Determining the spatiotemporal variability in the annual streamflow volume plays a relevant role in hydrology with regard to improving and implementing sustainable and resilient policies and practices of water resource management. This study investigates annual streamflow volume trends in a newly assembled, consolidated, and validated data set of daily mean river flow records from more than 3000 stations which cover near-natural basins in more than 40 countries across Europe. Although the data set contains streamflow time series from 1900 to 2013 in some stations, the statistical analyses were carried out by including observations from 1950 to 2013 in order to have a consistent and reliable data set over the continent. Trends were detected by calculating the slope of the Theil–Sen line over the annual anomalies of streamflow volume. The results show that annual streamflow volume trends have emerged at European scale, with a marked negative tendency in Mediterranean regions, with about $-\mathrm{1}\times {\mathrm{10}}^{\mathrm{3}}$ m³/(km² yr⁻²), and a generally positive trend in northern ones, with about 0.5×10³ m³/(km⁻² yr⁻²). The annual streamflow volume trend patterns appear to be in agreement with the continental-scale meteorological observations in response to climate change drivers. In the Mediterranean area, the decline of annual streamflow volumes started in 1965, and since the early 1980s, volumes have consistently been lower than the 1950–2013 average. The spatiotemporal annual streamflow volume patterns observed in this work can help to contextualize short-term trends and regional studies already available in the scientific literature, as well as to provide a valid benchmark for further accurate quantitative analysis of annual streamflow volumes.

This study addresses the estimation of flow-duration curves (FDC) in ungauged sites through the catchment classification. Forty-six catchments in the Upper Po river basin (Italy) were analyzed and classified through two different frameworks: the first scheme consists of the application of two clustering methods in a series considering six streamflow signatures, and the second one treats indexes of climate, physiography, soil, and land-use with the same clustering procedure. Catchments have been classified into three homogeneous groups: the first one is characterized by the lowest runoff and flash-flood events, the second one includes maximum runoff, and the third one shows intermediate behaviour. The estimation of FDCs was done using a lognormal distribution, whereas the regionalization was constructed applying a stepwise multiple linear regression, followed by a leave-one-out cross-validation. The results show great performance improvement when the regionalization model is found by taking account...

Many rainfall–runoff (RR) models are available in the scientific literature. Selecting the best structure and parameterization for a model is not straightforward and depends on a broad number of factors, including climatic conditions, catchment characteristics, temporal/spatial resolution and model objectives. In this study, the RR model ‘Modello Idrologico Semi-Distribuito in continuo’ (MISDc), mainly developed for flood simulation in Mediterranean basins, was tested on the Seveso basin, which is stressed several times a year by flooding events mainly caused by excessive urbanization. The work summarizes a compendium of the MISDc applications over a wide range of catchments in European countries and then it analyses the performances over the Seveso basin. The results show a good fit behaviour during both the calibration and the validation periods with a Nash–Sutcliffe coefficient index larger than 0.9. Moreover, the median volume and peak discharge errors calculated on several flood events were less than 25%. In conclusion, we can be assured that the reliability and computational speed could make the MISDc model suitable for flood estimation in many catchments of different geographical contexts and land use characteristics. Moreover, MISDc will also be useful for future support of real-time decision-making for flood risk management in the Seveso basin.