New suspect in the investigation into the cause of flash floods

The aim of this contribution is to introduce a new possibility of the description of unsaturated porous media flow. The approach presented here is entirely different from the traditional ways (e.g. the Richards‘ Equation). It will be explained why the traditional models often fail to describe various observed phenomena. The new approach provides certain interesting forecasts, among others a possible mechanism for flash-flood formation. The authors explain why they think that, under specific conditions, porous medium discharge may substantially exceed the infiltration due to rainfall

Spatial (GIS) analysis of relief and lithology of the Vsetínské vrchy Mountains (Outer West Carpathians, Czech Republic)

By combination of digital elevation models (DEM) with digital geological maps within GIS environ- ment it is possible to detect manifestations of neotectonic movements. This technique has been demonstrated on the Vsetínské vrchy Mountains, a 367 km2 large area in the Outer West Carpathians, the Czech Republic. The results have shown that steeper hillslopes, higher local relief (LR), and greater Strahler's hypsometric integrals (SHI) correlate well with regions of increased resistance to erosion, as opposed to less resistant bedrock geology which is correlated with predominantly gentle slopes, lower LR, and smaller SHIs. These facts do not support former opinions on significant neotectonic block faulting of the Vsetínské vrchy Mountains during the "neotectonic period". The relief topography is concordant with underlying strata of variable resistance. Thus, it seems probable that the youngest evolution of the study area has been proceeding steadily. The topographic relief has experienced the state of dynamic equilibrium, which has been caused by the rebounding of the Epivariscan European Platform. There is no reason to assume the alternation of periods of tectonic standstill and strong, mainly vertical, movements during several "neotectonic phases"

Data for: On reliable identification of factors influencing wildlife-vehicle collisions along roads

Wildlife-vehicle crashes, cases and controls with attribute

Piping in loess-like and loess-derived soils : case study of Halenkovice site, Czech Republic

The soil piping that occurs on luvisols in the vicinity of the village of Halenkovice was studied for 5 years. These piping phenomena can only be found where arable land meets the forest or a belt of shrubbery. If there is a scarp in the locality, which usually changes from 6° in the field to approximately 30° in the forest, soil pipes are more likely to occur. Before the scarp, the slope flattens out and it is almost horizontal. This factor makes it possible for the overland flow to seep into the slope. This seepage results in soil piping, which is formed in loess loam and colluvial deposits. There are about 15 sites in the vicinity of the village of Halenkovice, where soil piping occurs. In one of them, Halenkovice 1 (an area of 900 m2) we closely studied 47 partial cavities. Their internal volume is 3.8 m3. The volume of the sink holes is 23 m3. There are two types of soil pipes – vertical, which on average tend to be shorter (40 cm) and lead the water under the surface, and soil pipes parallel with the slope, which are on average 81 cm long. Water flows through the pipes during a thaw or precipitation, which often takes away the top soil. The intensity of this process depends on the intensity of precipitation, which occurs outside the growing season, when there are no crops in the fields

Data for: On reliable identification of factors influencing wildlife-vehicle collisions along roads

Wildlife-vehicle crashes, cases and controls with attributesTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

COVID-19 related travel restrictions prevented numerous wildlife deaths on roads: A comparative analysis of results from 11 countries

Millions of wild animals are killed annually on roads worldwide. During spring 2020, the volume of road traffic was reduced globally as a consequence of the COVID-19 pandemic. We gathered data on wildlife-vehicle collisions (WVC) from Czechia, Estonia, Finland, Hungary, Israel, Norway, Slovenia, Spain, Sweden, and for Scotland and England within the United Kingdom. In all studied countries WVC statistics tend to be dominated by large mammals (various deer species and wild boar), while information on smaller mammals as well as birds are less well recorded. The expected number of WVC for 2020 was predicted on the basis of 2015–2019 WVC time series representing expected WVC numbers under normal traffic conditions. Then, the forecasted and reported WVC data were compared. The results indicate varying levels of WVC decrease between countries during the COVID-19 related traffic flow reduction (CRTR). While no significant change was determined in Sweden, where the state-wide response to COVID-19 was the least intensive, a decrease as marked as 37.4% was identified in Estonia. The greatest WVC decrease, more than 40%, was determined during the first weeks of CRTR for Estonia, Spain, Israel, and Czechia. Measures taken during spring 2020 allowed the survival of large numbers of wild animals which would have been killed under normal traffic conditions. The significant effects of even just a few weeks of reduced traffic, help to highlight the negative impacts of roads on wildlife mortality and the need to boost global efforts of wildlife conservation, including systematic gathering of roadkill data

COVID-19 related travel restrictions prevented numerous wildlife deaths on roads: A comparative analysis of results from 11 countries

Millions of wild animals are killed annually on roads worldwide. During spring 2020, the volume of road traffic was reduced globally as a consequence of the COVID-19 pandemic. We gathered data on wildlife-vehicle collisions (WVC) from Czechia, Estonia, Finland, Hungary, Israel, Norway, Slovenia, Spain, Sweden, and for Scotland and England within the United Kingdom. In all studied countries WVC statistics tend to be dominated by large mammals (various deer species and wild boar), while information on smaller mammals as well as birds are less well recorded. The expected number of WVC for 2020 was predicted on the basis of 2015–2019 WVC time series representing expected WVC numbers under normal traffic conditions. Then, the forecasted and reported WVC data were compared. The results indicate varying levels of WVC decrease between countries during the COVID-19 related traffic flow reduction (CRTR). While no significant change was determined in Sweden, where the state-wide response to COVID-19 was the least intensive, a decrease as marked as 37.4% was identified in Estonia. The greatest WVC decrease, more than 40%, was determined during the first weeks of CRTR for Estonia, Spain, Israel, and Czechia. Measures taken during spring 2020 allowed the survival of large numbers of wild animals which would have been killed under normal traffic conditions. The significant effects of even just a few weeks of reduced traffic, help to highlight the negative impacts of roads on wildlife mortality and the need to boost global efforts of wildlife conservation, including systematic gathering of roadkill data

Using Deep Learning to Construct a Real-Time Road Safety Model; Modelling the Personal Attributes for Cyclist

This paper is concerned with the modelling of cyclist road traffic crashes by considering the personal attributes, i.e. gender and age of the cyclists. There are 21 different types of variables considered for each crash, which broadly fall into spatial, infrastructure, and environment categories. The study area of Tyne and Wear county in the north-east of England is selected for investigation. Six deep learning-based safety models are constructed using historic crash data. The effectiveness of deep learning methodology for road safety analysis is demonstrated, and it is found that spatial, infrastructural, and environmental conditions affect the safety interactions of a particular cyclist. These variables can be used for determining/predicting safety for a rider at a location. The model can predict age and gender of the rider, which is likely to be the most unsafe based upon the specific input variables. The significant accuracy is obtained for the constructed models with an overall accuracy of 84%. It is hoped that the proposed models can help in better designing of cyclist network, design, and planning, which will contribute to a sustainable transportation system