Abstract. Since the beginning of this century, Europe has been experiencing severe drought events (2003, 2007, 2010, 2018 and 2019) which have had adverse impacts on various sectors, such as agriculture, forestry, water management, health and ecosystems. During the last few decades, projections of the impact of climate change on hydroclimatic extremes have often been used for quantification of changes in the characteristics of these extremes. Recently, the research interest has been extended to include reconstructions of hydroclimatic conditions to provide historical context for present and future extremes. While there are available reconstructions of temperature, precipitation, drought indicators, or the 20th century runoff for Europe, multi-century annual runoff reconstructions are still lacking. In this study, we have used reconstructed precipitation and temperature data, Palmer Drought Severity Index and available observed runoff across 14 European catchments in order to develop annual runoff reconstructions for the period 1500–2000 using two data-driven and one conceptual lumped hydrological model. The comparison to observed runoff data has shown a good match between the reconstructed and observed runoff and their characteristics, particularly deficit volumes. On the other hand, the validation of input precipitation fields revealed an underestimation of the variance across most of Europe, which is propagated into the reconstructed runoff series. The reconstructed runoff is available via Figshare, an open-source scientific data repository, under the DOI https://doi.org/10.6084/m9.figshare.15178107, (Sadaf et al., 2021).

Abstract. Since the beginning of this century, Europe has been experiencing severe drought events (2003, 2007, 2010, 2018, and 2019) which have had adverse impacts on various sectors, such as agriculture, forestry, water management, health, and ecosystems. During the last few decades, projections of the impact of climate change on hydroclimatic extremes were often capable of reproducing changes in the characteristics of these extremes. Recently, the research interest has been extended to include reconstructions of hydro-climatic conditions to provide historical context for present and future extremes. While there are available reconstructions of temperature, precipitation, drought indicators, or the 20th century runoff for Europe, long-term runoff reconstructions are still lacking (e.g, monthly or daily runoff series for short periods are commonly available). Therefore, we considered reconstructed precipitation and temperature fields for the period between 1500 and 2000 together with reconstructed scPDSI, natural proxy data, and observed runoff over 14~European catchments to calibrate and validate the semi-empirical hydrological model GR1A and two data-driven models (Bayesian recurrent and long short-term memory neural network). The validation of input precipitation fields revealed an underestimation of the variance across most of Europe. On the other hand, the data-driven models have been proven to correct this bias in many cases, unlike the semi-empirical hydrological model GR1A. The comparison to observed historical runoff data has shown a good match between the reconstructed and observed runoff and between the runoff characteristics, particularly deficit volumes. The reconstructed runoff is available via figshare, an open source scientific data repository under the DOI https://doi.org/10.6084/m9.figshare.15178107, (Sadaf et al., 2021).

Abstract. Climate change impact on avalanches is ambiguous. Fewer, wetter, and smaller avalanches are expected in areas where snow cover is declining, while in higher altitude areas where snowfall prevails, snow avalanches are frequently and spontaneously triggered. In the present paper, we assess 39 years (winters of 1979–1999 to 2002–2020) of avalanche activity related to meteorological and snow drivers in the Krkonoše Mountains, Czechia, Central Europe. The analysis is based on an avalanche occurrence dataset for mostly south, south-easterly oriented 60 avalanche paths and related meteorological and snowpack data. Since 1979, 179/531 wet-snow/slab avalanches have been recorded. The aim is to analyze changes in avalanche activity: frequency and magnitude, and detect driving weather variables of wet and slab avalanches with quantification of variable importance. Especially, the number of wet avalanches in February and March has increased in the last three decades, while the number of slab avalanches has decreased with decadal variability. Medium, large, and very large slab avalanches seem to decline with decadal variability since 1961. The results indicate that wet avalanches are influenced by 3-day maximum and minimum air temperature, snow depth, wind speed, wind direction, and rainfall. Slab avalanche activity is determined by snow depth, rainfall, new snow, and wind speed. Air temperature-related variables for slab avalanches were less important than rain and snow-related variables based on the balanced random forest (RF) method. Surprisingly, the RF analysis revealed less significant relationship between new snow sum and slab avalanche activity. This could be because of the wind redistributing snow in storms in low altitude mountains. Our analysis allows the use of the identified wet and slab avalanche driving variables to be included in the avalanche danger levels alerts. Although it cannot replace operational forecasting, machine learning can allow for additional insights for the decision-making process to mitigate avalanche hazard.

The so-called Key Enabling Technologies (KETs) are high-tech and promising technologies that are expected to significantly contribute to solving the societal and environmental challenges, which modern European society is facing. Thus, European Commission significantly supports the innovations relating to KETs. Focusing at one of KETs — nanotechnology, the aim of the paper is to present current complex evaluation system of KETs based on technology indicators and applying it at specific use case of moderate and lead nanotechnology-performing countries. Furthermore, the two-dimensional (technology and economic) assessment of the nanotechnology-performing companies in each country is done in order to support the KETs evaluation results. The quantitative and qualitative data of secondary research by various open databases are used. Finally, the necessity of complex evaluation of KETs implementation applying principles of the sustainable development and technology dimension is addressed.

Abstract. Climate change impact on avalanches is ambiguous. Fewer, wetter, and smaller avalanches are expected in areas where snow cover is declining, while in higher-altitude areas where snowfall prevails, snow avalanches are frequently and spontaneously triggered. In the present paper, we (1) analyse trends in frequency, magnitude, and orientation of wet- and slab-avalanche activity during 59 winter seasons (1962–2021) and (2) detect the main meteorological and snow drivers of wet and slab avalanches for winter seasons from 1979 to 2020 using machine learning techniques – decision trees and random forest – with a tool that can balance the avalanche-day and non-avalanche-day dataset. In terms of avalanches, low to medium–high mountain ranges are neglected in the literature. Therefore we focused on the low-altitude Czech Krkonoše mountain range (Central Europe). The analysis is based on an avalanche dataset of 60 avalanche paths. The number and size of wet avalanches in February and March have increased, which is consistent with the current literature, while the number of slab avalanches has decreased in the last 3 decades. More wet-avalanche releases might be connected to winter season air temperature as it has risen by 1.8 ∘C since 1979. The random forest (RF) results indicate that wet avalanches are influenced by 3 d maximum and minimum air temperature, snow depth, wind speed, wind direction, and rainfall. Slab-avalanche activity is influenced by snow depth, rainfall, new snow, and wind speed. Based on the balanced RF method, air-temperature-related variables for slab avalanches were less important than rain- and snow-related variables. Surprisingly, the RF analysis revealed a less significant than expected relationship between the new-snow sum and slab-avalanche activity. Our analysis allows the use of the identified wet- and slab-avalanche driving variables to be included in the avalanche danger level alerts. Although it cannot replace operational forecasting, machine learning can allow for additional insights for the decision-making process to mitigate avalanche hazard.