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The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains: 2521 historical floods with impact data (1870-2020); 237 further historical floods with significant impacts, but without precise impact data (1950-2020) Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020). Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders): HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020) HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020) HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020) HANZE_list_of_references.csv - List of all references used in the catalogues HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods Regions_v2010_simplified.zip - Map of subnational regions (v2010) Regions_v2021_simplified.zip - Map of subnational regions (regions v2021) v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected. This work was supported by the German Research Foundation (DFG) through project "Decomposition of flood losses by environmental and economic drivers" (FloodDrivers), project no. 449175973 

For more detailed information, one can visit 10.1039/D4TC00466C Sanjay Sahare thanks project No. 2021/43/P/ST3/02599 co-funded by the National Science Centre and the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska–Curie grant agreement no. 945339.

Rainbow trout (Oncorhynchus mykiss) has become by far the most frequently farmed freshwater fish species in Turkey, whereas very little is known about its establishment and invasiveness potential. We explored this potential through a combination of Maxent habitat suitability model and the Aquatic Species Invasiveness Screening Kit (AS-ISK) on the river basin scale by generating an overall risk score (ORS). The outcome of this approach was also incorporated with the spatial analysis of native salmonid species by generating a relative vulnerability score (RVS) to prioritize susceptibility of native species (or populations) and to propose risk hotspots by identifying their potential geographic overlap and interaction with O. mykiss. Results suggest that the northern basins (Eastern Black Sea, Western Black Sea and Marmara basins) are the most suitable basins for O. mykiss. According to the Basic Risk Assessment (BRA) threshold scores, O. mykiss is classified as “high risk” for 3 (12.0%) of the 25 river basins screened (Western Black Sea, Eastern Black Sea and Maritza-Ergene), and as “medium risk” for the remaining basins. The climate change assessment (CCA) scores negatively contributed the overall invasiveness potential of O. mykiss in 22 (88.0%) of the river basins and resulted in zero contribution for the remaining three, namely Aras-Kura, Çoruh river and Eastern Black Sea. The ORS score of river basins was lowest for Orontes and highest for Western Black Sea, whereas it was lowest for Konya-closed basin and highest for Eastern Black Sea, when CCA was associated. The micro-basins occupied by Salmo rizeensis had the highest mean habitat suitability with O. mykiss. Among the all species, S. abanticus had the highest RVS, followed by S. munzuricus and S. euphrataeus. The overall outcome of the present study also suggests that the establishment and invasiveness potential of O. mykiss may decrease under future (climate warmer) in Turkey, except for the northeast region. This study can provide environmental managers ...

The dataset CO2_flux_measurements_Acoculco contains data on CO2 fluxes, coordinates (UTM), air temperature, atmospheric pressure measured in selected sites belonging to the Acoculco Geothermal Field: in particular, the areas named Lagunilla, Alcaparrosa, Los Azufres and also the area between them were investigated. CO2 flux measurements were performed using the accumulation chamber method. The dataset Field_meas_Acoculco_waters reports the ID, coordinates (UTM), Altitude (m.a.s.l.), temperature, flow rate, pH, Electrical Conductivity and Dissolved Oxygen for water samples collected in the central sector of the Acoculco geothermal field, but also in other sectors located inside and outside the Acoculco caldera. Total depth is also included for samples collected from water wells. The dataset Chemical_isotopic_data_Acoculco_waters reports major and minor chemical components and stable isotopic composition for hydrogen and oxygen determined in collected water samples in Acoculco geothermal field. Calculated partial pressures (in bars and log10-value) and CO2 concentrations of dissolved CO2 were also included. The dataset Chemical_isotopic_data_Acoculco_gas reports chemical and isotopic data for collected samples from Los Azufres and Alcaparrosa natural gas manifestations.

The data contains field-collected data of brown trout populations in 102 Norwegian reservoirs with variable environmental characteristics. The brown trout data (i.e. response variables) include estimates of: "Biomass" (grams of fish per 100m2 net per night); "Density" (number of fish per 100m2 net per night); "Mean weight" (mean wet mass in grams); "Mean condition" (mean Fulton's condition factor); and "Mean maturity length" (mean total length of mature females in millimeters). All abbreviations for different variables (columns) are explained in the paper. Many reservoirs ("Lake") have various names, some including Norwegian letters (æ, ø & å). Hence, we recommend to use coordinate data (EPSG:4326; "decimalLongitude" and "decimalLatitude") and Norwegian national lake ID numbers ("Lake_nr"; managed by the Norwegian Water Resources and Energy Directorate; www.nve.no) to locate the reservoirs. The variables "Year", "Month" and "Day" refer to times when survey fishing was conducted. Lake morphometry data ("A"=surface area, "SD"=shoreline development) is obtained from NVE database. The lake climatic and catchment data ("T"=mean July air temperature, "NDVI"= Normalized Difference Vegetation Index, and "SL"=terrain slope) is obtained and measured as described by Finstad et al. (2014; DOI: 10.1111/ele.12201). Other abbreviations include: "FC"=presence of other fish species (1=absent, 2=present); "GS"=gillnet series (1=Nordic, 2=Jensen); and "ST"=brown trout stocking (0=no stocking, 1=stocking). The water level regulation (WLR) metrics include: ): "WLR_magnitude"= maximum regulation amplitude; "WLR_frequency"=relative proportion of weeks with a sudden rise or drop in water level; and "WLR_duration"=the relative proportion of weeks with exceptionally low water levels.

Example script to transform MAXENT models to binary presence-absence matrices.

Log and boot files of game 211

Above- and below-ground biomass samples obtained in the Corpus Christi Bay and Mission-Aransas Bays, Texas, USA between November 2017 and February 2018.