In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of 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 performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF. KeywordsHSPF Model, Artificial Neural Network (ANN), Runoff Simulation, Balkhichai River Watershed * Corresponding author. F. Amirhossien et al. 204 IntroductionStreamflow is one of the most important processes in the hydrological cycle and its prediction is vital for water resources management and planning [1]. Computer simulation models of watershed hydrology and artificial intelligent techniques are widely used for runoff simulation and forecasting. The use of watershed models is increasing due to the growing demands of improving runoff quantity.Over the last decades, artificial intelligent techniques have been introduced and widely applied in hydrological studies as powerful alternative modelling tools, such as Artificial Neural Network (ANN) [2]-[6], and fuzzy inference system (FIS) [7]- [9]. In addition, Shamseldin [10] (1997), Kumar et al. [11] and Mutlu et al. [12] compared ANNs with different input variables for runoff simulation. The comparisons showed that the ANN models applying both with rainfall and discharge as input variables gave better results than the models with rainfall as the input. When the model utilizes of rainfall values as the input variables, the simulated hydrographs do not match the measured hydrographs so well [13] [14]. Although better fits between the simulated and measured hydrographs have been reported in other studies, where additional variables such as temperature [15], evaporation [16] and, soil moisture [17] have been included as inputs for the ANN model.HSPF is a semi-distributed, conceptual model that combines spatially distributed physical attributes into the hydrologic response units. In this model, surface runoff is simula...

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 9 50 km spatial resolution on the basis of B2 scenario, is used for the base 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.

The purpose of this study is to survey the impact of climate change on the runoff of Gharehsoo River in northwest of Iran. In this research the outputs of monthly precipitation and temperature data of PRECIS model, which is a regional climate model with 50 × 50 km resolution on the basis of B2 scenario, have been utilized for base (1961-1990) and future (2071-2100) periods. The output results of PRECIS model show that the average temperature of watershed increased up to 2˚C-5˚C. In addition, future precipitation is more than the base precipitation on January, February, March, September and December. The observed data of 1996-2002 used for calibration of HSPF model and the data of 2003-2004 were used for HSPF validation. The present monthly patterns for precipitation and temperature were estimated using the geostatistical techniques and the future monthly patterns were retrieved by the combination of future monthly PRECIS data and monthly patterns of precipitation and temperature. Then, the base and future precipitation and temperature patterns were introduced to validate HSPF model for the simulation of monthly runoff in the base and future periods. The results show that in the future, the discharge of Gharehsoo River watershed decreases in all of the months. In addition, the peak discharge in the future period happens one month earlier, in April, because of increase of temperature and earlier beginning of snow melting season. Finally the sensitivity analysis was performed on the monthly runoff. The results showed that monthly discharge increases 0.3%-35.6% and decreases 0.3%-32.6% due to 20% increase and decrease of precipitation, respectively. In addition, 1˚C and 2˚C increase of temperature leads to 0%-8% and 0.1%-15% decrease of average monthly discharge, respectively.