84 The Future of Water in African Cities
Nairobi, Kenya: Dealing with the Gap between Supply and Demand Table 4.1 Key Characteristics and Location of Nairobi, Kenya Population (2009)
3,140,000
Estimated daytime population (2010)
5,000,000
Projected annual population growth
2.50–5.00%
Economic activity
Industrial, commercial
Water consumption per person served
n.a.
Utility water coverage
63%
Source: Eckart et al, 2012. Note: n.a. = not available.
Nairobi presents a classic case of how the gap between water supply and demand might grow over time (see Chapter 1). Since 1985, the population of Nairobi has grown from approximately 1.2 million in 1985 to 3.2 million in 2010. During the same period, water demand grew slightly faster, from 203,000 cubic meters per day to 579,000 cubic meters per day (AWSB, 2012). In 2009, Nairobi experienced a crisis as water levels in the main reservoir became very low and water supply to the city had to be severely rationed. The water crisis provided a strong impetus to change. Work on a new water master plan has been ongoing for some time and is expected to be finalized during the summer of 2012. An Agence Française de Développement funded project to reduce nonrevenue water, with a focus on water losses, has started. Ongoing efforts to enhance the bulk water storage and transfer infrastructure have been complemented by additional work on IUWM in Nairobi, prepared as a case study for this book (Eckart et al., 2012). Nairobi faces major uncertainties in assessing the gap between future water demand and supply. The case study reviewed the population, income, and water consumption per capita projections. Even small changes to the assessment of the high- and low-growth scenarios for population and unit consumption result in a large range of possible demand in 20 years (see Figure 4.1). Other sources of uncertainty, including the impact of future climate variability and climate change, add to the degree of uncertainty about projected water supply and demand. This illustrates the need to implement adaptive systems that can cope with uncertainty as suggested by the principles of IUWM.