This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted.

In our era, the rapid increase of parallel programming coupled with high‐performance computing (HPC) facilities allows for the use of two‐dimensional shallow water equation (2D‐SWE) algorithms for simulating floods at the “hydrological” catchment scale, rather than just at the “hydraulic” fluvial scale. This approach paves the way for the development of new operational systems focused on impact‐based flash‐floods nowcasting, wherein hydrodynamic simulations directly model the spatial and temporal variability of measured or predicted rainfall on impacts even at a street scale. Specifically, the main goal of this research is to make a step to move toward the implementation of an effective flash flood nowcasting system in which timely and accurate impact warnings are provided by including weather radar products in the HPC 2D‐SWEs modelling framework able to integrate watershed hydrology, flow hydrodynamics, and river urban flooding in just one model. The timing, location, and intensity of the street‐level evolution of some key elements at risk (people, vehicles, and infrastructures) are also discussed considering both calibration issues and the role played by the spatial and temporal rainfall resolution. All these issues are analyzed and discussed having as a starting point the flood event which hit the Mandra town (Athens, Greece) on the 15 November 2017, highlighting the feasibility and the accuracy of the overall approach and providing new insights for the research in this field.

A new, three-parameter friction model at the boundaries of free surface flow is proposed. The model is valid for all flow and roughness characteristics and it is proposed to replace the widely used Manning equation in the flood simulation models. In the gauged flow domains, the parameters of the model can be calibrated directly using the appropriate field data. For the ungauged flow domains, the parameters are estimated following a four-step procedure: (1) selection of roughness zones; (2) generation of synthetic data from water depth—flow velocity—roughness height combinations; (3) calculation of shear stresses using a physically based equation; and (4) estimation of the model parameters through regression toward the results of this equation. The proposed friction model is used in the case study of the Tous dam break in Spain in 1982, as it was simulated by the two-dimensional hydrodynamic model FLOW-R2D. The results of this application show that the proposed friction model performs slightly better than the commonly used Manning equation.

Machine learning has been employed successfully as a tool virtually in every scientific and technological field. In hydrology, machine learning models first appeared as simple feed-forward networks that were used for short-term forecasting, and have evolved into complex models that can take into account even the static features of catchments, imitating the hydrological experience. Recent studies have found machine learning models to be robust and efficient, frequently outperforming the standard hydrological models (both conceptual and physically based). However, and despite some recent efforts, the results of the machine learning models require significant effort to interpret and derive inferences. Furthermore, all successful applications of machine learning in hydrology are based on networks of fairly complex topology that require significant computational power and CPU time to train. For these reasons, the value of the standard hydrological models remains indisputable. In this study, we suggest employing machine learning models not as a substitute for hydrological models, but as an independent tool to assess their performance. We argue that this approach can help to unveil the anomalies in catchment data that do not fit in the employed hydrological model structure or configuration, and to deal with them without compromising the understanding of the underlying physical processes.