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194 results for “Tributaries”
PIE LTER nutrient grab samples collected between December 1998 and July 2017 in the mainstem, tributaries, and headwater streams in the Ipswich and Parker River watersheds, Massachusetts.
Data set of Ipswich and Parker Rivers and tributaries in Massachusetts collected between December 1998 and July 2017 by MBL, UNH and/or members of the Ipswich and Parker River watershed associations. The data is primarily nutrient content of the streams, but there are some physical descriptors (i.e. discharge, temperature, dissolved oxygen) for some dates and sites. Site description file for all stations sampled in WAT-UNH-IPPR-Synoptic file can be found in WAT-UNH-IPPR-Synoptic-Sites file.
Fig. 4 in Food resource partitioning among species of Astyanax (Characiformes: Characidae) in the Lower Iguaçu River and tributaries, Brazil
Fig. 4. Relative frequency (%) of the diet overlap index of Astyanax species pairs in the Low Iguaçu River and tributaries in Brazil. Ab=A. bifasciatus; Ad=A. dissimilis; Ag=A. gymnodontus; Al=A. lacustris; Am=A. minor. Low <0.39; intermediate =0.40-0.59; high> 0.60.
Fig.1 in Food resource partitioning among species of Astyanax (Characiformes: Characidae) in the Lower Iguaçu River and tributaries, Brazil
Fig.1. Study area showing the sampling sites (numbers 1 to 25) in the Lower Iguaçu River and tributaries in Brazil.
Stochastic modeling of sediment connectivity for reconstructing sand fluxes and origins in the unmonitored Se Kong, Se San, and Sre Pok tributaries of the Mekong River
<p>Sediment supply to rivers, subsequent fluvial transport, and the resulting connectivity on network-scales are often sparsely monitored or subject to major uncertainty. Hence, we propose to adopt stochastic modeling approaches for studying network sediment connectivity. We demonstrate such a stochastic approach for modeling sand connectivity in the major, poorly monitored Se Kong, Se San, and Sre Pok (3S) tributaries of the Mekong River. Specifically, we run many random initializations of the CASCADE modeling framework for sediment connectivity in a Monte Carlo approach in order to quantify how unknown properties of sediment sources translate into uncertainty regarding network sediment connectivity. We identify a reduced ensemble of model realizations that reproduces downstream observations of sediment transport. This ensemble presents an inverse stochastic approximation of the spatial distribution, magnitude, and variability of transport capacity, sediment flux, and bed material grain size in the entire network (i.e., upscaling point observations to the entire network). The approximated magnitude of sediment flux in each tributary is controlled by reaches of low transport capacity (“bottlenecks”). These “bottlenecks” limit the ability of the inverse stochastic approximation to predict sediment transport in the upper parts of the catchment but they allow a clear partitioning of sand deliveries from the 3S to the Mekong, with the Se Kong delivering less (1.9 Mt/yr) and coarser (median grain size: 0.4 mm) sand than the Se San (5.3 Mt/yr, 0.22 mm) and Sre Pok (11 Mt/yr, 0.19 mm).</p>
Supplementary material (part 2) for "On the effect of tributary valleys on thermally driven winds in the main valley: a case study in the Inn Valley"
<p>Part 2 of the supplementary material for the master's thesis "On the effect of tributary valleys on thermally driven winds in the main valley: a case study in the Inn Valley." (Deidda 2023, available at <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-134436">https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-134436</a>). The supplementary material consists in two parts: part 1 includes scripts, datasets, model setup files and figures (available at <a href="https://zenodo.org/records/8089010">https://zenodo.org/records/8089010</a>), while part 2 includes the model output needed to create the figures (see description below).</p><p>This directory contains part of the model output used for the thesis. The model used is WRF-ARW version 4.4 (Skamarock et al. 2021). Information on the model setup is available in the thesis, Section 2.2. For storage limitations, only the files needed to create the graphs presented in the thesis are available.</p><p>The file format is <i>type_domain_date.nc </i>where <i>domain</i> is "d01" or "d02" (respectively for the outer or inner domain, see Section 2.2 in the thesis), while <i>type</i> is "wrfout", "mean_out", or "Averaged", where:</p><ul><li>"wrfout" is the instantaneous WRF output;</li><li>"mean_out" is the time-averaged WRF output, computed using the fork <a href="https://github.com/matzegoebel/WRFlux">WRFlux</a>.</li><li>"Averaged" is a combination of time-averaged and instantaneous outputs. These files were created using the the script <i>Create_intermediate_files.py, </i>available in the <a href="https://zenodo.org/records/8089010">software directory </a>(in Scripts.zip). These files were computed to have a lighter and WRF-like formatted data containing the averaged fields needed for the analysis.</li></ul>
F I G U R E 3 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 3 (a) Posterior distribution of the difference between the true value and posterior distribution estimates (in percentage of the true value) of annual total smolt abundance obtained from DM (Dirichlet-multinomial) model M5 (simulation exercise). Each line is a year, and the 10 (one for each replicate) distributions are overlaid. The overall average difference is indicated in the left corner of the panel and represented by a red dashed vertical line. (b) C.V. of the posterior distributions of total abundance obtained from DM model M5. Each blue dot corresponds to the C.V. of a unique year/replicate, and each dashed blue horizontal line corresponds to the average C.V. of a replicate across years. The average C.V. across years and replicates (red dashed line) is also indicated in the top-right corner of the panel.
F I G U R E 4 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 4 (a–c) Posterior distribution of the relative contribution of the smolt abundance associated with each upstream rotary screw trap (RST) and (d) posterior distribution of the relative contribution of the "rest" of the smolt abundance in relation to the total abundance for the DM (Dirichletmultinomial) model M5 (simulation exercise). The red dots correspond to the true values in the simulated dataset.
F I G U R E 1 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 1 Restigouche River catchment map with main streams and lakes. Red dots indicate the location of the rotary screw traps (RST). Black polygons indicate subcatchments draining into upstream RSTs. Top-right panels: zoom on the location of the two downstream RSTs and example of a smolt with a streamer tag. Photo credit: Marie-Camille St-Amour.
F I G U R E 5 in Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments
F I G U R E 5 (left panels, a–e) Posterior distributions of the estimated annual catchability θt,k at each rotary screw trap (RST), (middle panels, f– h) posterior distributions of the annual smolt abundance estimates associated with each upstream RST (Nmt,i), (i) the total smolt abundance for the Restigouche catchment (Nmtot), and (right panels, j–m) posterior distributions of the annual relative proportions of the total smolt abundance t of the Restigouche catchment estimated for each upstream RST and for the unsampled areas (rest, bottom-right panel) using the Restigouche dataset. In the catchability panels (a–e), the dashed gray line is the median, and the light and dark gray areas indicate the 2.5th–97.5th and 25th– 75th interquantile ranges, respectively, of the hyperparameter μθ, that is, the average catchability of each RST of the time series. In the relative k proportions panels (f–i) the thick dashed colored line in the top three panels indicates the average proportion of the total catchment abundance estimated at the upstream RST. The thick dashed gray lines indicate the proportions of the Restigouche catchment wetted areas of the subbasins sampled at the upstream RST(j–l) and the wetted area for the remainder of the Restigouche catchment not sampled by any upstream RST (m); the proportions for the rest of the catchment area change over years based on the specific upstream RSTs that operated in a given year. For all panels, the colored dot is the median, and the thin and thick vertical segments indicate the 2.5th–97.5th and 25th–75th interquantile ranges, respectively.
Savi et al., 2020 -- tributary-main-channel interaction experiments -- Experiment No Change 2 subset as netCDF files
<p><strong>Overview</strong></p> <p>Zip file contains two netCDF files with a subset of data from the "No Change 2" (NC2) experiment conducted by Savi et al., 2020 and published in Earth Surface Dynamics (<a href="https://doi.org/10.5194/esurf-8-303-2020">https://doi.org/10.5194/esurf-8-303-2020</a>) with the original data available via the Sediment Experimentalists Network Project Space SEAD Internal Repository (<a href="https://doi.org/10.26009/s0ZOQ0S6">https://doi.org/10.26009/s0ZOQ0S6</a>). Topographic scan data were re-formatted into the netCDF file "T_NC2_scans.nc", and overhead imagery was extracted from the video of the experiment approximately once every minute of experimental time and RGB band data is provided in the formatted netCDF file "T_NC2_images.nc". These data were formatted into netCDF files for easy loading into the "deltametrics" analysis toolbox.</p> <p> </p> <p><strong>Additional Details</strong></p> <p>Re-packaging the scan data from the .tif files was straightforward. From the metadata spreadsheet, we know the times at which the scans were taken (and can eliminate the redundant scan). From the paper itself we know the resolution of the topographic scans is 1 mm in the horizontal and vertical. We also know the input discharges, both water and sediment, through both the main channel and tributary, from the paper. We provide these values as metadata in the netCDF files. The scans form the 'eta' field representing the topography in the file. The packaged up netCDF file is called 'T_NC2_scans.nc'.</p> <p>Overhead imagery from the T_NC2_Complete21fps.wmv video file was extracted using the following command:</p> <blockquote> <p>ffmpeg -i T_NC2_Complete21fps.wmv -r 21 T_NC2_frames/%04d.png</p> </blockquote> <p>This command utilizes the ffmpeg tool to extract the frames at a rate of 21 frames per second (-r 21) as the file name implies that is the rate at which the overhead photos were combined into a video. The NC designation indicates that this experiment was performed with no change in the input conditions in either the main or tributary channels.</p> <p>The experiment ran for a total of 480 minutes. A total of 1466 images were obtained from the ffmpeg extraction. This translates to an image approximately every 20 seconds of real time (480 minutes / 1466 frames * 60 seconds/minute = 19.6453 seconds / frame). We sample every 3rd frame, which gives us images roughly once a minute (489 frames in all), to create the subset of data re-packaged as a netCDF file for deltametrics. Dimensions for the pixels were approximated based on our knowledge of the topographic scan resolution. Assuming the extents of the scans and overhead images are the same (although they are not), we calculate the number of millimeters per pixel in the x and y directions for the overhead images. We assume the pixels are more likely to be square than rectangular, so we average these values and assign this as the distance per pixel in both the x and y dimensions for these data.</p> <p>Script used to re-package this dataset is available as a <a href="https://gist.github.com/elbeejay/f7603e712cf0ea30a8215b902715d222">GitHub Gist</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Savi, Sara, et al. "Interactions between main channels and tributary alluvial fans: channel adjustments and sediment-signal propagation." Earth Surface Dynamics 8.2 (2020): 303-322.</p> <p>Physical experiments on interactions between main-channels and tributary alluvial fans<br> S. Savi, Tofelde, A. Wickert, A. Bufe, T. Schildgen, and M. Strecker<br> https://doi.org/10.26009/s0ZOQ0S6</p>
Variability in the discharge of the Mississippi River and tributaries from 1817 to 2020
<p>This is a two-hundred-year long dataset of the annual average, minimum, and maximum discharges at five stations draining the Mississippi River watershed: at Clinton, IA, Herman, MO, St. Louis, MO, Louisville, KY, and Vicksburg, MS. The data are useful to test for increases in the three discharge metrics, and correlations with air pressure differentials represented in the North Atlantic Oscillation (NAO) Index. These data may be useful for climate change assessments through modeling or synthetic assessments using other data sets.</p>
Figure 7 in Fish diversity of a tributary of the Meta River, in the flat highlands of the colombian Orinoquia
Figure 7. Canonical Correspondence Analysis (CCA), relating the structure of the fish community with environmental variables and sampling sites on the Mitimiti stream.
Figure 6 in Fish diversity of a tributary of the Meta River, in the flat highlands of the colombian Orinoquia
Figure 6. Hierarchical clustering dendrograms generated from component dissimilarity matrices βruz-bal for the hydrological periods of Mitimiti stream.
Figure 5 in Fish diversity of a tributary of the Meta River, in the flat highlands of the colombian Orinoquia
Figure 5. Species accumulation curve estimated through Hill numbers (0=Richness; 1=Shannon; 2=Simpson), for the sampling stations of Mitimiti stream.
Figure 4 in Fish diversity of a tributary of the Meta River, in the flat highlands of the colombian Orinoquia
Figure 4. Species accumulation curve estimated through Hill numbers (0=Richness; 1=Shannon; 2=Simpson), for the hydrological periods of Mitimiti stream.
Figure 3 in Fish diversity of a tributary of the Meta River, in the flat highlands of the colombian Orinoquia
Figure 3. Live photographic record of the species collected in the Mitimiti stream. A. Amblydoras gonzalezi, B. Platydoras armatulus, C. Pimelodella longibarbata, D. Rhamdia laukidi, E. Ancistrus triradiatus, F. Rineloricaria eigenmanni, G. Hemisorubim platyrhynchos, H. Pseudoplatystoma metaense, I. Sorubim lima (juvenile), J. Achirus novoae, K. Apistogramma hongsloi, L. Cichla orinocensis, M. Crenicichla saxatilis, N. Mesonauta egregious. 1 cm scale bar.
Figure 2 in Fish diversity of a tributary of the Meta River, in the flat highlands of the colombian Orinoquia
Figure 2. Live photographic record of the species collected in the Mitimiti stream. A. Acestrorhynchus falcirostris, B. Schizodon scotorhabdotus, C. Brycon falcatus, D. Salminus hilarii, E. Tetragonopterus argenteus, F. Caenotropus labyrinthicus, G. Chilodus punctatus, H. Boulengerella cuvieri, I. Hoplerythrinus unitaeniatus, J. Hemiodus unimaculatus, K. Pygocentrus cariba, L. Brachyhypopomus brevirostris, M. Eigenmannia cf. limbata, N. Corydoras axelrodi, Ñ. Corydoras cf. cortesi. 1 cm scale bar.
Рис. 2. МежгоΔовое соотношение чисΛенного обиΛия семейств герпетобионтных жесткокрыΛых в пойме (2008–2011 гг.) Fig. 2. Inter-annual ratio of numerical abundance of herpetobiont beetle families in the floodplain (2008–2011) in Population Dynamics For Herpetobiont Beetles (Coleoptera) In The Floodplain Of A Small Tributary In The Lower Reaches Of The Irtysh
Рис. 2. МежгоΔовое соотношение чисΛенного обиΛия семейств герпетобионтных жесткокрыΛых в пойме (2008–2011 гг.) Fig. 2. Inter-annual ratio of numerical abundance of herpetobiont beetle families in the floodplain (2008–2011)
Рис. 1. ÀенΔрограмма схоΔства (коэффициент Жаккара) состава насеΛения герпетобионтных жесткокрыΛых поймы разных Λет (2008–2011 гг.) Fig. 1. The dendrogram of faunistic similarity (Jacquard coefficient) for the population composition of herpetobiont beetles in different years (2008–2011) in Population Dynamics For Herpetobiont Beetles (Coleoptera) In The Floodplain Of A Small Tributary In The Lower Reaches Of The Irtysh
Рис. 1. ÀенΔрограмма схоΔства (коэффициент Жаккара) состава насеΛения герпетобионтных жесткокрыΛых поймы разных Λет (2008–2011 гг.) Fig. 1. The dendrogram of faunistic similarity (Jacquard coefficient) for the population composition of herpetobiont beetles in different years (2008–2011)
Fig. 3 in Influence of a large dam and importance of an undammed tributary on the reproductive ecology of the threatened fish matrinxã Brycon orthotaenia Günther, 1864 (Characiformes: Bryconidae) in southeastern Brazil
Fig. 3. Histological sections of Brycon orthotaenia testis stained with Hematoxilin-eosin: (a) resting stage with seminiferous tubules cointaining only spermatogonia (S), (b)(c) maturing/mature with seminiferous tubules full of spermatozoa (Z) in acidophilic secretion (arrows) and (d) spent testis with few spermatozoa (Z) in the lumen (L) in acidophilic secretion (arrow). Bar = (a) 30 µm, (b), (d) 70 µm, (c) 80 µm.
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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
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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.
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