The dataset presents three layers of geothermal data from Iceland based on "Atlas of Geothermal Resources in Europe" (2002). Heat-flow density, Temperature at 1000 meters and Temperature at 2000 meters (1:10 000 000).

Serwis danych WMS PODGIK Wejherowo

WMS

GDR_E Web Map Service (ArcIMS 9.3.1)

Small-Scale NHD Hydrography vector basemap. The National Hydrography Dataset (NHD) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance and stewardship. For additional information, go to http://nhd.usgs.gov.

Vantaan karttarajapinta (intranet)

This gridded product visualizes 1960 - 2014 water body nitrate concentration (umol/l) in the North Sea domain, for each season (winter: December – February; spring: March – May; summer: June – August; autumn: September – November). It is produced as a Diva 4D analysis, version 4.6.9: a reference field of all seasonal data between 1960-2014 was used; results were logit transformed to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. Every step of the time dimension corresponds to a 10-year moving average for each season. The depth dimension allows visualizing the gridded field at various depths.

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