This study assesses the potential of a hierarchical space-time model for monthly low-flow prediction in Austria. The model decomposes the monthly low-flows into a mean field and a residual field, where the mean field estimates the seasonal low-flow regime augmented by a long-term trend component. We compare four statistical (learning) approaches for the mean field, and three geostatistical methods for the residual field. All model combinations are evaluated using a hydrological diverse dataset of 260 stations in Austria, covering summer, winter, and mixed regimes. Model validation is performed by a nested 10-fold cross-validation. The best model for monthly low-flow prediction is a combination of a model-based boosting approach for the mean field and topkriging for the residual field. This model reaches a median R2 of 0.73. Model performance is generally higher for stations with a winter regime (best model yields median R2 of 0.84) than for summer regimes (R2 = 0.7), and lowest for the mixed regime type (R2 = 0.68). The model appears especially valuable in headwater catchments, where the performance increases from 0.56 (median R2 for simple topkriging routine) to 0.67 for the best model combination. The favorable performance results from the hierarchical model structure that effectively combines different types of information: average low-flow conditions estimated from climate and catchment characteristics, and information of adjacent catchments estimated by spatial correlation. The model is shown to provide robust estimates not only for moderate events, but also for extreme low-flow events where predictions are adjusted based on synchronous local observations.

Intermittent rivers are prevalent in many countries across Europe and in Mediterranean countries outside Europe, but little is known about the temporal evolution of intermittency characteristics and their relationships with climate variability. In this study, a trend analysis is performed on the annual and seasonal number of zero-flow days, the maximum duration of dry spells and the mean date of the zero-flow events, on a database of 452 rivers in European and in Mediterranean countries outside Europe, with varying degrees of intermittence. In addition, the relationships between flow intermittence and climate are investigated using the Standardized Precipitation Evapotranspiration Index (SPEI) and six climate indices describing large scale atmospheric circulation. Results indicated a strong spatial variability of the seasonal patterns of intermittence and the annual and seasonal number of zero-flow days, which highlights the controls exerted by local catchment properties. Most of the detected trends indicate an increasing number of zero-flow days which also tend to occur earlier in the year, in particular in Southern Europe. The SPEI is found to be strongly related to the annual and seasonal zero-flow day occurrence in more than half of the stations for different accumulation times between 12 and 24 months. Conversely, there is a weak dependence of river intermittence with large-scale circulation indices. Overall, these results suggest increased water stress in intermittent rivers that may affect their biota and biochemistry and also reduce available water resources.