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Drought is one of the main threats to food security and ecosystem productivity. During the past decades, Europe has experienced a series of droughts that caused substantial socioeconomic losses and environmental impacts. A key question is whether there are some similar characteristics in these droughts, especially when compared to the droughts that occurred further in the past. Answering this question is impossible with traditional single-index approaches and also short-term and often spatially inconsistent records. Here, using a multidimensional machine learning-based clustering algorithm and the hydrologic reconstruction of European drought, we determine the dominant drought types and investigate the changes in drought typology. We report a substantial increase in shorter warm-season droughts that are concurrent with an increase in potential evapotranspiration. If shifts reported here persist, then we will need new adaptive water management policies and, in the long run, we may observe considerable alterations in vegetation regimes and ecosystem functioning.

Projected changes of warm season (May–September) rainfall events in an ensemble of 30 regional climate model (RCM) simulations are assessed for the Czech Republic. Individual rainfall events are identified using the concept of minimum inter-event time and only heavy events are considered. The changes of rainfall event characteristics are evaluated between the control (1981–2000) and two scenario (2020–2049 and 2070–2099) periods. Despite a consistent decrease in the number of heavy rainfall events, there is a large uncertainty in projected changes in seasonal precipitation total due to heavy events. Most considered characteristics (rainfall event depth, mean rainfall rate, maximum 60-min rainfall intensity and indicators of rainfall event erosivity) are projected to increase and larger increases appear for more extreme values. Only rainfall event duration slightly decreases in the more distant scenario period according to the RCM simulations. As a consequence, the number of less extreme heavy rainfall events as well as the number of long events decreases in majority of the RCM simulations. Changes in most event characteristics (and especially in characteristics related to the rainfall intensity) depend on changes in radiative forcing and temperature for the future periods. Only changes in the number of events and seasonal total due to heavy events depend significantly on altitude.

Finite element analysis of concrete-to-rebar bond specimens under dynamic loading was conducted. This involved detailed modelling of the reinforcing steel bar ribs and the concrete keys in between. The modelling aimed to realise insight into the local structural phenomena and create a solid base for the prediction of bond behaviour under varying conditions. The analysis was performed in 3D with the explicit finite element code LS-Dyna. The proposed Soil and Foam Failure material model for concrete is described and definition of the material parameters is discussed. The numerical results are compared to experimental data obtained during dynamic push-in bond experiments carried out at the Technische Universität Dresden. Strain signals, slip measurements, bond stresses and crack patterns are analysed. The obtained signals are decomposed based on fundamentals of wave propagation. Significant influential factors like the experimental set-up geometry are hence identified. The capability of the model to predict key aspects of bond behaviour under dynamic loading is demonstrated and its applicability for future parametric studies is highlighted. [ABSTRACT FROM AUTHOR]

Characteristics of rainfall events in an ensemble of 23 regional climate model (RCM) simulations are evaluated against observed data in the Czech Republic for the period 1981-2000. Individual rainfall events are identified using the concept of minimum inter-event time (MIT) and only heavy events (15% of events with the largest event depths) during the warm season (May-September) are considered. Inasmuch as an RCM grid box represents a spatial average, the effects of areal averaging of rainfall data on characteristics of events are investigated using the observed data. Rainfall events from the RCM simulations are then compared to those from the at-site and area-average observations. Simulated number of heavy events and seasonal total precipitation due to heavy events are on average represented relatively well despite the higher spatial variation compared to observations. RCM-simulated event depths are comparable to the area-average observations, while event durations are overestimated and other characteristics related to rainfall intensity are significantly underestimated. The differences between RCM-simulated and at-site observed rainfall event characteristics are in general dominated by the biases of the climate models rather than the areal-averaging effect. Most of the rainfall event characteristics in the majority of the RCM simulations show a similar altitude-dependence pattern as in the observed data. The number of heavy events and seasonal total precipitation due to heavy events increase with altitude, and this dependence is captured better by the RCM simulations with higher spatial resolution. [ABSTRACT FROM AUTHOR]