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Water Body Checklists: North Pacific Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the North Pacific Ocean region using effechecka and a modified polygon from the International Hydrographic Association.

Water Body Checklists: South Pacific Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the South Pacific Ocean region using effechecka and a modified polygon from the International Hydrographic Association.

Water Body Checklists: Indian Ocean Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Indian Ocean region using effechecka and a modified polygon from the International Hydrographic Association.

Water Body Checklists: Arctic Ocean Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Arctic Ocean region using effechecka and modified polygons from the International Hydrographic Organization.

opencc-by-4.0Oct 2024View details →

Data from: A robust model for the assessment of oil spill hazards over land and water bodies

This repository contains all the data required to generate the results and figures reported in the article: A robust model for the assessment of oil spill hazards over land and water bodies.  Pablo Vallés, Sergio Martínez-Aranda, Reinaldo García & Pilar García-Navarro  Fluid Dynamic Technologies TFD-I3A, Universidad de Zaragoza, Spain, 2024 Author: Sergio Martínez Aranda Email: sermar@unizar.es Summary of the content: *FILE* BSLmodel_code.c  :  Implementation of the BSL model in the software OILFlow2D (Hydronia LLC) *ZIP-FOLDER* testOilChannel  :  Synthetic test 1: Oil spill over water channel with parabolic velocity profile     Contains:     *FILE* plotter2D.m  :  Matlab file for plotting the article figures     *FILE* readVTK_hu.m  : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, h, modU variables at cells     *FOLDER* graphics  :  Contains the output figures for the article     *FILE* free_surface_profiles_impCent.mat  :  Matlab structure containing the water level results along the longitudinal center profile for all the cases tested     *FILE* vel_profiles_impCent.mat  :  Matlab structure containing the velocity results along the cross-section x=900m for all the cases tested     *FOLDER* hydro_shear_layer  :  Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations     *FOLDER* BSL_disabled  :  Folders containing the raw simulation results with the BSL model disabled          Contains:         *FILES* stgpuXX.vtk  :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges         *FOLDERS* BSL_impCent_CdXpXXXX  :  Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd         Contains:         *FILES* stgpuXX.vtk  :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges          *ZIP-FOLDER* testOilBay  :  Synthetic test 2: Oil spill from land to a rotating water bay      Contains:     *FILE* plotter2D.m  :  Matlab file for plotting the article figures     *FILE* readVTK_zhvel.m  : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, z, h, u, v variables at cells     *FOLDER* graphics  :  Contains the output figures for the article.     *FOLDER* hydro_shear_layer :  Folder with the 2D hydrodynamics rotating fields, including VTK files, for the Bottom Shear Layer used in the simulations     *FOLDER* BSL_disabled  :  Folders containing the raw simulation results with the BSL model disabled          Contains:         *FILES* stgpuXX.vtk :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges             *FOLDERS* BSL_impCent_CdXpXXXX  :  Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd         Contains:         *FILES* stgpuXX.vtk :  VTK files with the 2D fields of the oil layer variables at different times         *FILE* deltat.out  :  File with the evolution of the time step and the inlet-outlet discharges                   *ZIP-FOLDER* caseSpillTilenga  :  Realistic case: Oil spill hazard assessment in the White Nile - Tilenga Project      Contains:     *FILE* Qgis_project.qgz  :  Portable QGIS project for plotting the article figures     *FOLDER* geoData  :  Contains the georeferenced data used for the simulation setup     *FOLDER* images  :  Contains the output figures for the article     *FOLDER* hydro_shear_layer :  Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations     *FOLDER* spills  :  Folders containing the OilFlow2D project files to perform the simulation of the six spill scenarios reported in the article         *FOLDERS* spill_XXX_XX  :  Folders containing raster files with the oil spreading results at different times for the six spill scenarios reported in the article

opencc-by-4.0Jan 2023View details →

A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?

Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.

opencc-by-4.0Mar 2021View details →

Global Water Body Levels Derived from ICESat-2

This dataset contains water level records derived from ICESat-2 for 227,386 lakes spanning Oct 14, 2018 to July 16, 2020. These records have been updated to reflect an error in the calculation of lake area in the previous version which led to an overestimation of lake area at high latitudes. For details on how this correction was performed, please see the 2023 Addenda to Cooley et al (2021). A complete description of the method used to derive water level from ICESat-2 can be found in Cooley et al (2021), but is briefly summarized below: <ol> <li>We create a conservative water mask modified from the Global Surface Water Occurrence (GSWO) product (Pekel et al., 2016);</li> <li>We intersect ATL08 mean terrain height returns with this water mask, requiring water bodies to receive at least three ICESat-2 point observations on the same day to be included in the analysis;</li> <li>We filter observations based on the mean standard deviation of returns, among other factors;</li> <li>We aggregate observations to monthly timesteps;</li> <li>We calculate seasonal variability in water level as the maximum minus the minimum monthly water level over the 22-month period.</li> </ol> This dataset contains: <ol> <li>ICESat2_lake_variability_v2_updated.shp: A shapefile containing lake points and summary statistics (i.e. height variability, storage variability, etc) *updated to include corrected lake area and storage values</li> <li>ICESat2_lake_height_time_series_v2_updated.csv: A csv file of the monthly water height time series used to calculate the global lake level variability *updated to include corrected lake area values</li> <li>265 water mask GeoTiffs: Water masks created from GSWO which we intersect with ICESat-2 data to produce the water level time series *unchanged from previous version</li> <li>ICESat2_mask_reference_v2_updated.csv – A csv file which lists the corresponding water mask for each water body in the dataset *updated to include corrected lake area values</li> <li>USGS_height_validation_v2_updated.csv – A csv file containing the height comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses *updated to include corrected lake area values</li> <li>USGS_range_validation_ v2_updated.csv – A csv file containing the range comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses *updated to include corrected lake area values</li> <li>California_storage_validation_v2_updated.csv – A csv file containing the storage comparison between ICESat-2 and California Department of Water Resource gauges used for validation and uncertainty analyses *updated to include corrected lake area and storage values</li> </ol> See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Cooley et al. 2021) and contact Sarah Cooley at <a href="mailto:scooley2@uoregon.edu">scooley2@uoregon.edu</a> with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset.  Cooley, S.W., Ryan, J.C., and Smith, L.C., (2021), Human alteration of global surface water storage variability, Nature, https://doi.org/10.1038/s41586-021-03262-3

FIGURE 3 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 3 Mass–standard length (M–LS) relationships (MLR) determined for exercised () and control () Salmo trutta cohorts over 0–32 weeks from treatment initiation. Each cohort included LS00 individuals (n = 6) as a common origin

FIGURE 1 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 1 (a) Landmark positions () on Salmo trutta parr that were digitised twice and then averaged to minimize measurement error. (b) Shape changes associated with principal components (PCs) 1–3. PCs were derived from a between-group PC analysis of Procrustes superimposed landmarks., Consensus shape with numbered landmark positions;, Shape changes associated with each PC. Shape changes are scaled to observed PC scores: Left hand side shape changes (back outlines) are scaled to the minimum value observed across the sample on each respective PC (shown below the image) and right hand side shape changes (black outlines) are scaled to the maximum value observed across the sample on each respective PC. PC1 describes a change in head size, PC2 describes dorso-ventral arching of the body and PC3 describes changes in overall robustness and body depth

Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)

opencc-by-4.0Apr 2024View details →

Fig. 2 in Investigation of Ichthyophthirius multifiliis infection in fish from natural water bodies in the Lhasa and Nagqu regions of Tibet

Fig. 2. Pairwise comparisons of locality. Localities: BZ, Boqu Zangbo (river); CEL, Cuoe lake; CNL, Cuona lake; CW, Chabalang Wetland; LR, Lhasa River; LW, Lalu Wetland; SL, Selincuo Lake ZZ, Za'gya Zangbo (river). *: Indicates a significant difference between location 1 and location 2.

opencc-by-4.0Apr 2024View details →

Fig. 1 in Investigation of Ichthyophthirius multifiliis infection in fish from natural water bodies in the Lhasa and Nagqu regions of Tibet

Fig. 1. The map shows the sampling locations of fish in Tibet: 1. North shore of Selincuo (89.112679E, 32.102116N, 4558 m altitude); 2. Za'gya Zangbo (river) (90.723403E, 32.437662N, 5060 m altitude); 3. Boqu Zangbo (river) (89.292002E, 31.606048N, 4579 m altitude); 4. Boqu estuary (89.354410E, 31.762732N, 4554 m altitude); 5. Cuona lake (91.547087E, 32.012987N, 4726 m altitude); 6. Cuoe lake (91.545788E, 31.474142N, 4541 m altitude); 7. Lalu Wetland (91.099541E, 29.668837N, 4989 m altitude); 8. Chabalang Wetland (90.832207E, 29.380565N, 3590 m altitude).

FIGURE 2 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 2 (a) Principal component (PC) () C00, () C04, () C10, () C20, () C32, () E04, () E10, () E20, and () E32 and (b) linear discriminant (LD) scores for Salmo trutta treatment groups (C, control; E, exercise) across experimental weeks (i.e., age 00 (control sample before treatment initiation) to 32 (32 weeks of treatment); n = 6 individuals per group). PC1 and PC3, derived from a between-group PC analysis of Procrustes superimposed landmarks corrected for the arching artefact (PC2). LD1 and LD2, derived from a LD analysis on the corrected principal component scores. Ellipses demarcate 95% confidence intervals; O, group centroids. N.B. The change of direction for head size on LD1 resulting from a negative association with PC1 (see Table 2)

FIGURE 4 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr

FIGURE 4 Box plots showing median (), 25th–75th percentiles () and range () of Salmo trutta condition at length (KÞ for exercised () and control () Salmo trutta cohorts across the experimental period (i.e., age) weeks 4–32 after treatment initiation (n = 6 per group). *, significant differences of pairwise least-squares means between exercised and control cohorts; different lower-case letters (black, exercise; grey, control) denote significant differences of pairwise least-squares means within treatments across the experimental period

opencc-by-4.0Oct 2024View details →

Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest

This is the data repository for the second version (revised) of the manuscript "Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest" submitted to Geophysical Research Letters on 10 January 2025.

opencc-by-4.0Aug 2021View details →

Multi-year dataset for groundwater level, temperature, and chemical and isotopic compositions of different water bodies in an alpine catchment on the northeastern Qinghai-Tibet Plateau, China

Here we provide the multi-year dataset for groundwater level, temperature, and chemical and isotopic compositions of different water bodies in an alpine catchment on the northeastern Qinghai-Tibet Plateau, China. The first file contains monitoring data, including groundwater levels and ground temperatures. The second file includes the results of the sample analyses as well as the numbers and locations of the sampling sites.

opencc-by-4.0May 2019View details →

Fig. 4 in Diversity And Distribution Of Naked Amoebae In Water Bodies Of Sumy Region (Ukraine)

Fig. 4. Ordination of amoebae species complexes in different water body types by environmental factors (nonparametric multidimensional scaling, MDS).

opencc-by-4.0May 2019View details →

Fig. 3 in Diversity And Distribution Of Naked Amoebae In Water Bodies Of Sumy Region (Ukraine)

Fig. 3. Similarity of naked amoebae species complexes, according to the Chekanovsky–SØrensen index (cluster probability shown as % at the nodes, bootstrap 1000).

opencc-by-4.0May 2019View details →

Fig. 2 in Diversity And Distribution Of Naked Amoebae In Water Bodies Of Sumy Region (Ukraine)

Fig. 2. Naked amoebae found in water bodies of the Sumy Region: A — Saccamoeba sp. ×1240; B — Vexillifera sp. ×1240; C, D — Vannella lata ×1240; E — Cochliopodium sp. ×1240; F, G — Pellita digitata ×1240; H — Mayorella vespertilioides ×1240; I — Mayorella sp. ×1240; J, K — Thecamoeba sphaeronucleolus ×1240; L, M, N — Stenamoeba stenopodia ×1240; O, P, Q — Thecamoeba sp. ×1240; R — Acanthamoeba sp. (cysts) ×1240. S, T — Vahlkampfia sp. ×1240.