cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.0f02af18e5344ae7a65dfa7fe1444f34
title: EU-MOHP v013.1.0 Dataset

description: The presented dataset EU-MOHP v013.1.0 provides cross-scale information on the multiorder hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures “lateral position” (LP) as a relative measure of the position between the stream and the catchment divide, “divide stream distance” (DSD) as sum of the distances to the nearest stream and divide and “stream distance” (SD) as an absolute measure of the distance to the nearest stream. These three measures are calculated for several hydrologic orders to reflect different spatial scales. Its spatial extent covers major parts of the European Economic Area (EEA39) which also largely coincides with physiographical Europe. Although there might be many potential use cases, this dataset serves predominantly as valuable static environmental predictor variable for hydrogeological and hydrological modelling such as mapping or regionalization tasks using machine learning.
DOI: 10.15468/dl.uzxtbp
title: Occurrence Download

description: A dataset containing 3516613619 species occurrences available in GBIF matching the query:
{
 "or" : [
 "TaxonKey is Zostera marina",
 "TaxonKey is Phyllospadix torreyi",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3516613619 records from 73790 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049858-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.865b564023954262be42f853e610b8ab
title: Hobo sensors

description: This dataset is composed of 4 moisture and temperature sensors located in forested environment in the Sumiyoshigawa catchment (mid-catchment). The original dataset only shows 9 - 11 June 2021, but it will be updated as further data is collected. The exact location of the sensors is in a GIS shapefile that goes with the XLS files. Hobo 3 is by a wadeable river (3 m wide and about 20 cm deep at mode-flow), and the height is 25 from the ground.
Hobo 4 is 1.5 m high on a tree stem.
Hobo 2 and 5 are at the same location. Hobo 2 is located just above the ground, the lower part of the sensor touches the ground, and Hobo 5 is 1 m above the ground, on a tree stem.
DOI: 10.15468/dl.nr2mru
title: Occurrence Download

description: A dataset containing 4683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-1.545664 40.975057, -1.546260 41.015642, -1.599885 41.015642, -1.599268 40.975057, -1.545664 40.975057))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 4683 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049958-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.b84968e8235a47989f8b2f93ad8de488
title: camels annual peak streamflow

description: Annual peak flows for the CAMELS basins; downloaded from USGS National WaterInformation System (https://nwis.waterdata.usgs.gov/usa/nwis/peak; accessed 10 June 2021)
DOI: 10.4211/hs.c7739f47e2ca4a92989ec34b7a2e78dd
title: camels peak annual flow and return period

description: Peak flow observations for each of the CAMELS basins from the USGS National Water Information System (https://nwis.waterdata.usgs.gov/usa/nwis/peak; accessed 10 June 2021).

We  used  all  available  annual  peak  flows  for  each  of  the  CAMELS  basins  and  fit  these  values  to  the  Pearson  Type  III distribution with log transformation using the method of moments, as described in U.S. Interagency Committee on Water Data,Bulletin 17b (IACWD, 1982). The probability density function is: f(x|\tau, \alpha, \beta) = \frac{(\frac{x-\tau}{\beta})^{\alpha-1}exp(-\frac{x-\tau}{\beta})}{|\beta|\gamma(\alpha)}, The CAMELS basins are suitable for this method under the assumptions of flood flows that are not appreciably altered by reservoir regulation, watershed changes or where the possibility of unusual events, such as dam failures. 

We used Matlab code from Mathworks File Exchange to fit the peak annual flow events to the distribution to obtain return period estimates for each basin (Burkey, 2009). These return period calculations can also be done with free and open source software available from the USGS (https://water.usgs.gov/software/PeakFQ/; accessed 10 June 2021). We classified the water year of each basin (basin-year) according to the return period of its observed peak annual discharge
DOI: 10.4211/hs.c63ddcb50ea84800a529c7e1b2a21f5e
title: RHESSys Morris Sensitivity Analysis Data Repository for Smith et al.

description: This repository provides the Regional HydroEcologic Simulation System (RHESSys) model data and code, sensitivity analysis data and code, paper figures, and supplementary information for the following article published in HESS:
Smith, J.D., L. Lin, J.D. Quinn, and L.E. Band. (2022). Guidance on evaluating parametric model uncertainty at decision-relevant scales. Hydrology and Earth System Sciences. 26, 2519–2539 https://doi.org/10.5194/hess-26-2519-2022

See the Readme.docx file for a description of all directories and files in the repository.
DOI: 10.15468/dl.uu46u8
title: Occurrence Download

description: A dataset listing the 263 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Liste de quelques espèces à statuts particuliers de l'herbier du Centre National de Floristique (CNF) en Côte d'Ivoire"
 ]
}

 The dataset's 263 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049952-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.a22182704dcb42b2baf36c7400a2921c
title: South Fork Discharge Data

description: Field measurements of discharge and stream channel bathymetry are provided from near the abutment of the former South Fork Dam (breached in 1889 causing the Johnstown Flood of 1889).  This, combined with 15-minute stream stage data (HOBO logger) helped in the establishment of a new stream gage (and stream rating curve over the period of study) in Johnstown Flood National Memorial in the South Fork of the Little Conemaugh River, Cambria County, PA, USA.  Logger data from the South Fork are made available here.

Using Stage IV Precipitation Data from UCAR extracted for the Little Conemaugh and South Fork basins via R (example provided for April 17 for the Little Conemaugh) and stream gage data from East Conemaugh (via USGS; station 03041000), unit hydrographs (optimized via a merit function for peak discharge) were established for storms exceeding 8 hours in duration.  These unit hydrographs were then convolved with design storms to probe possible peak discharges in the Little Conemaugh watershed and the South Fork sub-basin.  Unit hydrographs and design storm analyses are provided here.
DOI: 10.4211/hs.19935a37955b49bfb1642c41f2e8753a
title: Droughts and floods for natural-regulated catchment pairs in the United States

description: I here present a spatial set of natural-regulated catchment pairs to study the effect of reservoir regulation on local and regional flood and drought characteristics in the United States.
This data set is composed of the following components:
1) List of catchment pairs (paired_catch_list.txt) containing the USGS IDS of the 114 paired natural ('nat_id') and regulated ('reg_id') gauge.
2) Folder with shapefiles of gauge locations and catchment boundaries of natural and regulated catchments (folder shapefiles).
3) Folder with streamflow time series corresponding to the natural and regulated gauges
4) Folder with extracted mean drought and flood characteristics for the natural and regulated gauges

The dataset accompanies the article 'Reservoir regulation affects droughts and floods at local and regional scales' by Brunner 2021 published in ERL: https://iopscience.iop.org/article/10.1088/1748-9326/ac36f6
DOI: 10.4211/hs.a7a54f183a3d4f1c828933c468237c0d
title: Data for La Follette et al (MCA on rock moisture and streamflow)

description: This is an old version of model outputs used for a modeling project. Therefore, this resource does not correspond to any manuscript.
DOI: 10.4211/hs.f70cefe684b746c6b37dd4ca056a6b34
title: Urban Water Demand Regression Modeling for California Water Suppliers

description: Urban water demand modeling with regression identifies explanatory factors of water use in cities. A generalized demand modeling approach was developed for over 400 urban water supply agencies in California. Using standardized data from self-reported sources for agencies across the state, a batch-processing approach was used to create standardized urban water demand models. The models were developed to test the validity of a simplified and generalized demand modeling approach using monthly available data. Semilog, multivariate regression models were developed for each urban water supply agency. Consumption from residential (single- and multi-family), commercial, industrial, and institutional water use were considered as outcome variables. Explanatory variables include indicator variables for months in a calendar year, periods of water conservation requirements during a 2011-16 severe drought, population, and water rates. The models were of reasonable fit, with adjusted R-squared values ranging from 0.6-0.99. Visual inspection revealed that the monthly models captured trends with reasonable accuracy. The time frame for models was 2013-18, a period with standardized available data through statewide reporting. The modeling approach has been subsequently further extended to incorporate additional climate variables (precipitation and evapotranspiration) for sector-specific models. The models are intended to understand explanatory factors of demand through a generalized modeling approach and not intended to be used for water supply operations without further refinement and testing. The approach can be adapted to many types of cities.
DOI: 10.4211/hs.5fab6f2a877a4b80bad8769ca4a32962
title: Hydrologic Model Driving Data and Geophysical Measurements - Uncoupled and Coupled Brute Force Hydrogeophysical Inversions

description: Synthetic hydrologic model driving data (rainfall, snowmelt, potential evaporation, potential transpiration) and assumed soil data (hydraulic properties and texture) are provided for uncoupled and coupled brute force hydrogeophysical inversion tests. 

For field data applications of the brute force hydrogeophysical inversions, raw and inverted electrical resistivity tomography data, hydrologic model driving data (rainfall, snowmelt, potential evaporation, potential transpiration), and measured soil characteristics (water retention and texture) are provided for the 2018 water year (1 October 2017 to 30 September 2018) from the No Name watershed in southeast Wyoming.
DOI: 10.4211/hs.48c6a00bb6c449afbe33b67250cd1ae7
title: Data and code repository: a mutual information theory-based approach for assessing uncertainties in deterministic multi-category precipitation forecasts

description: The very nature of weather forecasts and verifications and the way they are used make it impossible for one single or absolute standard of evaluation. However, little research has been conducted on verifying deterministic multi‐category forecasts, which is based on the attribute of uncertainty. The authors propose a new approach using two mutual information theory‐based scores for assessing the comprehensive uncertainty of all categories and the uncertainty for a certain category in deterministic multi‐category precipitation forecasts, respectively. Specifically, the comprehensive uncertainty is defined as the average reduction in uncertainty about the observations resulting from the use of a predictive model to provide all categories forecasts; the uncertainty of a certain category is defined as the reduction in uncertainty about the observations resulting from the use of a predictive model to provide a certain category forecast. By applying the proposed approach and traditional verification methods, the four precipitation forecasting products from the China Meteorological Administration, European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and United Kingdom Meteorological Office were verified in the Dahuofang Reservoir Drainage Basin, China. The results indicate that: (a) the proposed approach can better capture the changing patterns of uncertainties with lead times and distinguish the forecasting performance among different forecast products; (b) the proposed approach is resistant to the extreme bias; (c) the proposed approach needs a careful choice of bin width; and (d) the bias analysis is necessary before verifying the uncertainties in precipitation forecasts.
DOI: 10.4211/hs.aadf2681d15f48748d49c1533d117e9e
title: Hourly flow data from hydropower facilities in CONUS

description: This data resource supports Marshall & Grubert (2021). The study presents operational hydropower parameters calculated based on six years of hourly data from 158 dams, and estimates power generation in these hours based on monthly hydropower generation data reported to the Energy Information Administration (EIA). More details on calculated parameters are available in the associated manuscript. The data are distributed as a Shiny application. The application can be run locally by downloading the data package, accessed at: https://adrienne-marshall.shinyapps.io/hydropower/, or users can use the data included for their own analyses (further documentation in the readme.md further down this page).


References: 
Marshall, A. M., & Grubert, E. (2022). Hydroelectricity modeling for low-carbon and no-carbon grids: Empirical operational parameters for optimization and dispatch models. Earth’s Future, n/a(n/a), e2021EF002503. https://doi.org/10.1029/2021EF002503
DOI: 10.4211/hs.460846941c814818b617e49a2f09254a
title: Storm surges, Waves, Hydrodynamics and Vegetation Surveys in Magothy Bay Natural Area Preserve, VA, USA (2014-2017)

description: These datasets include measurements of hydrodynamic (currents and water levels) and wave conditions, vegetation bio-mechanic characteristics (biomass, stem height, diameter, and density), and topo-bathymetric features during the period of (2014-2017) that were measured in the field during extreme events, regular tidal cycles, and over different seasons. This dataset provides the information for the campaigns in Magothy Bay Natural Area Preserve, Virginia, USA. Hydrodynamic measurements were carried out with Acoustic Doppler Current Profilers (ADCPs) (Aquadopp Nortek 2 MHz) and RBR D-wave sensors; vegetation surveys included the measurements of vegetation height, diameter and stem spacing using randomly placed 0.25 m2 quadrats on the ground surface. The sensors, topo-bathy data and vegetation measurement’s locations are georeferenced using a differential GPS Trimble R4. SAV measurements (when present) were carried out by using haphazardly placed 0.25m2 quadrats. At each site, the team measured 1) total SAV percent cover, 2) percent cover of each individual species, 3) canopy height, 4) epiphyte presence on SAV leaf blades, and 5) water depth. All the field procedures, data processing, equipment, and project methodology are described in the QAPP document. 
This field work is part of the project “Quantifying storm surge attenuation by wetlands” funded by the US Department of the Interior (DOI) & National Fish and Wildlife Foundation (NFWF) as part of the Hurricane Sandy Relief Program (Award#43932). The project is a collaboration between George Mason University and the United Stated Geological Survey (USGS). This project quantified the ability of salt marshes in the Chesapeake Bay to attenuate coastal hazards; including the attenuation of storm surge and the reduction of wave energy by these natural ecosystems. The project documented the interaction of storm surges and waves with marshes by measuring hydrodynamic conditions in the field during extreme events (waves, currents and water levels), vegetation characteristics and topo-bathymetric surveys in 4 natural preserves in the Chesapeake Bay during the extent of the project, including several coastal storms and hurricanes.
DOI: 10.4211/hs.b6d0ade225af4a3388e8bdb01f586c6c
title: Storm surges, Waves, Hydrodynamics and Vegetation Surveys in Eastern Shore of Virginia National Wildlife Refuge, VA, USA (2014-2017)

description: These datasets include measurements of hydrodynamic (currents and water levels) and wave conditions, vegetation bio-mechanic characteristics (biomass, stem height, diameter, and density), and topo-bathymetric features during the period of (2014-2017) that were measured in the field during extreme events, regular tidal cycles, and over different seasons. This dataset provides the information for the campaigns in Eastern Shore of Virginia National Wildlife Refuge, Virginia, USA. Hydrodynamic measurements were carried out with Acoustic Doppler Current Profilers (ADCPs) (Aquadopp Nortek 2 MHz) and RBR D-wave sensors; vegetation surveys included the measurements of vegetation height, diameter and stem spacing using randomly placed 0.25 m2 quadrats on the ground surface. The sensors, topo-bathy data and vegetation measurement’s locations are georeferenced using a differential GPS Trimble R4. SAV measurements (when present) were carried out by using haphazardly placed 0.25m2 quadrats. At each site, the team measured 1) total SAV percent cover, 2) percent cover of each individual species, 3) canopy height, 4) epiphyte presence on SAV leaf blades, and 5) water depth. All the field procedures, data processing, equipment, and project methodology are described in the QAPP document. 
This field work is part of the project “Quantifying storm surge attenuation by wetlands” funded by the US Department of the Interior (DOI) & National Fish and Wildlife Foundation (NFWF) as part of the Hurricane Sandy Relief Program (Award#43932). The project is a collaboration between George Mason University and the United Stated Geological Survey (USGS). This project quantified the ability of salt marshes in the Chesapeake Bay to attenuate coastal hazards; including the attenuation of storm surge and the reduction of wave energy by these natural ecosystems. The project documented the interaction of storm surges and waves with marshes by measuring hydrodynamic conditions in the field during extreme events (waves, currents and water levels), vegetation characteristics and topo-bathymetric surveys in 4 natural preserves in the Chesapeake Bay during the extent of the project, including several coastal storms and hurricanes.
DOI: 10.4211/hs.fc396f95ca8f40659161c16773d7a538
title: Storm surges, Waves, Hydrodynamics and Vegetation Surveys in Deal Island, MD, USA (2018)

description: These datasets include measurements of hydrodynamic (currents and water levels) and wave conditions, vegetation bio-mechanic characteristics (biomass, stem height, diameter, and density), and topo-bathymetric features during the period of (2018) that were measured in the field during extreme events, regular tidal cycles, and over different seasons. This dataset provides the information for the campaigns in Deal Island, Maryland, USA. Hydrodynamic measurements were carried out with Acoustic Doppler Current Profilers (ADCPs) (Aquadopp Nortek 2 MHz) and RBR D-wave sensors; vegetation surveys included the measurements of vegetation height, diameter and stem spacing using randomly placed 0.25 m2 quadrats on the ground surface. The sensors, topo-bathy data and vegetation measurement’s locations are georeferenced using a differential GPS Trimble R4. SAV measurements (when present) were carried out by using haphazardly placed 0.25m2 quadrats. At each site, the team measured 1) total SAV percent cover, 2) percent cover of each individual species, 3) canopy height, 4) epiphyte presence on SAV leaf blades, and 5) water depth. All the field procedures, data processing, equipment, and project methodology are described in the QAPP document. 
This field work is part of the project “Quantifying storm surge attenuation by wetlands” funded by The Nature Conservancy. The project is a collaboration between George Mason University, The Nature Conservancy and The Maryland Department of Natural Resources (DNR).
DOI: 10.4211/hs.4795712c165847fca6f7e3bf409b3366
title: Storm surges, Waves, and Hydrodynamics in Dameron Marsh Natural Area Preserve, VA, USA (2013-2016)

description: These datasets include measurements of hydrodynamic (currents and water levels) and wave conditions, vegetation bio-mechanic characteristics (biomass, stem height, diameter, and density), and topo-bathymetric features during the period of (2013-2016) that were measured in the field during extreme events, regular tidal cycles, and over different seasons. This dataset provides the information for the campaigns in Dameron Marsh Natural Area Preserve, Virginia, USA. Hydrodynamic measurements were carried out with Acoustic Doppler Current Profilers (ADCPs) (Aquadopp Nortek 2 MHz) and RBR D-wave sensors; vegetation surveys included the measurements of vegetation height, diameter and stem spacing using randomly placed 0.25 m2 quadrats on the ground surface. The sensors, topo-bathy data and vegetation measurement’s locations are georeferenced using a differential GPS Trimble R4. SAV measurements (when present) were carried out by using haphazardly placed 0.25m2 quadrats. At each site, the team measured 1) total SAV percent cover, 2) percent cover of each individual species, 3) canopy height, 4) epiphyte presence on SAV leaf blades, and 5) water depth. All the field procedures, data processing, equipment, and project methodology are described in the QAPP document. 
This field work is part of the project “Quantifying storm surge attenuation by wetlands” funded by the US Department of the Interior (DOI) & National Fish and Wildlife Foundation (NFWF) as part of the Hurricane Sandy Relief Program (Award#43932). The project is a collaboration between George Mason University and the United Stated Geological Survey (USGS). This project quantified the ability of salt marshes in the Chesapeake Bay to attenuate coastal hazards; including the attenuation of storm surge and the reduction of wave energy by these natural ecosystems. The project documented the interaction of storm surges and waves with marshes by measuring hydrodynamic conditions in the field during extreme events (waves, currents and water levels), vegetation characteristics and topo-bathymetric surveys in 4 natural preserves in the Chesapeake Bay during the extent of the project, including several coastal storms and hurricanes.