Analysis of water losses in seven small and medium-sized water distribution networks in the south region of Cameroon (Central Africa)

doi:10.21203/rs.3.rs-3918815/v1

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Analysis of water losses in seven small and medium-sized water distribution networks in the south region of Cameroon (Central Africa)

https://doi.org/10.21203/rs.3.rs-3918815/v1

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This paper analyzes and evaluates water losses in seven small and medium-sized water supply networks in the south region of Cameroon in order to contribute to improving the most effective approaches to reducing water losses in Sub-Saharan Africa's water supply networks. Water losses were analyzed using a variety of indices as well as indices recommended by the International Water Association. The results lead to the conclusion that all the water supply networks achieved mixed results compared to national data and very weak results compared to international standards. The failure intensity index didn’t perform well for all the analyzed water supply networks, leading to the conclusion that the water supply networks studied are not technically in good condition. The percentage water loss indices for all the water supply networks range from 6.06–87.25%. Only the Ambam water supply network performed well in terms of unit water loss. The non-revenue water basic index values in all the studied water supply networks range from 18.37–92.21%. The real leakage balance index in all the studied water supply networks ranges from 27.31 to 2,916.30 dm³/connection/day. Results also suggest that the infrastructure leakage index ranged from 0.02 to 122.65 and that there is a need for improvement to enhance profitability with better network pressure management, active leakage control, and network maintenance. This assessment, however, offers only a current snapshot of the status of water loss in the water supply networks analyzed and should be repeated at regular intervals to follow changes in general and local conditions.

Environmental Engineering

Water loss

failure frequency index

water loss indices

water supply network

This paper analyzes and evaluates water losses in seven water supply networks in the south region of Cameroon.
Water losses were analyzed using a variety of indices as well as indices recommended by the International Water Association.
The results of the analysis lead to the conclusion that all the water supply networks analyzed achieved mixed results compared to national data and very weak results compared to international standards.

Water loss is defined as water produced but not sold to customers or used by or through the utility (Al-Washali et al., 2016). Water losses are divided into two categories: real losses and apparent losses (Wu et al. 2011). Real losses, also known as physical losses, are water that spills out of pipes and other bursts and leakages. The apparent losses, or commercial losses, are water that is used by customers and not paid for. The apparent losses include illegal water consumption and data handling or billing errors.

Water losses and the resulting financial losses are a major issue for water utilities all over the world (Ociepa et al., 2019). According to Liemberger and Wyatt (2019), the global volume of non-revenue water (water losses) is estimated at 346 million cubic meters per day, or 126 billion cubic meters per year, costing water utilities approximately USD 39 billion per year. In Africa, water losses ranged from 5–70% of the system input volume (WSF-AF et al. 2009; AfWA and USAID 2015).

In Cameroon, data on water losses in the water distribution network are very scarce and dispersed compared with the severity of the problem. Very little data is available on the volume of water losses around the country. However, estimations from Liemberger and Wyatt (2019) indicate that water losses in Cameroon represent 40% of the system input volume, which costs the water utility about USD 27.7 million every year. More recently, a study conducted by Mvongo et al. (2023) in the Kribi water supply network indicated that water losses range from 26.81–32.18% of the system input volume.

The impact of water losses is significant at different levels. On the technical level, leaks have an impact on the technical stability of the water supply system, the network's operational age, water quality, and water service quality (Al-Washali et al., 2016). At the environmental level, in order to alleviate pressure on water resources, leakages should always be tackled as they cause considerable water wastage. Otherwise, water awareness and conservation initiatives in the household sector are insignificant since the amount of water saved by regulating water use inside premises isn't as substantial as the water that leaks out of water distribution systems.

In order to enhance the technical condition of water supply networks, water utilities must build systems for controlling and evaluating water losses, make organizational changes, and develop and implement modernization initiatives. In every company, an evaluation of water losses should precede decisions concerning the repair or modernization of the water supply system (Lee et al., 2012). Due to the optimization of water production costs, reducing water losses must be a major issue for water supply companies. By reducing the losses, plants can save money for investment. Water utilities all over the world must implement and apply modern standards for water loss management. This is why many countries (Austria, Germany, Switzerland, Spain, etc.) have been developing and implementing special programs to analyze and assess water losses (Jin and Piratla, 2016; Ociepa et al., 2019).

Although many water loss management strategies exist, in many developing countries, such as Cameroon, the concept of water loss management has received little attention, despite the severity of the problem. Therefore, this paper discusses the analysis and assessment of water losses in seven small and medium water supply networks in the south region of Cameroon. The purpose of this paper is to adapt the methodologies for water loss evaluation applicable to small and medium water distribution networks in Cameroon (Central Africa). Indeed, small and medium-sized drinking water distribution networks have specific performance issues, ranging from difficulties in gathering the data needed to assess water losses to a lack of personnel and financial resources for efficient operations (Haider et al., 2014). In addition, numerous small and medium-sized water distribution systems are experiencing severe drinking water shortages as a result of periodic droughts brought on by climate change (Klosok-Bazan et al., 2021). Monitoring, examining, and eliminating water losses are thus the only ways to improve the issue. The article also provides information on the current state of water losses in order to mobilize the national and international efforts required.

Cameroon, like many countries in sub-Saharan Africa, has seen the urban water supply sector evolve significantly over the past three decades. Indeed, when Cameroon gained independence in 1960, water production and distribution activities in urban areas were carried out by several players, in particular, the Central Electric Power Distribution Company (CCDEE), which ensured the production and distribution of water in Yaoundé, Maroua, Mbalmayo, and Nkongsamba; the Water and Sanitation Company (SOCEA), which produces and distributes water in Douala; the Electricity Company of Cameroon (ENELCAM) which ensures the production and distribution of water in Dschang, Ebolowa, Edéa, Foumban and Kribi; and the municipal authorities which ensure the production and distribution of water in the towns of Bafang, Bafia, Garoua, Mbanga, and Sangmélima. This multiplicity of actors and the extreme diversity of conventions that govern their intervention generate many constraints that slow down the development of the water sector (Kouasi, 2008).

In order to deal with this situation, the State of Cameroon will first create the Provisional Water Service of Cameroon (SPEC) in 1964 and then the National Water Company of Cameroon (SNEC) on May 22, 1967. SNEC is in charge of drinking water production and distribution activities in urban areas. With Cameroon's adherence to the "Structural Adjustment Program" of the 1990s, SNEC was included on the list of companies to be privatized in 1999 (Weghmann & Lobina, 2018). The strategy adopted was to sell 51% of the company's share capital to a partner of reference with proven technical capacities and sufficient financial standing to carry out the investments within the framework of a new concession contract. This process was declared unsuccessful due to the low attractiveness of the proposed concession regime to investors in the sector.

Drawing lessons from this failure, the State of Cameroon is changing its strategy and proposing a new privatization scheme. This scheme is based on the leasing of the drinking water production and distribution sectors through a Public-Private Partnership (PPP). This strategy revolves around the triangular State/Heritage Company/Farmer relationship, which is organized into two main contracts, in particular a Concession and Infrastructure Management Contract (State and Heritage Company) and a Lease Contract (State, Heritage, and Farmer Company). To materialize the new configuration of the urban water sector, the Heritage Company was created on December 31, 2005, under the name of Cameroon Water Utilities Corporation (Camwater). In 2008, the process of privatization of SNEC was concluded with the signing of a 10-year lease contract for the management and operation of urban water infrastructure between the State, Camwater, and Camerounaise des Eaux (CDE), a subsidiary of the Office National de l’Eau Potable (ONEP), Morocco's national company.

In 2018, the lease contract between the State, Camwater, and CDE expired and was not renewed. Cameroon’s decision to renationalize water production and distribution by not renewing the contract with CDE was taken after years of poor service quality and overbilling (Weghmann & Lobina, 2018). Following Decree No. 2018/144 of February 20, 2018, Camwater took over the leasing missions that the State had entrusted to the CDE, in particular the operation of the public service for the production, transport, and distribution of drinking water in urban and peri-urban areas. Of the 213 urban centers conceded to Camwater, only 117 have water supply networks.

3.1. Study area

The area covered by this study is the South region of Cameroon. These are precisely the water supply networks located in the towns of Ambam, Campo, Ebolowa, Kribi, Meyomessala, Sangmélima, and Zoétélé (Fig. 1). They are managed by Camwater, a public company in charge of drinking water supply in urban and peri-urban areas of Cameroon. Ambam is located 90 km south of the town of Ebolowa and 243 km from Yaoundé. Its population is estimated at 35,525 (BUCREP, 2005). Campo is located on the shore of the Gulf of Guinea, 64 km south of the town of Kribi. Its city center is 4 km north of the mouth of the Ntem River, which marks the border with Equatorial Guinea. Its population is estimated at 5,139 (BUCREP, 2005).

Ebolowa is located 158 kilometers south of Yaoundé. It is connected to the Gabonese and Equatoguinean borders by a 120 km axis and to the Atlantic Ocean (Kribi) by two unpaved axes, one of 160 km via Akom II and another of 180 km by Lolodorf. Its population is estimated at 94,612 inhabitants. Kribi is a popular seaside destination known for its white-sand beaches and deep-water port. It is found in the coastal lowlands of the Cameroonian coast, with an average altitude of 20 m. The population is estimated at 50,000 inhabitants (BUCREP, 2005).

Meyomessala is located on the D84 road, 51 kilometers northeast of Sangmélima. Its population is estimated at 2,728 inhabitants (BUCREP, 2005). The town of Sangmélima is located on the southern Cameroon plateau at an altitude of 711 m, 170 km south of Yaoundé, 120 km east of Ebolowa, and 130 km from the Gabonese border. Its population is estimated at 86,538 (BUCREP, 2005). The town of Zoétélé is located on the D34 road, 54 kilometers north of Sangmélima. As the crow flies, Zoétélé is 37 km from Sangmélima and 90.5 km from Ebolowa. Its population is estimated at 5,319 (BUCREP, 2005).

The south region of Cameroon is subject to an equatorial climate, with rainfall varying from 1,500 to 2,000 mm (MINEE & GWP 2009). The average annual temperature is around 25°C. Its hydrographic network is quite dense, with many rivers: Ntem, Dja, Lokoundje, Lobe, and Kienké. The geological unit is the crystalline basement (Defo et al. 2016; Lienou et al. 1999).

3.2. Data collection

The data used covered the period of 2018–2022 and was obtained from the regional delegation of the Camwater for the south region of Cameroon, which included: water supplied to the network, used for social welfare purposes, non-production, and production purposes; network length; quantity and length of water supply connections; material and age of the network; the number of recipients, average pressure in the tested network; and the number of failures in individual years.

3.3. Data processing

Water losses were analyzed using a variety of indices (failure intensity index, percentage ratio of water loss, unit water loss per capita, real leakage balance, and infrastructure leakage index) and the standard water balance recommended by International Water Association (IWA). In fact, the water balance is a method that is widely used and acknowledged to evaluate water losses in a water distribution system. It is feasible to track the true and apparent loss rates, as well as their sub-components, in the water balance. The water balance has been simplified and adapted to the specificities of small and medium-sized water distribution networks, in particular the difficult access to technical data used to calculate water losses and the limited capacities of the actors in charge of the management of drinking water networks in Cameroon. Thus, the balance of water used is composed of four components: Water Supplied to the Network (SIV), Water Use for Own Needs (UAC), Water Sold (BAC) and Water Loss in the Distribution System (CARL). The American Water Works Association (AWWA), International Water Association (IWA), and Water Band Index (WBI) recommendations were followed in the evaluation of water loss indices. Table 1 presents a summary of the water loss indicators used.

Table 1

Summary of the water loss indicators used
Indices	Formulas	Formulas parameters	Sources
Failure intensity index (λ)	\(\lambda =\frac{N}{L\times t}\)	Where λ is expressed in the number of failures per kilometer of pipe and per year (failures/km/year), N is several failures per year, L is the total length of the distribution pipes (km), and t is a time in which a given number of failures occurring is equal to 1 year	Lambert et al. (2014)
Water loss (WS)	\(WS=\frac{CARL}{SIV}\times 100\)	Where WS is in percentage (%), CARL (Current Annual Real Losses) is the water loss in the distribution system (m³/year), and SIV (System Input Volume) is the water supplied to the network (m³/year)
Current Annual Real Losses (CARL)	\(CARL=SIV-UAC-BAC\)	Where CARL is in m³/year, UAC (Unbilled Authorised Consumption) is the water used for own needs of the Kribi water distribution network (m³/year), and BAC (Billed Authorised Consumption) is the water sold (m³/year)
Unit water loss (Q_los)	\({Q}_{los}=\frac{CARL\times 1000}{IN\times 365}\)	Where Q_los is in dm³/inhabitant/day, and IN is the number of inhabitants using the water supply system.
Unit coefficient of water loss per km (q_s)	\({q}_{s}=\frac{CARL}{Lm}\)	Where q_s is in m³/(km.h), CARL is the Current Annual Real Losses (m³/year), and L_m is the length of mains (km)
Hydraulic load index (q₀)	\({q}_{0}=\frac{SIV}{\left({L}_{m}\right)\times 365}\)	Where q₀ is the hydraulic load index (m³/km/day), L_m is the length of mains (km) and SIV is the System Input Volume (m³/year)
Real Leakage Balance index (RLB)	\(RLB=\frac{CARL\times 1000}{{N}_{c}\times 365}\)	Where RLB is in dm³/connection/day, CARL is the Current Annual Real Losses (m³/year), and N_C is the number of service connections (pcs).	Lambert et Hiner (2000)
Non-Revenue Water Basic (NRWB)	\(NRWB=\left(\frac{SIV-BAC}{SIV}\right)\times 100\)	Where NRWB is in percentage (%), SIV is the System Input Volume (m³/year) and BAC is the Billed Authorised Consumption (m³/year)
Unavoidable Annual Real Losses (UARL)	\(UARL=(18\times {L}_{m}+0.8\times {N}_{C}+25\times {L}_{p})\times 0.365\times p\)	Where UARL is in m³/year, L_m is the length of the mains (km), L_p is the length of private service pipes from the property boundary to the meter (km), p is the average pressure in the tested network (mCE), and 0.365 is the conversion factor per year and m³.
Infrastructure Leakage Index (ILI)	\(ILI=\frac{CARL}{UARL}\)	Where CARL is the Current Annual Real Losses (m³/year), and UARL is the Unavoidable Annual Real Losses (m³/year)

4.1. Results

4.1.1. Characterization of the water supply networks of the study area

Table 2 presents the length of the water supply networks, the number of services connections and the average pressure of the water supply networks of the study areas based on data from 2018–2022.

Table 2

Characteristics of the water Supply Networks of the study area
Water supply Network	Length of the Water Supply Network (km)			Number of Service connections (Nc)	Average Pressure in the Network p (m H₂0)
Water supply Network	Length of the Water Supply Network (Lm)	Length of the Private Services Pipes (Lp)	Total Length (Lm + Lp)	Number of Service connections (Nc)	Average Pressure in the Network p (m H₂0)
Ambam water supply network (AWSN)
2018	5.56	1.39	6.95	501	32
2019	5.56	1.39	6.95	507	30
2020	5.56	1.39	6.95	529	29
2021	5.56	1.39	6.95	548	28
2022	5.56	1.39	6.95	670	27
Campo water supply network (CWSN)
2018	7.14	1.79	8.93	117	29
2019	7.14	1.79	8.93	119	29
2020	7.14	1.79	8.93	122	26
2021	7.14	1.79	8.93	128	24
2022	7.14	1.79	8.93	128	24
Ebolowa water supply network (EWSN)
2018	44.90	11.23	56.13	4,222	30
2019	44.90	11.23	56.13	4,354	30
2020	45.02	11.26	56.28	4,585	30
2021	45.02	11.26	56.28	5,565	30
2022	45.02	11.26	56.28	5,676	30
Kribi water supply network (KWSN)
2018	27.77	6.94	34.71	2,967	25
2019	27.77	6.94	34.71	3,190	25
2020	27.77	6.94	34.71	3,366	25
2021	27.77	6.94	34.71	3,657	25
2022	27.77	6.94	34.71	3,860	25
Meyomessala water supply network (MWSN)
2018	26.08	6.52	32.60	228	26
2019	26.08	6.52	32.60	237	26
2020	26.08	6.52	32.60	245	26
2021	26.08	6.52	32.60	256	26
2022	26.08	6.52	32.60	256	26
Sangmélima water supply network (SWSN)
2018	40.50	10.13	50.63	1,967	30
2019	40.50	10.13	50.63	2,071	30
2020	40.50	10.13	50.63	2,157	30
2021	44.26	11.07	55.33	2,242	30
2022	44.26	11.07	55.33	2,288	30
Zoétélé water supply network (ZWSN)
2018	12.84	3.21	16.05	262	25
2019	12.84	3.21	16.05	266	25
2020	12.84	3.21	16.05	278	25
2021	12.84	3.21	16.05	298	25
2022	12.84	3.21	16.05	301	25

Analysis of the data contained in Table 2 leads to the conclusion that the Ebolowa water supply network is the longest water supply network with the length (including water supply connections) of 56.28 km operate by the Camwater in the south region of Cameroon. The Ambam water supply network uses the shortest length of water pipes of the seven water supply analyzed, i.e., 6.95 km. The Ebolowa water supply has the largest number of service connections (5,676) while the highest average pressure in the network (30 m H₂0) is found in Sangmélima water supply network and in the Ebolowa water supply network.

4.1.2. Water Balance

Table 3 presents the summary of the water balance in the study area between 2018 and 2022. This Table shows that the KWSN has introduced the highest volume of SIV in the network, with an average value of 1,252,470 m³/year between 2018 and 2022. He is followed by the EWSN (1,064,721 m³/year), SWSN (693,605 m³/year), MWSN (439,870 m³/year), AWSN (81,879 m³/year), ZWSN (30,897 m³/year), and CWSN (27,069 m³/year). Based on this data, the water supply network can be classified into three categories: medium-size water supply networks (KWSN and EWSN) with SIV greater than 1,000,000 m³/year; small water supply networks (SWSN and MWSN) with SIV between 100,000 and 1,000,000 m³/year; and very small water supply networks (AWSN, ZWSN, and CWSN) with SIV less than 100,000 m³/year.

Table 3 also shows that the classification of the water supply network based on the UAC is similar to that based on the SIV. Indeed, the water supply network with the highest UAC value is the KWSN, with an average value of 125,412 m³/year. He is followed by EWSN (70,016 m³/year), SWSN (39,357 m³/year), MWSN (27,873 m³/year), AWSN (4,920 m³/year), CWSN (3,478 m³/year), and ZWSN (2,754 m³/year). Regarding BAC, analysis of the data in Table 3 shows that EWSN has the largest volume of BAC, with an average value of 709,616 m³/year. He is followed by KWSN (469,519 m³/year), SWSN (336,740 m³/year), MWSN (256,550 m³/year), AWSN (56,167 m³/year), ZWSN (12,532 m³/year), and CWSN (9,942 m³/year). Analysis of Table 3 also shows that the KWSN has the highest volume of water lost between 2018 and 2022, with an average value of 657,539 m³/year. He is followed by SWSN (317,508 m³/year), EWSN (285,089 m³/year), MWSN (155,448 m³/year), AWSN (20,793 m³/year), ZWSN (15,612 m³/year), and CWSN (13,649 m³/year).

Table 3

Summary in water balance in 2018–2022
Year	Water Supplied to the Network, SIV (m³/year)	Water Use for Own Needs, UAC (m³/year)	Water Sold, BAC (m³/year)	Water Loss in the Distribution System CARL (m³/year)
Ambam water supply network (AWSN)
2018	66,445	2,991	32,002	31,452
2019	75,342	9,248	48,449	17,645
2020	98,288	7,436	71,020	19,832
2021	99,201	2,982	72,375	23,844
2022	70,120	1,941	56,988	11,191
Campo water supply network (CWSN)
2018	51,144	7,164	17,251	26,729
2019	33,473	2,909	14,010	16,554
2020	33,965	6,230	9,383	18,352
2021	11,622	847	5,443	5,332
2022	5,141	240	3,625	1,276
Ebolowa water supply network (EWSN)
2018	1,161,920	127,100	856,780	178,040
2019	1,308,200	69,600	671,541	567,059
2020	1,037,907	54,913	756,388	226,606
2021	794,080	44,066	648,194	101,820
2022	1,021,500	54,400	615,179	351,921
Kribi water supply network (KWSN)
2018	519,495	127,422	206,944	185,129
2019	1,494,924	227,891	310,197	956,836
2020	1,255,324	78,251	439,247	737,826
2021	1,736,683	115,926	744,428	876,329
2022	1,255,925	77,572	646,777	531,576
Meyomessala water supply network (MWSN)
2018	274,899	23,568	205,426	45,905
2019	313,046	44,492	249,591	18,963
2020	542,947	26,247	255,910	260,790
2021	619,535	28,554	329,275	261,706
2022	448,925	16,503	242,547	189,875
Sangmélima water supply network (SWSN)
2018	498,024	12,200	211,859	273,965
2019	721,151	43,960	288,748	388,443
2020	812,778	48,258	395,245	369,275
2021	789,584	52,119	435,826	301,639
2022	646,487	40,248	352,023	254,216
Zoétélé water supply network (ZWSN)
2018	31,355	2,924	12,185	16,246
2019	28,878	3,565	14,571	10,742
2020	34,211	3,114	17,136	13,961
2021	33,590	1,667	2,617	29,306
2022	26,453	2,498	16,151	7,804

4.1.3. Pipe Failure Rate

The failure intensity index is one of the most important indices for assessing the technical condition of a water supply network (Ociepa et al. 2019). Table 4 presents the mean values of the failure intensity index in the water supply networks analyzed.

Table 4

Mean values of failures intensity
Water supply network	Failure Intensity λ (failure/km/year)					Mean	Median	Min	Max
Water supply network	2018	2019	2020	2021	2022	Mean	Median	Min	Max
Ambam	10.79	24.32	30.07	22.16	22.30	21.93	22.30	10.79	30.07
Campo	15.12	8.51	2.02	19.48	2.35	9.50	8.51	2.02	19.48
Ebolowa	6.50	8.12	8.05	8.85	5.05	7.31	8.05	5.05	8.85
Kribi	6.80	12.65	10.05	6.42	5.30	8.25	6.80	5.30	12.65
Meyomessala	0.77	1.35	1.81	1.84	1.01	1.36	1.35	0.77	1.84
Sangmélima	2.41	3.56	2.03	2.95	1.74	2.54	2.41	1.74	3.56
Zoétélé	0.75	3.74	1.93	7.35	4.24	3.60	3.74	0.75	7.35

The analysis of the data in Table 4 shows that the failure intensity index generally presents a sawtooth evolution in all the networks studied. The Ambam water supply network has the highest failure intensity index, with an average value of 21.93 failures per km per year. He is followed by the Campo water supply network (9.50 failures per km/year), the Kribi water supply network (8.25 failures per km/year), the Ebolowa water supply network (7.31 failures per km/year), the Zoétélé water supply network (3.60 failures per km/year), the Sangmélima water supply network (2.54 failures per km/year), and the Meyomessala water supply (1.36 failures per km/year).

According to the recommendation of the standard PN-EN 60300-3-4:2008, the failure intensity index should not exceed 0.3 failure per km/year for main lines and 0.5 failure per km/year for distribution lines (Gwodziej-Mazur and Witochowski 2021). In this study, all the analyzed water supply networks didn’t meet this criteria. This leads to the conclusion that the water supply networks studied are not technically in good condition. Water losses in the water supply network, on the other hand, are determined not only by the number of failures but also by the rate and duration of water outflow from the broken pipe. The diameter of the hole and the pressure in the network, in turn, affect the outflow rate (Ociepa 2021; Rak and Misztal 2017; Rak and Sypie´n 2013).

4.1.4. Water Loss Indices

Table 5 presents water loss indices for the water supply networks analyzed. Analysis of the percentage water loss indices (Table 5) reveals that all the water supply networks in the analyzed five years have achieved mixed results, with values ranging from 6.06–87.25%. The highest average value of water indices was achieved by KWSN (50.19%), followed by ZWSN (49.31%), SWSN (46.37%), CWSN (45.29%), MWSN (31.07%), AWSN (26.19%), and EWSN (25.56%). Guérin-Schneider (2001) considers the percentage of water loss indices as good (between 0 and 20%), medium (between 20% and 30%), weak (between 30% and 40%), and very weak (greater than 40%). According to the above interpretation and based on the average value of WS, the AWSN and the EWSN have achieved medium performance. The MWSN has achieved a weak performance, while the other water supply networks (CWSN, KWSN, SWSN, and ZWSN) have achieved a very weak performance. However, it should be stressed that only utilizing the water loss rate index to assess excessive loss is insufficient. Furthermore, the value of the indicator is influenced by the amount of water utilized to meet the needs of the water supply facility itself, which is provided as an estimate by the facility. It is not suggested to use it to compare water loss in different water distribution systems for these reasons (Ociepa 2021). It can only be used to evaluate the variation of water loss over time in the same water distribution system (Kwietniewski 2013).

Analysis of the NRWB index values presented in Table 5 also reveals that this index has achieved mixed results in all the studied water supply networks, with values ranging from 18.37–92.21%. KWSN (62.01%) had the highest average value of water indices, followed by ZWSN (58.35%), CWSN (55.89%), SWSN (51.83%), MWSN (38.25%), AWSN (32.21%), and EWSN (32.04%). These average values of NRWB are higher than the average values reported for the Cameroonian systems except for AWSN and EWSN, where the value of NRWB is below the average value of the Cameroonian water supply system. According to AfWA and USAID (2015), the average NRWB for Cameroonian water supply systems is around 33%. In developing countries, the NRWB threshold for high-performing water utilities is believed to be around 23% (Singh et al., 2014), 15% in North America, and 13% in Western Europe (Chini and Stillwell, 2018). NRWB index values greater than 23% were found in all of the water supply networks studied. This finding is consistent with findings from other studies in Sub-Saharan Africa, where NRWB ranged from 25% in Western Africa to 41% in Eastern Africa (Minlo et al., 2023; Mvongo et al., 2023; Hoko and Chipwaila, 2017; Harawa et al., 2016; AfWA and USAID, 2015).

The real leakage balance index (RLB), which measures water losses per day per water supply connection, has achieved mixed results in all the studied water supply networks, with values ranging from 27.31 to 2,916.30 dm³/connection/day. The greatest average value of RLB indices was found in MWSN (1,703.99 dm³/connection/day), followed by KWSN (525.42 dm³/connection/day), SWSN (407.50 dm³/connection/day), CWSN (312.12 dm³/connection/day), ZWSN (151.71 dm³/connection/day), AWSN (107.01 dm³/connection/day), and EWSN (103.92 dm³/connection/day). According to Ociepa et al. (2019), the permitted daily water losses per water supply connection are assumed to be 100 m³/connection/day. In light of the foregoing, all water supply networks performed poorly. Furthermore, the unit water loss per capita, with values ranging from 0.68 to 46.10 dm³/inhabitant/day, demonstrates the poor condition of the investigated water supply networks. The highest average value of unit water loss indices was found in MWSN (17.54 dm³/inhabitant/day), followed by KWSN (15.12 dm³/inhabitant/day), SWSN (10.05 dm³/inhabitant/day), EWSN (8.26 dm³/inhabitant/day), ZWSN (8.04 dm³/inhabitant/day), CWSN (7.28 dm³/inhabitant/day), and AWSN (1.60 dm³/inhabitant/day).

Another analyzed index was the unit water loss index per kilometer (q_s) of water supply system. The highest average value of unit water loss index per kilometer was found in KWSN (2.70 m³/km/h), followed by SWSN (0.87 m³/km/h), EWSN (0.72 m³/km/h), MWSN (0.68 m³/km/h), AWSN (0.43 m³/km/h), CWSN (0.22 m³/km/h), and AWSN (0.14 m³/km/h). According to Guérin-Schneider (2001), the unit water loss index per kilometer for a water supply system with fewer than 5,000 service connections should be less than 0.21 m³/km/h. Only the AWSN did well in terms of unit water loss, according to this assessment.

Table 5

Water loss indices for the Kribi water distribution network
Year	WS (%)	Qlos (dm³/inhabitant/day)	RLB (dm³/connection/day)	NRWB (%)	qo (m³/(km.h)
Ambam water supply network (AWSN)
2018	47.34	2.43	172.00	51.84	0.65
2019	23.40	1.36	95.35	35.69	0.36
2020	20.20	1.53	102.71	27.74	0.41
2021	24.00	1.84	119.21	27.04	0.49
2022	16.00	0.86	45.76	18.73	0.23
Campo water supply network (CWSN)
2018	52.26	14.25	625.90	66.27	0.43
2019	49.45	8.83	381.12	58.15	0.26
2020	54.03	9.78	412.13	72.37	0.29
2021	45.88	2.84	114.13	53.17	0.09
2022	24.82	0.68	27.31	29.49	0.02
Ebolowa water supply network (EWSN)
2018	15.32	5.16	115.53	26.26	0.45
2019	43.35	16.42	356.82	48.67	1.44
2020	21.83	6.56	135.41	27.12	0.57
2021	12.82	2.95	50.13	18.37	0.26
2022	34.45	10.19	169.87	39.78	0.89
Kribi water supply network (KWSN)
2018	35.64	4.26	170.95	60.16	0.76
2019	64.01	22.00	821.78	79.25	3.93
2020	58.78	16.97	600.55	65.01	3.03
2021	50.46	20.15	656.52	57.14	3.60
2022	42.33	12.22	377.30	48.50	2.19
Meyomessala water supply network (MWSN)
2018	16.70	46.10	551.61	25.27	0.20
2019	6.06	19.04	219.21	20.27	0.08
2020	48.03	261.91	2,916.30	52.87	1.14
2021	42.24	262.83	2,800.79	46.85	1.15
2022	42.30	190.69	2,032.05	45.97	0.83
Sangmélima water supply network (SWSN)
2018	55.01	8.67	381.59	57.46	0.77
2019	53.86	12.30	513.87	59.96	1.09
2020	45.43	11.69	469.04	51.37	1.04
2021	38.20	9.55	368.60	44.80	0.78
2022	39.32	8.05	304.41	45.55	0.66
Zoétélé water supply network (ZWSN)
2018	51.81	8.37	169.88	61.14	0.14
2019	37.20	5.53	110.64	49.54	0.10
2020	40.81	7.19	137.59	49.91	0.12
2021	87.25	15.10	269.43	92.21	0.26
2022	29.50	4.02	71.03	38.94	0.07

The Infrastructure Leakage Index (ILI) can be used to compare different water distribution systems. The ILI is a ratio-the lower the ILI, the better managed the water network is, and a high ILI indicates that the water network is poorly managed (Winarni 2009). Figure 2 presents the evolution of the ILI between 2018 and 2022.

Figure 2 shows that the ILI in the EWSN is below the upper limit for good conditions according to IWA criteria (ILI ≤ 2.5) and the WBI Banding for developing countries (ILI ≤ 4), with a range of values between 0.02 and 0.55. It implies good conditions for the EWSN. The ILI index is above the upper limit for good conditions according to IWA criteria, and the WBI banding in the MWSN and ZWSN has a range of values from 23.94 to 122.65. In the KWSN, ILI index values are higher than the upper limit for good conditions according to IWA criteria. They are also higher than the upper limit for good conditions according to WBI banding criteria for developing countries, except in 2021, where the ILI is below the limit. In the other water supply networks (AWSN, CWSN, and SWSN), the ILI presents a sawtooth evolution. In the AWSN, values range from 1.69 to 5.03, while in the CWSN, values range from 0.53 to 9.46. In the SWSN, values range from 0.66 to 8.80.

The range value of ILI (0.02 to 122.65) in the study area is similar to those reported in Sub-Saharan Africa. According to AfWA and USAID (2015), the ILI in Sub-Saharan Africa ranges from over 1500 in one state in Nigeria to as low as 1.0 in the Ugandan town of Entebbe, indicating the continent's vast variety of water network management competency (and incompetence). The ILI index, on the other hand, should be utilized when the number of connections exceeds 5,000, the density of connections exceeds 20 per km of water supply network, and the network pressure is at least 0.25 MPa (McKenzie and Lambert 2003). In 2021 and 2022, only EWSN fits these standards. As a result, ILI index findings should be interpreted with extreme caution.

The hydraulic load index for the water supply network was taken into consideration in the water loss study. Table 6 presents the hydraulic load index for the water supply networks analyzed.

Table 6

Hydraulic load index for the water supply network analysed
Water supply network	Hydraulic load index q₀ (m³/km/day)					Mean	Median	Min	Max
Water supply network	2018	2019	2020	2021	2022	Mean	Median	Min	Max
Ambam	26.19	29.70	38.75	39.11	27.64	32.28	29.70	26.19	39.11
Campo	15.69	10.27	10.42	3.57	1.58	8.30	10.27	1.58	15.69
Ebolowa	56.71	63.85	50.53	38.66	49.73	51.90	50.53	38.66	63.85
Kribi	41.00	118.00	99.09	137.08	99.13	98.86	99.13	41.00	137.08
Meyomessala	23.10	26.31	45.63	52.07	37.73	36.97	37.73	23.10	52.07
Sangmélima	26.95	39.02	43.98	39.10	32.01	36.21	39.02	26.95	43.98
Zoétélé	5.35	4.93	5.84	5.73	4.52	5.27	5.35	4.52	5.84

Table 6 shows that the highest average value of hydraulic load index was found in KWSN (98.86 m³/km/day), followed by EWSN (51.90 m³/km/day), MWSN (36.97 m³/km/day), SWSN (36.21 m³/km/day), AWSN (32.28 m³/km/day), CWSN (8.30 m³/km/day), and ZWSN (5.27 m³/km/day). Water losses in a distribution system are proportional to the hydraulic load of the network (Ociepa 2021). In general, reducing network load results in reduced water loss. However, a review of the hydraulic load indices in the examined water supply network reveals a sawtooth evolution, making a clear relationship between load and water losses impossible to establish.

4.2. Discussion

4.2.1. Water losses performances

The results of the analysis lead to the conclusion that all the water supply networks analyzed achieved mixed results compared to national data (WSF-AF et al. 2009; AfWA and USAID 2015; Liemberger and Wyatt, 2019) and very weak results compared to international standards. This figure of poor performance in terms of water loss management is similar to those identified in various studies (Bhagat et al. 2019; AfWA and USAID 2015; Makaya 2015; Jacobsen et al. 2012; Minlo et al., 2023; Mutikanga 2012; Mutikanga et al. 2009; WSF-AF et al. 2009) in Sub-Saharan Africa. These poor performance are due to technical, economic, political and social, factors that affect the sustainability of water supply systems. These factors, often mentioned in the literature by several authors (Makaya 2015; AfWA and USAID 2015; Frauendorfer & Liemberger 2010; Vairavamoorthy and Mansoor 2006) are interrelated.

On the technical level, the main challenge is the age of the water supply networks. This situation is due to the economic crisis that Cameroon experienced in the 1990s and which caused a lack of investment for more than two decades in the urban water supply sector. The majority of pipe materials (steel, cast iron, PVC, etc.) have deteriorated over time and network programs are slow to be implemented. Previous research (Rezaei et al. 2015; Hu & Hubble 2007) found that pipe age increases failure rates and water losses. Furthermore, the majority of water supply networks are unplanned, and there is no current map of the network that allows one to track its evolution in space and time. Several authors (Shamsaei et al. 2013; Mutikanga et al. 2009) indicate that controlling water loss and managing system operations in an "unplanned" water distribution network is complex and is a significant source of non-revenue water in cities. In addition, high level of water losses is also influences by poor operation and maintenance policies including ineffective record keeping systems, inadequate technical skills and technology, and water meter inaccuracies. Camwater has adopted a passive water losses management strategy, meaning that Camwater, through its Leaks Research Brigade (LRB), repairs only those leaks that are visible. This is not enough since 90% of the leaks are usually not visible on the surface (Frauendorfer & Liemberger 2010). Furthermore, the lack of material, financial and human resources limits the action of the LRB.

Economically, the financial viability of Camwater is the main barrier to the effective implementation of water loss control programs in drinking water distribution networks. Indeed, the solvency and liquidity of this company are not guaranteed due to the high level of debt. Thus, the liquidity ratio of 0.89 in 2020 indicates that Camwater's activities do not generate enough cash to meet short-term commitments, while the solvency ratio of 2.2 shows high indebtedness relative to the level of equity (CTR, 2021). Three main reasons could explain such a situation:

The first is the high level of water losses in the water distribution networks managed by Camwater. Indeed, on the basis of an average tariff of 329 FCFA/m³, the cumulative cost of water losses in the seven networks studied during the five years considered is estimated at 2.4 billion FCFA (approximately 4.4 million USD). At national level, Camwater is losing nearly losing nearly 27.7 million USD (about 15.2 billion of CFA francs) per year (Liemberger and Wyatt, 2019). Therefore, Camwater can survive for long if it loses a significant portion of its marketable product (treated water). Furthermore, when treated water is lost, the costs of water collection, treatment, and distribution rise, water sales fall, and large capital expenditure plans are frequently encouraged to fulfill the ever-increasing demand (Frauendorfer & Liemberger 2010).

The second reason is the administration of water prices in Cameroon. Indeed, in order to facilitate access to drinking water in Cameroon, the government has opted for an equalization system that ensures that the price of water is the same in the area granted to Camwater. However, the current price per cubic meter of water does not cover all the charges related to the water service (CTR, 2021). This creates a shortfall that is not systematically covered by the State of Cameroon in the form of a subsidy. This observation differs from that observed in Algeria, where the loss of earnings is covered by the state in the form of a subsidy (Boukhari and Miras, 2019). Morocco, on the other hand, applies a vast system of equalization between consumption brackets, between trades, and between territories (Lydec, 2016) to seek viable cost recovery in operating expenses and, as far as possible, in investment expenses in the capital (Boukhari and Miras, 2019). Thus, the performance of Camwater would be improved on the condition either of applying the real prices or of systematically taking charge each year of the shortfall linked to the administration of the price of water.

The third reason relates to unpaid consumption bills, starting with those of the State of Cameroon, which hinder the performance of Camwater. Indeed, although state receivables have fallen sharply compared to 2018, falling from 45.6 billion FCFA (around 70.8 million USD) to 15.4 billion FCFA (23.9 million USD) (CTR, 2021), they still remain significant (CTR, 2021), they still remain significant. It is estimated that unpaid bills as of November 1st, 2022 are around 32 billion CFA francs (58,2 million USD). Thus, in order to achieve financial balance, it is necessary to set up a new economic model that would ensure the financial viability of Camwater.

On the political level, the main challenge is the centralization of the production and distribution of water. Indeed, the terms of the concession contract for the public water service in urban and peri-urban areas create a kind of monopoly serving the cause of democratizing access to water in Cameroon and destroying the incentives for improved management and governance. Water management should be decentralized and placed under the responsibility of municipalities. Better, an incentive policy should be set up to encourage private initiatives going in the direction of the multiplication of the constructions of boreholes and other castles of less scale being able to make it possible to curb the deficits at the local level. In addition, it is important to address the behavior of the community in the fight against water losses in the network. Indeed, water loss is not just an engineering problem but also reflects a sociocultural situation that requires changes in community behavior and attitudes toward water usage (Frauendorfer & Liemberger, 2010). Certain community attitudes favor illegal connections to the network (fraud) and the theft of equipment such as meters and their accessories. Since the beginning of 2022, 750 subscriber water meters in Douala, 900 meters in Yaoundé, and around fifty meters in Mbalmayo and Maroua have been stolen.

4.2.2. Availability and quality of data

The evaluation of water losses in small and medium-sized water distribution networks that is reported in this study shouldn't be interpreted as demonstrating an independent evaluation of water losses. Data provided by the utility itself was used to conduct the assessment. The study as it is now offers a useful overview of the current patterns and problems with water losses in the southern region of Cameroon. Furthermore, when assessing water losses in the water distribution network in the Southern Cameroon region, most water distribution network face the following problems:

no reliable information on the real length of the network. Existing maps often show only a fraction of the existing network. This leads to an underestimation of the UARL and an overestimation of the values of the ILI and the failure intensity index, the unit water loss per kilometer, and the hydraulic load index.
the number of connections to the water service is not known in the networks studied. The number of clients was therefore used instead of the number of connections. In practice, the number of clients will, in most cases, be greater than the number of connections. This leads to an overestimation of the UARL and an underestimation of the ILI and the RLB.
pressure data and pressure loggers are not available. The average pressure in the different networks was estimated from the design data of the different drinking water distribution networks. It is generally very high compared to the actual values observed in the field. This leads to an overestimation of UARL and an underestimation of ILI.
high level of apparent losses (difficult to estimate) and therefore unreliable and imprecise volume of real losses.

The data used in this study are also subject to errors and uncertainties. These errors add up in the NRW and real loss calculation volumes. Uncertainty will always exist in the calculated values of water losses (NRW, apparent losses, and real losses), regardless of the approach or procedures used to assess water losses. Striving to quantify uncertainty is a useful strategy for dealing with it. Although uncertainty estimates have long been common practice in hydrological measurements, such as the measurement of river flows, their applications to estimates of water losses in drinking water distribution networks remain embryonic. However, there is software available to calculate water balance that allows users to specify 95% confidence limits for all elements entered and automatically calculates 95% confidence limits for water losses. (AL-Washali et al., 2020; Liemberger and McKenzie, 2003). Using 95% simply indicates that the estimates have a wider range of acceptable confidence levels than more precise estimates.

The aim of this study was to analyze and evaluate water losses in seven water supply networks in the south region of Cameroon in order to contribute constructively to the debate on the most effective approaches to reducing water losses in Sub-Saharan Africa's water supply networks. Results show that the simplified water balance provided a useful overview of the current patterns and problems with water losses in small and medium-sized water distribution networks in sub-Saharan Africa, where access to technical data used to calculate water losses is difficult and the capacities of the utility to conduct water audits are limited. Results also suggest that passive loss management is ineffective for reducing water losses in Sub-Saharan Africa's urban water supply. There is a need for improvement to enhance profitability with better network pressure management, active leakage control, and network maintenance. Future work may analyze the correlation between accident rates and damage in small and medium-sized water supply networks. This can be done using the risk matrix method. This method was previously used by Rak et al. (2019) with good results and can’t be adapted for use in sub-Saharan Africa.

Conflict of interest

The authors declare no conflict of interest.

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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The authors declare no competing interests.

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Analysis of water losses in seven small and medium-sized water distribution networks in the south region of Cameroon (Central Africa)

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Abstract

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Highlights

1. Introduction

2. Evolution of the context of water supply in urban areas in Cameroon

3. Materials and methods

3.1. Study area

3.2. Data collection

3.3. Data processing

4. Results and discussion

4.1. Results

4.1.1. Characterization of the water supply networks of the study area

4.1.2. Water Balance

4.1.3. Pipe Failure Rate

4.1.4. Water Loss Indices

4.2. Discussion

4.2.1. Water losses performances

4.2.2. Availability and quality of data

5. Conclusion

Declarations

Conflict of interest

Data availability statement

Funding

References

Additional Declarations

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