Variability assessment of simulated recharge resulted from precipitation using different GCMs, case study: west shore of Lake Urmia, Iran

Variations associated with recharge rate prediction are studied by a comparison between the simulated recharge based on weather data generated by Long Ashton Research Station Weather Generator (LARS-WG), which is a downscaling model with spectra of global circulation models (GCMs). The study area included aquifer of Urmia Plain (West Shore of Lake Urmia) located in Iran, Middle East. The aquifer area was classified based on soil composition gradation. Hydrologic Evaluation of Landfill Performance (HELP) model and percolation into vertical column were used for 1-dimensional modelling (pseudo-2-D modelling) of the recharge of various zones of Urmia Plain. The data required for modelling of weather parameter and recharge include average daily temperature, daily rainfall, and solar radiation. The recharge modelling was performed for time periods of 2020–2040, 2041–2060, 2061–2080, and 2081–2100 and five GCMs based on the 5th report issued by Intergovernmental Panel on Climate Change (IPCC), namely, EC-EARTH, GFDL-CM3, HadGEM2, MIROC5, and MPI-ESM-MR, under RCP 8.5 and RCP 4.5 scenarios. The recharge results of periods were compared between the GCMs. This variability in recharge prediction indicated that GCMs are highly important tools for water management planning.