# Monitoring Nonrevenue Water Performance in Intermittent Supply

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## Abstract

**:**

## 1. Introduction

## 2. Research Methodology

#### 2.1. Overview of Sana’a Water Supply

#### 2.2. Analysis of NRW and SIV Trends

^{3}/year) is nonrevenue water, SIV (m

^{3}/year) is the system input volume and BC (m

^{3}/year) is the billed consumption.

#### 2.3. NRW Component Assessment

^{3}/year) is the apparent losses, RL (m

^{3}/year) is the real losses, UAC (m

^{3}/year) is the unbilled authorised consumption and WL is the volume of lost water. The volume of UAC was estimated by auditing and analysing the records of the Sana’a water utility. The volume of the AL was estimated using the Apparent Loss Estimation equation, as elaborated by AL-Washali, Sharma and Kennedy [27]. The volumes of real losses (RL) and water loss were then calculated using Equations (2) and (3). Accordingly, the International Water Association (IWA) standard water balance was established for the Sana’a water supply system for 2009. As the data of 2015 are incomplete, the same estimated proportions of NRW components in 2009 were used to calculate the NRW components for the year 2015, and the results were then compared.

#### 2.4. NRW Performance Indicators

^{3}/km of mains/day, litres/connection/day and litres/property/day. The KPIs for comparing internal/external leakage between different systems are the Unavoidable Annual Real Losses (UARL), Infrastructure Leakage Index (ILI), average pressure, value of leakage Euro/m

^{3}and repair frequencies.

^{3}/year) and UARL (m

^{3}/year) is the unavoidable annual RL, which can be calculated from Equation (8).

_{m}is the length of mains in km, N

_{c}is the number of service connections, L

_{p}is the total length of private underground connection pipes in m (between the edge of the street and customer meters) and P

_{ave}is the average operating pressure of the meters. Vermersch, Carteado, Rizzo, Johnson, Arregui and Lambert [28] suggested that the Apparent Loss Index could be used, which can be calculated in a similar manner to the infrastructure leakage index using Equation (9).

^{3}/year) is the current annual AL and RAAL (m

^{3}/year) is the reference annual AL, which represents 5% of the volume of the billed, authorised metered consumption, excluding exported water.

#### 2.5. Normalising the NRW PIs Using w.s.p. Adjustment

^{3}/year) is the normalised NRW and ${\mathrm{T}}_{\mathrm{avg}}$ is the average supply time in the system (h/day).

#### Sensitivity Analysis of the Average Supply Time (T_{avg})

_{avg}. The influence of T

_{avg}on the volume of NRW was plotted on a curve, the equation was deduced and the step-slopes of the T

_{avg}–NRW curve were analysed. Accordingly, the critical points of the curve and high-sensitivity cases were determined.

#### 2.6. Normalising the NRW Using Regression Analysis

#### Extracting the Actual NRW Trend

## 3. Results and Discussion

#### 3.1. Fluctuations in the NRW Volume

#### 3.2. NRW Components

^{2}) of NRW components. The proportions of NRW components were assumed to remain the same, and these proportions were used in the analysis of 2015, as shown in Table 1.

#### 3.3. NRW PIs

#### 3.4. Normalised NRW Using w.s.p. Adjustment

_{avg}, which is used as an adjustment factor. It was found that when T

_{avg}decreases, NRW increases. The power function of the curve is lim NRW = + ∞ when T

_{avg}approaches zero from the right to the left. The derivatives of (T

_{avg}) cannot determine the critical points of this curve. However, when benchmarking the volume of NRW at T

_{avg}= 24 h/day and analysing the step-slopes of the curve from right to left, the results show that the volume of NRW will be doubled at T

_{avg}= 12 h/day. Similarly, NRW will increase by 200%, 500% and 2300% when T

_{avg}is 8, 4 and 1 h/day, respectively. The lower the T

_{avg}, the more sensitive the normalised NRW volume. For the case of Sana’a, where T

_{avg}was 4.4 and 0.6 h/day for 2009 and 2015, respectively, the normalised NRW volume becomes more sensitive. Thus, the accuracy of the analysis of all NRW components and PIs is significantly influenced by the calculation of T

_{avg}.

_{avg}also has uncertainties for intermittent supply systems. In Sana’a, estimating the supply time for each distribution area within a network of 369 distribution areas is complicated, as the time of the distribution valves’ closures and pumping hours of the wells and headworks must be recorded. These data are not currently available, and there would be uncertainties in their collection. Therefore, estimating the supply time for each distribution area would require significant effort and commitment. Such uncertainties significantly undermine the accuracy of normalising the NRW levels and PIs through this approach.

#### Is the NRW Status Progressing or Regressing?

#### 3.5. Normalised NRW Using Regression Analysis

^{2}= 0.66), even with the poor data obtained for some years during the analysis period. The NRW-SIV correlations were also strong for annual data obtained for five years (2011–2015) for the full-scale system (Figure 5b), as well as for a DMA within the network (Figure 5c).

_{2015}was 9.01 MCM, while that of NRW

_{2009}was 8.64 MCM. This slight difference in the level of NRW is reasonable in Sana’a and more rational than what was suggested by w.s.p. adjustment. While unnormalised NRW has exhibited a reduction in the NRW level by 81%, normalisation of the NRW level by regression analysis suggested an increase in the NRW level of 4%, and the w.s.p. approach suggested an increase of 36%.

_{avg}. Developing a correction or “reduction” factor curve similar to that in Figure 4, but in the 4th quadrant, could be useful for conducting a more rational benchmarking of different systems with different T

_{avg}.

#### Extracted NRW Status Trend

## 4. Conclusions

- The volume and PIs of NRW all vary in direct proportion to the SIV. This is critical for monitoring the level and PIs of NRW for water systems with fluctuating SIV and utilities that are shifting from intermittent to continuous supplies. An increase in the NRW level does not necessarily indicate worse performance, as it could be due to an increase in the amount of supplied water. Additionally, a decrease in the NRW level does not necessarily mean better performance, as it could be due to a decrease in the supplied water. Therefore, normalisation is necessary to properly monitor and benchmark NRW PIs for intermittent supplies.
- The ’when-system-is-pressurised’ adjustment, which is often used for normalising RL indicators, could be extended to normalise the volumes of NRW, AL, RL and their PIs. However, this principle leads to an overestimation of the AL, which are still difficult to monitor. This is because, when the demand is fully met, any increase in the system input volume contributes to RL, while the AL remain the same. Another limitation of this approach is the sensitivity of the average supply time (T
_{avg}), as its uncertainties significantly undermine the accuracy of the normalised NRW PIs, including those of the RL. In addition, this approach is likely biased towards water systems with an increasing water supply and vice versa. For water systems with a T_{avg}of less than 8 h/day, the results of this approach become more uncertain. Finally, it is not certain whether this approach indicates the actual extent of NRW progression or regression. - For monitoring the NRW status of an individual water supply system, the NRW volume and PIs can be normalised through regression analysis. This approach reflects the actual behaviour of the NRW status and provides more rational progression and regression extents. However, this approach can only be used for monitoring the NRW for individual systems, and not for a comparison of different systems.
- Comparing and benchmarking a water supply system to other systems with reasonable accuracy does not appear to be possible. More analysis is required to allow proper benchmarking using ’when-system-is-pressurised’ adjustment, particularly for extending it to AL benchmarking. Until then, for more rational benchmarking through this adjustment, a correction factor curve for T
_{avg}should be developed to enhance the monitoring of the NRW progression and reflect the situation of NRW for a given system among other systems with different supply patterns. - Once an NRW monitoring tool is available, NRW management should start by network partitioning into DMAs, pressure management, active leakage detection surveys, active customer meter replacement policy and the detection of unauthorised uses. Moving towards a smart network is effective in NRW management, using smart metering, smart data acquisition and on-time acting and control.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Nomenclature

(24/7) | Continuous supply |

θ | 95% confidence limits |

∆A | Uncertainty of variable A |

AL | Apparent loss |

ALI | Apparent loss index |

BC | Billed consumption |

CAAL | Current annual apparent losses |

CARL | Current annual real losses |

DMA | District metered area |

ILI | Infrastructure leakage index |

L_{m} | Length of mains |

L_{p} | Length in m of underground connection private pipes |

MCM | Million cubic metre |

N_{c} | Number of service connections |

NRW | Nonrevenue water |

P_{ave} | Average operating pressure in metres |

PIs | Performance indicators |

RAAL | Reference annual apparent losses |

RA | Regression analysis |

RL | Real losses |

SD | Standard deviation |

SD^{2} | Variance |

SIV | System input volume |

T_{avg} | Average supply time |

UAC | Unbilled authorised consumption |

UARL | Unavoidable annual real losses |

UC | Unauthorised consumption |

WL | Water loss |

w.s.p. | When system is pressurised |

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**Figure 3.**Fluctuation in the NRW volume according to changes in SIV: (

**a**) monthly basis for the full-scale system; (

**b**) annual basis for the full-scale system; (

**c**) monthly basis for DMA-1 in Hadda Zone, Sana’a.

**Figure 4.**Sensitivity of the normalised NRW volume (w.s.p.) to the average supply time in Sana’a water distribution system.

**Figure 5.**NRW–SIV regression equations; (

**a**) monthly volumes for 2005–2015; (

**b**) annual volumes for 2011–2015; (

**c**) monthly volumes for a DMA in Sana’a; and (

**d**)–(

**h**) monthly volumes for 2011, 2012, 2013, 2014 and 2015, respectively.

Volume 2009 | θ | SD | SD^{2} | Volume 2015 | |
---|---|---|---|---|---|

m^{3}/year | ± % | m^{3}/year | m^{3}/year | ||

NRW | 8,637,692 | 5 | 227,452 | 5 × 10^{10} | 1,604,557 |

UAC | 114,152 | 20 | 11,648 | 1 × 10^{08} | 21,205 |

AL | 5,686,452 | 18 | 531,632 | 8 × 10^{10} | 1,100,093 |

RL | 2,837,088 | 40 | 578,363 | 1 × 10^{11} | 483,259 |

NRW Component | PI | 2009 | 2015 | ∆ % |
---|---|---|---|---|

NRW | m^{3}/year | 8,637,692 | 1,604,557 | −81% |

NRW | % | 39% | 22% | −44% |

NRW | m^{3}/c/year | 97 | 17 | −83% |

RL | m^{3}/year | 2,837,088 | 527,024 | −81% |

RL | L/c/d | 87 | 15 | −83% |

RL | L/c/d/m pressure | 9 | 2 | −83% |

ILI | - | 9 | 2 | −82% |

ILI | w.s.p. | 48 | 62 | +29% |

AL | m^{3}/year | 5,686,452 | 1,056,328 | −81% |

AL | m^{3}/c/year | 64 | 11 | −83% |

ALI | - | 8 | 4 | −57% |

Component | PI | 2009 | 2015 | ∆ % |
---|---|---|---|---|

T_{avg} | h/day | 4.40 | 0.60 | −86% |

T_{avg} | day/year | 66.92 | 9.13 | −86% |

SIV | m^{3}/day w.s.p. | 333,106 | 815,500 | +145% |

NRW | m^{3}/day w.s.p. | 129,081 | 175,842 | +36% |

NRW | m^{3}/c/year w.s.p. | 530 | 678 | +28% |

NRW | % w.s.p* | 39% | 22% | −44% |

NRW | % w.s.p** | 89% | 98% | +10% |

RL | L/c/d w.s.p. | 477 | 610 | +28% |

RL | L/c/d/ m pres. w.s.p. | 5 | 6 | +28% |

AL | m^{3}/c/year w.s.p. | 349 | 446 | +28% |

ILI | w.s.p. | 48 | 62 | +29% |

ALI | w.s.p. | 8 | 4 | −57% |

ALI | w.s.p.*** | 45 | 145 | +219% |

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## Share and Cite

**MDPI and ACS Style**

AL-Washali, T.; Sharma, S.; AL-Nozaily, F.; Haidera, M.; Kennedy, M. Monitoring Nonrevenue Water Performance in Intermittent Supply. *Water* **2019**, *11*, 1220.
https://doi.org/10.3390/w11061220

**AMA Style**

AL-Washali T, Sharma S, AL-Nozaily F, Haidera M, Kennedy M. Monitoring Nonrevenue Water Performance in Intermittent Supply. *Water*. 2019; 11(6):1220.
https://doi.org/10.3390/w11061220

**Chicago/Turabian Style**

AL-Washali, Taha, Saroj Sharma, Fadhl AL-Nozaily, Mansour Haidera, and Maria Kennedy. 2019. "Monitoring Nonrevenue Water Performance in Intermittent Supply" *Water* 11, no. 6: 1220.
https://doi.org/10.3390/w11061220