417 
Meter ID 0.182 
Bathrooms 0.441 
People 0.184 
Floors 0.357 
Meter type 0.264 
Meter brand 0.279 
Consumption 0.457 
Pressure −0.286 
VariablePC 1
Date −0.417 
Meter ID 0.182 
Bathrooms 0.441 
People 0.184 
Floors 0.357 
Meter type 0.264 
Meter brand 0.279 
Consumption 0.457 
Pressure −0.286 

Estimated future water endowment

In addition to the characteristics of the users, information was collected on individual consumption per week, with the purpose of estimating the real water endowment of the sector (CONAGUA 2007), with a result of 150 L/person/day. This result is important for real modeling and to determine, in the near future, the behavior of continuous flow and demand. This would be achieved using the EPANET simulation model.

Demand forecast calculations, based on consumption analysis, resulted in the following:

  • 3,500 user accounts in the sector.

  • 3 users per account means 10,500 users in total.

  • 150 L/person/day equals 1,605,500 L/day or 18.6 L/sec.

The Riberas de Sacramento sector has a flow meter that generates a graph of the actual consumption flow in the sector that allows us to compare data and draw some conclusions. The research allowed us to identify the characteristics of consumption in Riberas del Sacramento: actual supply in the area (150 L/person/day), hours of highest consumption (17.00–21.00), water flow rate with the highest value in L/sec (500–1,000 L/h) and classification of users regarding the type of consumption in general (2 m3 to 4 m3 average per week).

This analysis will be useful for forecasting future demand, based on per capita consumption. Water consumption in liters per capita per day was correlated with the pattern of supply, green areas, family size and age of the householder (Fan et al. 2013). For this reason, the characteristics of the population in a study are important to detect certain variants that cause effects on the results (Willis et al. 2013).

The analysis of information on the housing characteristics of users reflects important trends in the future behavior of the sector. The vast majority of the users surveyed are classified as: three-member family, one-storey house. They do not have a meter and are connected to the network with a hose. This is representative of the sector. The consumption of these users will be reflected in the global analysis of the consumption information.

Multivariate analysis of the consumption of the target population indicated that there was an opposite relationship between pressure and consumption. That is, flow pressure did not affect consumption, nor did higher pressure increase consumption. As pressure and consumption were on opposite sides of the components graph; it is assumed that in this study what reflects more the amount of consumption of the users are characteristics such as the number of bathrooms and the number of floors of the house. The pressure is not analyzed in depth because the service is intermittent and most of the time that there is good pressure is dedicated to filling water networks and water tanks in the houses.

Another important observation is the direct relationship between consumption and the number of toilets in the dwelling. The number of similar users reflects a trend that can be considered a pattern when it comes to normalizing their consumption to a detected average value. The correlation between variables resulted in several graphs with a common result between consumption values and living conditions by stratigraphic location according to the classification performed. By means of the previous stratigraphic analysis, the basis of this research, it was possible to find a uniformly distributed sample that reflects the general behavior of the sector. The study evidences the pattern and magnitude of consumption data, previously recorded in the equipment, sub-classified by the characteristics of the people living in the site: inhabitants of the house, size of the dwelling, number of bathrooms and the type of meter installed in the register. Not having a meter is a peculiar characteristic that defines non-discriminate consumption.

It is difficult to establish, from the results of this research, the exact time in which the supply behaves on a continuous delivery and not intermittent mode. It can only be said that, thanks to the multivariate analysis of the consumption data and its correlation with the particular characteristics of the users, it is certain that the study area had a non-permanent supply due to the flow variations.

This modality forced users to develop consumption practices, including storage in water tanks and cisterns, and to reduce consumption during the hours when the supply valves are closed. An important factor to consider is the detection of the impact generated by the constant filling of individual storage tanks that affects the time taken by the network to re-establish a continuous flow of water.

It is important to gather this information for future analysis to identify consumption patterns in the area, which can also be adapted to any other area of the city, based on a multivariate analysis of the specific characteristics of the users already detected and their actual consumption. The intermittent water supply invariably affects the consumption pattern of the user under the schedule that is being discussed since it forces them to use a minimum of 1 hour to fill the tank that is used as a reserve for water during the hours of the day when water is not supplied. These patterns could be elaborated with the combination of the analysis of a study performed with Contazara domestic water meters and with pulse domestic water meters to complement the first analysis and the specific detection of the exact moments of change from permanent to non-permanent flow by recording the pressure behavior by means of autonomous equipment designed for that purpose.

The comparison of the daily supply calculation from particular measurements of the users under study and the actual consumption of all users in the sector, gives us a similarity that encourages a favorable extrapolation of the study to another region or sector and to generate reliable data for forecasting future demand based on the growth of the population. This result is not close to the standard of current values, billed by consumption in the sector, of the regulatory agency of the drinking water service and differs from what is considered in the literature for sectors with similar characteristics and average water endowments proposed in the country. In other words, the water endowment for each particular community has to be based on a previous analysis such as the one performed in this research in order to be more realistic.

This study functions as a preliminary reconnaissance of the area to later carry out an analysis of the same sector, using pulse sensor meters to determine the stochasticity of the users' consumption and its correlation with a hybrid demand pattern that is better adapted to the type of potable water supply existing in the area. Note that analyses can be made not only in a continuous supply but also in an intermittent supply that is intended to transition to a continuous supply. As limitations for the improvement of this study, it should be mentioned that it will be difficult for the Riberas de Sacramento sector to change to a continuous supply due to the lack of knowledge of the operating agency regarding the benefits that it entails and because they are accustomed to an intermittent supply which in a very comfortable way has allowed them to manage the distribution of water volumes at their discretion. It is intended that through this study we begin to have knowledge of water consumption in intermittent supply to be compared later in another study with a continuous supply and see results that would lead to a change in thinking about the intermittent supply that causes many limitations.

All relevant data are included in the paper or its Supplementary Information.

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