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59 results for “stream networks”
Seasonal carbon dioxide concentrations and fluxes throughout Denmark's stream network
<h2>About</h2> <p>Repository containing code and data for the study:</p> <p><strong>Title</strong> Seasonal carbon dioxide concentrations and fluxes throughout Denmark’s stream network</p> <p><strong>Authors</strong> Kenneth Thorø Martinsen<sup>1</sup>, Kaj Sand-Jensen<sup>1*</sup>, Victor Bergmann<sup>1</sup>, Tobias Skjærlund<sup>1</sup>, Johan Emil Kjær<sup>1</sup>, Julian Koch<sup>2</sup></p> <p><strong>Affiliations</strong> <sup>1</sup>Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark <sup>2</sup>Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen, Denmark</p> <p><strong>Corresponding author</strong> *Correspondence: <a href="mailto:ksandjensen@bio.ku.dk">ksandjensen@bio.ku.dk</a></p> <p><strong>GitHub repository</strong> <a href="https://github.com/KennethTM/denmark_stream_co2">https://github.com/KennethTM/denmark_stream_co2</a></p> <div> <h2>Contents</h2> <a href="https://github.com/KennethTM/denmark_stream_co2#contents"></a></div> <p>The repository contains R and Python scripts for the analysis and figures.</p> <p>The <code>data/insitu_flux</code> directory contains the <em>in-situ</em> CO<sub>2</sub> flux measurements in Excel format.</p> <p>The <code>data/modeling</code> directory contains artifacts from modeling.</p> <p>The <code>data/products</code> directory contains products resulting from the analysis (predicted CO<sub>2</sub> concentrations and estimated CO<sub>2</sub> fluxes throughout Denmark) in csv format.</p>
GIS data - Model sítě vodních toků v ČR | GIS data - Water Stream Network Model for the Czech Republic
<p>GeoTIFF layers (8 x 8 m) containing a modeled network of watercourses in the landscape of the Czech Republic: (1) "streams_def_cr.tif" layer - network of natural watercourses with Strahler order determination; (2) "vodni_toky.tif" layer - model of potentially navigable watercourses. For a detailed description of layers, see https://doi.org/10.5281/zenodo.3367296</p>
Stream network for the SAFE Project derived from SRTM data
<b>Description: </b><p>This zipfile contains linear geometries showing a channel network across the SAFE Project landscape. The network was calculated using the GRASS hydrology tool <code>r.stream.extract</code> from SRTM elevation data and the resulting flow accumulation predictions (see <a href="https://zenodo.org/record/3490488">https://zenodo.org/record/3490488</a> and <a href="https://zenodo.org/record/3490687">https://zenodo.org/record/3490687</a>).<br><br>Note that these networks are derived entirely from remotely sensed data, but do form a single interconnected network across the wider SAFE landscape. Other stream network data are available () but the provenance of these are not well known and they only cover part of the SAFE network.<br><br>Details of the geoprocessing can be found here: <a href="https://www.safeproject.net/dokuwiki/safe_gis/stream_networks">https://www.safeproject.net/dokuwiki/safe_gis/stream_networks</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492009">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_SRTM_Stream_network_metadata.xlsx, SRTM_Channels_network.zip</p><p><b>SAFE_SRTM_Stream_network_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_Channels_network.zip</b></p><p>Description: Shapefile containing 54413 calculated segments forming a channel network across the wider SAFE landscape.</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet Properties)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 17</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>cat</b>: Identity of segment (Field type: id)</li><li><b>type_cd</b>: Segment type (see also type) (Field type: numeric)</li><li><b>ID</b>: Identity of segment (Field type: id)</li><li><b>length</b>: Length of network segment (Field type: numeric)</li><li><b>n_ponts</b>: Number of points in segment (Field type: numeric)</li><li><b>sourceX</b>: X coordinate of source point of segment (Field type: numeric)</li><li><b>sourceY</b>: Y coordinate of source point of segment (Field type: numeric)</li><li><b>sinkX</b>: X coordinate of sink point of segment (Field type: numeric)</li><li><b>sinkY</b>: Y coordinate of sink point of segment (Field type: numeric)</li><li><b>type</b>: Segment type - is the segment a source or outflow segment (connected at one end only, type_cd = 0) or internal (connected at both ends, type_cd=1) (Field type: categorical)</li><li><b>snkChnn</b>: Identity of sink channel for this segment (Field type: id)</li><li><b>srcChnn</b>: Identities of source channels for this segment (Field type: id)</li><li><b>nSourcs</b>: Count of source channels (Field type: numeric)</li><li><b>sorcElv</b>: Elevation of start point of segment (Field type: numeric)</li><li><b>sinkElv</b>: Elevation of sink point of segment (Field type: numeric)</li><li><b>sorcFlw</b>: Incoming flow at segment source (Field type: numeric)</li><li><b>sinkFlw</b>: Outgoing flow at segment sink (Field type: numeric)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Alternative stream network data for the SAFE Project
<b>Description: </b><p>This zipfile contains three shapefiles of linear geometries showing channel networks across the SAFE landscape.<br></p><ul><br><li>RIVERS_UTM.shp: From Igor Lysenko's SAFE pack, this is a river network with two areas, covering Maliau and the area around the SAFE experimental region. The network density is much higher in Maliau than SAFE and the channel network around SAFE is not complete. This is projected in UTM50N WGS84.</li><br><li>LFEriver_SL.shp: This is provided (by Sarah Luke?) through Clare Wilkinson's GIS files and adds a critical missing stream for the Logged Forest Edge (LFE) catchment. This is projected in UTM50N WGS84.</li><br><li>all_rivers.shp: This is provided through Clare Wilkinson's GIS files and provides streams for a single region covering Danum and the SAFE experimental region but not sampled watersheds in Oil Palm plantations to the south of SAFE. The original file is missing projection information (no .prj file) but other files in the same dataset are projected in RSO Timbalai 1948 and using this projection fits with the context of other data. The version uploaded onto Zenodo has been reprojected into UTM50N WGS84.</li><br></ul><br><br>Although the files are not consistent, they do contain channels and channel data that are referenced in some studies. The provenance of these files are unknown, although the following suggests that they may be traced using GPS or from imagery:<br><ul><br><li>Incomplete coverage within the regions they cover.</li><br><li>Treatment of larger rivers, changing from a single line feature showing the stream centreline (?) to double lines showing the river banks.</li><br><li>Variation in network density: the western edge of all_rivers.shp shows a vertical band about 4 km wide of higher stream density than the rest of the region; RIVERS_UTM.shp shows marked differences in stream density between SAFE and Maliau.</li><br></ul><p></p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492064">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_Alternative_Stream_network_metadata.xlsx, Preexisting_SAFE_river_files.zip</p><p><b>SAFE_Alternative_Stream_network_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>Preexisting_SAFE_river_files.zip</b></p><p>Description: Contains three shapefiles of channel networks.</p><p>This file contains 3 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet LFEriver_SL)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 1</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>Id</b>: Identity of river (Field type: id)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet RIVERS_UTM)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 1</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>NAME</b>: Local name of segment (Field type: comments)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet all_river_UTM50N_WGS84)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 10</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>FNODE_</b>: Unknown (Field type: numeric)</li><li><b>TNODE_</b>: Unknown (Field type: numeric)</li><li><b>LPOLY_</b>: Unknown (Field type: numeric)</li><li><b>RPOLY_</b>: Unknown (Field type: numeric)</li><li><b>LENGTH_MET</b>: Length of channel segment in metres (Field type: numeric)</li><li><b>RIV_YSC_</b>: Unknown (Field type: numeric)</li><li><b>RIV_YSC_ID</b>: Unknown (Field type: numeric)</li><li><b>CODE</b>: Unknown (Field type: numeric)</li><li><b>NAME</b>: Local name of segment (Field type: comments)</li><li><b>AREA</b>: Local area of segment (Field type: comments)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Data from: Within-reach temperature heterogeneity is limited in a southern Appalachian stream network, southeastern USA
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Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
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Data from: Environmental and spatial effects on co-occurrence network size and taxonomic similarity in stream diatoms, insects, and fish
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Data from: Combining geostatistical and biotic interactions modelling to predict amphibian refuges under crayfish invasion across dendritic stream networks
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Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
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Data for: Thermal vulnerability in a mountain stream network: Temporal, spatial, and biological data
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Saturated and unsaturated hydraulic conductivity for Saddle Stream Network, 2017
Saturated and unsaturated hydraulic conductivity are important surface hydrologic properties that dictate the how fast water moves into the subsurface. Saturated and unsaturated hydraulic conductivity were measured at approximately 25 locations throughout the Niwot Ridge Saddle Stream Catchment in 2017. Saturated hydraulic conductivity was calculated from saturated infiltration rates determined using a single-ring infiltrometer. Unsaturated hydraulic conductivity was calculated from unsaturated infiltration rates determined using a mini-disk portable tension infiltrometer. Both measurements had over an order of magnitude variation across the catchment, suggesting a wide range in hydrologic function. Replicate measurements within 1m^2 were often comparable, but at some sites showed large differences. These differences likely reflect the small-scale variability in subsurface characteristics across the catchment.
Soil loss on ignition for Saddle Stream Network, 2017
Soil loss on ignition (LOI), as a proxy for total soil organic matter, is an important control on surface hydrology. To measure soil LOI across the Niwot Ridge LTER Saddle Stream Catchment, surface (0 – 10 cm) soil samples (n = 84) were collected on roughly a 60 x 60 m grid, including sensor network sites. After drying, sieving to < 2mm, and grinding to a fine powder, a ~20 g subsample of soil was combusted at 550°C for 4 hours. LOI is reported as a percent difference between dry weights at 550°C and 105°C. The average LOI was 19.23%, but soils had a wide range from 4.80% to 46.77%. This dataset is useful for studies of spatial patterns in organic matter accumulation, biogeochemical process rates, and surface hydrology.
Soil moisture and temperature surveys for Saddle Stream Network, 2017
Surface soil moisture and temperature reflect patterns in snowmelt and hydrologic connectivity in alpine ecosystems. In the summer of 2017, surface soil moisture and temperature were measured on eight surveys at 84 locations in the Niwot Ridge Saddle Stream catchment. The eight surveys were July 3, 2017 – September 9, 2017. Overall, soil moisture showed the influence of snowmelt early in the season and equilibration of soil moisture during the summer monsoon. Seasonal patterns in soil temperature were opposite that of soil moisture in that temperatures increased until the summer monsoon, which cooled surface soils temporarily. Soil moisture was highly variable between locations in that some locations remained consistently dry, while others remained consistently wet. Still others experienced an order of magnitude of variation in soil moisture over the course of the season. These data are useful for validating hydrologic models and remotely sensed thermal and moisture properties, informing growing season plant phenology, and understanding hydrologic connectivity in alpine ecosystems.
Soil texture for Saddle Stream Network, 2017
Soil texture is an important hydrologic control. To measure soil texture across the Niwot Ridge LTER Saddle Stream Catchment, surface (0 – 10 cm) soil samples (n = 84) were collected on roughly a 60 x 60 m grid, including sensor network sites. After drying and sieving to less than 2mm, soil size fractions (percent sand, silt, and clay) and corresponding soil texture were determined by laser particle size analysis. Soil size fractions and texture are reported using both United States Department of Agriculture and World Reference Base soil classification standards. Soils were one of four textures: loamy sand, silt loam, sandy loam, and loam. Over half of the soils sampled were sandy loam. This dataset is useful for hydrologic modeling studies and other studies related to understanding alpine hydrology.
Stream Order for drainage to streams in the Ipswich or Parker River Network - ASCII Raster File
This datalayer is a gridded data set that identifies for each pixel the stream order that drainage from the pixel initially enters into the river network. The resolution is 120 m x 120 m. Based on the flow direction in: WAT-RGIS-120m-FlowDirection.asc. First order streams at the 120m resolution are equivalent to third order when calculated at the pixel level from the drainage direction grid (i.e. 3rd order pixels are equivalent to 1st order streams, 4th order pixels are second order streams, etc). Idenitify the distribution of inputs from land to streams of different sizes.
Data from: Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks
The network architecture of streams and rivers constrains evolutionary, demographic, and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho giant salamander, Dicamptodon aterrimus, in stream networks of Idaho and Montana, USA. We used microsatellite data to test population structure models by (1) examining hierarchical partitioning of genetic variation in stream networks and (2) testing for genetic isolation by distance along stream corridors versus overland pathways. Replicated sampling of streams within catchments within three river basins revealed that hierarchical scale had strong effects on genetic structure and gene flow. AMOVA identified significant structure at all hierarchical scales (among streams, among catchments, among basins), but divergence among catchments had the greatest structural influence. Isolation by distance was detected within catchments, and in-stream distance was a strong predictor of genetic divergence. Patterns of genetic divergence suggest that differentiation among streams within catchments was driven by limited migration, consistent with a stream hierarchy model of population structure. However, there was no evidence of migration among catchments within basins, or among basins, indicating that gene flow only counters the effects of genetic drift at smaller scales (within rather than among catchments). These results show the strong influence of stream networks on population structure and genetic divergence of a salamander, with contrasting effects at different hierarchical scales.
Ephemeral stream water contributions to United States drainage networks
<p>Results from our CONUS ephemeral stream modeling study. Included in this release is a set of csv files for each CONUS drainage basin (USGS HUC level 4 basins). These lookup tables can be joined to NHD-HR hydrography (https://www.usgs.gov/national-hydrography/nhdplus-high-resolution). CSV columns are as follows:</p> <ul> <li>NHDPlusID: NHD-HR reach ID. Use this to join to hydrography shapefiles.</li> <li>perenniality: Result of our ephemeral classification model (see paper for methods).</li> <li>percQEph_reach: Main model result. See equation S1 in paper.</li> </ul> <p> </p> <p>Some notes:</p> <ul> <li>Note that continental-scale models prioritize capturing broad patterns across large domains, so we caution against over-interpreting results for individual river reaches.</li> <li>Great Lakes are not included, though the model does route through the lakes for topological consistency.</li> <li>Foreign streams are recast as 'non-ephemeral' to be copacetic with figures in the paper. See paper for more details.</li> <li>Some reaches present in the NHD-HR are not included in our results for a variety of reasons. See paper for details.</li> </ul> <p> </p>
Species and environmental datasets from Sierra Nevada, CA (USA) streams in lake-stream networks
<p>A major goal of community ecology is understanding the processes responsible for generating biodiversity patterns along spatial and environmental gradients. In stream ecosystems, system specific conceptual frameworks have dominated research describing biodiversity change along longitudinal gradients of river networks. However, support for these conceptual frameworks has been mixed, mainly applicable to specific stream ecosystems and biomes, and these frameworks have placed less emphasis on general mechanisms driving biodiversity patterns. Rethinking biodiversity patterns and processes in stream ecosystems with a focus on the overarching mechanisms common across ecosystems will provide a more holistic understanding of why biodiversity patterns vary along river networks. In this study, we apply the Theory of Ecological Communities (TEC) conceptual framework to stream ecosystems to focus explicitly on the core ecological processes structuring communities: dispersal, speciation, niche selection, and ecological drift. Using a unique case study from high elevation networks of connected lakes and streams, we sampled stream invertebrate communities in the Sierra Nevada, CA to test established stream ecology frameworks and compared them to the TEC framework. Local diversity increased and β-diversity decreased moving downstream from the headwaters, consistent with the <em>river continuum concept</em> and the <em>small but mighty</em> framework of mountain stream biodiversity. Local diversity was also structured by distance below upstream lakes, where diversity increased with distance below upstream lakes, in support of the <em>serial discontinuity</em> <em>concept</em>. Despite some support for the biodiversity patterns predicted from the stream ecology frameworks, no single framework was fully supported, suggesting "context dependence". By framing our results under the TEC, we found species diversity was structured by niche selection, where local diversity was highest in environmentally favorable sites. Local diversity was also highest in sites with small community sizes, countering predicted effects of ecological drift. Moreover, higher β-diversity in the headwaters was influenced by dispersal and niche selection, where environmentally harsh and spatially isolated sites exhibit higher community variation. Taken together our results suggest that combining system specific ecological frameworks with the TEC provides a powerful approach for inferring the mechanisms driving biodiversity patterns and provides a path toward generalization of biodiversity research across ecosystems.</p>
Monthly pCO2, gas transfer velocity and CO2 efflux rate in global streams and rivers (the GRADES river networks)
<p>The datasets contain monthly partial pressure of dissolved CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>, µatm), gas transfer velocity (<em>k</em>, m d<sup>-1</sup>), and CO<sub>2</sub> efflux rate (g C m<sup>-2</sup> d<sup>-1</sup>) estimates for global streams and rivers in both comma separated values (csv) and GeoTIFF formats. These datasets served as the base for monthly CO<sub>2</sub> emission estimates for global streams and rivers and can be used to support regional terrestrial carbon balances. Original vector-version of products is based on the Global Reach–level A priori Discharge Estimates for SWOT or GRADES river networks, which have ~ 3 million individual reaches of global streams and rivers. The GeoTIFF version has a spatial resolution of approximately 0.0083º (1 km). The datasets also contain direct CO<sub>2</sub> measurements compiled from the literature (Supplementary Data 1).</p>
Data from: Parallel cognitive processing streams in human prefrontal cortex: parsing areal-level brain network for response inhibition
<p>Multiple cognitive processes are recruited to achieve adaptive behavior. However, it is poorly understood how such cognitive processes are implemented in temporal cascades of human cerebral cortical areas as processing streams to achieve behavior. In the present study, we identify cortical processing streams for response inhibition and examine relationships among the processing streams. Functional magnetic resonance imaging (MRI) and time-resolved single-pulse transcranial magnetic stimulation (TMS) reveal three distinct critical timings of transient disruption in the functionally essential cortical areas that belong to two distinct cerebrocortical networks. Furthermore, single-pulse TMS following suppression of the ventral posterior inferior frontal cortex (vpIFC) with repetitive TMS reveals information flow from the vpIFC to the presupplementary motor area (preSMA) within the same network but not to the dorsal posterior inferior frontal cortex (dpIFC) across different networks. These causal behavioral effects suggest two parallel processing streams (vpIFC-preSMA versus dpIFC-intraparietal sulcus) that act concurrently during response inhibition.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.