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128 results for “River deltas”
Рис. 2. Основные места концентрации фуражирующих особей Bombus distinguendus в АрхангеΛьской обΛасти: 1 — Разнотравно-зΛаковый Λуг с Trifolium pratense и Trifolium repens в окрестностях гороΑа Мезень; 2 — Разнотравно-зΛаковый Λуг по обочине Αороги с Centaurea scabiosa в окрестностях сеΛа ХоΛмогоры; 3 – Агроценоз со Stachys palustris в ΑеΛьте реки Северная Δвина; 4 — РуΑераΛьное сообщество с Chamaenerion angustifolium в ΑеΛьте реки Северная Δвина Fig. 2. Typical foraging habitats of Bombus distinguendus in Arkhangelsk Oblast: 1 — Meadow with Trifolium pratense and Trifolium repens near the town of Mezen; 2 — Roadside meadow with Centaurea scabiosa near the village of Kholmogory; 3 — Agricultural habitat with Stachys palustris in the delta of the Northern Dvina River; 4 — Ruderal community with Chamaenerion angustifolium in the delta of the Northern Dvina River in Bombus distinguendus Morawitz, 1869 (Hymenoptera: Apidae) in Arkhangelsk Oblast, Russia: Distribution, ecology and conservation
Рис. 2. Основные места концентрации фуражирующих особей Bombus distinguendus в АрхангеΛьской обΛасти: 1 — Разнотравно-зΛаковый Λуг с Trifolium pratense и Trifolium repens в окрестностях гороΑа Мезень; 2 — Разнотравно-зΛаковый Λуг по обочине Αороги с Centaurea scabiosa в окрестностях сеΛа ХоΛмогоры; 3 – Агроценоз со Stachys palustris в ΑеΛьте реки Северная Δвина; 4 — РуΑераΛьное сообщество с Chamaenerion angustifolium в ΑеΛьте реки Северная Δвина Fig. 2. Typical foraging habitats of Bombus distinguendus in Arkhangelsk Oblast: 1 — Meadow with Trifolium pratense and Trifolium repens near the town of Mezen; 2 — Roadside meadow with Centaurea scabiosa near the village of Kholmogory; 3 — Agricultural habitat with Stachys palustris in the delta of the Northern Dvina River; 4 — Ruderal community with Chamaenerion angustifolium in the delta of the Northern Dvina River
Figure 3 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 3. Distribution of phytoplankton groups in the study area identified as a result of cluster analysis. Green – Co_1, Blue – Co_2, Red – Co_3.
Figure 2 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 2. Similarity dendrogram demonstrating the phytoplankton groups identified based on the quantitative characteristics of communities at stations (relative abundance of species).
Figure 5 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 5. MDS diagram showing groups of environmental conditions identified based on similarity in the distribution of concentrations of the primary nutrients and temperature at stations.
Figure 1 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 1. Scheme of stations in the study area. 1 – The river part of the research area, 2 – the kultuk zone, and 3 – the sea part of the avandelta.
Figure An2. Distribution of mineral phosphorus (a), silica (b), nitrate (c) and nitrite nitrogen (d). in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure An2. Distribution of mineral phosphorus (a), silica (b), nitrate (c) and nitrite nitrogen (d).
Figure 8 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 8. Distribution of biotopic conditions identified as a result of cluster analysis of factors reflecting the intensity of production and destruction processes (AOU, Chl-a, and Pheo). Blue – A, Green – B, Purple – M6 station, Red – M5 station.
Figure 7 in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure 7. Types of biotopic conditions identified by production and destruction characteristics (AOU, Chl-a, and Pheo).
Data for "Impacts of ozone-vegetation interactions on ozone pollution episodes in North China and the Yangtze River Delta"
<p>Data for "Impacts of ozone-vegetation interactions on ozone pollution episodes in North China and the Yangtze River Delta"</p>
Colville River Delta Sea Ice Model and Output Data Files
<p>In this study, we used a 1D Delft3D-FLOW model to simulate the temporal development of the Colville River Delta, AK during the most active Arctic seasons. Simulations focused on the deltaic clinoform (i.e., the cross-sectional view of a delta) and used a floating barge structure to mimic the effects of sea ice on surface waters. Delft3D simulations were coupled with modules written in MATLAB and outputs were process in MATLAB.</p><p>The dataset includes an example model run file (Delft3D-FLOW and MATLAB) and output of results from simulations used to assess Arctic delta development under sea ice. </p><p>Files include:</p><ol><li>Example Delft3D-FLOW model setup file, MATLAB run script, and ice files for a 1500-year simulation.</li><li>Processed MATLAB structures and metadata for model results<ol><li>Long-term Delta Developmental Outputs (1500-year simulations)<ol><li>Ice-free</li><li>Ice-affected</li><li>Ice-free with waves</li><li>Ice-affected with waves</li></ol></li><li>Varying Sea Ice Characteristics Outputs (500-year simulations)<ol><li>Ice matrix (six simulations)</li></ol></li><li>Future Arctic Delta Scenarios Outputs (450-year simulations) <ol><li>Scenario A</li><li>Scenario B</li></ol></li></ol></li></ol>
Dataset for 'Can restoring water and sediment fluxes across a mega-dam cascade alleviate a sinking river delta?'
<p>Please cite this dataset and corresponding manuscript at <a href="https://doi.org/10.1126/sciadv.adn9731">10.1126/sciadv.adn9731</a> if data were used in any way.</p> <p>Correspondence to Prof Lu Xi Xi at geoluxx@nus.edu.sg</p>
Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta: supporting data - high water levels
<p>This dataset contains modelled high water levels in the Guayas delta supporting the paper 'Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta' published in HESS 2024.</p> <p>The dataset is organised through two folders:</p> <ul> <li>mangroves: all data from the scenarios with mangroves included in the domain</li> <li>no_mangroves: all data from the scenarios without mangroves included in the domain</li> </ul> <p>Each folder is further divided along 6 subfolders:</p> <ul> <li>I: El Niño Ocean & river</li> <li>II: El Niño Ocean</li> <li>III: El Niño river</li> <li>IV: Neutral</li> <li>I_50per: 50 % increase in seaward El Niño anomalies</li> <li>I_150per: 150 % increase in seaward El Niño anomalies</li> </ul> <p>Each folder contains two files:</p> <ul> <li>vars.csv - each row represents a mesh node and there are three columns: <ul> <li>X: x value of the model mesh node [m]</li> <li>Y: y value of the model mesh node [m]</li> <li>HIGH_WATER: high water level [m]</li> </ul> </li> </ul>
Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada
<p>Dataset representing functional lake-to-channel connectivity in the Mackenzie Delta, NWT, Canada between 1984 and 2022 (final.class_20230324.feather), developed using Landsat 5, 7, and 9 optical imagery. </p> <p>Data in folders corresponds to data processing steps in scripts: https://doi.org/10.5281/zenodo.14618991</p> <p>Associated with manuscript: Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada in WRR: Dolan, W., Pavelsky, T. M., & Piliouras, A. (2024). Remote sensing of multitemporal functional lake‐to‐channel connectivity and implications for water movement through the Mackenzie River Delta, Canada. <em>Water Resources Research</em>, <em>60</em>(4), e2023WR036614. https://doi.org/10.1029/2023WR036614</p>
The hourly PM2.5 observation data over the Pearl River Delta region
<p>This dataset contains the hourly PM<sub>2.5</sub> observation data over the PRD region used in the JGR-Atmospheres paper.</p>
Fig.1 in Infection Of Predatory Fish With Larvae Of Eustrongylides Excisus (Nematoda, Dioctophymatidae) In The Delta Of The Dnipro River And The Dnipro-Buh Estuary In Southern Ukraine
Fig.1. Sites of the material collection.
Fig. 6. S in Infection Of Predatory Fish With Larvae Of Eustrongylides Excisus (Nematoda, Dioctophymatidae) In The Delta Of The Dnipro River And The Dnipro-Buh Estuary In Southern Ukraine
Fig. 6. S-shaped bend of intestinal tube in E. еxcisus larva from perch. x400 magniFIcatoin.
Figure An1. Distribution of salinity (a), temperature (b), dissolved oxygen (c), and AOU (d). in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure An1. Distribution of salinity (a), temperature (b), dissolved oxygen (c), and AOU (d).
Figure An3. Distribution of pCO2 (a), Chl-a (b), and Pheo (c). in Phytoplankton assemblages under hydrochemical conditions of the Volga River Delta
Figure An3. Distribution of pCO2 (a), Chl-a (b), and Pheo (c).
Three-dimensional soil organic carbon density by logarithmic function and coefficient scaling in Yangtze River Delta, China
<h3>Three-dimensional soil organic carbon density (SOCD) dataset with 90-m resolution generated by Lin, S., Zhu, Q., Yin, B., Yang, G., Liao, K., Lai, X., Guo, C., 2025. Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China. Environmental Modelling & Software, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2025.106582" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.envsoft.2025.106582.</span></span></a></h3> <h3>Here, based on the best performance, the three-dimensional SOCD generated by LF corrected with coefficient scaling method were provided. The accurate SOCD maps with the spatial resolution of 90-m at any specific depth interval can be generated by our method. This dataset includes:</h3> <ul> <li>Spatial distribution map of parameter 1 (p1) of LF (LF_p1.tif)</li> <li>Spatial distribution map of parameter 2 (p2) of LF (LF_p2.tif)</li> <li>The calculation code and fitted functions of scaling coefficient a, k of LF (fitted_fx_scalingcoff.m)</li> <li>Readme.docx</li> </ul> <p>Note: the unit of SOCD is kg m-2; the spatial distribution maps provided by this dataset does not mask any water bodies.</p> <p><strong>How to use our dataset? Please refer to our article and Readme.docx for more details.</strong></p> <p> </p> <p> </p>
Data from: Developing and applying a macroinvertebrate‐based multimetric index for urban rivers in the Niger Delta, Nigeria
<p class="MsoCommentText">Urban pollution of riverine ecosystem is a serious concern in the Niger Delta region of Nigeria. No biomonitoring tool exists for the routine monitoring of effects of urban pollution on riverine systems within the region. Therefore, the aim of this study was to develop and apply a macroinvertebrate-based multimetric index for assessing water quality condition of impacted urban river systems in the Niger Delta area of Nigeria. Macroinvertebrate and physico-chemical samples were collected from 11 stations in eight river systems. Based on the physico-chemical variables, the stations were categorised into three impact categories namely; least impacted stations (LIS), moderately impacted stations (MIS) and heavily impacted stations (HIS). Seventy seven (77) candidate metrics were tested and only five: Hemiptera abundance, %Coleoptera+Hemiptera, %Chironomidae+Oligochaeta, Evenness index and Logarithm of relative abundance of very large body size (>40-80 mm) were retained and integrated into the final Niger Delta urban multimetric index (MINDU). The validation data set showed a correspondence of 83.3% between the index result and the physico-chemically-based classification for the LIS and a 75% correspondence for the MIS. A performance of 22.2% was recorded for the HIS. The newly developed MINDU proved useful as a biomonitoring tool in the Niger Delta region of Nigeria, and can thus be used by environmental managers and government officials for routine monitoring of rivers and streams subjected to urban pollution.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.