3.1. Evaluation of Water Loss Performance
The infrastructure leakage index PIILI
was applied to evaluate and compare the performance of the 40 WDNs. As shown in Figure 3
a, the PIILI
ranged from 0.15 to 12 for the investigated WDNs. In particular, smaller systems with low water demands had a wide variation of that PI
. Consequently, there was no correlation between water demand (as an indicator for population size) and PIILI
, which was indicated by a pearson correlation coefficient of 0.24. In total, 9 WDNs, having a water demand less than 0.4 million m3
(corresponding to 300–7000 inhabitants), had a value less than 1.0. These results are in accordance with the findings of a recent study, in which 24 of 54 investigated WDNs in Austria had a PIILI
lower than 1.0 [31
]. As described by the authors, there are two possible explanations for such low values. Basically, PIILI
is calculated as a ratio between real losses and real losses reduced to technically unavoidable levels. First, continuous night flow measurements allow for a rapid identification of leakages in small WDNs, therefore, real losses can be decreased significantly. Secondly, design, installation, and maintenance efforts are high in Austria due to local standards and guidelines for reducing the reported number of failures and water losses. Consequently, the number of failures in Austria is lower than assumed in the calculation of technically unavoidable losses, supporting lower PIILI
values. Another explanation is that water losses in small WDNs can be too low to measure. In this case, water losses are often underestimated, resulting in lower PIILI
Another disadvantage of PIILI
is the large number of network parameters required for calculation. In this context, Figure 3
b–d compare the simpler PIV
(ranging from 3 to 104%), PIL
(ranging from 0.02 to 1.15 m3
/h/km), and PIC
(ranging from 25 to 1290 L/day/service connection) with PIILI
. Since water consumption instead of system input was used as a comparative value for calculation of these PI
s, values above 100% are possible (occurring in one investigated WDN). In this case, (estimated) water losses were higher than water consumption of all consumers. As can be seen, the pearson correlation coefficient is for all configurations was very high (above 0.95), indicating that all simpler PI
s are a good substitution for PIILI
in smaller WDNs. However, only PIV
has the advantage of a practical evaluation. The advantage of PIV
is that it can be easily calculated, whereas PIL
can be used only for parts of the WDNs due to limitations regarding service connections. We suggest using PIV
for small WDNs (less than 10,000 inhabitants) and PIILI
for medium networks (greater than 10,000 inhabitants) in Tyrol.
To test the robustness of the chosen approach (estimation of missing demand and number of service connections based on number of inhabitants, overnight stays, and number of buildings), Table 2
shows correlation coefficients between the investigated PI
s with PIILI.
for different missing data techniques (complete datasets, available datasets, or estimation of missing values). Although different missing data techniques were applied, the correlation coefficients did not change significantly, and, therefore, the obtained relations are robust in terms of data availability. Additionally, complete, and missing datasets are color-coded in Figure 3
, Figure 4
and Figure 5
. However, simplified missing data techniques (e.g., single imputation) imply that uncertainties of variables affect the results and robustness of the chosen approach. In contrast, more complex procedures, such as multiple imputation, include uncertainty analysis by generating multiple datasets with plausible values [39
]. Moreover, uncertainty and sensitivity analyses can be carried out to investigate the influence of uncertain input parameters [42
]. However, these tools are time-consuming, and are, therefore, in conflict with the requirements of PI
s for small municipalities and local authorities, which have to deal with limited time and human resources for goal-oriented water loss management. In this context, the PI
s and also the data collection should be as simple as possible for a practical application. Consequently, detailed and time-consuming uncertainty and robustness analyses are not the focus of this work. Therefore, the results are subject to statistical fluctuations, but, as can be seen from the figures, this simple approach can compensate very well for a lack of basic data.
3.2. Identification of Water Losses
Leakage detection campaigns were utilized in 9 WDNs to detect and localize network failures. The results were divided into different network elements, namely: main pipes, service connections, hydrants, and valves; these are summarized in Table 3
. As can be seen, failures were the most frequent at service connections with an average value of 4.5 failures per WDN. One reason for this can be retrofitting of existing WDNs with house connections, whereby for this process the main network is usually subsequently bored. In total, an average of 1.8 L/s (or 6.5 m3
/h) of drinking water is lost through faulty service connections. The second most common failure was found in hydrants, with an average of 1.9 hydrants per WDN showing a leakage. Interestingly, an average water loss of 0.2 L/s (or 0.7 m3
/h) is the lowest for faulty hydrants compared that of to other network elements. In contrast, failures in the main pipes occur on average 1.6 times per WDN, causing drinking water losses of 1.2 L/s (or 4.3 m3
/h). For the last investigated network element, the valves, a total of 0.6 faulty valves per network were detected, corresponding to an average additional water loss of 0.4 L/s (or 1.5 m3
/h). For the total network, water losses were on average 3.8 L/s (or 13.7 m3
/h), with a peak value of 13.0 L/s (or 46.8 m3
3.3. Experiences Regarding Rehabilitation
As shown before, the volume-related PIV
represents a good substitution of the complex leakage infrastructure index PIILI
for smaller WDNs and is therefore used to discuss experiences with rehabilitation measures in Tyrol. As expected, water losses show a clear correlation with average year of construction, as can be seen in Figure 4
. The investigated WDNs were constructed on average between 1962 and 1999. With increasing age, water losses increase sharply, whereas on average younger WDNs show significantly lower water losses.
Further important findings are:
Rehabilitation: Although one WDN was very old, it has the lowest PI (PIV is 7%). The initial WDN was constructed in the 1930s with first extensions and then renovations in the 1940s and 1950s, respectively. In the last few years, the network operators have put a lot of effort into renewing the system, reducing both average year of construction (now 1999) and water losses.
Rehabilitation planning using survival curves: Only one of the investigated WDNs documented repair work due to bursts and leaks in a detailed way such that they could be used for estimating expected service life of pipes with the same construction year. Consequently, rehabilitation planning using survival curves does not provide statistically relevant information for network renewal as the data base is too small in small WDNs.
On-line hydraulic monitoring: One WDN with less than 1000 connected inhabitants had water losses of 3% of the total water demand. To achieve such a low value, the network operator installed an online monitoring system several years ago. By continuously measuring the system inputs, irregularities and anomalies can be detected, and bursts and leaks can be repaired relatively quickly. In contrast, none of the investigated WDNs used pressure sensors for leakage detection as pressure fluctuation are low due to overdesigned pipes (e.g., regulations about fire flow and minimum pipe diameter) and low water extractions (e.g., distributed system with a low number of connected inhabitants).
Leakage detection campaigns: Two larger WDNs had a PIV between 20 and 30%, corresponding to a PIILI between 2 and 3, although one of them had a very complex network structure with different pressure zones. To obtain these relatively low levels, leakage detection campaigns were carried out every year to detect failures in the WDNs. As reports from the network operators indicate, lower values of water losses were hard to achieve, but could be maintained through annual inspection. Additionally, repairing bursts and leakages, which are detected by applying leakage detection campaigns, can reduce water losses significantly. For example, a WDN with approximately 1500 inhabitants could reduce water losses from 45 to 7% within two months.
a,b show the correlation of real network length and construction year from the pipe information system with results of the network survey in 2012. Interestingly, there is a difference between the level of knowledge of network operators and the actual WDN characteristics. For example, differences regarding network length were up to 36 km, whereas there was a considerable discrepancy in age of up to 18 years. As discussed above, average construction year can be related to amount of water losses, whereby older construction usually correlates with high water loss. Failure rates and associated leakages increase with pipe age [43
] raising the amount of water loss. Consequently, values of PI
s (e.g., PIILI
, or PIV
) also increase, showing a worsening of system performance. Therefore, the promotion of pipe information systems should be expanded further in future to obtain a conclusion on actual age and to implement appropriate measures.