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1,188 results for “Delta”
MONNALISA_NUM_DA_0_SA_30_AR_5_TR_0.4_Delta_0
Upload of the numerical solutions for the Reference plane of the MONNALISA Project. Please, refer to the README file of this folder and to the articles associated.
MONNALISA_NUM_DA_45_SA_30_AR_5_TR_0.4_Delta_-20
Upload of the numerical solutions for the Reference plane of the MONNALISA Project. Please, refer to the README file of this folder and to the articles associated.
MONNALISA_NUM_DA_0_SA_30_AR_5_TR_0.4_Delta_-15
Upload of the numerical solutions for the Reference plane of the MONNALISA Project. Please, refer to the README file of this folder and to the articles associated.
MONNALISA_NUM_DA_0_SA_30_AR_5_TR_0.4_Delta_10
Upload of the numerical solutions for the Reference plane of the MONNALISA Project. Please, refer to the README file of this folder and to the articles associated.
MONNALISA_NUM_DA_0_SA_30_AR_5_TR_0.4_Delta_35
Upload of the numerical solutions for the Reference plane of the MONNALISA Project. Please, refer to the README file of this folder and to the articles associated.
WALOWA (WAve LOads on WAlls) - Large-scale Experiments in the Delta Flume on Overtopping Wave Loads on Vertical Walls
<p>Coasts of low lying countries are often comprised of a gentle foreshore and shallow waters, followed by a dike and a promenade. At the end of the promenade buildings or storm walls are constructed. This setting makes it possible for waves to overtop the dike and impact on the storm wall or building. Especially during storm season the overtopping waves induce large loads on these structures. New scenarios for climate change and sea level rise make it worthwhile to invest in research regarding overtopping wave loads.</p> <p>Within the European project 'Wave Loads on Walls' (WaLoWa) model tests in the Delta flume (The Netherlands) were conducted. It is the aim to study overtopping wave loads on storm walls and buildings. The project is coordinated by Ghent University (Belgium), in cooperation with TU Delft (The Netherlands), RWTH Aachen (Germany), University of Bari, University of L'Aquila, University of Calabria and University of Florence (Italy) and Flanders Hydraulics Research (Belgium). The project is financed by a grant by Hydralab+ in the framework of the EC Horizon 2020 program.</p> <p>A model geometry comprised of a sandy beach, a sloping dike, promenade and wall structure was built into the Delta flume. The beach alone consists of 1000m³ sand material and was an essential part of the structure, to obtain the broken wave conditions similar to reality. Waves representing a storm with a 1000 year return period and an additional water level to account for sea level rise result in the tested superstorm conditions.</p> <p>Measurements of the water surface elevation were taken close to the paddle, along the mildly sloping foreshore and at the dike toe location by resistance type wave gauges mounted to the flume side wall. The bathymetry of the sandy foreshore was measured by a mechanical profiler before and after the test. The overtopping flow properties thickness and velocity were measured by resistance type wave gauges, ultra-sonic distance sensors, paddle wheels and an electro-magnetic current meter installed along the promenade. Finally, the impact forces and pressures on the wall were measured by compression load cells and pressure sensors respectively. The data-set was complemented by a number of synoptic measurements, such as laser scan profiles, GoPro images, High-speed camera images, Digital camera images. Due to its large storage size, these data are provided on request.</p>
Inundation maps of Danube Delta for 10 dates within the period 2016/10/05 to 2017/08/01 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/10/05 to 2017/08/01 were generated for Danube Delta based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Danube_Delta_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. The regions, which are manually denoted as affected by clouds, are denoted with 2. 'Date' is in the form YYYY_MM_DD.</p>
Danube Delta NDWIs collection from 2011 to 2014
<p>Normalized Difference Water Index (NDWI) (signed integer file, scale factor 0.001) of Danube Delta Protected Area (PA).</p>
Danube Delta (Romania) - NEVERMORE Climate Dataset
<p>The dataset consist of the historical and climate projection (CMIP6) for gridded atmospheric variables and the climate hazards/extreme events alongside the return values (likelihood) of hazards/extreme events. The dataset was developed during NEVERMORE project as part of WP3 from CMCC and NCSRD.</p>
Data from: Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing data
<p>Data from the paper:</p> <p><em>Amos, S., Mengistu, S., Kleinschroth, F. (2021): Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing.</em></p> <p>Based on Landsat 5, 7, 8, RapidEye Ortho, and Sentinel-2 satellite imagery, we manually mapped the settlements of the Dasanech people in the most populated parts of the Omo River Delta in Ethiopia from 1992 to 2019 using QGIS. We used the data to answer the following questions: (1) How have pastoralist settlements in the delta changed in extent and persistence over the past three decades? And (2) how have the settlements changed structurally during the construction, filling, and operation of Gibe III Dam?</p> <p>We conducted two independent remote sensing analyses. Firstly, we used Landsat data from 1992 to 2019 to track land that is inhabited by pastoralists people within the evergreen part of the Delta. Secondly, the higher spatial resolution of the RapidEye Ortho (5m) and Sentinel-2 (10m) images allowed the detailed identification of settlements as well as infrastructure (tin-roof houses and road) in the Delta during a shorter period from 2009 to 2019. <strong>For more information on the data, please refer to the README.txt or the paper.</strong></p>
Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022).</p> <p>All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.)</p> <p>The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times. </p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p><br> The resize command used was:</p> <p><br> #to resize and then quarter<br> #Dir structure is:<br> # --Desktop<br> # |- originals<br> # |- resized<br> # |- quarters</p> <p>`cd originals`<br> `mogrify -resize 2000x2000 -path ../resized *.jpg`</p> <p>#then quarter them<br> `cd ..`<br> `cd resized`</p> <p>`mogrify -crop 2x2@ +repage -path ../quarters *.jpg`</p> <p>For full size images, please download the jpegs directly from NOAA.</p>
Socio-economic development of global river deltas from gridded data
<p>Crop, population, and GDP values in the world's major river deltas, derived from publicly available gridded datasets. </p> <p>v0: Dec. 2022</p> <p>v1: Jan 2023 (added Metadata)</p>
Distribution models for riparian landbirds and waterbirds in the Sacramento-San Joaquin Delta
<p><strong>SUMMARY</strong><br> Distribution models for 9 riparian landbird species and 6 groups of waterbird species in the Sacramento-San Joaquin River Delta of California. </p> <p><strong>DESCRIPTION</strong><br> These predictive models were developed to relate the probability of species or group presence as a function of the surrounding landscape, facilitating predictions of species presence or absence over the entire landscape. Each .RData object is structured as a list containing individual model objects of class `gbm` for each species or group.</p> <p>Models were developed using Boosted Regression Trees, implemented in R using the R packages `dismo` (Hijmans et al. 2021) and `gbm` (Greenwell et al. 2020). Models were developed from pre-existing bird survey data, including 2,547 surveys for riparian landbirds conducted at 716 unique locations throughout the Central Valley of California during the breeding season (May and June), 2011–2019, and 7,820 surveys for waterbirds conducted at 504 unique locations in the Delta during the fall (July 15–November 15) and winter (November 17–March 5) seasons, 2013–14 and 2014–15. Waterbird models were developed for each of the fall and winter seasons, with 46 species grouped into 6 distinct groups defined by similar habitat requirements, foraging style, and diet. </p> <p>These models were used to predict the distribution of each species and group across a baseline Delta landscape (representing land cover in 2018), and these predictions were used to identify Priority Bird Conservation Areas in the Delta. In addition, the models were used to predict distributions for alternative scenarios of future landscape change, and to evaluate the net change from the baseline distributions in the total area of suitable habitat. These models are required for evaluating the change in Biodiversity Support benefits using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial scenario analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala K, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T. (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta.</li> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta.</li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi:10.5281/zenodo.7718620. https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>Literature Cited:</strong></p> <ul> <li>Greenwell B, Boehmke B, Cunningham J, Developers G (2020). <em>gbm: Generalized Boosted Regression Models</em>. R package version 2.1.8. https://CRAN.R-project.org/package=gbm</li> <li>Hijmans RJ, Phillips S, Leathwick J, Elith J (2021). <em>dismo: Species Distribution Modeling</em>. R package version 1.3-5. https://CRAN.R-project.org/package=dismo</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE, Sesser KA, Reiter ME, Shuford WD, Golet GH, Hickey CM, Gardali T. 2023. Distribution models for riparian landbirds and waterbirds in the Sacramento-San Joaquin Delta. doi: 10.5281/zenodo.7531945</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo. (https://doi.org/10.5281/zenodo.7531945)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> Not Planned</p> <p><strong>DATE</strong><br> These models were developed 2019-2022, based on bird survey data collected 2011-2019.</p> <p><strong>FIELD DEFINITIONS</strong><br> N/A</p> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <p>BRT_models_riparianlandbirds.RData:</p> <ul> <li><strong>NUWO:</strong> Nuttall's Woodpecker (<em>Picoides nuttallii</em>)</li> <li><strong>ATFL: </strong>Ash-throated Flycatcher (<em>Myiarchus cinerascens</em>)</li> <li><strong>BHGR: </strong>Black-headed Grosbeak (<em>Pheucticus melanocephalus</em>)</li> <li><strong>LAZB: </strong>Lazuli Bunting (<em>Passerina amoena</em>)</li> <li><strong>COYE:</strong> Common Yellowthroat (<em>Geothlypis trichas</em>)</li> <li><strong>YEWA: </strong>Yellow Warbler (<em>Setophaga petechia</em>)</li> <li><strong>SPTO: </strong>Spotted Towhee (<em>Pipilo maculatus</em>)</li> <li><strong>SOSP:</strong> Song Sparrow (<em>Melospiza melodia</em>)</li> <li><strong>YBCH: </strong>Yellow-breasted Chat (<em>Icteria virens</em>)</li> </ul> <p>BRT_models_waterbirds.RData:</p> <ul> <li><strong>geese:</strong> Geese <ul> <li>Greater White-fronted Goose (<em>Anser albifrons</em>)</li> <li>Snow Goose (<em>Anser caerulescens</em>)</li> <li>Ross's Goose (<em>Anser rossii</em>)</li> <li>Cackling Goose (<em>Branta hutchinsii</em>)</li> <li>Canada Goose (<em>Branta canadensis</em>)</li> </ul> </li> <li><strong>dblr: </strong>Dabbling ducks, including: <ul> <li>Wood Duck (<em>Aix sponsa</em>)</li> <li>Gadwall (<em>Mareca strepera</em>)</li> <li>American Wigeon (<em>Mareca americana</em>)</li> <li>Mallard (<em>Anas platyrhynchos</em>)</li> <li>Blue-winged Teal (<em>Spatula discors</em>)</li> <li>Cinnamon Teal (<em>Spatula cyanoptera</em>)</li> <li>Northern Shoveler (<em>Spatula clypeata</em>)</li> <li>Northern Pintail (<em>Anas acuta</em>)</li> <li>Green-winged Teal (<em>Anas carolinensis</em>)</li> </ul> </li> <li><strong>divduck: </strong>Diving ducks (<em>Note: this model was only developed for the winter season</em>) <ul> <li>Canvasback (<em>Aythya valisineria</em>)</li> <li>Ring-necked Duck (<em>Aythya collaris</em>)</li> <li>Lesser Scaup (<em>Aythya affinis</em>)</li> <li>Bufflehead (<em>Bucephala albeola</em>)</li> <li>Common Goldeneye (<em>Bucephala clangula</em>)</li> <li>Hooded Merganser (<em>Lophodytes cucullatus</em>)</li> <li>Common Merganser (<em>Mergus merganser</em>)</li> <li>Ruddy Duck (<em>Oxyura jamaicensis</em>)</li> </ul> </li> <li><strong>crane: </strong>Cranes <ul> <li>Greater Sandhill Crane (<em>Antigone canadensis tabida</em>)</li> <li>Lesser Sandhill Crane (<em>Antigone canadensis canadensis</em>)</li> </ul> </li> <li><strong>shore: </strong>Shorebirds <ul> <li>Western Sandpiper (<em>Calidris mauri</em>)</li> <li>Least Sandpiper (<em>Calidris minutilla</em>)</li> <li>Dunlin (<em>Calidris alpina</em>)</li> <li>Black-necked Stilt (<em>Himantopus mexicanus</em>)</li> <li>American Avocet (<em>Recurvirostra americana</em>)</li> <li>Greater Yellowlegs (<em>Tringa melanoleuca</em>)</li> <li>Lesser Yellowlegs (<em>Tringa flavipes</em>)</li> <li>Long-billed Dowitcher (<em>Limnodromus scolopaceus</em>)</li> <li>Short-billed Dowitcher (<em>Limnodromus griseus</em>)</li> <li>Wilson's Snipe (<em>Gallinago delicata</em>)</li> </ul> </li> <li><strong>cicon: </strong>Herons/Egrets (Ciconiiformes) <ul> <li>Great Blue Heron (<em>Ardea herodias</em>)</li> <li>Great Egret (<em>Ardea alba</em>)</li> <li>Snowy Egret (<em>Egretta thula</em>)</li> <li>Cattle Egret (<em>Bubulcus ibis</em>)</li> <li>Green Heron (<em>Butorides virescens</em>)</li> <li>Black-crowned Night-Heron (<em>Nycticorax nycticorax</em>)</li> </ul> </li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes: </strong>birds, landbirds, songbirds, waterbirds, waterfowl, shorebirds, distribution, habitat</li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
Supplemental tables for a study of the seasonal Impacts of the Physical Environment on Biogeochemical Cycles in Arctic Lakes of the Mackenzie River Delta
<p>submitted abstract</p> <p>We conducted two- and six-year-long deployments of continuous water samplers (OsmoSamplers) and sensors (Temperature, pressure, light level, dissolved oxygen (DO) and conductivity) in nine lakes within the mid- to outer-delta region of the Mackenzie River and documented biogeochemical fluctuations (Mn, Fe, sulfate, and DO), defined physical processes that that drive such fluctuations, and constrained the impact of lake solutes on annual riverine fluxes. Five lakes were in the mid-delta region near Inuvik, NT, two lakes were in the outer delta, and two lakes were on the Arctic coastal plain and were not impacted by the Mackenzie River. In general, temperature minima occurred in September/October, indicative of ice formation, and distinct hydrostatic pressure (water level) anomalies occurred in May/June associated with ice breakup, lasting for days to months and impacting lake levels up to 4.2 m higher than “normal”. Such anomalies coincide with a dramatic change in solute concentrations. Systematic changes in solute concentrations indicate redox-driven biogeochemical reactions, salt exclusion during ice formation, and continuous to sporadic exchange of river water. Redox reactions were regulated by DO inputs stemming from atmospheric, photosynthetic, and riverine sources. During ice-covered periods dissolved sulfate may be conservative but was generally removed. Manganese and iron concentrations showed phases of production and removal during ice-covered periods, but both were produced overall. Calculated solute fluxes from lake waters alone to the Arctic Ocean may only impact yearly riverine fluxes for solutes that exceed ten times the river concentration prior to ice breakup (e.g., Mn and Fe).</p>
Transitions in flooding intensity on an experimental delta
<p>Information about past environments is stored in sedimentary rocks via biogeochemical markers stored in the sediments. Using these markers, the signal of paleoclimate and other environmental factors can be reconstructed from the strata. However, because sediment accumulation occurs stochastically, the stratigraphic record is often difficult to reconstruct with confidence. It is generally thought that with a sufficient sample size though, noise averages out, and the true signal can be reconstructed. This assumption is valid when the statistics of erosion and deposition remain steady throughout the interval of interest. In fact, it is known that changes in climate can alter the statistics of erosion and deposition, but the impact of this effect on paleoclimate reconstructions remains poorly understood. </p> <p>This dataset describes a set of physical delta experiments conducted at the Tulane University Sediment Dynamics and Stratigraphy Laboratory. Throughout the experiment, the level of flooding intensity that the delta was exposed to alternated between two end-member values, with transitions of varying durations. We monitored channel dynamics, and reconstructed synthetic climate records from the strata to see how the changing statistics of sediment accumulation impacted the preservation of environmental signals in the strata. </p> <p>This dataset is an HDF5 dataset, which is a general format. The data largely consist of a set of 3D arrays that contain 2D topography and imagery data, where the third dimension is time. Each data object is paired with a 1D vector that links datasets across the time dimension, since data were collected at different intervals. The appropriate linking datasets are also included as CSVs.</p>
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</p>
Methane and carbon dioxide fluxes from vegetated and open water zones of lakes in the Peace-Athabasca Delta, Alberta, Canada, 2019
Shallow areas of lakes, known as littoral zones, emit disproportionately more methane than open water but are sometimes ignored in upscaled estimates of lake greenhouse gas emissions. Littoral zone coverage may be estimated through synthetic aperture radar (SAR) mapping of emergent aquatic vegetation, which only grows in water less than ~1.5 m deep. In an accompanying publication, we combine airborne SAR mapping with field measurements of littoral and open-water methane flux to assess the importance of littoral zones to landscape-scale methane emissions. This dataset contains the field measurements of chamber methane flux from vegetated littoral zones and open water used for the accompanying publication. Measurements come from 24 distinct sampling events of 15 lakes in the Peace-Athabasca Delta, Alberta, Canada in July through August, 2019. The dataset also includes within-lake locations, carbon dioxide measurements, simple characterizations of vegetation type, and associated limnological and meteorological measurements, when available: water and air temperature, water depth, wind speed and direction, and relative humidity.
Temperature Thresholds for Aquatic Species in the Sacramento San-Joaquin Delta
The effects of increasing water temperature on species in the Sacramento San-Joaquin Delta is of growing importance for conservation and resource management; however, species metrics of temperature sensitivity vary across agency groups and can result in varied understanding and predictions of species vulnerability and modeling. Here, we provide a catalogue of key temperature metrics for several species in the Sacramento San Joaquin Delta that may be used as a standard for scientists studying temperature effects and resource management of native and non-native species. The dataset includes both documented physiological thresholds for suboptimal and upper temperature tolerances from metrics such as growth or critical and lethal limits, as well as calculated minimum, maximum and mean temperature values of species catch in the Delta from long-term monitoring surveys from 1954-2022. Species include critical estuarine fishes such as Delta Smelt, Longfin Smelt, Chinook Salmon, and sturgeon, as well as the species they interact with such as invasive fishes and invertebrates, cyanobacteria, and aquatic vegetation. The dataset includes values for different life-stages where possible and references for information. This information can help guide conservation measures and species management needed to lessen the impact stressful temperature conditions.
Interagency Ecological Program: Phytoplankton monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 2008-2024
The State Water Resources Control Board (SWRCB) sets water quality objectives to protect beneficial uses of water in the Sacramento-San Joaquin Delta and Suisun Bay. These objectives are met by establishing standards mandated in water right permits issued to the Department of Water Resources and U.S. Bureau of Reclamation by the SWRCB. The standards include minimum Delta outflows, limits to Delta water export by the State Water Project (SWP) and the Central Valley Project (CVP), and maximum allowable salinity levels. In 1971, the State Water Resources Control Board (SWRCB) established Water Right Decision 1379 (D-1379). This Decision contained new water quality requirements for the San Francisco Bay-Delta Estuary. D-1379 was also the first water right decision to provide terms and conditions for a comprehensive monitoring program to routinely determine water quality conditions and changes in environmental conditions within the estuary. The monitoring program described in D-1379 was developed by the Stanford Research Institute through a contract with the SWRCB. Implementation of the monitoring program began in 1972, as SWRCB, DWR, and USBR met to define their individual responsibilities for various elements of the monitoring program. In 1978, amendments to water quality standards were implemented and resulted in Water Right Decision 1485 (D-1485). More recently these standards were again amended under the 1995 Water Quality Control Plan and Water Right Decision 1641 (D-1641) established in 1999. The SWP and CVP are currently operated to comply with the monitoring and reporting requirements described in D-1641. D-1641 requires DWR and USBR to conduct a comprehensive environmental monitoring program to determine compliance with the water quality standards and also to submit an annual report to SWRCB discussing data collected. The phytoplankton monitoring program is one element of DWR’s and USBR’s Environmental Monitoring Program (EMP) conducted under the Interagency Ec
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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.
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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.