Prediction of Discharge Using Artificial Neural Network and IHACRES Models Due to Climate Change

Abstract

Understanding of climate change and its impacts on river discharge has affected the quality and quantity of water and also supplying water requirements for drinking, agriculture and industry. Therefore, prediction of precipitation and temperature by climate models as well as simulation and optimization of their runoff with suitable models are very important. In this study, four climate models of the Fifth Coupled Model Inter comparison Project (CMIP5) and RCP8.5 scenario were used to forecast future precipitation and temperature for the next two periods including 2020-2052 and 2053-2085. Mean Observed Temperature-Precipitation (MOTP) method was used to reduce the uncertainty of climate models and the change factor method was used to downscale the climate data. Then, the Lumped-conceptual Identification of unit Hydrographs and Component flows from Rainfall, Evaporation and Stream flow data (IHACRES) model and multi-layer Artificial Neural Network (ANN) model were employed to estimate the effects of these parameters on the Khorramrood River runoff. The neural network model is written and implemented using Scikit-Learn library and the Python programming language. The comparison of performance of ANN models with different input variables like monthly precipitation, monthly precipitation of previous months, monthly discharge, monthly discharge of previous months, monthly temperature was made to find the best and most efficient network structure. The results showed that the precipitation in Khorramrood River basin based on the weighted combination model decreased by 8.18 % and 9.75 % in the first and the second periods, respectively, while the temperature increased by 1.85 and 4.22 °C, respectively. The discharge parameter in the calibration and validation period in the IHACRES model based on criteria to evaluate the parameters of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), The Coefficient of Determination (R), and the Nash-Sutcliffe Efficiency (NSE) performed better than the artificial neural network model. However, due to the small differences of these changes, the predictions were performed for both periods and using both models and the results indicated that future discharge in the IHACRES model decreased by 12.72 % during the first period and by 20.3 % in the second period, while the model of artificial neural network showed decrease rates of 2.12 % and 6.97 %, respectively.