Abstract. In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10 min data for 96 stations and corresponding R factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary, and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood, and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R factor) as well as the effect of record length and spatial coverage are also addressed. Finally, the contribution of each source of uncertainty is quantified. The average R factor for the area of the Czech Republic is 640 MJ ha−1 mm h−1, with values for the individual stations ranging between 320 and 1520 MJ ha−1 mm h−1. Among various spatial interpolation methods, the GLS model relating the R factor to the altitude, longitude, mean precipitation, and mean fraction of precipitation above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R factor is estimated for individual sites. Our results revealed that reasonable estimates of the R factor can be obtained even from relatively short records (15–20 years), provided sufficient spatial coverage and covariates are available.

A miniature lightweight portable Raman spectrometer and a palm-sized device allow for fast and unambiguous detection of common gemstones mounted in complex jewels. Here, complex religious artefacts and the Ring Monstrance from the Loreto treasury (Prague, Czech Republic; eighteenth century) were investigated. These discriminations are based on the very good correspondence of the wavenumbers of the strongest Raman bands of the minerals. Very short laser illumination times and efficient collection of scattered light were sufficient to obtain strong diagnostic Raman signals. The following minerals were documented: quartz and its varieties, beryl varieties (emerald), corundum varieties (sapphire), garnets (almandine, grossular), diamond as well as aragonite in pearls. Miniature Raman spectrometers can be recommended for common gemmological work as well as for mineralogical investigations of jewels and cultural heritage objects whenever the antiquities cannot be transported to a laboratory. This article is part of the themed issue ‘Raman spectroscopy in art and archaeology’.

Abstract:Cities are complex socioecological systems that are particularly vulnerable to the impacts of climate change and are also exposed to other trends, such as urbanization and population aging. Due to the changing climate, days with extreme temperatures are expected to become more numerous, which is particularly important for urban areas, where the urban heat island phenomenon is observed. This study presents an example of a spatially explicit potential climate change impact assessment of heatwaves integrating both science and stakeholder participation for three large Czech cities (Prague, Brno, and Pilsen). Stakeholder participation exercises were used to prioritize climate change risks, provide impetus and opportunity for knowledge co-production, and support adaptation planning. Potential climate change impacts of heatwaves in the three Czech cities for the current baseline and for the future (2021-2040) using Representative Concentration Pathways (RCPs)-RCP 4.5 and RCP 8.5, were mapped at two levels describing "in-city" and "inter-city" comparison. When comparing the potential impact of heatwaves across the three cities ("inter-city"), the most affected city is Brno, with 10.5% of its area in the very high impact category for the baseline and both RCPs. The "in-city" comparison shows the differences between the baseline and future scenarios of each city. The assessment of heatwaves' impacts was further used to support urban adaptation planning.

Abstract. In the present paper, the rainfall erosivity factor (R-factor) for the area of the Czech Republic is assessed. Based on 10-minute data for 96 stations and corresponding R-factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, also the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R-factor) as well as the effect of record length and spatial coverage are addressed. Finally, the contribution of each source of uncertainty is quantified. The average R-factor for the area of the Czech Republic is 64 MJ ha−1 cm h−1, with values for the individual stations ranging between 32 and 152 MJ ha−1 cm h−1. Among various spatial interpolation methods, the GLS model relating R-factor to the mean altitude, longitude, mean precipitation and mean excess above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R-factor is estimated for individual sites. Our results revealed that reasonable estimates of the R-factor can be obtained even from relativelly short records (15–20 years), provided sufficient spatial coverage and covariates are available.

Among other important factors, vegetation cover strongly affects the hydrological processes in mountain catchments. In this paper, we present the results of field infiltration measurements at the location of various vegetation covers, together with an estimation of the infiltration characteristics of a small mountain forest catchment. Measured steady infiltration rate values were extrapolated on the basis of the dominant plant species distribution in the catchment. We determined which plant species are dominant, and infiltration tests were carried out where these species were located in selected sites in the catchment. The characteristic steady infiltration rates were averaged for each dominant vegetation species. The percentages of dominant plant species were determined for established points placed in a regular network throughout the basin. An extrapolation of the directly measured infiltration values to these established points was calculated using (1) percentages of the dominant plant species determined at these points, and (2) characteristic infiltration rates averaged for these species. An infiltration map was created from the infiltration values calculated for the established points.