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59 results for “river surface”
Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing for "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea" paper.
<p>Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing as well as selected model output used for analysis and producing figures in the paper "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea". Contact Bronwyn Cahill if you have questions at: bronwyn.cahill@io-warnemuende.de</p>
A long-term (1981-2020) 1-km daily extreme and mean near surface air temperature product over Yellow River Basin of China
<p>The dataset includes the semless 1-km daily extreme and mean near surface air temperature products over Yellow River Basin of China. The fourth version is from 1 January 2011 to 31 Decmber 2020.</p>
Data on frequency-wavenumber spectra of water waves from videos of the river surface: River Sheaf, UK, Feb-Jun 2019
<p>This data set contains sequences of orthorectified images of the free surface of the River Sheaf, Sheffield, United Kingdom (Latitude: 53.373056$^\circ$ Longitude: -1.463913$^\circ$ (WGS 84)), recorded between February and June 2019, as well as their 3D space-time Fourier power spectrum, and gauging survey data of the stage and flow discharge.</p>
10Be concentrations constraining surface age and valley growth rate in a seepage-derived drainage network in the Apalachicola River basin, Florida
<p class="Head1"><span><span>Measuring rates of valley head migration and determining the timing of canyon-opening are insightful quantifications for the history and evolution of planetary surfaces. Horizontal spatial gradients of <em>in situ-</em>produced cosmogenic nuclide concentrations provide a framework for assessing the migration of these and similar topographic features. We developed a theoretical model for the concentration of <em>in situ</em> produced cosmogenic radionuclides in valley walls during retreat of a valley head. The retreat rate is inversely proportional to the magnitude of the spatial concentration gradient and proportional to local nuclide accumulation rates. By solving for a spatial gradient in concentration along a valley parallel transect, we created an expression for the explicit determination of valley head retreat, termed unzipping. We applied this theory to a developing seepage-derived drainage network along the Apalachicola River, Florida, USA. Sample concentrations along a valley margin transect vary systematically from 2.9 x 10<sup>5</sup> atoms/g to 3.5 x 10<sup>5</sup> atoms/g resulting in a gradient of 160 atoms/g/m, and from this value a valley head retreat rate of 0.025 m/y is found. The discrepancy between overall network age and current rates of valley head migration suggests intermittent network growth which is consistent with glacial-interglacial precipitation variations during the Pleistocene. This method can be applied to a wide range of Earth-surface environments. For the <sup>10</sup>Be system, this method should be sensitive to unzipping rates bounded between 10<sup>-6</sup> m/y and 10<sup>0</sup> m/y.</span></span></p>
Bathymetric and surface digital model of the river-floodplain system corresponding to the Duero river reach between Toro and Zamora (Castilla y León).
<p>Digitally edited digital model to represent correctly and with hydraulic criteria the bridges, weirs, dips, roads and walls present in the area.</p>
Bathymetric and surface digital model of the urban reach of the Douro River through Zamora (Castilla y León)
<p>The RAR file is composed of all files related to the Digital Bathymetric Model (DBM) and the Digital Surface Model (DSM). Among these files there are: the original rasters files; the files digitally modified to correctly represent all the hydraulic characteristics of the Duero River and those urban characteristics of Zamora; the rasters that represent the error made in both models, calculated by geostatistics; the files related to the geostatistics process. Also, it also includes the integration of both models (DBM plus DSM) to form a High Resolution Urban Digital Model (HRDUM) of the Duero River in it passes through Zamora.</p>
The simulation results of runoff components and estimated surface water demands for the Yarkant River basin
<p>This dataset contains two directories as below. </p> <p>1. Hydrological_Simulation</p> <p>Historical_runoff_simulation.csv : the four runoff components at monthly scale (km^3/month) aggregated from daily runoff simulation results for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP1-2.6 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP2-4.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP5-8.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "48 experiments" contains the four runoff components at monthly scale (km^3/month) simulated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p> <p>2. Water_Demand_Estimation</p> <p>Historical_water_demand_estimation.csv : the monthly water demands (km^3/month) from irrigation (including wheat, cotton, and corn) and other sectors for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP1-2.6 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP2-4.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP5-8.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "48 experiments" contains the monthly water demands (km^3/month) estimated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p>
DSM Water Level: An UAV photogrammetry dataset for determination of river surface level using machine learning
<p>Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the water surface level of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. Machine Learning can be used to correct these disturbances as well as to extract a single water surface elevation value. The presented dataset contains raw photogrammetric orthophotos and DSMs of areas representing parts of a small river and the corresponding DSMs with corrected water surface disturbances. Also a single ground truth value of mean water surface level for each DSM sample is provided. This allows the dataset to be used for supervised training of a neural network performing a denoising or regression task.</p> <p>Acknowledgement: some of the samples were extracted from photogrammetric data acquired by Bandini et. al (https://doi.org/10.5281/zenodo.3519888)</p>
10Be concentrations constraining surface age and valley growth rate in a seepage-derived drainage network in the Apalachicola River basin, Florida
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Synthetic river datasets built for testing and development of the Surface Water and Ocean Topography mission discharge algorithms
<p><strong>1.Summary</strong></p> <p>Datasets used for testing the performance of discharge estimation algorithms built in support of the Surface Water and Ocean Topography satellite mission. The benchmarking manuscript entitled “Exploring the factors controlling the performance of the Surface Water and Ocean Topography mission discharge algorithms” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.</p> <p><strong>2.File description</strong></p> <p>The dataset is divided into four groups: 1-Ideal data, 2-Varying Temporal Sampling, 3-Measurement Uncertainty, and 4-SWOT Sampling and Uncertainty. Ideal data contains daily measurements with no observational uncertainty. Varying Temporal Sampling downsamples the ideal measurements considering different temporal frequencies with complete sets assuming: 1 measurement every 2 days, 3 days, 4 days, 5 days, 7 days, 10 days, and 21 days. The measurement uncertainty set adds errors to cross-sectional heights and widths, which are used to compute reach average height, width, and slope considering error corruption. The final set SWOT Sampling and Uncertainty accounts for SWOT temporal sampling and measurement uncertainty. Sets containing uncertainty have extra height, width, and slope attributes with the word true appended to the attribute name. Such attributes represent the uncorrupted measurements at the cross-section and reach scales. Height, width, and slopes for the SWOT sampling and Uncertainty dataset containing the value of negative 9999 denote points that are not observed at a particular location and time step.</p> <p>Data will be contained in one NetCDF file per river. The file contains the following groups and variables:</p> <p><strong>/River_Info/</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>/XS_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>Z: Bed elevation in meters. Dimension: Cross-section, time step.</p> <p>xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.</p> <p>X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.</p> <p>longitude: Cross-section longitude in decimal degrees. Dimension: Cross-section,1.</p> <p>latitude: Cross-section latitude in decimal degrees. Dimension: Cross-section,1.</p> <p>W: River width in meters. Dimension: Cross-section, time step.</p> <p>Wtrue: River width in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>Q: Discharge (m<sup>3</sup>/s). Dimension: Cross-section, time step.</p> <p>H: Water surface elevation in meters. Dimension: Cross-section, time step.</p> <p>Htrue: Water surface elevation in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>A: Cross-sectional area of flow in m<sup>2</sup>. Dimension: Cross-section, time step.</p> <p>P: Wetted perimeter in meters. Dimension: Cross-section, time step.</p> <p>n: Manning’s roughness. Dimension: Cross-section, time step.</p> <p><strong>/Reach_Timeseries/</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.</p> <p>W: Reach averaged river width in meters. Dimension: Reach, time step.</p> <p>Wtrue: Reach averaged river width in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the width value with no uncertainty.</p> <p>Q: Reach averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.</p> <p>Htrue: Reach averaged water surface elevation in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.</p> <p>S: Reach averaged water surface slope in meters per meter. Reach, time step.</p> <p>Strue: Reach averaged water surface slope in meters per meter. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the slope value with no uncertainty.</p> <p>A: Reach averaged area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p><strong>References</strong></p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: <a href="https://doi.org/10.1016/j.gloplacha.2014.01.011">https://doi.org/10.1016/j.gloplacha.2014.01.011</a>.</p> <p> </p>
Data in the paper Accuracy Evaluation of River Surface Flow Field Measurement Methods based on Satellite Video
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Raw datasets for paper "River Surface Velocimetry Reconstruction via Graph-enhanced Neural Operator"
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Measurement report: Surface exchange fluxes of HONO during the growth process of paddy fields in the Huaihe River Basin, China
<p>Raw data for Meng et al. submitted to ACP.</p>
Time series of electrical conductivity, temperature, relative stream stage and total pressure recorded in surface water and streambed sediments of River Erpe and River Ammer, Germany, and the Sturt River, South Australia
<p>Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
Sensitivity of atmospheric river vapor transport and precipitation to uniform sea-surface temperature increases
<p>This is the companion data for the manuscript of the same title, originally submitted to JGR: Atmospheres on 02/08/2020 and resubmitted on 06/30/2020. Specifically, this dataset corresponds to the resubmitted version. Note that the only change is the addition of code example to generate kernel density estimates of Hadley cell edge, subtropical jet, and eddy-driven jet positions. </p> <p><strong>modelParameters: </strong>this folder contains the scripts I ran on NERSC Cori in 2019 to initialize the CESM2.0/CAM5 model runs. </p> <ul> <li>qobs_script_newcase.sh: creates all cases, for the Baseline as well as the +xK SST runs</li> <li>docn_comp_mod.F90: original CAM5 aquaplanet SST distributions; "QOBS" is used for my "Baseline" experiments</li> <li>plusxK_docn_comp_mod.F90: modified SST distributions, for x=(2,4,6); these are simply uniform additions to the QOBS SST distributions</li> <li>Macros.make & env_mach_specific.xml: configuration files to run CESM2.0 on Cori at the time</li> <li>user_nl_cam: namelist for CAM5; specifies some model run parameters as well as output variables.</li> </ul> <p><strong>detectionParameters: </strong>this folder contains the scripts I ran on NERSC Cori in 2019 to detect tropical cyclones and atmospheric rivers. Use this code for reference purposes only (i.e., to see which parameters were used to detect ARs or TCs). It will not run as-is.</p> <p><strong>All other subfolders </strong>provide working examples of code used to generate figures (all in Jupyter notebooks) for the manuscript; folder names are descriptive. </p> <p>Unfortunately, model output was large (~12 TB). Hence, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. </p> <p>For more details, refer to the manuscript, or contact me (eelliott@ucdavis.edu). </p>
Glacier surface elevation estimates for a glacier in the Poiqu river basin, Central Himalaya.
<p>This zip folder contains tif files representing glacier surface elevation estimates under two different scenarios of thinning towards the year 2100, informed by past (2000-2015) thinning rates which have been extrapolated into the future. </p> <p>These results are presented in the paper by Allen et al. 2022, <em>Glacial lake outburst flood hazard under current and future conditions: worst-case scenarios in a transboundary Himalayan basin, </em>Natural Hazards and Earth System Science, https://doi.org/10.5194/nhess-22-1-2022.</p>
Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers"
<p>Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers", whose manuscript will be submitted by 10/25/2023</p> <p><br>The dataset contains the necessary data to generate the figures in the paper with the code in the link <a href="https://doi.org/10.5281/zenodo.10958491" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10958491</a> whose Github reference is <a href="https://github.com/meteorologytoday/paperfigures-2024-AR-SST-response" target="_blank" rel="noopener">https://github.com/meteorologytoday/paperfigures-2024-AR-SST-response</a></p> <p> </p>
Surface rock cover soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Version 2: Unfortunately, errors were found in the original training data preparation in version 1. This version corrects those errors and has resulted in cross validation accuracy increases (R<sup>2</sup>) from ~0.4 to ~0.55 for both rock cover and representative rock size.</p> <p>Repository includes maps of surface rock cover (sfragcov) and dominant surface rock size (sfragsize) as defined by United States soil survey program. </p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Press. A hybrid approach for predictive soil property mapping using conventional soil survey data. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_model_additional_elements.extension</p> <p>Example: sfragsize_r_2D_QRF.tif</p> <p>Indicates dominant surface fragment size (sfragsize) using a 2D model employing a quantile regression forest (QRF). This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. If the first name is sfragcov, it indicates that the layer is for surface fragment cover.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (2019) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C.,2019, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma</p>
The measurements and three operational numerical forecasts of surface wind speed over Pearl River Estuary during 2018–2021.
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OpenNeuro
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