Abstract. Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).

Abstract. Besides other non-behavioural factors, low-light conditions significantly influence the frequency of traffic accidents in an urban environment. This paper intends to identify the impact of low-light conditions on traffic accidents in the city of Cluj-Napoca, Romania. The dependence degree between light and the number of traffic accidents was analysed using the Pearson correlation, and the relation between the spatial distribution of traffic accidents and the light conditions was determined by the frequency ratio model. The vulnerable areas within the city were identified based on the calculation of the injury rate for the 0.5 km 2 areas uniformly distributed within the study area. The results show a strong linear correlation between the low-light conditions and the number of traffic accidents in terms of three seasonal variations and a high probability of traffic accident occurrence under the above-mentioned conditions at the city entrances/exits, which represent vulnerable areas within the study area. Knowing the linear dependence and the spatial relation between the low light and the number of traffic accidents, as well as the consequences induced by their occurrence, enabled us to identify the areas of high traffic accident risk in Cluj-Napoca.

In rural regions, characterized by a large number of small basins, some of them torrential and others without permanent flow, high intensity convective rainfall generate overland flow, not always on the same path. Overland hillslope flow, followed by a dangerous peak discharge, can happen almost anywhere at any time. The purpose of this paper is to present a GIS module for computation of possible peak flow in any point of a hillslope. The workflow is based on the rational method, but it is applied rather differently by avoiding the previous delineation of watersheds and working on a raster cell level. The module that we present is more than just an automation of known GIS procedures because the result of this model also includes the path of the maximum flow and the peak flows, which would otherwise remain unknown. There is a possibility of another path for the peak flow under different rainfall and antecedent moisture conditions. In this case, the module needs to be rerun for the new conditions when a new torrential rainfall event occurs. The module provides an easy-to-understand graphical representation of peak flow paths, maximum flows and associated maps that can be useful for further decisions and prevention measures.

No abstract

No abstract