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edi52/100

National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.

Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.

openCC (other)May 2025View details →
zenodo48/100

RAPID input and output files corresponding to "River Network Routing on the NHDPlus Dataset"

<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RAPID input and output files that were used in the study reported in:</p> <ul> <li>David, C&eacute;dric H., David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets and Victor Eijkhout (2011), River Network Routing on the NHDPlus Dataset, Journal of Hydrometeorology, 12(5), 913-934. DOI: 10.1175/2011JHM1345.1.&nbsp;</li> </ul> <p>&nbsp;</p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.&nbsp;</p> <p>&nbsp;</p> <p><strong>Time format</strong></p> <p>The times reported in this description all follow the ISO 8601 format.&nbsp; For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1<sup>st</sup> 2000 (2000-01-01), Central Standard Time (-06:00).&nbsp; Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step.&nbsp; For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps.&nbsp; The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1<sup>st</sup> 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1<sup>st</sup> 2000, Universal Time.</p> <p>&nbsp;</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus.&nbsp;</li> <li>The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis.&nbsp; &nbsp;</li> <li>Outputs from a simulation using the community Noah land surface model with multiparameterization options (Noah-MP, Niu et al. 2011, http://www.jsg.utexas.edu/noah-mp). &nbsp;The simulation was run by Guo-Yue Niu, and produced 3-hourly time steps from 2004-01-01T00:00+00:00 to 2008-01-01T00:00+00:00. &nbsp;Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <p>&nbsp;</p> <p><strong>Software</strong></p> <p>The following software were used to produce files in this dataset:</p> <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.0.0.&nbsp; Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com).&nbsp;</li> <li>Microsoft Excel (https://products.office.com/en-us/excel).&nbsp;</li> <li>CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html).&nbsp;</li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers).&nbsp;</li> </ul> <p>&nbsp;</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to two study domains:</p> <ul> <li>The combination of the San Antonio and Guadalupe River Basins, TX.&nbsp; RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 5,175).&nbsp; The temporal range corresponding to this domain is from 2004-01-01T00:00-06:00 to 2007-12-31 T00:00-06:00.</li> <li>The Upper Mississippi River Basin.&nbsp; RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 182,240).&nbsp; The temporal range corresponding to this domain spans 100 fictitious days.</li> </ul> <p>&nbsp;</p> <p><strong>Description of files for the San Antonio and Guadalupe River Basins</strong></p> <p>All files below were prepared by C&eacute;dric H. David, using the data sources and software mentioned above.&nbsp;</p> <ul> <li><em>rapid_connect_San_Guad.csv.</em>&nbsp; This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). &nbsp;For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches.&nbsp; A value of zero is used in place of NoData.&nbsp; The river reaches are sorted in increasing value of COMID.&nbsp; The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.&nbsp; This file was prepared using ArcGIS and Excel.</li> <li><em>m3_riv_San_Guad_2004_2007_cst.nc.&nbsp; </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. &nbsp;The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00. &nbsp;The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP.&nbsp; This file was prepared using ArcGIS and a Fortran program.</li> <li><em>kfac_San_Guad_1km_hour.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using a wave celerity of 1 km/h.&nbsp; This file was prepared using a Fortran program.</li> <li><em>kfac_San_Guad_celerity.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using the wave celerity numbers of Table 2 in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_1.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_2.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_3.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_4.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_1.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (17) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_2.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (18) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_3.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (19) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_4.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (21) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>basin_id_San_Guad_hydroseq.csv. &nbsp;</em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins.&nbsp; The river reaches are sorted from upstream to downstream. &nbsp;The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.&nbsp; This file was prepared using Excel.</li> <li><em>Qout_San_Guad_1460days_p1_dtR=900s.nc.</em> &nbsp;This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p2_dtR=900s.nc.&nbsp; </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p3_dtR=900s.nc.&nbsp; </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p4_dtR=900s.nc. &nbsp;</em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p1_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p2_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p3_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p4_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_San_Guad_2004_2007_full.csv.&nbsp; </em>This CSV file contains the list of COMIDs of rivers containing USGS gauges and with full daily data record.&nbsp; &nbsp;The river reaches are sorted in increasing value of COMID.&nbsp; The time range used for determining a full record is daily from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00.&nbsp; The values were computed using the following NHDPlus field: COMID.&nbsp; This file was prepared using ArcGIS, HydroGET, and Excel.</li> <li><em>Qobs_San_Guad_2004_2007_full.csv.&nbsp; </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). &nbsp;The river reaches have the same COMIDs and are sorted similarly to <em>gage_id_San_Guad_2004_2007_full.csv</em>.&nbsp; The time range for the daily values is from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00.&nbsp; The values were computed using the following NHDPlus field: COMID, and the observations from NWIS.&nbsp;&nbsp; This file was prepared using ArcGIS, HydroGET, and Excel.</li> </ul> <p>&nbsp;</p> <p><strong>Description of files for the Upper Mississippi River Basin</strong></p> <p>All files below were prepared by C&eacute;dric H. David, using the data sources and software mentioned above.&nbsp;</p> <ul> <li><em>rapid_connect_Reg07.csv.&nbsp; </em>This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs).&nbsp; For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches.&nbsp; A value of zero is used in place of NoData.&nbsp; The river reaches are sorted in increasing value of COMID.&nbsp; The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.&nbsp; This file was prepared using ArcGIS and Excel.&nbsp;</li> <li><em>m3_riv_Reg07_100days_dummy.nc.&nbsp; </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>.&nbsp; The time range for this file is for 100 fictitious days.&nbsp; The values were computed using a unique value of 1 cubic meter for all river reaches and all time steps.&nbsp; This file was prepared using a Fortran program.</li> <li><em>kfac_Reg07_2.5ms.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (22) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.&nbsp;&nbsp;&nbsp;&nbsp;</li> <li><em>xfac_Reg07_0.3.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>.&nbsp; The values were computed based on Equation (22) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.&nbsp;&nbsp;&nbsp;&nbsp;</li> <li><em>basin_id_Reg07_hydroseq.csv.&nbsp; </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the Upper Mississippi River Basin.&nbsp; The river reaches are sorted from upstream to downstream.&nbsp; The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.&nbsp; This file was prepared using Excel.</li> <li><em>Qout_Reg07_100days_pfac_dtR900s.nc.</em> This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_Reg07_hydroseq.csv</em>.&nbsp; The time range for this file spans 100 fictitous days. The values were computed using the Muskingum method with parameters of Equation (22) in David et al. (2011).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> </ul> <p>&nbsp;</p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>The confluence of the Missouri River and the Upper Mississippi River upstream of Saint Louis, MO was overlooked.&nbsp; The contribution from the Missouri River is therefore not accounted for in the network connectivity corresponding to the Upper Mississippi River Basin.&nbsp; This has no effect on the conclusions of David et al. (2011) since the Upper Mississippi River Basin was studied with synthetic data and solely to evaluate parallel performance of RAPID.</p> <p>&nbsp;</p> <p><strong>Funding</strong></p> <p>This work was partially supported by the U.S. National Aeronautics and Space Administration under the Interdisciplinary Science Project NNX07AL79G; by the U.S. National Science Foundation under project EAR-0413265: CUAHSI Hydrologic Information Systems; by Ecole des Mines de Paris, France; and by the American Geophysical Union under a Horton (Hydrology) Research Grant.</p>

opencc-by-4.0Sep 2011View details →
edi48/100

LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S

Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.

openCC (other)Jul 2021View details →
zenodo44/100

Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al

<p>CO2 and CH4 emissions and several associated environmental variables &nbsp;were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>

opencc-by-4.0Jan 2024View details →
zenodo44/100

River network and hydro-geomorphological parameters at 1/12° resolution for global hydrological and climate studies

<p>Global scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and the increasing computing capacities, have allowed better performances of RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides flow direction of all grid cells. River networks may be derived at various spatial resolution by upscaling high resolution hydrography data.<br> This paper presents a new global scale river network at 1/12&deg; derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the Hierarchical Dominant River Tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is here provided with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography).<br> The new river network and parameters are assessed by comparing the performances of two global scale simulations with the CTRIP model, one with the current spatial resolution (1/2&deg;) and the other with the new spatial resolution (1/12&deg;). It is shown that CTRIP at 1/12&deg; overall outperforms CTRIP at 1/2&deg;, demonstrating the added value of the spatial resolution increase.<br> The new river network and the consistent hydro-geomorphology parameters may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"

<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1.</p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.&nbsp;</p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate.&nbsp; The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location.&nbsp; As a result, all values of &quot;measured&quot; discharge errors (i.e. Bias, STDE, and RMSE) are incorrect.&nbsp; This issue did not affect the values of &quot;estimated&quot; errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented.&nbsp; The figures published in the manuscript can all be recreated using the files in which &quot;BUG_DO_NOT_USE&quot; was appended to the name.&nbsp; Correct figures can also be created using corresponding file names that were not so appended.&nbsp;</p> <p>Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper.&nbsp; The slope of the daily discharge STDE trend initially reported as <span class="math-tex">\(\alpha = 0.3876\)</span> in Figure 5c changes to <span class="math-tex">\(\alpha = 0.4507\)</span> after correction.&nbsp; The resulting value of the ideal inflation factor hence changes from <span class="math-tex">\(I = {1 \over 0.3876} \approx 2.58\)</span> to <span class="math-tex">\(I = {1 \over 0.4507} \approx 2.22\)</span>. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to <span class="math-tex">\(I = 2.58\)</span> than to <span class="math-tex">\(I = 1\)</span> or <span class="math-tex">\(I = 5\)</span>, <em>i.e.</em> the three values that were evaluated.</p> <p>Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: &quot;timeseries_rap_exp01.csv&quot;, &quot;timeseries_rap_exp18.csv&quot;, and &quot;stats_rap_exp18.csv&quot;. Faulty versions of the same files have &quot;BUG_NETCDF4&quot; appended to their names. Correct files have been recreated with file names that were not so appended.&nbsp;</p>

opencc-by-4.0Mar 2020View details →
zenodo44/100

Daily observed, modelled, and infilled river flows for an Irish hydrometric reference network of river flow stations

<p>Here we present a dataset of observed, modelled, and infilled daily river flow data relating to the newly updated Irish Hydrometric Reference Network (IHRN) of high-quality gauging stations located across the Republic of Ireland. Internationally applied selection criteria, analysis of historical observations and flow gauge metadata, stakeholder feedback, and trend assessments aided in the identification of the network&rsquo;s 51 stations. A combination of the GR4J conceptual hydrological model and a backpropagation neural network driven by catchment specific precipitation and temperature extracted from gridded datasets was used to model flows that subsequently infilled gaps in the observational record for each of the series (from commencement of each station&rsquo;s record till the end of 2022). Also included are the 2.5 and 97.5 quantile values for each station&rsquo;s modelled data, which represent the upper and lower uncertainty bounds of the respective ensemble flows derived during the flow generation process. As well as providing a useful means for evaluating the impact of changing climatic conditions on Irish catchments, the IHRN data offers utility for assessing catchment based impacts for flow extremes, and the generation of both historical reconstructions and future climate projections for a range of flow regimes across the island of Ireland.</p>

opencc-by-4.0Oct 2024View details →
zenodo40/100

Greenhouse gas dynamics in river networks fragmented by drying and damming

<p>River fragmentation by drying and damming is occurring more frequently in the Anthropocene, yet there is limited research about its effects on greenhouse gas (GHG) fluxes. These fragmented rivers have the potential to be important sources of GHGs to the atmosphere through both similar and dissimilar mechanisms. Our objective was to review the literature of the individual and the interactive effects of fragmentation by drying and damming on GHG fluxes in river networks, identifying the magnitudes and drivers of CO<sub>2</sub>, methane (CH<sub>4</sub>), and N<sub>2</sub>O flux rates. We conducted a systematic search of studies addressing the separate and interactive effects of drying and damming on GHG fluxes from running waters. The search was primarily conducted using Web of Science for studies published from 1900 to January 2021.&nbsp;For the 42 studies about rivers impacted by drying, 54 about damming, and 6 about their interactive effects, we collected a suite of qualitative and quantitative information. The major proximal drivers of GHG emissions in river networks impacted by drying were sediment moisture, sediment temperature, sediment organic matter content and sediment texture. In networks impacted by damming, the major proximal drivers were water temperature, dissolved oxygen, and chlorophyll ɑ. We found research lacking in non-arid climates for drying, and on small water retention structures for damming. We propose a conceptual model where the spatial distribution of fragmentation is the principle driver of GHG fluxes at the network scale.&nbsp;</p>

opencc-by-4.0Jun 2022View details →
zenodo40/100

IW-NET sample data-set: River Weser IW network

<p>This data-set provides a sample of the data that was utilized for the analysis performed in the context of the IW-NET research project. The data have been collected via publicly available sources and are offered in this package as a sample. The use case the data refer to is River Weser, in northern Germany. The following table explains the contents of each of the uploaded files.</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserWaterway.json/content" target="_blank" rel="noopener noreferrer">RiverWeserWaterway.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserRelationsWaysNodes.json/content" target="_blank" rel="noopener noreferrer">RiverWeserRelationsWaysNodes.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/IWTWeather.json/content" target="_blank" rel="noopener noreferrer">IWTWeather.json</a></div> </td> <td>Weather reports from 6 stations in the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/unCitiesDE.json/content" target="_blank" rel="noopener noreferrer">unCitiesDE.json</a></td> <td>UN/LOCODE data in json format.</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/MMSI.xlsx/content" target="_blank" rel="noopener">MMSI.xlsx</a></div> </td> <td>Correspondance of MMSI codes to Vessel registration country</td> </tr> </tbody> </table> <p>These resources were combined with AIS data logs from vessels active in the area - which, for legal reasons, cannot be made publicly available to provide powerful insights into the logistics operations and their intricacies.</p>

opencc-by-4.0Dec 2023View details →
zenodo40/100

Supplementary Data for "Interplay of river and tidal forcings promotes loops in coastal channel networks"

<p>This dataset contains supplemental data required to reproduce the results of the paper&nbsp;<em>Interplay of river and tidal forcings promotes loops in coastal channel networks</em>&nbsp;(in review at Geophysical Research Letters). We provide raw and extracted channel network data for 19 river deltas/coastal marsh sites. For each site, the following files are provided:</p> <p><strong>XXX_base.tif</strong> : the raw binary mask of the river channel network<br> <strong>XXX_clipper.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : polygon(s) used to clip the raw mask<br> <strong>XXX_clipped.tif</strong> : the binary mask of the river channel network after being clipped by XXX_clipper.shp<br> <strong>XXX_filled.tif</strong> : the binary mask after filling islands via the method specified in the paper<br> <strong>XXX_inlet_nodes.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : locations of the inlet nodes; used by RivGraph<br> <strong>XXX_shoreline.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : location of the shoreline; used by RivGraph<br> <strong>XXX_links.json</strong> : GeoJSON file containing the geometries, connectivities, and widths of each link in the network<br> <strong>XXX_nodes.json</strong> : GeoJSON file containing the locations of each node of the network<br> <strong>process_XXX.py</strong> : the python script used to generate the above files</p> <p>All files listed below (except .py files) are georeferenced (i.e. can be opened with QGIS, ArcGIS or another GIS). Exceptions to the provided files include:</p> <p><strong>Barnstable</strong>: no &quot;base.tif&quot; is provided. Use &quot;filled.tif&quot;.<br> <strong>GBM</strong>: some hand-cleaning was performed on &quot;filled.tif&quot;.<br> <strong>Mackenize</strong>: &quot;clipper.shp&quot; is not provided, but &quot;clipped.tif&quot; is.<br> <strong>Mississippi</strong>: &quot;clipper.shp&quot; is not provided as the mask was made from a shapefile.</p> <p>In order to run process_XXX.py, the RivGraph package will need to be installed. Instructions<br> can be found at https://github.com/jonschwenk/RivGraph.</p>

opencc-by-4.0Feb 2022View details →
zenodo40/100

Fig. 1 in Trophic network of aquatic macroinvertebrates along an altitudinal gradient in a Neotropical mountain river

Fig. 1. Static models of trophic networks at three sites in the Gaira River representing basal resources (red), intermediate consumer (orange) and top predators (yellow) and the interactions among them. SL, San Lorenzo, upper sector; LV, La Victoria, middle sector; PM, Puerto Mosquito, lower sector; D, dry season; R, rainy season.

opencc-by-4.0Aug 2018View details →
zenodo40/100

Fig. 2 in Trophic network of aquatic macroinvertebrates along an altitudinal gradient in a Neotropical mountain river

Fig. 2. Representation of the standard deviation of vulnerability (SD-V) of the main food sources (FPOM, fine particulate organic matter; CPOM, coarse particulate organic matter; PT, plant tissue) calculated from the ratios recorded in the guts of the macroinvertebrates sampled in the elevational gradient of Gaira River during the dry and rainy seasons. Arrow thickness indicates the vulnerability importance.

opencc-by-4.0Aug 2018View details →
zenodo40/100

Data archive for 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'

<p>This repository contains the code and databases used in the paper 'Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot'.&nbsp;</p> <p>The 'Mekong River Barrier Database (MRBD)' folder contains the basin-scale barrier database developed in this study. This database contains more than 13,000 unique barriers, which were identified by using the convolutional neural networks-based object detection method from Google Earth&rsquo;s satellite imagery.</p> <p>The 'FCOS' folder contains the barrier detection model (FCOS ResNext-101-FPN), trained for detecting river barriers from remotely sensed images within the MMDetection framework.&nbsp;The 'FCOS_x101_v2' folder contains the enhanced FCOS model.</p> <p>The 'R_script' folder contains R files used in the paper. Coordinate.R was used to extract coordinates from bounding boxes in each TIF image. CAFI.R was used to calculate the CAFI index in each sub-catchment.</p> <p>The 'Barrier image training set' folder contains over 10,000 river barrier satellite images and their associated JSON files, forming the 'training, validation, and test datasets' used during the model training process. This dataset is made available to the user community in raw, in the hope that others will contribute to its future development, thereby enhancing its use and utility.</p> <p>For more information on the MMDetection framework, refer to the&nbsp;following GitHub repository:&nbsp;<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a></p>

opencc-by-4.0May 2023View details →
dryad40/100

Data and code from: River network connectivity reductions dominate declines in the richness of plateau fish species under climate change in the upper Yangtze River Basin

Open the record for dataset details and reuse information.

publicMay 2025View details →
dryad40/100

Data from: Multiple stressors in river networks: Local and downstream effects on freshwater macroinvertebrates

Open the record for dataset details and reuse information.

publicOct 2025View details →
dryad40/100

Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)

Open the record for dataset details and reuse information.

publicMar 2020View details →
zenodo36/100

Schematic Multimodal Network In the Ohio River Basin

<p>The topological connectivity of the components of Ohio Basin, including the waterway segments, highways, and railways serving the OD demand of commodities</p>

opencc-by-4.0Mar 2022View details →
zenodo36/100

N2 and  N2O concentrations and fluxes for the six large river networks in China

<p>This dataset is a collation of 4-year direct measurement values of N<sub>2</sub> and&nbsp; N<sub>2</sub>O concentrations and fluxes for the six large river networks in China, along with information on location, hydrological, physical, and chemical conditions of the 56 sampling sites. These data will be very valuable for balancing the regional and global N budget. In addition, the Monte Carlo simulation code for upscaling and analyzing uncertainties are presented here.</p> <p>&nbsp;</p>

opencc-by-4.0Sep 2021View details →
zenodo36/100

Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks

<pre>The data are part of the doctoral work entitled: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks.</pre>

opencc-by-4.0Jul 2022View details →
zenodo36/100

Analyses, data and figures related to: "Connecting ships: Using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and visualise wood use patterns"

<p>Analyses, data and figures related to: &quot;Connecting ships: using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and to visualise patterns of wood use&quot; by Ronald M. Visser (Saxion University of Applied Sciences, Deventer, the Netherlands) and Yardeni Vorst (Vorst wood research, Zaandam, the Netherlands) submitted to the International Journal of Wood Culture</p>

openother-openOct 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record