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Annual primary productivity of Spartina alterniflora in control and fertilized plots at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA, 1999-2025.

Annual productivity is estimated from aboveground biomass in Spartina alterniflora-dominated salt marsh plots on the Rowley River within the Plum Island Ecosystems (PIE) LTER site. Aboveground biomass is determined non-destructively.

openCC (other)Feb 2022View details →

Nekton individual data from flume net collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.

The flume nets were deployed with the purpose of capturing salt marsh nekton. Nekton species were identified to the lowest taxonomic level using species keys. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.

openCC (other)Feb 2022View details →

Nekton species counts and density from flume net collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.

openCC (other)Nov 2022View details →

Benthic algae chlorophyll measurements for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA.

openCC (other)Feb 2022View details →

Marsh plant species shoot height, weight and diameters for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA.

Marsh plant species shoot height, weight and diameters for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2012 and at Clubhead Creek in 2005 and from 2009 till 2019.

openCC (other)Jan 2020View details →

Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018

Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018. The water budget includes precipitation, evapotranspiration, stream flows, water withdrawals, sewer export and public water supply imports.

Aboveground biomass at control and fertilized plots in a Spartina patens-dominated salt marsh, Rowley River, Plum Island Ecosystem LTER, MA (2000-2025).

Aboveground biomass is determined destructively at control and fertilized sites approximately monthly during the growing season at a Spartina patens salt marsh on the Rowley River within the Plum Island Ecosystems (PIE) LTER site.

Plant heights at control and fertilized plots in a Spartina alterniflora-dominated marsh, Law's Point, Rowley River, Plum Island Ecosystem LTER, MA (1999-2025).

Plant heights are measured during the growing season in permanent plots at a Spartina alterniflora-dominated salt marsh on the Rowley River within the Plum Island Ecosystems (PIE) LTER site. Plant heights are converted to plant weight using an algorithm to generate a non-destructive estimate of aboveground plant biomass.

Aboveground plant biomass and density in control and fertilized plots in a Spartina alterniflora-dominated marsh, Rowley River, Plum Island Ecosystem LTER, MA (1999-2025).

Aboveground plant biomass and density is determined non-destructively during the growing season in permanent control and fertilized plots in a Spartina alterniflora-dominated salt marsh at Laws Point on the Rowley River within the Plum Island Ecosystems (PIE) LTER site.

Plant heights from permanent plots in a Spartina alterniflora-dominated marsh, Nelson Island, Parker River National Wildlife Refuge, Plum Island Ecosystems LTER, MA (2019-2025).

Plant heights are measured during the growing season in permanent plots at a Spartina alterniflora-dominated salt marsh on Nelson Island, Parker River National Wildlife Refuge, within the Plum Island Ecosystems (PIE) LTER site. Plant heights are converted to plant weight using an algorithm to generate a non-destructive estimate of aboveground plant biomass.

openCC (other)May 2023View details →

PIE LTER marsh sediment porewater nutrient concentrations from Spartina sp. and Typha sp. sites along the Parker River and Rowley River, MA.

Marsh sediment porewater nutrient concentrations [NH4+, NO3-, DOC, TDN, H2S] and salinity are reported from Spartina sp. and Typha sp. sites along the Parker and Rowley Rivers, MA. Porewater peeper poles are used for collection and the poles are located in the vicinity of the marsh water table sites for the Railroad, Typha, Shad and Nelson sites.

opencc-by-4.0Jan 2020View details →

RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins

This database contains Inputs and intermediate files of the RAPID model pre-processor (RRR), and also outputs from the RRR (i.e., Inputs for RAPID); which were used by Sikder et al. [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. If you use this RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins in your work, please cite: Sikder et al., [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>. The database contains; <ul> <li>Global River basin and Network Shapefiles: HydroSHEDS.tar.gz</li> <li>Extracted Basin Shapefile:                            MIGBM_basin.tar.gz</li> <li>Extracted River Network Shapefiles:             MIGBM_res_ntwk.tar.gz    (Note: res = fine or coarse)</li> <li>Catchment Files:             rapid_catchment_as_riv_res.csv                     (Note: res = fine or coarse)</li> <li>Connectivity Files:           rapid_connect_res_MIGBM.csv                       (Note: res = fine or coarse)</li> <li>Coordinate Files:             coords_res_MIGBM.csv                                   (Note: res = fine or coarse)</li> <li>Base Parameter Files:     pfac_res_MIGBM_1km_hour.csv                     (Note: p = k or x; res = fine or coarse)</li> <li>Sort Files:                         sort_res_MIGBM_topo.csv                              (Note: res = fine or coarse)</li> <li>Sorted Basin Files:           riv_bas_id_res_MIGBM_topo.csv                    (Note: res = fine or coarse)</li> <li>Coupling Files:                 rapid_coupling.tar.gz</li> <li>Parameter Files:              rapid_param.tar.gz</li> <li>Volume Files:                   m3_riv_res_MIGBM_20000101_20091231_prj_LSMsr_tr_utc.nc          (Note: res = fine or coarse; prj = GLDAS or GLDAS.2.0 or GLDAS.2.1 or ECMWF; LSM = CLM, MOS, NOAH, VIC, ERAint; sr = 10 or 025; tr = 3H or D)</li> </ul>   Other necessary links associated with this database: RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a> RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a> GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a> ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a>   References: Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a> David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a> Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a> Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>

opencc-by-4.0Dec 2019View details →

River Surface Reflectance Database (RiverSR)

RiverSR database (River Surface Reflectance) v1.1.0 This database contains Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. The surface reflectance values across bands (red, green, blue, nir, swir1, swir1) represent the median reflectance of pixels detected as water within each Landsat scene that are within the boundaries of each reach represented by NHDPlusV2 centerlines. Surface reflectance is therefore geo-referenced to river center lines with network topology (NHDPlusV2) for quick geospatial analysis. Files: 1) Metadata (riverSR_v1.1_metadata.docx): Description of all data files associated with this repository.  2) Surface reflectance database (riverSR_usa_v1.1.feather). Feather files are text files readable in R and python with the feather package and this table is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv).

opencc-by-4.0Sep 2011View details →

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

Corresponding peer-reviewed publication This dataset corresponds to all the RAPID input and output files that were used in the study reported in: <ul> <li>David, Cédric 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. </li> </ul>   When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.    Time format The times reported in this description all follow the ISO 8601 format.  For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1st 2000 (2000-01-01), Central Standard Time (-06:00).  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.  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.  The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1st 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1st 2000, Universal Time.   Data sources The following sources were used to produce files in this dataset: <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus. </li> <li>The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis.   </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).  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.  Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul>   Software The following software were used to produce files in this dataset: <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.0.0.  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). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul>   Study domain The files in this dataset correspond to two study domains: <ul> <li>The combination of the San Antonio and Guadalupe River Basins, TX.  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).  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.  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).  The temporal range corresponding to this domain spans 100 fictitious days.</li> </ul>   Description of files for the San Antonio and Guadalupe River Basins All files below were prepared by Cédric H. David, using the data sources and software mentioned above.  <ul> <li>rapid_connect_San_Guad.csv.  This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs).  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.  A value of zero is used in place of NoData.  The river reaches are sorted in increasing value of COMID.  The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.  This file was prepared using ArcGIS and Excel.</li> <li>m3_riv_San_Guad_2004_2007_cst.nc.  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 rapid_connect_San_Guad.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00.  The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP.  This file was prepared using ArcGIS and a Fortran program.</li> <li>kfac_San_Guad_1km_hour.csv.  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  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.  This file was prepared using a Fortran program.</li> <li>kfac_San_Guad_celerity.csv.  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  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).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_1.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_2.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_3.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_4.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_1.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (17) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_2.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (18) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_3.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (19) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_4.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (21) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>basin_id_San_Guad_hydroseq.csv.  This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins.  The river reaches are sorted from upstream to downstream.  The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.  This file was prepared using Excel.</li> <li>Qout_San_Guad_1460days_p1_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>Qout_San_Guad_1460days_p2_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>Qout_San_Guad_1460days_p3_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>Qout_San_Guad_1460days_p4_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p1_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p2_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p3_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p4_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>gage_id_San_Guad_2004_2007_full.csv.  This CSV file contains the list of COMIDs of rivers containing USGS gauges and with full daily data record.   The river reaches are sorted in increasing value of COMID.  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.  The values were computed using the following NHDPlus field: COMID.  This file was prepared using ArcGIS, HydroGET, and Excel.</li> <li>Qobs_San_Guad_2004_2007_full.csv.  This CSV file contains daily averaged measured stream flow (in cubic meters per second).  The river reaches have the same COMIDs and are sorted similarly to gage_id_San_Guad_2004_2007_full.csv.  The time range for the daily values is from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00.  The values were computed using the following NHDPlus field: COMID, and the observations from NWIS.   This file was prepared using ArcGIS, HydroGET, and Excel.</li> </ul>   Description of files for the Upper Mississippi River Basin All files below were prepared by Cédric H. David, using the data sources and software mentioned above.  <ul> <li>rapid_connect_Reg07.csv.  This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs).  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.  A value of zero is used in place of NoData.  The river reaches are sorted in increasing value of COMID.  The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.  This file was prepared using ArcGIS and Excel. </li> <li>m3_riv_Reg07_100days_dummy.nc.  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 rapid_connect_Reg07.csv.  The time range for this file is for 100 fictitious days.  The values were computed using a unique value of 1 cubic meter for all river reaches and all time steps.  This file was prepared using a Fortran program.</li> <li>kfac_Reg07_2.5ms.csv.  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_Reg07.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (22) in David et al. (2011).  This file was prepared using a Fortran program.    </li> <li>xfac_Reg07_0.3.csv.  This CSV file contains a first guess of Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_Reg07.csv.  The values were computed based on Equation (22) in David et al. (2011).  This file was prepared using a Fortran program.    </li> <li>basin_id_Reg07_hydroseq.csv.  This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the Upper Mississippi River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.  This file was prepared using Excel.</li> <li>Qout_Reg07_100days_pfac_dtR900s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_Reg07_hydroseq.csv.  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).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> </ul>   Known bugs and limitations in this dataset or the associated manuscript. The confluence of the Missouri River and the Upper Mississippi River upstream of Saint Louis, MO was overlooked.  The contribution from the Missouri River is therefore not accounted for in the network connectivity corresponding to the Upper Mississippi River Basin.  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.   Funding 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.

opencc-by-4.0Nov 2022View details →

IRIS: ICESat-2 River Surface Slope

ICESat-2 River Surface Slope (IRIS) When using this data please cite Scherer D., Schwatke C., Dettmering D., Seitz F.: ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023. A detailed description of the methodology and validation is published in Scherer D., Schwatke C., Dettmering D., Seitz F. : ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry. Water Resources Research, <a href="http://doi.org/10.1029/2022WR032842">10.1029/2022WR032842</a>, 2022. 1. Summary The unique multibeam lidar altimeter of ICESat-2 is used to measure reach-scale water surface slope (WSS) every time the spacecraft’s orbit crosses a reach. The method of deriving WSS from simultaneous ICESat-2 ATL13 (Jasinski et al., 2021) observations is described in detail and validated in Scherer et al. (2022). In this ICESat-2 River Surface Slope (IRIS) dataset, we provide the minimum, average, and maximum slope derived with three different approaches (across, along, and combined) per reach. Additionally, we give the standard deviation and epochs of the derived WSS data. The reaches are defined by the SWOT River Database (SWORD, Altenau et al., 2021). An interactive map is available at <a href="https://dahiti.dgfi.tum.de/en/products/water-surface-slope/.">DAHITI</a>. 2. Version History IRIS v0: Only includes the reaches studied in Scherer et al. (2022). Based on ICESat-2 ATL13 v5, Cycle 1-13 (October 2018 to October 2021) and SWORD Version v1. IRIS v1: Global coverage (limited by ICESat-2 data availability and cloud cover). Based on ICESat-2 ATL13 v5, Cycle 1-16 (October 2018 to August 2022) and SWORD Version v2. IRIS v2: Global coverage with 6,083 additional reaches and 92,347 more observations compared to v1. Based on ICESat-2 ATL13 v6, Cycle 1-19 (October 2018 to April 2023) and SWORD Version v15. IRIS v2.1: Based on ICESat-2 ATL13 v6, Cycle 1-19 (October 2018 to April 2023) and SWORD Version v16. IRIS v2.2: 3,251 additional reaches and 58,862 new observations compared to v2.1. Based on ICESat-2 ATL13 v6, Cycle 1-20 (October 2018 to August 2023) and SWORD Version v16. IRIS v2.3: 1,595 additional reaches and 32,590 new observations compared to v2.2. Based on ICESat-2 ATL13 v6, Cycle 1-21 (October 2018 to October 2023) and SWORD Version v16. IRIS v2.6: 2,755 additional reaches and 362,136 new observations compared to v2.3. Based on ICESat-2 ATL13 v6, Cycle 1-23 (October 2018 to May 2024) and SWORD Version v16. IRIS v2.9: 2,485 additional reaches and 184,549 new observations compared to v2.6. Based on ICESat-2 ATL13 v6, Cycle 1-24 (October 2018 to August 2024) and SWORD Version v16. Fixed some broken geometries in the gpkg data. IRIS v3.0: Based on ICESat-2 ATL13 v6, Cycle 1-24 (October 2018 to August 2024) and SWORD Version v17. IRIS v3.2: 1,370 additional reaches and 210,951 new observations compared to v3.0. Based on ICESat-2 ATL13 v6, Cycle 1-26 (October 2018 to December 2024) and SWORD Version v17. 3. Data Format and Variable Description From Version 2.6, IRIS is also available as GeoPackage. The IRIS data is stored in a single NetCDF4 file which is structured in a single group containing the following variables: reach_id: The SWORD reach identifier [-] lon: Approx. centroid longitude of the SWORD reach [degrees east] lat: Approx. centroid latitude of the SWORD reach [degrees north] across_flag, along_flag, combined_flag: Flags indicating whether ICESat-2 [across/along/combined] slope is available (1) for the reach or not (0) [-] avg_across_slope, avg_along_slope, avg_combined_slope: Average (median) ICESat-2 [across/along/combined] slope for the reach [mm/km] min_across_slope, min_along_slope, min_combined_slope: Minimum ICESat-2 [across/along/combined] slope for the reach [mm/km] max_across_slope, max_along_slope, max_combined_slope: Maximum ICESat-2 [across/along/combined] slope for the reach [mm/km] std_across_slope, std_along_slope, std_combined_slope: ICESat-2 [across/along/combined] slope standard deviation for the reach [mm/km] n_across_slope, n_along_slope, n_combined_slope: Number of days with ICESat-2 [across/along/combined] slope observations for the reach [-] min_date_across_slope, min_date_along_slope:, min_date_combined_slope: First date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01] max_date_across_slope, max_date_along_slope:, max_date_combined_slope: Latest date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01] 4. References Scherer D., Schwatke C., Dettmering D., Seitz F.: ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023 Scherer D., Schwatke C., Dettmering D., Seitz F. (2022): ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry, Water Resources Research, https://doi.org/10.1029/2022WR032842 Jasinski M., Stoll J., Hancock D., Robbins J., Nattala J., Morison J., Jones B., Ondrusek M., Pavelsky T.M., Parrish C. and the ICESat-2-Science-Team (2021). ATLAS/ICESat-2 L3A Inland Water Surface Height, Version 5. [Dataset] Altenau E.H., Pavelsky T.M., Durand, M.T., Yang X., Frasson, R.P.d.M., Bendezu, L. (2021): SWOT River Database (SWORD) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898569

opencc-by-4.0Nov 2024View details →

RADIT: A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean

The Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset provides daily records of river discharge, temperature, and heat flux for 25 major Arctic-draining rivers from 1950 to 2023. Using machine learning methods and ERA5-Land reanalysis data, we reconstructed these key hydrological variables with high accuracy (most NSEs > 0.8). Due to licensing restrictions and to encourage adherence to the stated licenses of the original input data, this dataset only provides the reconstructed (filled) values. Users can obtain the complete historical observational data from their original publicly available sources as detailed in our documentation. By combining these original observations with our reconstructed data, a comprehensive and continuous daily dataset from 1950 to 2023 can be assembled. Clear instructions and links for downloading the original observational data used in this study can be found at: <a href="https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data" target="_blank" rel="noopener">https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data</a>. Should you encounter any issues or have questions, please feel free to contact the first author, Zihan Wang (zhwang2018@163.com).

opencc-by-4.0Oct 2023View details →

GRWSE-global river water surface elevation from sentinel-3

This dataset includes time series of Water Surface Elevation (WSE) of large rivers at over 3000 virtual stations. The WSE time series were created using Sentinel-3A and Sentinel-3B altimetry data.

opencc-zeroNov 2023View details →

Historical Weather, Load, Wind, and Solar Data for the Salt River Project

We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:<ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>

opencc-by-4.0Nov 2023View details →

Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.

<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4>There are two datasets in this data release:1. Model training dataset. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. 2. Model output dataset. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (Ritchie et al., 2018). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following Over et al., 2021) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):1. vegetation / other (0)2. water (1)3. sediment (2)4. large wood (3)<h3>1. Model training dataset.</h3>Imagery was labeled using a combination of the open-source software Doodler (Buscombe et al., 2021; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (Buscombe and Goldstein, 2022). Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.<h4>Files:</h4>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs2. Model_training_ images1of4.zip, 1.5 GB, imagery tiffs3. Model_training_ images2of4.zip, 1.5 GB, imagery tiffs4. Model_training_ images3of4.zip, 1.7 GB, imagery tiffs5. Model_training_ images4of4.zip, 1.6 GB, imagery tiffs<h3>2. Model output dataset.</h3>Imagery was labeled using a deep-learning based semantic segmentation model (Buscombe, 2023) trained specifically for the task using the Segmentation Gym (Buscombe and Goldstein, 2022) modeling suite. We use the software package Segmentation Gym (Buscombe and Goldstein, 2022) to fine-tune a Segformer (Xie et al., 2021) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of Xie et al. (2021), itself fine-tuned on ADE20k dataset (Zhou et al., 2019) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. <h4>Files:</h4>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)2. ElwhaMR_ imagery_ part1_ of_ 2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)3. ElwhaMR_ imagery_ part2_ of_ 2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)4. ElwhaLR_ imagery_ part1_ of_ 2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)5. ElwhaLR_ imagery_ part2_ of_ 2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)  This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. <h3>References</h3>• Buscombe, D. (2023). Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021). Human-in-the-loop segmentation of Earth surface imagery. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>• Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, pp.12077-12090.• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127, pp.302-321.