As a complex hydrological problem, rainfall-runoff (RR) modeling is of importance in runoff studies, water supply, irrigation issues, and environmental management. Among the variety of approaches for RR modeling, conceptual approaches use physical concepts and are appropriate methods for representation of the physics of the problem while may fail in competition with their advanced alternatives. Contrarily, machine learning approaches for RR modeling provide high computation ability however, they are based on the data characteristics and the physics of the problem cannot be completely understood. For the sake of overcoming the aforementioned deficiencies, this study coupled conceptual and machine learning approaches to establish a robust and more reliable RR model. To this end, three hydrological process-based models namely: IHACRES, GR4J, and MISD are applied for runoff simulating in a snow-covered basin in Switzerland and then, conceptual models’ outcomes together with more hydro-meteorological variables were incorporated into the model structure to construct multilayer perceptron (MLP) and support vector machine (SVM) models. At the final stage of the modeling procedure, the data fusion machine learning approach was implemented through using the outcomes of MLP and SVM models to develop two evolutionary models of fusion MLP and hybrid MLP-whale optimization algorithm (MLP-WOA). As a result of conceptual models, the IHACRES-based model better simulated the RR process in comparison to the GR4J, and MISD models. The effect of incorporating meteorological variables into the coupled hydrological process-based and machine learning models was also investigated where precipitation, wind speed, relative humidity, temperature and snow depth were added separately to each hydrological model. It is found that incorporating meteorological variables into the hydrological models increased the accuracy of the models in runoff simulation. Three different learning phases were successfully applied in the current study for improving runoff peak simulation accuracy. This study proved that phase one (only hydrological model) has a big error while phase three (coupling hydrological model by machine learning model) gave a minimum error in runoff estimation in a snow-covered catchment. The IHACRES-based MLP-WOA model with RMSE of 8.49 m3/s improved the performance of the ordinary IHACRES model by a factor of almost 27%. It can be considered as a satisfactory achievement in this study for runoff estimation through applying coupled conceptual-ML hydrological models. Recommended methodology in this study for RR modeling may motivate its application in alternative hydrological problems.

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Impacts of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) on hydroclimatic variables have been previously studied in various viewpoints. This study adds a new perspective by focusing on the influences of winter NAO and AO extreme phases on hydrological drought using a standardized streamflow index (SSFI) over Turkey and northern Iran. Moreover, the physical mechanisms associated with the extreme phases of NAO over different atmospheric conditions were investigated. The results concerning short-term droughts across Turkey revealed that wet conditions dominate particularly in the winter and following spring during the negative NAO and AO extreme phases while the NAO and AO impacts in Iran are only significant during simultaneous winter. Furthermore, the outputs of SSFI for the positive extreme phases of NAO and AO considerably differed in Turkey and Iran so that multiple drought events were detected in Turkey for all timescales as generally opposed to Iran. Western and eastern Turkey suffered from drought in various magnitudes during the positive extreme phases of NAO while fewer droughts were observed in Iran around the Caspian Sea during the negative NAO and AO extreme phases. Results of correlation analysis depicts that negative cycles are controlling wetness (drought) in Turkey (Iran) while there is a weak correlation between positive NAO cycles and SSFIs in the study area. In addition, our study showed that negative NAO and AO extreme phases could affect the hydrological drought stronger but in a shorter period compared with a longer period regarding the positive NAO and AO phases.

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