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This paper introduces HIDRA-D, a novel deep-learning model for basin scale dense (gridded) sea level prediction using sparse satellite altimetry and in situ tide gauge data. Accurate sea level prediction is crucial for coastal risk management, marine operations, and sustainable development. While traditional numerical ocean models are computationally expensive, especially for probabilistic forecasts over many ensemble members, HIDRA-D offers a faster, numerically cheaper, observation-driven alternative. Unlike previous HIDRA models (HIDRA1, HIDRA2 and HIDRA3) that focused on point predictions at tide gauges, HIDRA-D provides dense, two-dimensional, gridded sea level forecasts. The core innovation lies in a new algorithm that effectively leverages sparse and unevenly distributed satellite altimetry data in combination with tide gauge observations, to learn the complex basin-scale dynamics of sea level. HIDRA-D achieves this by integrating a HIDRA3 module for point predictions at tide gauges with a novel Dense decoder module, which generates low-frequency spatial components of the sea level field in the Fourier domain, whose Fourier inverse is an hourly sea level forecast over a 3 d horizon. When comparing 3 d forecasts against satellite absolute dynamic topography (ADT) data in the Adriatic, HIDRA-D achieves a 28.0 % reduction in mean absolute error relative to the NEMO general circulation model. However, while HIDRA-D performs well in open waters, leave-one-out cross-validation at tide gauges indicates limitations in areas with complex bathymetry, such as the Neretva estuary located in a narrow bay, and in regions with sparse satellite ADT data, like the northern Adriatic. Importantly, the model shows robustness to spatially-limited tide gauge coverage, maintaining acceptable performance even when trained using data from distant stations. This suggests its potential for broader applicability in areas with limited in situ observations.
Monitoring drought helps to reduce their economic and environmental impacts by enabling early warnings and better resource management planning. In Europe, there are several operational monitoring systems operating at national and regional scales. However, such monitoring systems are rarely validated, which complicates the decision-making process. Therefore, we evaluated six national drought monitoring products in Central Europe using a novel extreme event impact database compiled from national newspaper reports over the period 2000–2023. The drought monitoring indices used in the countries include the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized relative soil moisture with different aggregation periods. The area under receiver-operating characteristic curve (AUC) is used to assess the ability of the drought indices to detect impact occurrence. Spearman correlation coefficients (r) between the severity of the drought index and the number of reported impacts are used to assess their ability to capture impact severity. The highest AUC values were obtained for the drought monitoring products of Czechia, Croatia, and Slovenia ( AUC>0.8 ) while the lowest values were obtained for the monitoring product of Austria ( AUC<0.7 ). Impact severity was best captured in Poland (for some indices r>0.6 ), and worst in Slovakia, Slovenia, and Austria ( r<0.4 ). With an increasing aggregation period, the correlation generally decreases, while the AUC values show a non-linear pattern, peaking at an intermediate integration time of three to 6 months. The results of this study help to understand the strengths and weaknesses of drought monitoring products in each country and support the development of a common drought monitoring framework for Central Europe.
This paper presents a laboratory study on the physical properties of mortar incorporating expanded vermiculite. The basic cement-lime mortar matrix remained unchanged, while the proportions of vermiculite were varied at 10%, 20%, and 30%. The research results demonstrated a significant improvement in physical properties, including compressive and flexural strength, as well as the static modulus of elasticity. This study establishes relationships between these properties and examines the impact of expanded vermiculite on characteristics such as air void content, capillary suction of hardened mortar, total porosity (according to SIA 262, Appendix A), gas and air permeability, and capillary water absorption. Additionally, the water permeability of the mortar was tested, along with its internal resistance to freeze-thaw cycles, up to 200 cycles. The findings of this laboratory research contribute to filling an existing scientific gap, demonstrating that the physical properties of mortar with the addition of expanded vermiculite at 10%, 20%, and 30% are significantly improved compared to mortar without vermiculite.
The descriptors of the European avalanche danger scale, in use since 1993, are being revised. In June 2025 EAWS forecasters agreed on a new provisional wording and, in doing so, provided its content validation. Because the descriptors are first of all a public communication tool, we ran a comprehension validation: a survey testing whether end users read the wording as forecasters intend. It ran online from 3 February to 8 April 2026 in six languages and retained 17,335 responses after cleaning, of which 15,749 were completed. All wording comparisons were randomised between subjects. We evaluated each proposal against three conditions: correctness (do users recover the order forecasters intend?), simplicity (how easy is the task?), and increase (does the wording convey how much the danger increases from level to level?). For avalanche occurrence the main finding is reassuring: all four wording variants were ranked accurately (83–92% correct), and most differences that reach significance in the pooled data do not survive within individual languages. Only the variant that drops the distribution wording is clearly weaker. On the increase condition the variants do separate. Respondents placed the four statements on a continuous danger scale; wording level 2 as “in some places” rather than “in a few places” moves it closer to level 3 and shrinks the perceived difference between moderate to considerable from 36% to 31% of the distance between levels 1 and 4 (Cohen’s d = 0.39, p < 0.001). For size and impact the wording effect is large: adding size class words (small, medium, large, very large) to the impact wording raised accuracy from 61% to 95% and self-reported ease from 4.7 to 6.1 (Cohen’s d = 1.04). Most of that failure comes from using “bury and kill” at both levels 1 and 2, which makes them hard to tell apart. We also report how users read seven phrases that forecasters already use. For avalanche occurrence, the largest differences are between languages rather than between wordings.
The detailed spatial and temporal emission inventory, prepared on an hourly basis and at a 250 × 250-meter grid, coupled with the local dispersion modeling system GRAMM/GRAL, helps to identify the impact of primary emission sources on air pollution. Our study was conducted in the complex subalpine city of Ljubljana. We prepared a detailed emission inventory for the most contributive sectors, which are industry, transport, small combustion, and agriculture for NOx, PM10, PM2.5, NMVOCs, and NH3 emissions in the year 2021. The total estimated annual emissions for Ljubljana were 1,935 tons of NOₓ, 393 tons of PM10, 315 tons of PM2.5, 1,847 tons of NMVOCs, and 168 tons of NH3. A comprehensive uncertainty analysis was conducted to assess the reliability of the emission inventory. Results from the GRAMM/GRAL dispersion model illustrate the spatial distribution of pollutants, which closely follows the geographical patterns of emission sources. Statistical comparison between modeled and observed concentrations indicates moderate agreement for PM10 and stronger agreement for NO2. Additionally, PMF analysis at the Vič monitoring station shows a consistent representation of primary emission sources compared with the model results. Overall, the results of this study improve understanding of atmospheric emission dynamics and provide a scientific basis for evidence-based decision-making aimed at reducing pollutant concentrations and protecting public health.
