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253 results for “regional level”
Cuvette Centrale subset region - monthly water level data
<p>These data were derived using the methods described in the paper: https://www.mdpi.com/2072-4292/15/12/3099</p> <p>The filenames beginning with 'WL_monthly' contain the monthly minimum, maximum, mean, and standard deviation of the estimated daily water levels for a subset of the Cuvette Centrale region in the Central Congo Basin.</p> <p>The filenames beginning with just 'WL_' contain the minimum, maximum, mean, and standard deviation of the estimated daily water levels over the 20-month study period, March 2019 to October 2020.</p> <p> </p>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
PERCEIVE WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels
<p>The data set will contain all the shareable data collected and generated through the different tasks of WP5, that are interdependent. In particular, in Task5.1 we collected a bibliography, which is already included in the dataset. In Task5.2 we collected a large collection of data from different documentary sources and media: EU policies and reports, descriptions and reports created by Local Managing Authorities, newspaper articles, tweets, Facebook posts referred to EU CP policies. During Task5.3 we analyzed these data through Mallet software to elicit topics, as sets of words that co-occur together. The results of the linear regression analysis will be in this data set as well. The results of the analysis will consist of tables of texts and of numerical data.</p> <p>Collected data are only partly available online, therefore our generated data will have a unique value, as there is no comparable public source of data. Data will be helpful for all student and practitioners willing to understand how the concepts of Cohesion Policies, Europe and European identity are shaped in the public sphere.</p>
PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels
<p>This data set contains all the shareable data collected and generated through the different tasks of WP5, which are interdependent.</p> <p>In particular, in Task5.1 we collected a bibliography, which is the basis for our theoretical work.</p> <p>In Task5.2 we collected a large collection of data from different documentary sources and media: EU policies and reports, descriptions and reports created by Local Managing Authorities, newspaper articles, tweets, Facebook posts referred to EU CP policies. We don’t have the permission to share these data (as they are protected by copyright), but all the sources are described in Deliverable 5.2, which is public (see <a href="http://doi.org/10.6092/unibo/amsacta/5726">http://doi.org/10.6092/unibo/amsacta/5726</a> or <a href="http://doi.org/10.5281/zenodo.1318184">http://doi.org/10.5281/zenodo.1318184</a>).</p> <p>During Task5.3 we analyzed the textual content of data listed in Task5.2, to construct a database of discursive topics in Task5.4. Data set includes the description of topics (results of topic modeling), clusters of topics obtained both interpretively and algorithmically, and the relevant data regarding sentiment and semantic analyses.</p> <p>Task5.5 regards a statistical analysis linking public discourse and different definitions of being Europeans on the one hand with European identification on the other hand. The data set contains the measures of variables used to run the regression test, and the results of the test in tabular form.</p>
Regional Sea-level Budget from 1993-2016
<p>This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/.</p> <p>**<em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>** This has no impact on the results of the manuscript, as the 'axis error' occurred only when organising the files to be published. </p> <p>** Please cite the appropriate papers when using this data **<br> Please cite 'Regionalizing Sea-level Budget with Machine Learning Techniques' when using this data set. However, <strong>most of the data heavily relies on previous work and data sets by many authors,</strong> so please acknowledge that work by citing the original sources of the data (which can be found in the main text of 'Regionalizing Sea-level Budget with Machine Learning Techniques').<br> ** please check this carefully!**</p> <p>This repository contains the following files:</p> <p><strong>budget_components_ENS.nc</strong><br> Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). <em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>If required the individual data sets used for the ensemble, please contact the author. </p> <p><strong>masks.nc</strong><br> netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired.</p> <p><strong>dmaps_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl)</p> <p><strong>som_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl)</p> <p>The code to generate this data and the manuscript figures can be found at https://github.com/carocamargo/SLB</p> <p><br> Corresponding author: carolina.camargo@nioz.nl</p>
Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE
<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, <em>Climate of the Past,</em> 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul> <p> </p> <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul> <p> </p> <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul> <p> </p> <ul> <li><strong>Methodology:</strong> The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
Australian Statistical-Area (SA) Level Regions and Census Income Data (2011)
<p>The Australian Statistical Geography Standard (ASGS) defines a series of nested geographical areas in Australia known as Statistical Area (SA) Levels. SA3 regions are aggregations of SA2 regions, and SA2 regions are aggregations of SA1 regions. This data set contains the shapefiles of all SA1, SA2, and SA3 regions across Australia at the time of the 2011 census, originally downloaded from the Australian Bureau of Statistics (<a href="https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.001July\%20201.">ABS</a>).</p><p>This data set also contains income information from the 2011 census, at the SA1 and SA2 level in New South Wales (NSW). Specifically, it contains the number of families of various types within a range of weekly income brackets.</p><p>Sainsbury-Dale et al. (2023) used a subset of this data set in a study on poverty levels in an area of (NSW) surrounding Sydney. </p><p> </p><p><strong>References</strong></p><p>Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2023) "Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK", <i>Journal of Statistical Software</i>, to appear.</p>
SLIIDERS: Sea Level Impacts Input Dataset by Elevation, Region, and Scenario
<p>This record includes the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) dataset. It also includes source code to generate this product as well as necessary inputs that are not available for download elsewhere. Both the dataset and the source code are consistent with version 1.2. <strong>Note</strong>: The version associated with <a href="https://gmd.copernicus.org/articles/16/4331/2023/">Depsky et al., 2023</a> is v1.1.</p> <p>The zipped SLIIDERS Zarr store can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>sliiders-v1.2.zarr.zip</strong>: SLIIDERS. A global dataset containing 18 socioeconomic variables, reflecting present day socioeconomic and geophysical characteristics of 11,980 coastal regions and projecting capital stock, GDP, and population growth trajectories through 2100 for five SSPs and two economic growth models. These variables are used as inputs to the pyCIAM modeling platform detailed in Depsky et al. 2023.</li> <li><strong>sliiders-v1.2.nc</strong>: Same as the original SLIIDERS dataset, but in netcdf format.</li> </ul> <p><em>Inputs</em></p> <p>All provided inputs are manually created or adjusted points used to create the coastline segments of SLIIDERS:</p> <ul> <li><strong>ciam_segment_pts_manual_adds.parquet</strong>: A list of segment points manually added to those that come from the extreme sea level model CoDEC (<a href="https://doi.org/10.5281/zenodo.3660926">Muis et al. 2020</a>)</li> <li><strong>gtsm_stations_ciam_ne_coastline_snapped.parquet:</strong> Stations from CoDEC snapped to coastlines from <a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">Natural Earth</a></li> <li><strong>gtsm_stations_eur_tothin.parquet</strong>: A list of European points in CoDEC to thin. CoDEC provides ~10km resolution in Europe and ~50km elsewhere. For consistency, SLIIDERS uses ~50km spacing for its coastal segments globally.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>sliiders-1.2.zip</strong>: The source code used to generate SLIIDERS v1.1. See READMEs within this code for more details. This is consistent with release v1.2 of the code maintained on github at <a href="https://github.com/ClimateImpactLab/SLIIDERS">https://github.com/ClimateImpactLab/SLIIDERS</a></li> </ul>
Groundwater level data used in the manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions"
<p>The standardized semimonthly groundwater levels collected from 30 monitoring wells in Dalad County, China. The data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The data are used in our submitted manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions". If you find this dataset useful for your research, please consider to cite our manuscript upon publication. </p>
Replication package for "Why do people persist in sea-level rise threatened coastal regions? Empirical evidence on risk aversion and place attachment"
<p><strong>Steps to replicate the tables and figures in “Why do people persist in sea-level rise threatened coastal regions? Empirical evidence on risk aversion and place attachment”</strong></p> <p><em>by Ivo Steimanis, Matthias Mayer and Björn Vollan</em></p> <p><strong>General information:</strong></p> <ul> <li>Instructions for replication of the results using Stata. All do-files were created in Stata 16.</li> <li>There are 4 folders (DO-FILES, DTA-FILES, OUTPUT, XLS-FILES), in the replication package. Copy these folders to your computer in a common directory</li> </ul> <p> </p> <p><strong>Do-files:</strong></p> <ul> <li>In the DO-FILES folder run the <strong>“00_master.do”</strong> to replicate the results reported in the main manuscript and the supplementary materials. The results will be saved in the OUTPUT folder. All additional Stata packages will be automatically installed.</li> <li><strong>“01_merge_generate.do” </strong>merges the different datasets and creates additional variables using in the analysis</li> <li><strong>“02_analysis.do” </strong>provides the code to replicate all figures and tables reported in the main manuscript and supplementary materials</li> </ul> <p> </p> <p><strong>Data sets:</strong></p> <ul> <li>“bd_combine.dta”: cleaned survey data from Bangladesh</li> <li>“vn_combine.dta”: cleaned survey data from Vietnam</li> <li>“data_analysis.dta”: main data set with the survey data from Bangladesh and Vietnam merged</li> </ul>
Data for "Random forest-based modeling of stream nutrients at national level in a data-scarce region"
<p>The aim of the study was to model annual total nitrogen (TN) and total phosphorus (TP) concentrations at national level using an ML approach. We used water quality data originating from the Environmental Monitoring Database KESE to train RF models for nutrient concentration prediction in 242 catchments across Estonia. A total of 82 environmental variables were used as predictors in the models. In order to yield the best results, a feature selection strategy along with hyperparameter optimization was performed when building the models. The models are applicable for predicting nutrient loads on an annual level, e.g. for the purpose of reporting national level water quality statistics in regional projects, such as HELCOM. The results showed that this relatively basic RF modeling approach can have a performance similar to process-based models. Moreover, these models are easier to reuse and apply on a larger scale, since the required inputs can be derived from freely available datasets (e.g. satellite imagery)</p> <p>This repository contains the input data used for building the RF models and the files describing the modeling results.</p> <p>The description of the files is given in the README.txt file.</p> <p>Virro, H., Kmoch, A., Vainu, M. and Uuemaa, E., 2022. Random forest-based modeling of stream nutrients at national level in a data-scarce region. Science of The Total Environment, 840, p.156613.</p> <p><a href="https://doi.org/10.1016/j.scitotenv.2022.156613">https://doi.org/10.1016/j.scitotenv.2022.156613</a></p>
Virtual stations (TeroVIR ) and water level time series (TeroWAT) in West Africa and Arctic regions
<p>The dataset contains a sample of locations across Siberia and Africa, for which water-level time series were automatically derived from Sentinel-3 altimeters (methodology described in Machefer et al. 2022<sup>1</sup>) from year 2016 to year 2021, together with the in-situ station records and the area covered by the altimetry measurements. The purpose of this dataset is validation and exemplification of the methodology. </p> <p>The methodology described produces comprehensive water level records at a global scale based on altimetry satellite data. The validation against in-situ data was assessed in numerous environments in West Africa and complex locations such as Arctic rivers partially covered with ice.<br> <br> This dataset offers a sample of the records at 3 locations in West Africa (Kemacina [Mali], Koulikouro [Mali], Lokoja [Niger]) and in the sub-arctic region (Yakutsk [Russia]). The data are organised by Level 1 of <a href="http://www.hydrosheds.org/">HydroBASINS</a><sup>2 </sup>definition (ex: africa) in two folders, each containing: virtual stations (teroVIR) and insitu stations (insitu) as shapefiles with their associated metadata, the corresponding water level time series (teroWAT) in NetCDF, and the level 3 of HydroBASINS, corresponding to the largest river basins of each continent. Finally, a csv file (validation) presents the computed metrics assessing the accuracy of the processors.</p> <p>N.B.: time series with less than two common date points between insitu and teroWAT have not been assessed. </p> <p>[1] Machefer, M., Perpinyà-Vallès M., Escorihuela M.J., Gustafsson D., Romero L. (2022): Challenges and evolution of water level monitoring towards a comprehensive, world-scale coverage with remote sensing. Earth System Science Data (Under Reviewing)</p> <p>[2] Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.</p>
Рис. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1–4 — имаго (1, 2 — самцы; 3, 4 — самки). Δанные сбора имаго: 1 — Буреинский заповеΑник, верховье р. Правая Бурея, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ уровнем моря, 24.06.2014; 2 — Буреинский заповеΑник, верховье р. Правая Бурея, окрестности корΑона «Новый МеΑвежий», 900 м наΑ уровнем моря; 4.07.2016; 3, 4 — там же, 29–30.06.2018 Fig. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1 – 4 – adults (1, 2 – males; 3, 4 – females). Data labels for imago: 1 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, near Novyi Medvezhii cordon, 900 m above sea level, 4.07.2016; 3, 4 – at the same place, 29–30.06.2018 in On The Biology Of (Stichel, 1911) (Lepidoptera, Erebidae, Arctiinae) In Northern Amur Region
Рис. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1–4 — имаго (1, 2 — самцы; 3, 4 — самки). Δанные сбора имаго: 1 — Буреинский заповеΑник, верховье р. Правая Бурея, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ уровнем моря, 24.06.2014; 2 — Буреинский заповеΑник, верховье р. Правая Бурея, окрестности корΑона «Новый МеΑвежий», 900 м наΑ уровнем моря; 4.07.2016; 3, 4 — там же, 29–30.06.2018 Fig. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1 – 4 – adults (1, 2 – males; 3, 4 – females). Data labels for imago: 1 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, near Novyi Medvezhii cordon, 900 m above sea level, 4.07.2016; 3, 4 – at the same place, 29–30.06.2018
Spatially explicit regions of different suitability for Sentinel-2A and 2B Level-1C data.
<p>Spatially explicit regions of different suitability for Sentinel-2A and 2B Level-1C data. High suitability means a combination of high coverage and low average cloud cover. The suitability of data at a specific location is decreasing with either adecrease of number of scenes or an increase of average cloud cover.</p> <p>See here for more information: https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1572799</p>
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m. in The Ceratopogonidae (Insecta: Diptera) fauna of the Central Black Sea Region in Turkey
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m.
Figure 10. A in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 10. A thin biofilm that has practically grown into the surface of crumbling sandstone in the abrasive section of an open pseudolittoral (a). Fragment of colonial settlment by Halamphora borealis (b). Scale bars: a — 5 cm, b — 10 µm. Photos by Philipp Sapozhnikov, Olga Kalinina.
Figure 9 in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 9. Fragment of cheesy ("moss") biofilm (a) on flat blocks of sandstone, in the middle pseudolittoral zone. Mixed colonial settlements of Halamphora coffeaeformis and H. hybrida (b, c) growing in the form of "clouds" (flakes) on Enteromorpha filaments. Designations: h — cells of various species of Halamphora, ep — cell of Entomoneis paludosa. Puddles of the upper pseudolittoral, April 2023. Scale bar: a – 5 cm, b – 100 µm, c – 25 µm. Photos by Philipp Sapozhnikov.
Figure 3 in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 3. Map of microepiliton sampling points in various coastal locations in the city of Aktau: a - map of the Caspian Sea with a highlighted area of the coast of the Mangystau region, b - section of the coast of the Mangystau region with a highlighted area of the city of Aktau, c - coast in the area of the city of Aktau and its immediate suburbs, d - locations of sampling in October 2022, e - locations of sampling in April 2023.
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