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Exposure to climate hazards

This series refers to datasets related to the presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected by climate hazards, including flooding, wildfires and urban heat island effects. The datasets are part of the European Climate Adaptation Platform (Climate-ADAPT) accessible here: https://climate-adapt.eea.europa.eu/

Model Output Statistics for JELENIA GORA (12500)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Deutsch-polnische umweltentlastende Kooperatiosprojekte und Umweltbildungsvorhaben in grenznahen Regionen Deutschlands und Polens

Klimaanpassungsschule - Teil 2

Ziel: Durch eine Anschubfinanzierung des BMU gefördert erfasste die Charité für die medizinische Versorgung wichtige Themen, die durch den Klimawandel beeinflusst werden. Methode: Die Themen werden aus internistischer, pädiatrischer, pneumologischer, allergologischer und dermatologischer Sicht durch Experten (meist aus dem Allergie-Centrum Charité) aufgearbeitet und in Vorträgen, Fort- und Weiterbildung einschließlich E-learning verbreitet (www.klimanapassungsschule.de). Teil 2 des Projektes stellt die Fortführung des ursprünglichen Projektes (siehe Teil 1) dar. Die Arbeit wird 2014 aus eigenen Mitteln der Charité fortgesetzt. Die Aktivität wird durch eine Gemeinschaft aus der Stiftung Deutscher Polleninformationsdienst (PID), dem ECARF und dem Allergie-Centrum-Charité geleistet. Methode: Entwicklung von Bildungsmodulen für die Aus- und Weiterbildung von Pflegekräften und Ärzten zu den gesundheitlichen Auswirkungen des Klimawandels, Anpassungs- ,Aufklärungs- und Behandlungsmöglichkeiten. Adressaten: Das Angebot zur Aus- und Weiterbildung richtet sich an Ärzte, Schwestern, Pfleger u.a. in medizinischen Bereichen tätige Personen.

Ready-to-use version of the Eurasian Modern Pollen Database version 2, with 90 pollen taxa and 7634 sites

Ready-to-use version of the Eurasian Modern Pollen Database version 2 (EMPD2; Davis et al., 2020; Chevalier et al., 2019) that includes 90 taxa and 7634 modern pollen samples with pollen sums (excluding Pinus) higher or equal to 100 pollen grains (Tables 1 to 6). Table 7 contains 394 additional sites with pollen sums less than 100 pollen grains when excluding Pinus but higher or equal to 100 pollen grains when Pinus is included. Users can merge Tables 1 and 7 (8028 modern pollen samples) if they consider pollen sums (including Pinus) equal or higher than 100 pollen grains sufficient for accurate reconstructions. This ready-to-use version of the EMPD2 was initially built to do paleoclimatic reconstructions for Southern Europe. For users willing to do paleoclimate reconstructions in regions that may need to re-include some of the taxa that were removed, the intermediate version containing all the counts for the 840 initial taxa and the first grouping to 192 taxa is also available as Table 8.

Pollen-based climate reconstructions and syntheses in Europe

A fossil pollen dataset distributed across Europe (10° W - 43° E, 33° - 71° N) comprising 520 records was extracted from the LegacyPollen 1.0 database (Herzschuh et al., 2022) to reconstruct climatic variables including Annual temperature (TANN), Annual precipitation (PANN), Winter Temperature (December, January, February; TDJF), Summer Temperature (June, July, August; TJJA). Short records not reaching beyond 1 ka BP were also excluded to keep the dataset refined, as the syntheses aim to cover the entire Holocene (i.e., 11-1 ka BP). The modern pollen training dataset was integrated from Legacy Climate 1.0 (Herzschuh et al., 2023) and the EMPD2 (Davis et al., 2020). Two different approaches were applied in parallel to reconstruct climate variables from fossil pollen assemblages, namely Modern Analogue Technique (MAT) and Weighted Averaging Partial Least Squares (WAPLS). Reconstruction uncertainties were provided as Root Mean Squared Errors of Prediction (RMSEPs). All the reconstructions and tests were conducted using the rioja and analogue packages in R (R Core Team, 2019). The synthesized results were interpolated from all reconstructed climate records. The mean value of reconstructed climatic variables with the same ages was calculated before any interpolations. Due to the different chronological resolution of the time series, the sequences were then interpolated to equidistant time series of 50-year intervals. Two different interpolation methods were applied in R. The first is to use the interp.dataset function from rioja package with loess regression to interpolate the dataset as a whole. The second is to interpolate each complete record that can cover the Holocene (i.e., 11-1 ka) and has a mean resolution of less than 1ka separately using the corit package with linear regression and then calculate the mean of these records. To perform the latter interpolation, a total of 214 records covering the entire period between 11-1 ka BP were used. The Root Mean Squared Errors (RMSEs) were calculated for the synthesis results.

EU27_2020 basemap for EEA internal use

This Discomap web map service provides an EU-27 (2020) basemap for internal EEA use as a background layer in viewers or any other web application. It is provided as REST and as OGC WMS services, dynamic and cached. The cached service has a custom cache at the following scales: 1/50.000.000 1/42.000.000 1/36.000.000 (Europe's size) 1/30.000.000 1/20.000.000 1/10.000.000 1/5.000.000 1/2.500.000 1/1.000.000.

Floods Reference Spatial Datasets reported under Floods Directive - version 2.0, Jul. 2024

The Floods Directive (FD) was adopted in 2007 (https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:32007L0060). The purpose of the FD is to establish a framework for the assessment and management of flood risks, aiming at the reduction of the adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods in the European Union. ‘Flood’ means the temporary covering by water of land not normally covered by water. This shall include floods from rivers, mountain torrents, Mediterranean ephemeral water courses, and floods from the sea in coastal areas, and may exclude floods from sewerage systems. This reference spatial dataset, reported under the Floods Directive, includes the areas of potential significant flood risk (APSFR), as they were lastly reported by the Member States to the European Commission, and the Units of Management (UoM).

WISE WFD Reference Spatial Datasets reported under Water Framework Directive 2022 - INTERNAL VERSION - version 1.8, Mar 2025

The dataset contains information on the European river basin districts, the river basin district sub-units, the surface water bodies and the groundwater bodies delineated for the 3rd River Basin Management Plans (RBMP) under the Water Framework Directive (WFD) as well as the European monitoring sites used for the assessment of the status of the above mentioned surface water bodies and groundwater bodies. This data set is available only for internal use of the European Commission and the European Environment Agency. Please enter the publicly available version to access data: https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/bce2c4e0-0dad-4c42-9ea8-a0b82607d451 The information was reported to the European Commission under the Water Framework Directive (WFD) reporting obligations. The dataset compiles the available spatial data related to the 3rd RBMPs due in 2022 (hereafter WFD2022). See http://rod.eionet.europa.eu/obligations/780 for further information on the WFD2022 spatial reporting. Note: * This dataset has been reported by the member states. The subsequent QC revealed some problems caused by self-intersections elements. Data in GPKG-format should be processed using QGIS.

LegacyVegetation: European reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka

This data set presents the reconstructed vegetation cover for 1287 European sites based on harmonized pollen data from the data set LegacyPollen 2.0. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by accounting for taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were calculated using the aforementioned taxon-specific parameters and a Gaussian plume model for deposition and dispersal. Values for relative pollen productivity and fall speed from the synthesis from Wiezcorek and Herzschuh (2010) were updated with recent studies used to reconstruct vegetation cover. The average values from all Northern Hemisphere values were used where taxon-specific continental values were unavailable. As REVEALS was conceived to reconstruct vegetation from large lakes, only records originating from large lakes (>= 50h) are marked as "valid as site" in the dataset. Reconstructions from other records can be used when spatially averaging several together. An example script to do so is provided on Zenodo (https://doi.org/10.5281/zenodo.12800290). Reconstructed tree cover was validated using modern Landsat remote sensing forest cover. Reconstructed tree cover has much lower errors than the original arboreal pollen percentages. Reconstructions of individual taxa are more uncertain. We present tables with reconstructed vegetation cover for all continents with original parameters. As further details, we list a table with the taxon-specific parameters used, metadata for all records, and a list of parameters adjusted in the default version of REVEALSinR.

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