Simulation of Urmia Lake Level Changes and Its Uncertainty and Sensitivity to Water Budget Components

Document Type : Research Article

Authors

Abstract

This research work is an attempt to simulate and analyze monthly water level changes in Urmia Lake.
To this end, water budget, multiple regression and artificial neural networks (ANNs) approaches have
been investigated, using monthly data of effective components of water budget equation such as input
discharge, average rainfall and average evaporation. Furthermore, uncertainty and sensitivity analysis
were employed to compare the simulation methods capabilities. The results suggested that ANNs
model using monthly discharge, rainfall and evaporation as inputs gave best results with less
sensitivity, but greater uncertainty.