Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran

Abstract

This study represents a new strategy for assessing how climate change has impacted urban water demand per capita in Neyshabur, Iran. Future rainfall depths and temperature variations are projected using several general circulation models (GCMs) for two representative concentration pathway (RCP) (i.e., RCP45 and RCP85) scenarios using LARS-WG software. A simulator model is developed using the genetic programming (GP) model to predict future water demand based on projected climate variables of rainfall depth and maximum temperature. The period of 1996–2016 is selected as the base period. Three future periods, namely the near-future (2021–2040), middle future (2041–2060), and far future (2061–2080), are also employed to assess climate change impact on water demand. Results indicate significant increases in annual projected rainfall depth (14~53%), maximum temperature (0.04~4.21 °C), and minimum temperature (1.01~4.71 °C). The projected monthly patterns of rainfall depth and temperature are predicted to cause a 1-month shift in the water demand peak (i.e., it will occur in April instead of May) for all future periods. Furthermore, the annual water demand per capita is projected to increase by 0.5~1.2%, 1.5~3.2%, and (2.2~7.1%), during the near-, middle-, and far-future periods, respectively. The uncertainty associated with water demand is also projected to increase over time for RCP45. The mathematical expression of urban water demand based on climatic variables is vital to managing the water resources of Neyshabur. The methodology proposed in the present study represents a robust approach to assessing how climate change might affect urban water demand in cities other than Neyshabur and provides crucial information for decision-makers.