Section II Analysis of Water Use and the Factors that Affect It

Transcription

1 Section II Analysis of Water Use and the Factors that Affect It

2 Background This study was based in the capital city of United Arab Emirates (UAE), Abu Dhabi. The location of Abu Dhabi, within the larger geographic area, is shown in Figure 1. Other principal cities of the Emirate are Al Ain and Liwa. The UAE gained its independence in December of It has an approximate land area of 67,340 square kilometers, although most of its borders are not demarcated. The county is largely desert, with mountains in the north. The areas lying along the Persian Gulf are frequently humid. The UAE shares boundaries with Oman, Qatar and Saudi Arabia. 1 Figure 1: Regional map of area surrounding Abu Dhabi There are two main sources of usable water in Abu Dhabi Emirate: groundwater and desalinated seawater (desal). Groundwater is generally saline, and is used mainly for agriculture, so the main supply of domestic potable water is desal water. Reclaimed wastewater is used for irrigation purposes. In 2010 there were eight seawater desalination plants in the country, which supplied approximately one quarter of all fresh water used, and almost all of the drinking water. The annual freshwater consumption for the country, including landscape water use, is estimated to be 650 liters per person per day. 2 1 U.S. Library of Congress Country Studies (1993) 2 See

3 All three cities in the Emirate are supplied with desal water via transmission companies that are jointly owned by public and private holding companies. The production of desal water itself is in the hands of private or joint public/private companies that own and manage the electric generating plants. Distribution of fresh water is managed by public water companies. Responsibility for overseeing the operations of the water and electric generation sector is in the hands of the Regulation and Supervision Bureau, which is charged by law with ensuring the reliability of the water and electrical systems, which includes the goal of efficient use of resources following international standards. 3 The Abu Dhabi Environmental Agency (EAD) was established specifically to work towards achieving a sustainable water system that will allow the long-term, socio-economic development of the Emirates of Abu Dhabi. 4 The EAD stated the problem of water resources succinctly in its 2006 report, which, to paraphrase, stated that the Emirate faces the following challenges: Low precipitation rate (<100 mm/year) Low groundwater recharge rate (< 0.4% of use ) No reliable surface water resources One of the highest per capita water consumptions in the world. One of the key strategies for improving water management, and achieving a more sustainable system was to raise public awareness of the need to conserve water and use it efficiently. The purpose of the Residential End Uses of Water Study was to obtain highly detailed water use data, referred to as flow trace data, from a group of single family residences. These flow traces were to be analyzed down to the event level by trained and experienced analysts using the Trace Wizard program and the results of the analysis were to be compiled into a water use database. This database was then to be linked to a separate database containing physical and demographic information about the participating homes and the combination of the water use data and the survey data were to be used to explore the factors that affected water use in the sample. The water use database was also used to generate detailed statistical information on household and per capita water use. Flow trace data were obtained for three separate monitoring periods (MP). The first period, MP1, ran generally from June 12 through June 25, MP2 covered the period from September 23 through October 5, 2013, and MP3 ran from December 2 through December 15, There were a total of three, two week, data collection periods, that covered the early summer, fall, and winter seasons. The purpose of collecting data over three periods was to Abu Dhabi Emirate Water Resources Statistics 2006

4 examine the extent to which water use in the homes was affected by the season. When seasonality was not a factor in water use, as was the case for the non-landscape uses, the data from the three monitoring periods was averaged to provide values for analysis. Over the course of the study data were collected on a total of 151 homes for at least one monitoring period. During MP1 data were successfully collected from a total of 143 homes. During MP2 data were obtained from 148 homes, and during MP3 data were collected from 142 homes. During each period data were obtained from slightly different homes, so that when the data were combined there were data from a total of 151 homes. Sample Description Nationalities and Regional Groups There were a total of 151 homes in the study sample. Since Abu Dhabi is a very cosmopolitan city, the residents come from all over the world. During the survey the nationality of each household was determined, and the nationalities were grouped according to larger geographical or cultural regions. There were four regional origins used to describe the nationality of the participants: Arab expats, Asian expats, UAE nationals, and Western expats. Members of the study group came from 28 countries; Table 1 shows the number from each country, and to which regional group each country was assigned. Western expats comprised the largest group with 57 households, followed by Arab expats with 41, UAE National with 30, and Asian expats with 23. Table 1: Breakdown of households by nationality and regional group Nationality Region Arab Expats Asian Expats UAE Nationals Western Expats 1 American Australian Bahrain British Canadian Dutch Egyptian Emirati French German Indian Iraqi Total

5 Nationality Region Arab Expats Asian Expats UAE Nationals Western Expats 13 Irish Italian Japanese Jordanian Lebanese Norwegian Pakistani Palestinian South African Spanish Sudanese Swedish Swiss Syrian Tunisian Turkish Total Total Housing Compounds The study group of 151 homes had members in 10 compounds located throughout Abu Dhabi. Table 2 shows the number of homes from the study group located in each of the compounds, and the percent of the total group they represent. Table 3 shows the breakdown of the study group by housing compound and regional group. These tables show that the study group was not evenly distributed among compounds, and the regional groups were not evenly distributed either. The compound with the greatest representation was Sas Al Nakheel, which accounted for nearly half, or 46%, of the study group. The next largest group came from Seashore Villas, which accounted for only 18% of the study homes. Table 3 shows that some regional groups tended to be spread fairly evenly among compounds and some tended to gather in one or a few compounds. The Arab expats showed the most homes in the Seashore Villas compound, but were fairly evenly distributed in the other compounds. The Western and Asian expats, however, were strongly concentrated in the Sas Al Nakheel compound. Behind this were the two Al Qurm compounds with 8% each. Two compounds, Karama and Mushrif had a single representative each.

6 The approximate location of each of the compounds has been shown in Figure 3. This shows that from a geographical perspective the compounds in the study cover the main area of Abu Dhabi. The breakdown of the occupancy by housing compound is shown in Table 4. On average there were 5.1 persons per home in the study group. This includes only family members and helpers who sleep in the home. Day workers are not included as residents. Table 2: Distribution of study group by compounds Compound Frequency Percent Cumulative Percent Al Dhafra Al Khalidiya Village Al Maha Gardens Al Qurm Compound Al Qurm Gardens Golf Gardens Karama Area Mushrif Gardens Sas Al Nakheel Seashore Villas Total Table 3: Distribution of study group by housing compound and regional group Compound Name Number of Homes from Regional Group Total Arab expat Asian expat UAE national Western expat Al Dhafra Al Khalidiya Village Al Maha Gardens Al Qurm Compound Al Qurm Gardens Golf Gardens Karama Area Mushrif Gardens Sas Al Nakheel Seashore Villas Total

7 Temperature (Deg C) Table 4: Occupants by compound Compound Total Residents Family Members Al Dhafra Al Khalidiya Village Al Maha Gardens Al Qurm Compound Al Qurm Gardens Golf Gardens Karama Area Mushrif Gardens Sas Al Nakheel Seashore Villas Total Live-In Help Temperature During the Logging Periods Data were available on temperature and relative humidity during the three logging periods. The temperature data were the most useful for comparisons. FiFigure 2 shows the maximum, minimum, and average daily temperatures recorded during each logging period. There is clearly a downward trend in both the maximum and minimum temperatures during the three periods. The maximum, minimum, and average temperatures all dropped by approximately 10 degrees Celsius between MP1 and MP3. The relative humidity rose during the logging periods from an average of 53% in MP1 to 63% in MP3. The change in temperature was significant enough between MP1 and MP3 that any water use that was weather-related should have shown a response MP1 MP2 MP3 Max Min Ave Monitoring Period F i Figure 2: Temperature during monitoring periods

8 Figure 3: Location of compounds in the study 8

9 Description of Flow Trace Analysis In order to properly interpret the results of this study it is important to understand how flow trace analysis works, and consider its strengths and weaknesses. The goal of flow trace analysis is to disaggregate water use in a single family home based on a highly precise pattern of flow, over time, obtained from the main water meter for the house. The key is that the main water uses, such as toilets, clothes washers, dishwashers, irrigation systems, and showers in the home provide very clear flow patterns that are relatively easy to identify. Other uses, such as faucets, leaks, car washing and pools, are more ambiguous. The idea is to extract the information for the easily identified events, which leaves behind a smaller volume of water in the remaining categories. This smaller volume of water can then be analyzed statistically to examine the factors that appear to have an influence. Flow trace is a very good tool when understood in this way, but it does involve a degree of uncertainty and random error. When one balances the information provided by flow trace analysis against the practical impossibility of sub-metering a home to provide end use information of greater detail, its value is clear. Working with flow traces and the Trace Wizard program, an experienced analyst can determine the important information related to the daily household use for the key fixtures and appliances, and can determine the efficiency levels of these as measured by their volumes of use and flow rates. Water use for categories like faucets and leaks can overlap since sometimes events produced by a faucet may appear to be a leak, and vice versa. This is where the information from the surveys can be used to identify relationships between household characteristics and the end use in question. This process can help clarify the factors that are probably linked to the use. For example, leak events may sometimes include very small faucet uses, intermittent flows for automatic pool filling, ice machines, or continuous flows from certain water treatment systems. By modeling leakage against the presence of pools, home water treatment, automatic irrigation systems etc., it is possible to see what factors explain increased leakage or leak-like events. Leakage estimates should be tempered with the knowledge that in some cases what appears to be a leak may be a legitimate use that requires continuous flow. These types of issues tend to work on the fringes of the data. The main body of information provided by the analysis is the core household water use patterns and efficiency levels for the household. Each flow trace file obtained from Dornier was analyzed into individual water use events using the Trace Wizard software. During Trace Wizard analysis each event is characterized according to its end use, start time, duration, volume, maximum flow rate and mode flow rate. This is a stepwise process. Each trace is first checked to verify that the logged volume agrees with the meter volume. When the volumes agree then the trace can be analyzed as is. When the volumes do not agree further investigation is required. In some cases the data logger records the data but the volume recorded differs from that of the meter by a small amount. These traces usually are used with a correction factor applied so that the volumes agree. In other cases the volume of the data logger and the meter volumes differ by a substantial amount. These traces are opened for inspection. In some cases the trace files may contain a few

10 erroneous events, caused by infrequent electrical interference with the sensor, which causes extremely high flow rates to be recorded. If these are isolated events they can be removed manually during analysis, and the rest of the trace can be used. If the entire trace is contaminated with interference then it has to be discarded. In some cases the logger simply fails to record any data, in which case the trace is discarded and if necessary the site is relogged. After the volumes were evaluated and, if needed, correction factors applied, each of the traces with usable data was disaggregated into individual events. The Trace Wizard program is similar to an expert system in that the analyst identifies how events should be categorized according to fixture type, and then the program uses this information to find all similar events in the trace and assign them to the chosen fixture. For example, if on Day 1 of the trace a toilet is identified that has a volume of 6.5 liters, a peak flow of 8 lpm, and a duration of 50 seconds, these fixture parameters are adopted by the analyst. The program will then find other similar events throughout the duration of the logging period that match the first event. Each of these events is labeled as a toilet with no further intervention required on the part of the analyst. The analyst works through the flow trace to find all of the major fixtures, assigns the fixture parameters, and verifies that the fixtures have been identified successfully by the program. Occasionally the parameters from more than one fixture may be very similar, as is the case in Figure 4. In this example, one of the toilet profiles is identical to some of the clothes washer cycles. When this occurs it is necessary for the analyst to identify the events manually rather than using the Trace Wizard program to identify the events automatically. Figure 4: A trace that has identical parameters for one of the toilets and one cycle of the clotheswasher

11 When multiple events occur simultaneously it may be necessary for the analyst to identify events by inspection and separate these events manually. The analyst also identifies the first cycle of all clothes washer and dishwasher events in a trace. This allows the number of clothes washer and dishwasher cycles to be grouped into loads, from which the gallons per load can be determined. The analyst may need to evaluate other events on a case-by-case basis. Manual irrigation, pool filling, and baths can have enough variability from one trace to another that it can be difficult to develop a template that contains all of the necessary parameters to identify them automatically. Events such as these are identified through inspection by the analyst. Visual inspection may be necessary for identifying more common events as well. For example, if someone leaves a kitchen faucet running for 10 minutes while they wash the dishes it may look like a shower if it is flowing at a similar flow rate. In these cases classification of the event is a judgment call supported by factors such as frequency, time of day (showers are more likely to occur in the morning and late evening) and the proximity of other events (long periods of faucet use may be followed by the dishwasher). Each water use event in the flow trace is characterized by fixture type, flow rate, duration and volume. The analysis does not however, reveal the make or model of a fixture or appliance. The efficiency of devices like toilets, showers, and clothes washers is inferred from their measured volumes or flow rates. Following the initial disaggregation and analysis process, the trace is checked by another analyst to make sure there are no obvious errors and that events that require a judgment call seem reasonable. Once all questions are resolved, the trace is then ready for further processing, and the process is repeated on another trace. Simple traces can be analyzed in as little as 30 minutes. Analysis of complex traces may take several hours to complete. The level of complexity is normally related to the volume of water used in the home during the logging period and the frequency of events occurring simultaneously such as a continuous leak. Trace Wizard Identification of Common Household Fixtures Trace Wizard analysis provides a visual tool for identifying individual events that take place during the two-week data logging period. The most common events found during trace analysis are toilets, faucets, showers, clothes washers, dishwashers, irrigation events and leaks. Examples of these events follow, along with a description of a typical profile. While flow trace analysis is not perfect it performs very well in identifying the key household end uses. There are always ambiguous events that can be categorized differently by different analysts, and these create scatter to the results. Trace Wizard is at its best in identifying anything that is controlled by a timer or a mechanical device. These include toilets, dishwashers, clothes washers, and irrigation timers. Fixtures that are limited by a valve or which operate in a repeatable fashion such as showers or baths are also fairly easy to identify. The program deals with simultaneous events by splitting

12 out the super-event from the base event. This covers the situation of the toilet flush on top of the shower or irrigation. It also has the ability to split out events that run into each other, but this requires the analyst to manually identify the point at which one event ends and another begins. This covers the situation where a faucet is turned on before a toilet stops filling. The following sections provide some examples of how typical fixtures and appliances are recognized in flow trace analysis, and discuss issues encountered in dealing with each category of end use. Toilets Previous end use studies have found that there are, on average, five toilet flushes, per person, per day in the home. Therefore it was not unusual to find toilet flushes in a trace during the two week data logging period. Some homes had only one or two single flush toilets in use while other homes had as many as five, dual flush toilets. Some toilets may get used frequently if they are located in the main part of the house while others, such as the toilet in the master bathroom may only get used in the morning and evening. Toilet flush profiles in other words, peak flow, duration, and volume can vary markedly from one toilet to the next. Even the same make and model toilet can have different profiles depending on variables such as the water pressure, the adjustment of the fill line, or the weight of the flapper. Some toilets fill at a high flow rate and fill in less than a minute while others fill at a very low flow rate and may take several minutes to refill the tank. Figure 5 is an excellent example of three toilet flush profiles. The volume of the first toilet is 5.4 liters; the flow rate is 10.5 lpm with a fill time of 40 seconds. The volume of the second toilet is 4.9 liters; the flow rate is 1.1 lpm with a fill time of 4m 20s. The volume of the third toilet is 5.1 liters, the flow rate is 2.5 lpm and with a fill time of 2m 20s. It is typical to see a lot of toilet flushing first thing in the morning when the family is preparing to leave for work or school, early in the evening, and at bedtime. These are times when showering often takes place as well. Figure 5 is an example of the evening routine beginning with shower use (shown in red) and some faucet use (yellow) and three toilet flushes (green).

13 Flow rate: 10.5 lpm Volume: 5.4 liters Duration: 40s Flow rate: 1.1 lpm Volume: 4.9 liters Duration: 4m 20s Flow rate: 2.5 lpm Volume: 5.1 liters Duration: 2m 20s Figure 5: An example of three toilet profiles Faucets Faucets use is the most diverse of all end use categories and includes everything from bathroom faucets and bidets to filling buckets for car washing and watering plants. Faucets are characterized by their irregular and random type of pattern unlike toilets that have a set volume or clothes washers and dishwasher that have distinct flow patterns. Faucet flow rates can be highly variable as can faucet duration. The flow from kitchen and bathroom faucets are often restricted by aerators, and like showerheads, have a maximum flow rate (although no minimum flow). Figure 6 is an excellent example of numerous faucet events (yellow) of short duration and flow rates less than 8 lpm, typical of kitchen and bathroom faucets. Note the faucet events that are concurrent with toilet flushes that are likely to be due to hand washing. Utility sinks, hose bibs, and bathtubs are examples of faucets whose flow rate is generally unrestricted by aerators be quite high. Examples of this type of faucet use are shown in Figure 7.

14 Figure 6: Many short faucet events (shown in yellow) under 8 lpm. Note simultaneous faucet events with toilet flushing. Figure 7: Multiple faucet events with longer duration and higher flow rate

15 Showers Showers typically have one of two profiles. The shower profile in Figure 8 (shown in red) is representative of homes that have what is commonly referred to as a tub/shower combo, in which the shower and bathtub are operated by the same faucets. This results in a high flow when the faucets are turned on initially and the temperature is being adjusted; the diverter is then pulled, the shower head restricts the flow and the flow rate drops. The flow then remains constant until the faucets are turned off. The second shower profile, shown in Figure 9, is typical of a stall shower where the flow goes directly through the showerhead and is therefore limited by the flow rate of the showerhead. The flow rate of a showerhead is dependent on the flow rating of the showerhead and the operating water pressure. The second shower, occurring nearly two hours later is 6:30 minutes with the same flow rate. During the two hour time period shown are multiple toilet flushes (green), multiple faucet uses (yellow) and the first two cycles of the dishwasher (pink). A showering pattern that is seen in some households is demonstrated in Figure 10. The profile of these shower events is typical of a stall shower but the grouping of events is commonly seen with what are often referred to as military showers. During this water-saving type of shower the water is turned off for a minute or so for soaping, shaving, or shampooing and then restarted again for rinsing. The shower can be turned off at that faucet or with a device in-line with the showerhead that reduces the flow to nearly zero. Figure 10 is two showers and each shower is two cycles. Shower/tub combination with a diverter Figure 8: Shower representative of a shower/tub combination with a diverter

16 Stall showers Figure 9: Two stall shower events First cycle of first shower First cycle of second shower Figure 10: Two, multi-cycle, shower events

17 Clothes Washers Clothes washing occurred in nearly all of the homes during the three study periods. Only a few homes indicated that they didn t have a clothes washer or that they used a laundry service for all or most of their clothes washing. A few participants indicated that they had two clothes washers in the home. Most participants in the study group had one of three types of clothes washers: top loading, front loading, or twin tub. The clothes washer shown in Figure 11 is typical of a top loading clothes washer. Each cycle is similar in volume and represents filling of the clothes washer tub. Cleaning and rinsing is accomplished by agitating clothing in a volume of water sufficient to submerge the clothing. Top loading clothes washers often have several settings allowing for temperature adjustment, load size, and additional rinse cycle. Figure 12 is the profile of a high-efficiency clothes washer, typically a front loader. These machines are designed to use less water than the standard top-loading clothes washers by using a tumbling action that provides cleaning by continually dropping and lifting clothes through a small pool of water. Some even have sensors that detect the weight of the clothing and adjust the volume of water accordingly. The clothes washer loads, shown, in Figure 12 vary considerably in volume. The first load is 68 liters and takes approximately 50 minutes to complete. The second load is 30 liters and is completed in about 20 minutes. Twin tubs are similar to top loading machines in that the clothes are submerged in a volume of water. The tub can be filled either manually or mechanically. After washing, the clothes are placed in a second tub for rinsing. Identifying manually operated twin tubs in the Trace Wizard program can be challenging because there is no set time between cycles and the fill level (and therefore volume) of each cycle may have considerable variability.

18 Volume: 61.6 liters Flow rate: 9.8 lpm Duration: 6:40 m Volume: 71.6 Flow rate: 10.3 lpm Duration: 7:10 m Figure 11: Typical profile of a top loading clothes washer First cycle Volume: 9.6 liters First cycle Volume: 9.5 liters Figure 12: A clothes washing pattern representative of a high-efficiency front loading clothes washer

19 The results of a survey of study participants revealed a wide variety of clothes washer manufacturers and models. The most frequently reported clothes washer manufacturer was LG; of the 49 homes for which data was available 16 of them had LG clothes washers. Having the same manufacturer did not guarantee any similarity of the clothes washer profile. Clothes washing, shown in turquoise in Figure 11 and Figure 12, represent two different LG model clothes washers, each with very different profiles (the white stripes indicate the first cycle and the beginning of a clothes washer load). As with a standard top-loading clothes washer, the initial cycle is flagged as first cycle, which allows the total volume of the clothes washer to be calculated for statistical purposes. Top loading clothes washers are generally very easy to identify in the Trace Wizard program even when there is a considerable amount of concurrent water use. The similarity in volume, flow rate, and duration of the first and second cycles is characteristic. Front loading clothes washers can be considerably more challenging to identify due the variability in load size, and often much less obvious cycles. The water use pattern may resemble faucet or toilet use and it often takes a skilled analyst to find the clothes washer reliably, particularly when there is concurrent water use. Many other examples of clothes washers can be found in Appendix B. Dishwashers Although dishwashers are multiple cycle events, their water use typically accounts for less than 5% of the total indoor use. Because they are cyclical and there is very little variation in the flow rate or volume of the cycles, dishwasher events are usually easy to identify. And, like clothes washers, the first cycle of the dishwasher event is identified which enables the number of events to be counted. Figure 13 is an example of a dishwasher event with four cycles. Faucet use often precedes or occurs during dishwasher events as dishes are rinsed, or items are being hand-washed. In the flow trace analysis the dishwasher category includes only water being used by mechanical dishwashing machines. Water used for hand-washing of dishes would be counted as part of the faucet category.

20 Figure 13: Typical dishwasher event with multiple cycles. There are multiple faucet uses occurring that are likely due to food preparation and/or hand washing of dishes. Irrigation Large automatic irrigation events are the easiest to identify and are usually characterized by a large event consisting of several very distinct segments, each with its own duration and flow rate as the various zone valves open and close. Automatic irrigation is generally operated by a timer device that turns on the irrigation at a set time, on specified days, and irrigates multiple zones in sequence. The flow rate for each zone varies depending on the type and number of sprinkler heads located on that zone. Figure 14 shows an irrigation event that occurs on December 7 th at 5:20 PM. The event properties show that the volume of the irrigation event is 2643 liters with a mode flow of 29 liters per minute, a duration of 1 hour and 40 minutes. This event was repeated daily throughout the duration of the data logging period. The change in flow rate occurs ten times during the irrigation event and is indicative of different irrigation zones. There is a continuous 2 lpm leak that occurs for the duration of the trace. The irrigation accounts for 60% of the water use in this home; the leak is 32% of the total use. Drip irrigation is typically lower flow than overhead irrigation and may be operated manually or as a separate zone on an automatic irrigation system. Drip irrigation is generally used for non-turf type plants that require less water and less frequent watering than turf or other high water-use plants. Figure 15 is an example of a drip irrigation event with a flow rate of 1.7 lpm and duration of 48 minutes. The total volume of the event is 80 gallons. There is a shower, clothes washing and some faucet use that are running concurrently with the irrigation event. A key to recognizing this event as irrigation as opposed to some other use was the fact that it was repeated during the logging period at a similar time of day.

21 Figure 14: Large irrigation event consisting of ten zones. Concurrent, continuous, baseline leak Manual irrigation using a hose is the most challenging type of irrigation to identify. Flow rates may be variable depending on the type of plant material being irrigated. Manual irrigation of lawns may require a high flow rate and events of long duration; buckets may be filled multiple times or hoses turned on and off to irrigate potted plants which would look like faucet use. Trees and shrubs may be watered infrequently using a low flow rate for a long duration. The time of day, event duration, flow rate and frequency is likely to vary considerably in a home that irrigates manually. Figure 16 is an example of manual irrigation. The event is 949 liters, the mode flow is 21 lpm, and the event duration is 47 minutes. The survey indicates the presence of 3.25 m 2 of irrigated area and the presence of lawn, high water use plants, potted plants, and irrigation with a hose.

22 Figure 15: Drip irrigation event Figure 16: Manual irrigation event

23 Leakage and Continuous Events Leakage should be considered a unique end use, since conceptually it combines wasteful losses of water associated with potentially several types of specific fixtures or purposes. There are generally two types of leakage: intermittent and run-on or continuous. While leaks are generally easy to identify in Trace Wizard it is often impossible to identify the source of the leak. Although leaks can occur in any water using device they are most typically associated with toilets, faucets, irrigation, and swimming pools. Leaks are particularly difficult to detect when they are silent as is the case with a swimming pool that uses a float valve for automatic refilling. Leakage is flow that cannot be easily classified as a typical fixture, such as use for toilet flushing, clothes washing, faucets, showering, irrigation, or other commonly found household use. Leaks can be attributable to malfunctioning fixtures such as a leaking toilet or irrigation system or due to process uses, such as a reverse osmosis system, or a non-recirculating pond or fountain. The cause of flow attributed to leakage may be discovered during a site visit or from information provided on the survey returned by the homeowner. Often, however, this information is unavailable, and the cause of leakage remains unknown. Since the leak category represents such an important part of single family residential water use, looking further into the causes of these types of events would be beneficial. Leakage flow rates can be highly variable from a flow that is barely detectable by the water meter to a flow of several liters per minute. Figure 17 is an excellent example of a continuous leak. The figure is a screen capture from Trace 12S311 during the third logging period between 7:27 AM and 9:27 AM. The leak flow rate (shown in blue) is 2 lpm for the duration of the logging period and results in more than 36,000 liters of leakage or 32% of the total volume. There are two types of leaks commonly associated with faulty toilets: a continuous leak and an intermittent flapper leak. A continuous leak can occur when the flapper remains open and the bowl refill tube runs continuously in an attempt to maintain the level of the float. An improperly set float adjustment screw or a sticking flush handle can also result in continuous flow. A flapper leak is intermittent and occurs as a result of slow leakage from the tank. A float valve is set to maintain a certain water level in the tank; as the float drops below the set point the tank is refilled. Figure 18 is an example of a flapper leak. The leak (shown in blue) begins after the first toilet flush (green) at approximately 12:43 PM and ends with a toilet flush that occurs around 2:27 PM. Intermittent flapper leakage occurs throughout the trace and results in just 2,100 liters of use. Other examples of leakage can be found in Appendix B.

24 Figure 17: Continuous 2 lpm leak for the duration of the trace Figure 18: Flapper leak tends to end with a toilet flush

25 Water Use Data All of the water use data used for this study relied on water meters. There were two meters at each house: the utility meter and the study meter installed on the outlet from the roof tank. The data logger that recorded the flows used for the disaggregation was attached to the roof tank meter. Understanding the metering is important because ultimately the accuracy of the study depends on that of the meters. Meter Accuracy All water meters lose accuracy at very low flows, and this will result in a certain amount of under-registration by the meters. Naturally, if the meter fails to respond to flows these will not be picked up by the data logger and will not be included in the water use analysis contained in this report. Meter errors are a constant issue with all water systems which is why new, high quality, utility grade, water meters were used for this study. The water meters that were used for this study (Manuflo CT5-S20) are positive displacement type meters, which are rated to have a minimum registration of lpm, and to record these flows with 95% accuracy. These meters meet E.E.C. approvals for Class C use. Any meter with a very small continuous leak, just under the minimum registration, say, might allow flows in this range to pass unrecorded. In the worst case scenario this could account for approximately 100 lpd, which would be equal to 5% of the average recorded flows in the homes (see Table 8). It is highly unlikely that every home in the study had a continuous leak of 0.08 lpm, or that any such leak would be continuous. A value of 100 lpd of unrecorded flow can be considered the upper bound of unrecorded flow, and in all likelihood the unrecorded flow is much lower than this. It is not possible to say precisely, however, since we are talking about flows that are beyond the accuracy of the meters. We would expect the accuracy of the data used for this study to be equal to or better than other studies the Aquacraft has been involved in. First, the meters were all new; in most of the other studies we have done it was not possible to replace the existing meter with new units. Also, in most of the studies we have done the data loggers have relied on magnetic sensors to pick up the flows through the meter. Magnetic sensors work well, but are subject to noise from electrical interference. The water meters provided high resolution pulse outputs directly to the data loggers, and this eliminated a major source of error. As discussed below the agreement between the logged volumes and the meter volumes was very good. Arrangement of Meters There were three sources of water use data available for the study. The primary source was from the flow trace files obtained from the data loggers attached to the meters on the outlets of the roof tanks. The second source was the meter readings on the meters to which the data loggers were attached, and the third was the meter readings from the utility meters just below the point where the customer taps were located on the mains.

26 In order to evaluate the data it is important to understand the arrangement of the various water components serving the lots. Generally, the customer service line is tapped to the water main at a point close to the house. This is followed by a water meter located in a pit or on a wall near the property line. From here the service line connects to an underground cistern. The cistern is not pressurized and the water level in the tank is controlled by a float valve. There is an overflow pipe on the cistern so that if the float valve malfunctions any excess water is conveyed to a drain. Water is pumped from the storage cistern to the roof tank by a transfer pump where it is stored until used. In most cases water from the roof tank flows through a pressure pump that brings the water to a more convenient pressure for use in the home. In order to monitor water use the study installed a separate, high resolution, meter attached to a data logger on the water line outlet from the roof tank prior to the pressure pump. This allowed all water use from the roof tank to be recorded and analyzed. The data recorded by the data loggers was transmitted via cell phone network to the Dornier researchers who stored it and sent it on to Aquacraft for end use analysis. The high resolution water use data from the roof tank meters were the primary source of water use information used for this analysis. The typical arrangement of the piping from the water main to the outlet from the cisterns is shown in Figure 19. Inspection of the diagram shows that there are two areas where water could leave the system between the utility meter and the roof tank. The first is the cistern itself, which could overflow or leak. The second is from the piping into and out of the cistern. If there are leaks in these pipes then water would be lost. It is also possible that hose bibs could be attached to the pipes from which water could be withdrawn for a variety of outdoor uses such as car washing, irrigation, pool filling, pavement washing, etc. It should be noted that any water lost from or withdrawn from the piping upstream of the outlet from the roof tanks would not be recorded by the study meter, and hence would not be included in the water use analysis performed for this study.

27 Figure 19: Typical piping diagram between the water main and the outlet from the cistern Comparison of Logged Data to Meter Data In order to validate the results of the data on which this study was based the data provided by the data logging equipment were compared to the water meter readings from the smart meter on the roof tanks and from the ADDC utility meters. There are two things to consider in checking the total water deliveries recorded by the smart meters and data loggers used for this study. The first is whether the flows recorded by the data loggers matched those recorded by the smart meters, and the second is whether the flows recorded by the meters installed on the roof tanks match the consumption indicated by the utility meters. This will show whether the roof meters are recording all of the water use in the homes, or whether any water is delivered to the homes from the utility main that is lost or withdrawn from the system prior to the roof tanks. In order to verify that the data loggers were recording the flows through the roof meters accurately, the sub-contractors that were responsible for the meter installation read the smart meters at the time that they were installed and when they were removed. Both the meter reading and the date of the reading were recorded. This allowed the total volume of water passing through the meter to be determined and the average daily flow rate calculated.

28 The meters and loggers were installed in June 2013 and most were removed by the end of January The target was to install 150 meters; ultimately a total of 151 meters were installed. One hundred eleven of these had been removed, and the ending reading obtained, by the time the report was prepared. The average daily water use recorded by the smart meters was 1809 lphd. The average daily use recorded by the data loggers for these same 111 accounts was 1785 lpd. Table 5 summarizes the comparison of the flows recorded by the smart meters and those recorded by the data loggers and used for the disaggregation that formed the basis of this study. The fact that the logged volumes matched the meter volumes so closely is a strong indication that the data transmitted from the data loggers accurately reflected the volume of water that was recorded by the water meters. Since the water meters were new, and met high accuracy standards, it is safe to assume that the data from the loggers was an accurate reflection of the water use by the homes from the roof tanks during the logging periods. Table 5: Verification of accuracy of data logging data by comparison to smart meter readings Parameter Number of accounts for which smart meter readings were available by January 30, 2014 Average daily water use recorded by the smart meters for entire study period Average daily water use recorded by data loggers for same homes during three logging periods Value lpd 1785 lpd Projected annual household water use 651 M 3 In addition to reading the smart meters at the start and end of the logging period, in June and December respectively, the technicians also attempted to read the utility meters on these dates. The comparison between the smart meters and the ADDC utility meters was not as close as was the comparison with the smart meters. This is at least partly due to the fact that many of the utility meters were not working, and reading could only be obtained on a total of 70 of them. The average daily water use recorded by the 70 utility meters was 2700 lpd, and the daily use recorded by the smart meters for these same accounts was 2021 lpd. The volume recorded by the data loggers was 1883 lpd during the 6 weeks of their operation. The values for the smart meters and the data loggers were much closer than the readings from the ADDC meters, which was higher than either the smart meters or the loggers. Given the incompleteness of the data and the issues with the performance of the ADDC meters this is not conclusive, but does suggest that the utility meters were probably delivering more water to the homes than was being recorded by the roof meters and loggers during the study period.

29 Table 6: Validation of results with ADDC meter readings Parameter Number of accounts for which ADDC meter readings were available by January, 30, 2014 Average daily water use recorded by the ADDC utility meters for study period Average daily water use recorded by the smart meters for the 70 homes with ADDC readings Average daily water use recorded by data loggers for same homes during three logging periods Value lpd 2021 lpd 1883 lpd A third source of water use data for the study group was the historic billing data supplied by the ADDC. It was possible to obtain billing data for the period from April 2012 through March 2013 for 97 accounts from the study group. Not all of the data covered the entire year, but there were between 31 and 86 accounts that had data in every month. When combined, the data covered at least a partial year in 97 accounts. As shown in Table 7 the average daily water use for the 97 accounts from the study group for which billing data were available was 3427 lpd. The average daily use for the same accounts from the logging data was 1825 lpd. The water consumption based on the roof meters accounted for 53% of the billing meters. Table 7: Water use from Billing Data for 97 members of study group Month Number of Accounts with Data Ave Daily Water Use LPD APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR Average daily household and per capita consumption based on historic billing data Average daily household and per capita consumption based on roof meters lphd 672 lpcd lphd 358 lpcd

30 Average Measured Use (LPD) April May June July Aug Sep Oct Nov Dec Jan Feb Mar LPD Utility ( ) LPD Loggers (2013) Figure 20: Comparison between utility meter data and roof tank meters At this point, it is not possible to explain why there was such a large discrepancy between the billing data and the data from the roof meters, but there are a number of possibilities that could help explain it. The billing data are a year prior to the study period, and it is possible that the water use of the group changed between when the billing data were collected and when the data logging study occurred. It is possible that there was a systematic error in the billing data such that the wrong accounts were reported or the volumes were miscalculated. There could be an error in how the research team interpreted the billing data. It is also possible that there are no errors in the data or how it was interpreted, and that the amount delivered to the properties is nearly twice the amount that is run through the roof tanks and into the homes. It is not likely that there was an error in the data from the roof meters and data loggers. These were new, high quality meters, and they were spot-checked against known volumes prior to installation to verify that they were recording accurately. The volume recorded by the meters agreed very closely with the volumes determined from the data logging as shown in Table 5. So there is consistency between the data logging results and the data from the roof meters, and

31 one can conclude that there was no error in converting from pulse data recorded by the loggers and volumes of water in liters. Finally, during analysis of the flow traces the volumes indicated by the traces matched the expected volumes of use and flow rates for residential uses. For example, the toilet flush volumes were in the range of 3 to 10 liters, and showers were in the range of 10 lpm. If these were adjusted upwards to match the billing data they would be much too large. It is also difficult to imagine that there is a systematic error in how the billing data are recorded by the water agency. Random errors in readings or data entry are to be expected, but errors that affect all of the data are unlikely. With respect to how the billing data were analyzed by the researchers, the fact that two individuals analyzed the data independently, and came to similar results argues that the analysis was not erroneous. Water from the utility system is generally delivered to below ground storage tanks from which it is pumped to the roof tank for delivery to the home. There are two ways that water could be lost in this process. It is possible that either of the storage tanks could leak or that the on-site piping could have leaks. It is also possible that taps divert water from the system prior to the roof tank, and that the volume of water diverted from these taps is large enough to cause the difference. At this point the difference between the logged data and the historic billing data can only be reported and the matter left for additional study. The reader should keep in mind that when reference is made to total household use in the analysis that follows what is meant is the total water delivered from the roof tanks to the homes, and there is a chance that this volume does not include all of the water actually diverted from the utility system into the home. Total Recorded Household Use Total water use, as used in this report, means all water that flowed from the roof tanks into the homes. It does not necessarily include all water used at the home because it is known that many of the homes reported having various taps from which they drew water that bypassed the roof tanks and meters installed for the study. It is possible that other homes also drew water that bypassed the roof tank meters, but did not report this on the survey. It is highly likely that the water drawn from the roof tanks includes all in-home domestic uses, but it may not include all landscape uses and leakage or overflows from the cisterns. During the logging period, which ran from June through December 2013, the total water that flowed from the roof tanks into the homes was measured by a water meter installed on the outlet from the roof tank by the research team, and the flow through this meter was logged by a data logger, which recorded flows on a 10-second interval and transmitted the data via cell phone technology. As discussed above, the roof tank meters were read at the beginning and end of the logging period, and their register volume was compared to the logged volumes. The results of these readings confirmed that the loggers were accurately reflecting the flow from the tanks.

32 The total water use is an important number since it represents the amount of water that must be delivered to each home in order to satisfy its demands. Irrespective of the number of persons in the home, the types of end uses present, or any other explanatory factor, the total water demand must be met by the water utility in order to avoid shortages to the home. The individual characteristics of the home will help to explain the demand, but from the perspective of the water provider it is the total water demand that needs to be met and planned for. Table 8 provides the data on total water consumption for the study group on the basis of liters per household per day (lphd). Values are shown for each of the logging periods with average values presented for the three periods in order to provide results for the combined data. Total household use includes domestic uses, landscape uses, and leakage downstream of the roof tanks. Domestic use, as used in this report, is intended to include all events that are neither for large landscape uses nor leakage. They include mainly indoor uses, but may also include some uses that occur outdoors, such as car washing. In the final analysis, regression models will be used to attempt to determine the impact of known outdoor activities such as car washing on faucet uses. There are two types of leak events. There are many very small and short duration events that appear to be faucet drips or leaky toilets. These occur in most homes to one degree or another. Typically these small leaks do not account for large volumes of leakage. The other type of leak is the long duration, constant flow that may continue for days or even for the entire logging period. These are the leaks that account for large volumes of water. Table 8 through Table 11 show the average total recorded use, domestic use, leakage and landscape use, for MP1, MP2, and MP3, and the combined data for the three periods. Changes in water use in the categories are as reported. In examining the household water use during the three logging periods the only category that appears to show a clear trend is in the landscape use during MP3, which was significantly lower than that observed during the first two periods. Both domestic use and leakage showed variability, but did not show a downward trend as the year progressed. Table 8: Comparison of total daily water use Parameter Total Use MP1 1 Total Use MP2 Total Use MP3 Logging Start Data June 12, 2013 Sept 23, 2013 Dec 2, 2013 Average Mean ± 95% C.I. (lphd) 1772 ± ± ± ± 200 %of Total Household Use 100% 100% 100% 100% Median (lphd) N (homes) Trace length (days)

33 Std. Deviation (lphd) MP = Monitoring Period Table 9 shows the comparison of domestic use during the three periods. The data show that the use during the three periods was very consistent, averaging 882 lphd, which represents just over half of the total water use for the homes. There was no seasonal variability in the domestic use evident. Table 9: Domestic use comparison (leakage excluded) Parameter Domestic Use a MP1 Domestic Use MP2 Domestic Use MP3 Average Mean ± 95% C.I. (lphd) 851 ± ± ± ± 81 Percent of Total 48% 50% 60% 53% Median (lphd) N (homes) Trace length (days) Std. Deviation (lphd) a does not include leakage The leakage statistics are shown in Table 10. The average daily leakage during the first monitoring period was higher than the other two periods because there was one house that had a broken valve which resulted in a very high leakage rate of over 18,000 lpd. After this was repaired the average leakage dropped from an average of nearly 200 lpd to between 80 and 100 lpd. The maximum leakage rate during the second two periods was over 2500 lpd, however. In all three periods, the median leakage rate was 12 lpd or less. This means that half of the homes in the sample were leaking at these low rates, but a few high leaking homes were raising the average for the group. On average, the homes with leakage rates of over 125 lpd accounted for only 17% of the homes, but over 80% of the total leakage volume. Leakage shows no signs of seasonality if the high volume home from MP1 is discounted. Table 10: Leakage statistics Parameter Leakage Leakage Leakage MP1 MP2 MP3 Combined Mean ± 95% C.I. (lphd) 198 ± ± ± ± 83 Std. Deviation (lphd) Percent of Total 11% 5% 7% 7% Median (lphd) N (homes) Trace length (days)

34 Max Ave Leakage in a single home (lphd) Percent of homes with leakage > 125 lphd Percent of total leakage volume accounted for by houses in the +125 lphd bin 18, % 15% 15% 17% 91% 75% 77% 82% Table 11 shows the average household water used in the landscape category. These events share the common features of high volumes and flow rates that do not fit into any of the domestic use or leakage categories. In most cases they are clearly for irrigation purposes, but in some case they may be for other landscape related purposes as discussed above. In many of the large uses the events are clearly being controlled by irrigation timers. The fact that the landscape use is higher during MP2 than it was in MP1 is not that surprising given the variability in the category, as seen in the overlapping confidence intervals, and the fact that many people do not fine-tune their irrigation timers to account for changes in weather. Landscape use during MP3 was considerably lower than during either of the previous two monitoring periods, which is clearly related to the significantly cooler temperatures and higher relative humidity during MP3. Table 11: Landscape use comparison Parameter Landscape Use MP1 Landscape Use MP2 Landscape Use MP3 Combined Mean ± 95% C.I. (lphd) 723 ± ± ± ± 147 Percent of Total 41% 45% 33% 40% Median (lphd) N (homes) Trace length (days) Std. Deviation (lphd) Table 12 shows a summary of the household water use during the three monitoring periods and the average use. Of the three categories of use, only the landscape use appears to be affected by the season. Domestic use, which includes all of the typical household end uses of water, does not exhibit seasonal variability. Figure 21 is a graphical representation of the water use in the home for the combined monitoring periods, with 53% being used for domestic purposes, 40% for landscape and 7% for leakage.

35 Table 12: Summary of household use in first three monitoring periods and combined Parameter Total Use Domestic Use Leakage Landscape Use MeanMP1 (lphd) 1772 ± ± ± ± 172 MeanMP2 (lphd) 1827 ± ± ± ± 201 MeanMP3 (lphd) 1471 ± ± ± ± 132 CombinedMP1,2,3 (lphd) Percent of total for combined MP 1679 ± ± ± ± % 53% 7% 40% 673, 40% 882, 53% Domestic (lpd) Leakage (lpd) Landscape (lpd) 124, 7% Figure 21: Average daily household use breakdown during the combined monitoring periods Per Capita Use The following tables show the per capita water use statistics. The values were determined by dividing the household use for each home by the number of full-time residents in

36 the home and then averaging the results. The residents included both family members and livein helpers. Day workers were not included as residents. It should be noted that per capita use for landscape water has been calculated in Table 16. Expressing landscape use on a per capita basis is not typical since the amount of water used for landscapes should be a function of the size and type of landscape present plus the weather rather than the number of people in residence. The parameter is useful, however, in obtaining an overall average per capita use value. Per capita use followed the same patterns as did household use in that the only category that was clearly seasonal in nature was the landscape use. Domestic and leakage uses did not show seasonality. Domestic use was very consistent from period to period, averaging 168 lpcd. Leakage was skewed by the same few high leakage homes. The average per capita leakage rate was 23 lpcd, but the median rate was only 3. Landscape use was also skewed by a few homes. The average landscape use was 149 lpcd while the median use was only 49 lpcd. Table 13: Total Logged Use (LPCD) Parameter Total Use MP1 Total Use MP2 Total Use MP3 Combined Mean ± 95% C.I. (lpcd) 353 ± ± ± ± 43 Percent of Total 100% 100% 100% 100% Median (lpcd) N (homes) Trace length (days) Std. Deviation (lpcd) Table 14: Domestic Use (LPCD) Parameter Domestic Use Domestic Use Domestic Use Combined MP1 MP2 MP3 Mean ± 95% C.I. (lpcd) 162 ± ± ± ± 12 Percent of Total 46% 46% 56% 49% Median (lpcd) N (homes) Trace length (days) Std. Deviation (lpcd)

37 Table 15: Leakage (LPCD) Parameter Leakage Leakage Leakage Combined MP1 MP2 MP3 Mean ± 95% C.I. (lpcd) 32 ± ± 8 23 ± ± 12 Percent of Total 9% 5% 8% 7% Median (lpcd) N (homes) Trace length (days) Std. Deviation (lpcd) Table 16: Landscape Use (LPCD) Parameter Landscape Landscape Landscape Combined MP1 MP2 MP3 Mean ± 95% C.I. (lpcd) 159 ± ± ± ± 38 Percent of Total 45% 50% 36% 44% Median (lpcd) N (homes) Trace length (days) Std. Deviation (lpcd) Distribution of Total Daily Water Use Figure 22 and Figure 23 show the distribution of total household water use (LPD) and domestic use (LPD) in the study group for the combined monitoring periods. As is typical for residential water use, both of these distributions are log-normal, with the data skewed to the right by a small number of high users. In these distributions the average use is higher than the median use, which implies that more than half of the homes in the study group have water use less than the average. Total use tends to be more highly skewed since it includes leakage, which is very skewed, while domestic use includes only the intentional end uses and is less skewed by high users.

38 Frequencies % of Houses 30% 25% 20% 15% 10% 5% 0% ,00 0 1,40 0 1,80 0 Relative Frequency 1% 8% 26% 21% 11% 9% 7% 5% 5% 1% 3% 1% 1% 3% Cumulative Frequency 1% 9% 34% 56% 67% 75% 83% 87% 92% 93% 96% 97% 97% 100% 2,20 0 2,60 0 3,00 0 3,40 0 Total Use (LPD) 3,80 0 4,20 0 4,60 0 5,00 0 mor e 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Relative Frequency Cumulative Frequency Figure 22: Distribution of total logged household use for the combined periods 35% 30% 25% 20% 15% 10% 5% 0% ,000 1,300 1,600 1,900 2,200 2,500 2,800 3,100 3,400 3,700 more Relative Frequency 1% 12% 28% 31% 13% 8% 5% 0% 1% 1% 0% 0% 0% 1% Cumulative Frequency 1% 13% 40% 72% 85% 93% 97% 97% 98% 99% 99% 99% 99% 100% Domestic Use (LPD) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Relative Frequency Cumulative Frequency Figure 23: Distribution of domestic use during the combined periods (excluding leakage)

39 Average Daily Use (lphd) End Use Statistics A summary of the daily household water use by end use and per capita use are shown in the figures below. These figures also give a comparison to the end use data from the Residential End Uses of Water Study (REUWS) from , which was a national study of single family water use conducted in the United States and Canada in the mid-1990 s and has served as a benchmark for residential water use since its publication in A copy of this study can be obtained from the Water Research Foundation in Denver, Colorado. If one examines the household use patterns from Figure 24 the two categories that stand out as higher in the Abu Dhabi sample are showers and faucet uses. The other categories are statistically similar to the benchmark. When the per capita use patterns are examined in Figure 25 it is interesting to note that all of the categories, except for faucet uses, are either the same or lower than the North American sample from REUWS. The higher per capita faucet use is probably due to the fact that most of the homes have domestic helpers present during the day using water for various household purposes. Given the higher number of persons present in the homes during the day, and the fact that most of these persons were employed to do work that requires water, it would make sense for faucet uses to rise disproportionately to the number of residents Toilet Clothes washer Shower Faucet Leak Other Bathtub DW AbuDhabi Combined REUWS Aquacraft, Inc. (1999). Residential End Uses of Water. AWWARF Research Reports, American Water Works Association Research Foundation: 344 pgs.

40 Average Per Capita Use (lpcd) Figure 24: Comparison of household end uses for combined MP to 1999 REUWS study Toilet Clothes washer Shower Faucet Leak Other Bathtub DW AbuDhabi Combined REUWS Figure 25: Comparison of per capita use for combined MP to 1999 REUWS One of the key sets of results from this study is the descriptive statistics summaries for the homes during the monitoring periods. These statistics were generated from the raw event data in two steps: first the use summaries were summarized from the event data for each household. These have been presented in Appendix A. The raw event data consist of each individual water use event that was identified from the flow trace, and assigned a use category by the analyst using the Trace Wizard program. In total there were over one million water use events identified for each monitoring period and the entire database contained over three million event records. The household summary tables assembled for each monitoring period were built from the event tables and have one row for each home in the study group for which data were collected. The summary tables then determine the statistics of the group by taking sums, averages, medians etc., on the table of household values. These summary statistics are shown in Table 17, below. Each row in Table 17 is a value that is determined from the average of the individual home values in the study group. For example, the row, Total volume used during the logging period (lph), represents the average total volume of logged water used by the homes in the study group during each logging period. The query that creates the household summary table

41 (Appendix A) calculates the total volume of water use in the trace for all events and categories. This is recorded as the second field of the data table after the keycode. The values in the fifth row of Table 17 represent the average of the total use for the homes during the logging periods, which for MP2 was 25,570 L. This was the average total volume of water that flowed through the meters and recorded by the data loggers during the 14 days of MP2 for the 148 logged homes. In every case the values in the table are calculated in this way from the individual houses. One can also see many of these statistics in the last eight rows of Appendix A. One thing that stands out about the household use statistics is how similar they are for many categories among the monitoring periods. Items like the number of events per day for clothes washers and bathtubs, shower flow rates, shower volumes, and shower durations are remarkably similar for all monitoring periods. It should be kept in mind that the analysts have no idea what these statistics will look like when they are analyzing the traces. The statistics are calculated after all of the traces are completed. So, these results are not the result of a bias during analysis. Even if the intention was to obtain specific results it would be virtually impossible to do so during the analysis since there is no way to check the intermediate results. The more reasonable explanation is that the data show that the use patterns for these categories tend to be very constant over the year. Table 17: Average Household Values for MP1, MP2, MP3 and combined Parameter MP1 MP2 MP3 Combined Trace begins June 2013 Sept 2013 Dec 2013 June 2013 Trace ends July 2013 Oct 2013 Dec 2013 Dec 2013 Trace length days Number of homes with data Total volume used during the logging period (lph) Total domestic use (lph) Landscape total use (lph) Bathtub total use (lph) Clothes washer total use (lph) Total toilet use (lph) Dishwasher total use(lph) Faucet total use (lph) Leak total use (lph) Other total use (lph) Shower total use (lph) Number of bathtub events/hh

42 Number of clothes washer events/hh Number of dishwasher events/hh Number of faucet events/hh Number of leak events/hh Number of other events/hh Number of shower events/hh Number of toilet events/hh Total daily use (lphd) Domestic daily use (lphd) Landscape daily use (lphd) Bathtub (lphd) Clothes washer (lphd) Dishwasher (lphd) Faucet (lphd) Leak (lphd) Other (lphd) Shower (lphd) Toilet (lphd) Average clothes washer (l/load) Clothes washer loads per day Total shower minutes Average shower duration (min) Average shower volume (l) Average shower flow (lpm) Showers per day Total shower minutes per day Toilet flush volume Toilet flush standard deviation Number of flushes/hh less than 6L Number of flushes/hh less than 8.3L Average percent of flushes/hh < 6 L 44% 43% 46% 46% Average percent of flushes/hh < % 77% 77% 77% Table 18 shows the per capita statistics for MP1, MP2, MP3 and the average values. Figure 26 is a pie chart showing the percentage of each end use for domestic purposes for the

43 combined monitoring periods. Generally, the per capita use was very consistent between the three monitoring periods, with the exception of the landscape use category. Total use went down in the third monitoring period largely because landscape use decreased during that period. Although leakage did not return to the levels found in MP1 there appeared to be an increasing trend in MP3. The other categories were very consistent between the three periods. Of note is the fact that the average leakage rate for the homes is greater than water use for clothes washers. Table 18: Comparison of per capita average statistics Parameter MP1 MP2 MP3 Combined Residents per HH (excluding non-live-in helpers) Total Lpcd Landscape Lpcd Domestic Lpcd Bathtub Lpcd Clothes washer Lpcd Dishwasher Lpcd Faucet Lpcd Leak Lpcd Other Lpcd Shower Lpcd Toilet Lpcd

44 Other 0.27% Bathtub 1.64% DW 0.39% Toilet 22.09% Faucet 39.00% Clothes washer 11.86% Shower 25% Figure 26: Breakdown of domestic end uses by household during the combined MP Toilets Table 19 shows the statistics for toilet use during the three logging periods. These data show a high degree of consistency from period to period. Overall, the average flush volume for the toilets in the study was 6.7 lpf. Toilets were flushed an average of 29 times per day and at the rate of 5.7 times per person per day. The average percent of the flushes in each home that was less than 6 liters was 44%. The average percent of flushes per home that was less than 8.3 liters, which is the value that demarks the upper range of efficient toilets was 77% (allowing a margin of error). Notice that these two values are not based on the combination of all individual flushes. They are based on the household averages for the 151 homes. These averages will not be the same as the averages when all flushes are combined. Table 19: Toilet statistics for households Parameter MP1 MP2 MP3 Combined Average number of flushes per household during logging Average length of trace (days) Average flushes per day per household Average flushes per person per day (

45 persons) Average toilet flush volume (liters) Median flush volume (liters) Average % of flushes < 8.3 liters 77% 77% 77% 77% Average % of flushes < 6 liters 44% 43% 46% 44% Figure 27 shows the distribution of individual toilet flushes based on data from all monitoring periods. This includes approximately 178,000 flushes. The data are shown in tabular form in Table 20, which is also based on data from the combined data. These data show that 90% of all toilet flushes in the group were 9 liters or less, and that 62% of the flushes were at or below the 6 L efficiency benchmark. The percent of the individual flushes at or below the benchmarks is higher for individual toilets than for household averages since when homes have a mixture of toilets of different flush volumes a few high volume flushes will raise the average for the home.

46 Frequency 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% < Flush Volume (L) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% RV CV Figure 27: Distribution of individual toilet flushes for combined data (N=177,825) Table 20: Distribution of flush volumes for combined data Liters Events RV CF < % 13% % 28% % 45% % 62% % 75% % 84% % 90% % 94% % 97% % 99% % 99%

47 Frequencies % 99% % 100% % 100% % 100% % 100% % 100% % 100% Total 177,825 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% more Relative Frequency 0% 0% 0% 2% 0% 9% 11% 17% 12% 11% 18% 10% 7% 3% Cumulative Frequency 0% 0% 0% 2% 2% 11% 23% 40% 52% 62% 80% 90% 97% 100% Flush Volume (L) 0% Relative Frequency Cumulative Frequency Figure 28: Distribution of average household flush volumes for combined MP (N=151) Figure 29 and Figure 30 show the distributions of the percentage of homes in which the percent of flushes less than 6 and 8.3 lpf varies between 5% and 100%. The purpose of these

48 figures is to show how uniform the toilet mixtures are in the homes. Homes with either a low or high percentage of flushes less than the given benchmark are homes with the same type of toilet present in the home (either efficient or inefficient). Homes in the middle of the distribution are homes with a mixture of toilets, some of which meet the criteria and some do not. There is a wide range of mixtures for the 6 l criteria with most of the homes showing between 20% and 70% of the flushes at 6 l or less. When the higher criteria is used, to allow for adjustments or building-specific conditions, the distribution becomes much more weighted to the right hand side because a majority of homes have a high percentage of their flushes at or below 8.3 l. Approximately 60% of the homes have more than 80% of their flushes at 8.3 l or less. Figure 29: Percent of homes with percent of flushes < 6 liters in combined MP

49 Figure 30: Percent of homes with percent of flushes < 8.3 liters in combined MP