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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]

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 -1×103 m 3 /(km 2 yr -2), and a generally positive trend in northern ones, with about 0.5×103 m 3 /(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. [ABSTRACT FROM AUTHOR]

Future climate scenarios for the Mediterranean region indicate a possible decrease in annual precipitation associated with an intensification of extreme rainfall events in the coming years. A major challenge in this region is to evaluate the impacts of changing precipitation patterns on extreme hydrological events such as droughts and floods. For this, it is important to understand the impact of climate change on soil moisture since it is a proxy for agricultural droughts, and the antecedent soil moisture condition plays a key role on runoff generation. This study focuses on 10 sites, located in southern France, with available soil moisture, temperature, and precipitation observations for a 10-year time period. Soil moisture is simulated at each site at the hourly time step using a model of soil water content. The sensitivity of the simulated soil moisture to different changes in precipitation and temperature is evaluated by simulating the soil moisture response to temperature and precipitation scenarios generated using a delta change method for temperature and a stochastic model (the Neyman–Scott rectangular pulse model) for precipitation. Results show that soil moisture is more impacted by changes in precipitation intermittence than precipitation intensity and temperature. Overall, increased temperature and precipitation intensity associated with more intermittent precipitation leads to decreased soil moisture and an increase in the annual number of days with dry soil moisture conditions. In particular, a temperature increase of +4 ∘ C combined with a decrease of annual rainfall between 10 % and 20 %, corresponding to the current available climate scenarios for the Mediterranean, lead to a lengthening of the drought period from June to October with an average of +28 d of soil moisture drought per year. [ABSTRACT FROM AUTHOR]

The global availability of satellite rainfall products (SRPs) at an increasingly high temporal and spatial resolution has made their exploitation in hydrological applications possible, especially in data-scarce regions. In this context, understanding how uncertainties transfer from SRPs to river discharge simulations, through the hydrological model, is a main research question. SRPs' accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g. threat score, false alarm ratio and probability of detection) and/or continuous (e.g. bias, root mean square error, Nash–Sutcliffe index, Kling–Gupta efficiency index and correlation coefficient) performance scores. However, whether these scores are informative about the associated performance in river discharge simulations (when the SRP is used as input to a hydrological model) is an under-discussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of river discharge simulation. That is, the following research questions are addressed: is there any performance score that can be used to select the best performing rainfall product for river discharge simulation? Are multiple scores needed? And, which are these scores? To answer these questions, three SRPs, namely the Tropical Rainfall Measurement Mission (TRRM) Multi-satellite Precipitation Analysis (TMPA), the Climate Prediction Center MORPHing (CMORPH) algorithm and the SM2RAIN algorithm applied to the Advanced SCATterometer (ASCAT) soil moisture product (SM2RAIN–ASCAT) have been used as input into a lumped hydrologic model, "Modello Idrologico Semi-Distribuito in continuo" (MISDc), for 1318 basins over Europe with different physiographic characteristics. Results suggest that, among the continuous scores, the correlation coefficient and Kling–Gupta efficiency index are not reliable indices to select the best performing rainfall product for hydrological modelling, whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to absolute values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling–Gupta efficiency index on river discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information for addressing the SRP selection for hydrological modelling. [ABSTRACT FROM AUTHOR]