Abstract
Water scarcity is currently still a global challenge despite the fact that water sustains life on earth. An understanding of domestic water demand is therefore vital for effective water management. In order to understand and predict future water demand, appropriate mathematical models are needed. The present work used Geographic Information Systems (GIS) based regression models; Geographically weighted regression (GWR) and Ordinary Least Square (OLS) to model domestic water demand in Athi river town. We identified a total of 7 water determinant factors in our study area. From these factors, 4 most significant ones (household size, household income, meter connections and household rooms) were identified using OLS. Further, GWR technique was used to investigate any intrinsic relationship between the factors and water demand occurrence. GWR coefficients values computed were mapped to exhibit the relationship and strength of each explanatory variable to water demand. By comparing OLS and GWR models with both AIC value and R2 value, the results demonstrated GWR model as capable of projecting water demand compared to OLS model. The GWR model was therefore adopted to predict water demand in the year 2022. It revealed domestic water demand in 2017 was estimated at 721,899 m3 compared to 880,769 m3 in 2022, explaining an increase of about 22%. Generally, the results of this study can be used by water resource planners and managers to effectively manage existing water resources and as baseline information for planning a cost-effective and reliable water supply sources to the residents of a town.
Highlights
Water is one of the most important natural goods for maintenance of life in Earth
Ordinary Least Square (OLS) regression was used to provide insight into the variables that explained the spatial variation of domestic water demand across the entire study region
Using Geographic Information Systems (GIS)-based local model and global statistic to explore the relationship between water use or demand variation and the household variables affecting water demand, it was able to identify and understand some certain vital information concerning stationarity and non-stationarity in spatial dataset
Summary
Water scarcity is a global challenge that currently affects more than 40% of the total global population [1]. It is estimated that by 2025, an estimated 3.9 billion (or over 60%) of the world’s population will live in a water stressed environment [2]. Despite water being one of the most essential resources with great implications for development in Africa, the freshwater situation is still not encouraging [3]. According to United Nations [4], an estimation of more than 300 million people in Africa is currently living in a water-scarce environment and many water requirements for agriculture, sanitation, industry and domestic use in Africa cannot be met. The situation is even getting worse as a result of increased population growth, rapid urbanization and industrialization, increasing agriculture and lack of adequate capacity to manage existing freshwater resources
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