Datasets for tree occurrences at species level. The file is presented in comma separated values (.csv) with 6 columns indicating: 1)and 2) Coordinates in a ETRS89-LAEA reference coordinate system (EPSG:3015), representing the centroid of the INSPIRE compliant 1 km ×1 km European grid (X, Y) 3) The country where the forest plot was sampled (COUNTRY) 4) The name of the species sampled (SPECIES NAME) 5) The diameter class of the trunk, with value 1 for trunks with diameter smaller than 12 cm, value 2 for trunks with diameter larger than 12 cm (DBH), and value -9999 for trunks with unknown diameters. 6) Binary field stating whether or not the target occurrence falls within the species geographical range, measured as the extent of occurrence (EOO, see Technical validation paragraph for details).
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).
The alpine tundra is the highest elevation belt of high mountains. This zone is an important reservoir of freshwater and provides habitat to unique species. This study assesses projected changes in the areal extent of the alpine tundra climate zone in three warming levels in European mountains. The alpine tundra was delineated using the Köppen-Geiger climate classification. We used 11 regional climate model simulations from EURO-CORDEX disaggregated at a one-kilometre grid size representing the RCP4.5 and RCP8.5 scenarios in the 1.5, 2 and 3 °C warming levels. Mitigation represented by the 1.5 °C warming level reduces projected losses of the alpine tundra. However, even in this warming level the projected contraction is severe. In this case, the contraction in the Alps, Scandes and Pyrenees together is projected at between 44% and 48% of the present extent. The contraction is projected to climb in the 2 °C warming to above 57%, while the 3 °C warming would imply that the alpine tundra will be near to collapse in Europe with a contraction of 84% in the three regions, which host most of the alpine tundra in Europe. The projected changes have negative implications for a range of ecosystem services and biodiversity, such as habitat provision, water provision and regulation, erosion protection, water quality and recreational services.
Fossil pollen records are well-established indicators of past vegetation changes. The prevalence of pollen across environmental settings including lakes, wetlands, and marine sediments, has made palynology one of the most ubiquitous and valuable tools for studying past environmental and climatic change globally for decades. A complementary research focus has been the development of statistical techniques to derive quantitative estimates of climatic conditions from pollen assemblages. This paper reviews the most commonly used statistical techniques and their rationale and seeks to provide a resource to facilitate their inclusion in more palaeoclimatic research. To this end, we first address the fundamental aspects of fossil pollen data that should be considered when undertaking pollen-based climate reconstructions. We then introduce the range of techniques currently available, the history of their development, and the situations in which they can be best employed. We review the literature on how to define robust calibration datasets, produce high-quality reconstructions, and evaluate climate reconstructions, and suggest methods and products that could be developed to facilitate accessibility and global usability. To continue to foster the development and inclusion of pollen climate reconstruction methods, we promote the development of reporting standards. When established, such standards should 1) enable broader application of climate reconstruction techniques, especially in regions where such methods are currently underused, and 2) enable the evaluation and reproduction of individual reconstructions, structuring them for the evolving open-science era, and optimising the use of fossil pollen data as a vital means for the study of past environmental and climatic variability. We also strongly encourage developers and users of palaeoclimate reconstruction methodologies to make associated programming code publicly available, which will further help disseminate these techniques to interested communities.
Abstract. Strong winds may uproot and break trees and represent one of the major natural disturbances for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the climate change effects. Despite the importance of such natural disturbances, there are currently no spatially-explicit databases of wind-related impact at Pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND comprises 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. Correlation analyses performed 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. Overall, FORWIND represents a valuable and open-access spatial source to improve our understanding of the vulnerability of forests to winds and develop large-scale monitoring/modelling of natural disturbances. 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).
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