We investigate the seasonal memory properties of a large sample of European rivers in terms of high and low flows. We compute seasonal correlations between peak and low flows and average flows in the previous seasons and explore the links with various physiographic and hydro-climatic catchment descriptors. Our findings suggest that there is a traceable physical basis for river memory which in turn can be employed to reduce uncertainty and improve probabilistic predictions of floods and droughts.

The aim of this work is to investigate the stochastic nature and time evolution of the solar radiation process for daily and hourly scale, with the ultimate goal of creating a new cyclostationary stochastic model capable of reproducing the dependence structure and the marginal distribution of hourly solar radiation via the clearness index K_T.

A systematic framework for delineating Hydrological Response Units (HRUs) is proposed, based on a modified Curve Number (CN) approach that accounts, by means of classes, for the soil permeability, land cover, and drainage capacity of a river basin. The aim is to reduce the subjectivity introduced by HRUs and provide a parsimonious modelling scheme. Results showed optimal performance of the CN approach in calibration and particularly in validation, compared against alternative HRU approaches.