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Modelling rainfall runoff is important for several human activities. For example, rainfall runoff models are needed for water resource planning and water system design. In this regard, the daily runoff was modelled using the Genie Rural, a 4-parameter Journalier (GR4J), Genie Rural, a 6-parameter Journalier (GR6J), and the CemaNeige GR6J lumped conceptual models that were developed by the IRSTEA Hydrology Group. The main difference among the tested models is in the complexity and processes that are considered in the various model versions. As a case study, the non-homogeneous mostly karst Ljubljanica River catchment down to the Moste discharge gauging station was selected. Models were evaluated using various efficiency criteria. For example, base flow index (BFI) was calculated for the results of all tested models and observed discharges in order to compare low flow simulation performance. Based on the presented results we can conclude that in case of the non-homogeneous and karst Ljubljanica catchment the CemaNeige GR6J yields better modelling results compared to the GR4J and GR6J models. Compared to the GR6J and GR4J model versions, the CemaNeige CR6J also includes the snow module and improved methodology for the low-flow simulations that are also included in the GR6J model version.

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Rain-on-snow (ROS) floods can cause economic damage and endanger human lives due to the compound effect of rainfall and snowmelt, especially under climate change. In this study, possible future changes of seasonality, magnitude and frequency characteristics of ROS floods were investigated for the selected catchments in Slovenia, Europe. For this purpose, five global/regional climate models (GCM/RCM) combinations were applied using the RCP4.5 climate scenario for the period 1981–2100. To determine ROS floods’ characteristics in the future, a lumped conceptual hydrological model Génie Rural à 6 paramètres Journalier (GR6J) with snow module CemaNeige was applied. The results indicate that the number of ROS floods could increase in the future. Moreover, also the magnitudes of extreme ROS floods could increase, while a slight decrease in the median values of ROS flood magnitudes was observed. The strength of seasonality for a high-altitude catchment could decrease in the future. A slight shift in the average ROS floods’ timing could be expected. Furthermore, a catchment located in a temperate continental climate could have a different response to the climate change impact in comparison to a catchment located in a mountain climate with alpine characteristics. Additionally, differences among investigated climate models show a large variability.

Data-driven models and conceptual models have been utilized in an attempt to perform rainfall–runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall–runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Qt-1) and streamflow data the preceding two days (Qt-2) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.