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Model Output Statistics for RACIBORZ (12540)

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 ]

Model Output Statistics for RESKO-SMOLSKO (12210)

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 ]

Model Output Statistics for LESZNO-STRZYZEWICE (12418)

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 ]

Model Output Statistics for KATOWICE (KATTOWITZ) (12560)

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 ]

Model Output Statistics for SUWALKI (12195)

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 ]

Pollen record of a sediment core from the Neualbenreuth Maar, Germany

The Neualbenreuth Maar (49°58' N, 12°28' E, 601 m asl) is a filled up former maar lake, located within a presently swampy depression 2.5 km ESE of the village Neualbenreuth (NE-Bavaria, Germany). It represents one of four hitherto known volcanic structures of Pleistocene age along the NNW-SSE trending Tachov fault zone. The maar structure was detected by gravity surveys and was subsequently confirmed by the recovery of lake sediments by an exploratory drilling campaign in 2015. Within the scope of a pilot study, a set of 141 pollen samples collected from sediment depths between 17.7 to 96.0 m below the recent surface. The samples were analyzed in order to evaluate the potential of the sequence for detailed palaeoenvironmental investigations, and to estimate the age of the sedimentary record. The pollen analyses from the Neualbenreuth Maar sediments reveal a continuous record of vegetation and climate changes encompassing four interglacial stages and five cold periods. The dominance of cold and dry tolerant herbs and the sparse representation trees and shrubs during most parts of the sequence indicates open landscapes of steppe to woody-steppe character typically of late Middle and Late Pleistocene glacial periods in Central Europe. The pollen assemblages of the warm stage in the upper part of the core clearly support its correlation with the Eemian interglacial (MIS 5e). The three pre-Eemian warm stages represent terrestrial analogues of the marine isotope stages (MIS) 7e, 7c, and 7a within the Saalian glacial period. In Central Europe, which was strongly affected by glacial and periglacial processes during the major Middle and Late Pleistocene cold periods, palaeoecological evidence of the Saalian complex of alternating warm and cold stages is ambiguous so far. The Neualbenreuth record provides the first biostratigraphical sequence from this region covering MIS 8 to 5 without notable depositional gaps

Wolfsnachweise in NRW

Ausgehend von Westpolen breitet sich der Wolf seit rund 20 Jahren wieder in Deutschland aus – ohne menschliches Zutun. Fast 100 Jahre war der Wolf, abgesehen von einzelnen wandernden Wölfen, aus Deutschland verschwunden. Nach jahrhundertelanger Verfolgung galt er hierzulande als ausgerottet. Die Bestätigung eines Wolfrudels auf einem militärischen Übungsplatz in der sächsischen Oberlausitz im Jahr 2000 war eine wildbiologische Sensation. Aus Polen eingewandert, hatte sich im Osten Deutschlands ein Wolfspaar angesiedelt und Junge aufgezogen. Von der Oberlausitz aus verbreitete sich das Wolfsvorkommen in den Folgejahren nicht nur im Osten der Republik, sondern auch in nordwestliche Richtung zunächst nach Niedersachsen. Für das Monitoringjahr 2021/2022 wurden in Deutschland 161 Rudel, 43 Paare und 21 sesshafte Einzeltiere nachgewiesen. Der Schwerpunkt der Verbreitung umfasst die Bundesländer Brandenburg (47 Rudel), gefolgt von Niedersachsen (34 Rudel) und Sachsen (31 Rudel). Eine aktuelle Übersicht über die Wolfsvorkommen und Nachweise von Wolfsrudeln in Deutschland bietet die Dokumentations- und Beratungsstelle zum Thema Wolf des Bundes - DBBW.

Einzugsgebiete

Methodische Grundlage ist die LAWA-Richtlinie zur Bestimmung und Verschlüsselung oberirdischer Einzugsgebiete (EZG). Die EZG sind mit den benachbarten Bundesländern sowie mit Polen abgestimmt. Grenzübergreifende Gebiete sind geschnitten, d.h. die Flächenangabe gilt für das anteilige Gebiet in MV.

Floods Reference Spatial Datasets reported under Floods Directive - version 3.0, Mar. 2025

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).

Results of palynological analysis from 2020 of the varved MO-05 core from Lake Mondsee (Austria) section (249-526 cm)

This study reports a precisely dated pollen record with a 20-year resolution from the varved sediments of Lake Mondsee in the north-eastern European Alps (47°49′N, 13°24′E, 481 m above sea level). The analysed part of core spans the interval between 1500 BCE and 500 CE and allows changes in vegetation composition in relation to climatic changes and human activities in the catchment to be inferred. Intervals of distinct but modest human impact are identified at ca. 1450-1220, 740-490 and 340-190 BCE and from 80 BCE to 180 CE. While the first two intervals are synchronous with prominent salt mining phases during the Bronze Age and Early Iron Age at the nearby UNESCO World Heritage Site of Hallstatt, the last two intervals fall within the Late Iron Age and Roman Imperial Era, respectively. Comparison with published records of extreme runoff events obtained from the same sediment core shows that human activities (including agriculture and logging) around Lake Mondsee were low during intervals of high flood frequency as indicated by a higher number of intercalated detrital event layers, but intensified during hydrologically stable intervals. Comparison of the pollen percentages of arboreal taxa with the stable oxygen isotope and potassium ion records of the NGRIP and GISP2 ice cores from Greenland reveals significant positive correlations for Fagus and negative correlations for Betula and Alnus. This underlines the sensitivity of vegetation around Lake Mondsee to temperature fluctuations in the North Atlantic as well as to moisture fluctuations controlled by changes in the intensity of the Siberian High and the North Atlantic Oscillation (NAO) regime.

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