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In a warming climate, periods with lower than average precipitation will increase in frequency and intensity. During such periods, known as meteorological droughts, the decline in annual runoff may be proportionally larger than the corresponding decline in precipitation. Reasons behind this exacerbation of runoff deficit during dry periods remain largely unknown, and this challenges the predictability of when this exacerbation will occur in the future and how intense it will be. In this work, we tested the hypothesis that runoff deficit exacerbation during droughts is a common feature across climates, driven by evaporation enhancement. We relied on multidecadal records of streamflow and precipitation for more than 200 catchment areas across various European climates, which distinctively show the emergence of similar periods of exacerbated runoff deficit identified in previous studies, i.e. runoff deficit on the order of -20 % to -40 % less than what expected from precipitation deficits. The magnitude of this exacerbation is two to three times larger for basins located in dry regions than for basins in wet regions, and is qualitatively correlated with an increase in annual evaporation during droughts, in the order of +11 % and +33 % over basins characterized by energy-limited and water-limited evaporation regimes, respectively. Thus, enhanced atmospheric and vegetation demand for moisture during dry periods induces a nonlinear precipitation-runoff relationship for low-flow regimes, which results in an unexpectedly large decrease in runoff during periods of already low water availability. Forecasting onset, magnitude, and duration of these drops in runoff have paramount societal and ecological implications, especially in a warming climate, given their supporting role for safeguarding water, food, and energy. The outcome that water basins are prone to this exacerbation of runoff deficit for various climates and evaporation regimes makes further understanding of its patterns of predictability an urgent priority for water-resource planning and management in a warming and drier climate. [ABSTRACT FROM AUTHOR]

Reducing errors in streamflow simulations is one of the main issues for a reliable forecast system aimed to manage floods and water resources. Data assimilation is a powerful tool to reduce model errors. Unfortunately, its use in operational chains with distributed and physically based models is a challenging issue since many methodologies require computational times that are hardly compatible with operational needs. The implemented methodology corrects modelled water level in channels and root-zone soil moisture using real-time water level gauge stations. Model's variables are corrected locally, then the updates are propagated upstream with a simple approach that accounts for sub-basins' contributions. The overfitting issue, which arises when updating a spatially distributed model with sparse streamflow data, is hence here addressed in the context of a large-scale operational implementation working in real time thanks to the simplicity of the strategy. To test the method, a hindcast of daily simulations covering 18 months was performed on the Italian Tevere basin, and the modelling results with and without assimilation were compared. The setup was that currently in place in the operational framework in both cases. The analysis evidences a clear overall benefit of applying the proposed method even out of the assimilation time window. [ABSTRACT FROM AUTHOR]

During the autumn of 2011 two catastrophic, very intense rainfall events affected two different parts of the Liguria Region of Italy causing various flash floods. The first occurred in October and the second at the beginning of November. Both the events were characterized by very high rainfall intensities (> 100mm h-1) that persisted on a small portion of territory causing local huge rainfall accumulations (> 400mm6 h-1). Two main considerations were made in order to set up this work. The first consideration is that various studies demonstrated that the two events had a similar genesis and similar triggering elements. The second very evident and coarse concern is that two main elements are needed to have a flash flood: a very intense and localized rainfall event and a catchment (or a group of catchments) to be affected. Starting from these assumptions we did the exercise of mixing the two flash flood ingredients by putting the rainfall field of the first event on the main catchment struck by the second event, which has its mouth in the biggest city of the Liguria Region: Genoa. A complete framework was set up to quantitatively carry out a "what if" experiment with the aim of evaluating the possible damages associated with this event. A probabilistic rainfall downscaling model was used to generate possible rainfall scenarios maintaining the main characteristics of the observed rainfall fields while a hydrological model transformed these rainfall scenarios in streamflow scenarios. A subset of streamflow scenarios is then used as input to a 2- D hydraulic model to estimate the hazard maps, and finally a proper methodology is applied for damage estimation. This leads to the estimation of the potential economic losses and of the risk level for the people that stay in the affected area. The results are interesting, surprising and in a way worrying: a rare but not impossible event (it occurred about 50 km away from Genoa) would have caused huge damages estimated between 120 and EUR 230 million for the affected part of the city of Genoa, Italy, and more than 17 000 potentially affected people. [ABSTRACT FROM AUTHOR]

Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to be monitored. We then evaluate its operational application in Italy, a country characterized by various climatic conditions and topographic features. The proposed system exploits a modified conditional merging (MCM) algorithm to generate rainfall estimates by blending data from national radar and rain-gauge networks. Then, we use the merged rainfall fields as input for the distributed and continuous hydrological model, Continuum, to obtain real-time streamflow predictions. We assess its performance in terms of rainfall estimates from MCM, using cross-validation and comparison with a conditional merging technique at an event-scale. We also assess its performance against rainfall fields from ground-based data at catchment-scale. We further evaluate the performance of the hydrological system in terms of streamflow against observed data (relative error on high flows less than 25% and Nash–Sutcliffe Efficiency greater than 0.5 for 72% and 46% of the calibrated study sections, respectively). These results, therefore, confirm the suitability of such an approach, even at national scale, over a wide range of catchment types, climates, and hydrometeorological regimes, and for operational purposes. [ABSTRACT FROM AUTHOR]