Forecasting Domestic Water Demand Using Meteorological and Satellite Data: Case Study of Greater Beirut Area

Abstract

Greater Beirut Area (GBA) is expected to suffer from various socio-economic burdens caused by climate change impacts, including those related to rising temperatures, reduced water availability, increased heat waves and heat island effect, and others. This study addresses future changes in water demand in GBA through utilizing water demand patterns, meteorological data, and remote sensing data. Initially, the relationships between satellite remotely sensed data on Land Surface Temperature (LST) and other weather parameters were tested for correlation. Water demand models showed that LST and air temperature had a high positive correlation with temperature, positive correlation with solar radiation and wind speed, and high negative correlation with precipitation. Single variable linear regression models were developed to predict changes in domestic water demand using atmospheric pressure and temperatures (average, minimum, and maximum) (R2 > 0.5), and a multivariable linear regression model was obtained for the city of Beirut. In addition, temperature-based models were used to forecast future water demand under four climate Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0, and 8.5). The results showed an anticipated increase, during the dry period, of 45–90 thousand cubic meter per month on the short term (2020–2039) and 90–270 thousand cubic meter per month on the long term (2080–2099). Recommendations for the way forward were provided.