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In this study, the capability of two different types of model including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and Artificial Neural Networks (ANNs) as a data-driven model in simulating runoff were evaluated. The area considered is the Gharehsoo River watershed in northwest Iran. HSPF is a semi distributed Deterministic, continuous and physically-Based model that can simulate the hydrologic, associated water quality and quantity, processes on pervious and impervious land surfaces and streams. ANN is probably the most successful machine learning technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach understanding as to the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performance of ANN and HSPF models in calibration and testing stages are compared with the observed runoff values to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicate that simulated runoff by ANN were generally closer to observed values than those predicted by HSPF.

The purpose of this study is to investigate the impacts of climate change on the runoff of Gharehsoo River Basin in the northwest of Iran. In this research, the outputs of monthly precipitation and temperature data of PRECIS (Providing Regional Climates for Impacts Studies) model, a regional climate model with 50 × 50 km spatial resolution on the basis of B2 scenario, is used for the base (1961–1990) and the future (2071–2100) periods. The output results of PRECIS show that the average temperature of the watershed increased up to 2–5 °C, for the period spanning from 2070 to 2100. In addition, compare to the base period, we are expecting to receive more precipitation in future for the months of January, February, March, September and December. The artificial neural network (ANN) was applied to quantify the future discharge. The results show that in the future, the discharge of Gharehsoo River watershed decreases for all months. Moreover, the peak discharge in the future period happens 1 month earlier, due to increasing in the temperature and earlier start of snow melting season. Finally, 1 and 2 °C increase in temperature lead to 0.05–8.2 % and 0.1–13.4 % decrease of average monthly discharge, respectively.