cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.03c52d47d66e40f4854da8397c7d9668
title: NEON Continuous Discharge Evaluation

description: Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses.  As of 2022, The National Ecological Observatory Network (NEON) provides up to 5 years of continuous discharge and uncertainty estimates at 28 stream and river sites across the United States. NEON generated annual estimates using Bayesian rating curves that were parameterized based on hydraulic controls and point estimates of discharge collected via acoustic doppler current profilers, salt tracer releases, and flow meter measurements.  Inputs to the models were sensor-measured continuous surface water elevations. Here we evaluate the reliability of these discharge estimates, with four approaches. We (1) compared predicted to observed discharge values, (2) compared predicted to observed surface water elevation values, (3) compiled data availability, and (4) calculated the proportion of discharge estimates extrapolated beyond field measurement.  We provided diagnostic metrics and evaluations of continuous discharge estimates and continuous stage estimates by month for each site in which continuous discharge data was available as of December 2021, enabling users to rapidly query for suitable NEON data.

Please note, this evaluation is was performed on NEON Discharge REALSE-2022 data and provisional data after 09/30/2019. See new versions of this dataset for future NEON data releases or our code repository to update this analysis locally: https://github.com/spencerrhea/neon_discharge_eval

See publication for details on methods:
Rhea, S, et al. User-focused evaluation of National Ecological Observatory Network streamflow estimates, Sci. Data, 2023.

For more information on NEON sites, see their depictions on the NEON page: https://www.neonscience.org/field-sites/explore-field-sites
For detailed information of each NEON watershed, shapefiles and associated information can be found here: https://www.neonscience.org/data-samples/data/spatial-data-maps
DOI: 10.4211/hs.7bfbec84d95440e6a7842159fb2a27a1
title: Kalamazoo River Watershed (KRW) Nitrogen Data

description: Nitrogen monitoring data from the Kalamazoo River Watershed, Michigan, USA (2005-2006) reported in the following paper:
H. Qiu, J. Niu, D.G. Baas and M.S. Phanikumar, An integrated watershed-scale framework to model nitrogen transport and transformations, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2023.163348  (2023)
DOI: 10.4211/hs.a6bcc341413c4fb0b195b25ebe1bb3e6
title: Files and code for the paper "Unprecedented flooding foretold by stream network organization and flow regime" by S. Basso, R. Merz, L. Tarasova and A. Miniussi

description: Files and code for the paper "Unprecedented flooding foretold by stream network organization and flow regime" by S. Basso, R. Merz, L. Tarasova and A. Miniussi
DOI: 10.4211/hs.53c0ae4cf09b404fb19a77ed2018e186
title: River corridor sources dominate CO2 emissions from a lowland river network

description: Rivers and streams are control points for CO2 evasion to the air (fCO2), with rates often exceeding internal metabolic production (net ecosystem production, NEP). The difference is attributed to groundwater inputs enriched in CO2 from upland soil respiration, but this implies a terrestrial-to-aquatic C transfer far larger than estimated by terrestrial mass balance. One explanation is that riparian zones, neglected in most terrestrial mass balances, contribute a disproportionate fraction of observed fCO2, highlighting the integral role of river corridors (i.e., streams plus their adjacent wetlands) in landscape C export dynamics. To test this hypothesis, we measured fCO2, NEP, and the lateral CO2 contributions from both terrestrial uplands (TER) and riparian wetlands (RIP) for seven mid-order reaches in a lowland river network in north Florida, USA. NEP contributed nearly half of fCO2 on average, but the remaining CO2 evaded by the stream was generally far larger than measured TER, suggesting principally river corridor (RIP) origins. The relative importance of RIP vs. TER varied markedly between contrasting hydrogeologic settings: RIP contributed 60% of fCO2 where geologic confinement forces lateral drainage through riparian soils, but only 12% where unconfined karst results in deeper groundwater flowpaths that largely bypass riparian zones. On a unit area basis, the relatively narrow riparian corridor yielded 40 times more CO2 than the terrestrial uplands (33.77 vs. 1.38 g-C m-2 yr-1), resulting in river corridors sourcing the majority of fCO2 (NEP + RIP = 85%) to streams. Including riparian zones in the conceptual model for terrestrial-to-aquatic C transfer implies that true terrestrial CO2 subsidies to streams are smaller than previously estimated.
DOI: 10.4211/hs.89cfde573936487a9ab4ff406638465b
title: Cross-CZO storm-event stream water silicon stable isotope - discharge datasets from manuscript (USA, France, Canada)

description: *** Please see new addendum for updated fSi_diss ***
This resource contains accompanying stream water chemistry data (major cations, anions, DOC, Silicon, Germanium, and Silicon stable isotopes) presented in "Resiliency of silica export signatures when low order streams are subject to storm events" by Fernandez et al. The datasets represent seven storm events from six different catchments across the US, Canada, and France as part of an international cross-CZO collaboration supported through SAVI (Science Across Virtual Institutes).  

Files are provided in ".csv" format and are organized both with respect to individual sites (listed below) and as bulk metadata (designated 'AllSites' ).

CZO sites and storm events investigated for this study (site specific csv file name identifier is provided inside the brackets [ ] ):
[LaJara] - La Jara Creek, Jemez CZO (New Mexico, USA) - March to Late May 2017 snowmelt event
[ProvidenceCreekP303] - Providence Creek Subcatchment P303, Southern Sierra CZO (California, USA) - January 2018 rain event
[ElderCreek] - Elder Creek, Eel River CZO (California, USA) - January 2017 rain event
[Sapine ] - Sapine Creek, OZCAR (Mont Lozère, FRA) - October 2016 rain event and long term monthly sampling from 2013-2015 
[QuiockCreek] - Quiock Creek, OZCAR (Basse-Terre, Guadeloupe, FRA) - October 2015 rain event
[KwakshuaWatershed708] - Kwakshua Watershed 708, Hakai Institute (Calvert Island, British Columbia, CAN) - two rain events, September and October 2017

Site-specific lithology and associated chemical compositions used in calculations for fSi (proxy for fraction of Si remaining in solution) are provided in 'bedrock_compositions.xlsx'. Refer to the manuscript for further details.
DOI: 10.4211/hs.6625bdbde41c45c2b906f32be7ea70f0
title: Supporting data and tools for "Impact of data temporal resolution on quantifying residential end uses of water"

description: The files provided here are the supporting data and code files for the analyses presented in "Impact of data temporal resolution on quantifying residential end uses of water", an article submitted to the Water journal (https://www.mdpi.com/journal/water). The journal paper assessed how the temporal resolution at which water use data are collected impacts our ability to identify water end use events, calculate features of individual events, and classify events by end use. Additionally, we also explored implications for data management associated with collecting this type of data as well as methods and tools for analyzing and extracting information from it. The data were collected in the cities of Logan and Providence, Utah, USA in 2022 and are included in this resource. The code and data included in this resource allow replication of the analyses presented in the journal paper, and the raw data included allow for extension of the analyses conducted.
DOI: 10.4211/hs.7dce252d5ba349ffa5308873183ca6b4
title: Reproducible evaluation of 2021 drought conditions in Blacksmith Fork, UT

description: This resource includes a python Jupyter notebook to evaluate 2021 drought conditions in the Blacksmith Fork, UT using data retrieved from the USGS using the python dataretrieval library. The analysis contained within the Jupyter notebook is reproducible. This is created to fulfill Hydroinformatics Assignment 8 requirements.
DOI: 10.4211/hs.bc6cb876e5754ce88e8d386bd75d2e6a
title: Evaluating the Severity of 2021 Drought at a Local Water Resource in Utah

description: This resource provides the results of exploring the streamflow data of a local water gage to evaluate the severity of 2021 drought conditions on local water resources. More specifically, the site of interest is at Blacksmith Fork AB UP and L Co.’s Dam NR Hyrum, Utah. The site number is 10113500 located within Cache County Utah and Hydrologic Unit 16010203. The site has a drainage area of 263 square miles and a datum of gage that is 5,020.60 feet above NGVD29 (USGS, 2022). The following sections describe the data retrieval and exploration method, and the assessment of the severity of drought conditions in 2021 at the chosen site. The resulting notebook showed that the drought conditions in 2021 at the chosen site were particularly apparent and concerning, especially when compared to the site’s past data. This drought affected the water flow for the entire year of 2021, but the water flow was lowest during the late summer and fall months.
DOI: 10.4211/hs.a33f0c48612749f98b2940366d363ce9
title: Using the dataretrieval Package to Retrieve USGS NWIS Discharge Data and Statistics

description: The purpose of this resource is to provide a script that automates data retrieval from the USGS NWIS database. Included in this resource is a Jupyter Notebook script (dataretrieval_percentiles.ipynb), that contains detailed comments for data extraction automation using the dataretrieval library. The script uses USGS 10126000 Bear River near Corinne, UT as a case study, however, the inputs can easily be modified such that a similar analysis could be performed for any watershed in the United States as long as there is an active USGS gage station or a period of record for which the gage stations was active. In addition to the aforementioned script, this resource contains a readme.txt file with instruction for opening and executing the script using the HydroShare and CUAHSI JupyterHub interface.
DOI: 10.4211/hs.b8aff640caaf4eba93c8bb4bcd7e09db
title: Hydroinfomatics Assignment 8

description: The Jupyter notebook uses the CSV file in the resource and creates a box cum bar plot for removal efficiencies of different BMP categories selected for analysis. The entire process is automated, the plots will be created for each category and will be saved as a .PNG file.
DOI: 10.4211/hs.5e9f29b6184e40c18b5522db38e43f37
title: Nutrient Removal Analysis at California Wastewater Treatment Facilities

description: Monitoring data can be used to identify wastewater treatment facilities that perform nutrient removal based on wastewater effluent nutrient concentrations. A script was developed using self-reported data taken from the California Integrated Water Quality System Project (CIWQS) to clean, filter, and organize a batch dataset of effluent flows, constituent concentrations, and constituent mass loadings for wastewater treatment facilities in California. Data for each facility was used to develop assessments for potential nutrient removal in individual facilities. Data from 426 wastewater treatment facilities was accessed through CIWQS. 144 facilities had adequate data to perform the analysis for at least one year. After analysis, 95 facilities were thought to use some form of nutrient removal. This information can be used to quickly group facilities of interest for focused analysis in lieu of a centralized database of facility characteristics that includes treatment train configuration and NPDES permit requirements. The technique can be adapted to several constituents. This analysis was an exploratory venture and not intended to be used as a final assessment of treatment practices for facilities in California.
DOI: 10.4211/hs.20206a86cb6040a68141974659d89a94
title: Generalized Methods for Nitrogen Loading Assessment in Contributing Areas of Selected Lakes of Interest

description: Lakes worldwide are impaired by external nutrient loading due to human activities, specifically agricultural runoff. Excess nutrients can change nutrient dynamics in lakes and contribute to declining water quality. This study utilizes two publicly available datasets, NHDPlus V2 and StreamCat, to investigate and compare the rates of inorganic nitrogen wet deposition, biological nitrogen fixation, and nitrogen fertilizer application within the surrounding watershed of three lakes. To accomplish this, a relational database was created to relate StreamCat values with NHDPlus V2 watershed hierarchy using a unique StreamID. The data from each dataset was loaded into the relational database after performing a geospatial analysis to identify StreamIDs within the watershed for each lake of interest. SQLite queries were then performed to obtain rates of chosen parameters from the relational database in order to compare the three watersheds. This study demonstrated that generalized methods can be developed to relate NHDPlusV2 and StreamCat, in order to simplify querying of water quality data from StreamCat for the watersheds of selected lakes.
DOI: 10.4211/hs.bcbcfe823cc4432f8ce96c3048d4591f
title: Data Component Use Case for Overland Flow Simulation

description: This resource includes a Jupyter notebook to demonstrate how to use the CSDMS Data Component (https://csdms.colorado.edu/wiki/DataComponents) to download the topography dataset and use the Landlab component (https://landlab.readthedocs.io/en/master/) to delineate the watershed and simulate the overland flow for a study area in the Boulder County.

HydroShare users can run the Jupyter Notebook (overland_flow.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub"
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If you encounter "Kernel Restarting" error when running this notebook on CUAHSI JupyterHub, select "Kernel" -> “Shut Down All Kernels" -> "Restart Kernel and Clear All Outputs" and rerun this notebook.

Please go to https://github.com/gantian127/overlandflow_usecase to learn how to run this notebook on local PC or CSDMS JupyterHub.
DOI: 10.4211/hs.3ed0608d3bb84160a5714861632adcad
title: Storm surges, Waves, Hydrodynamics and Vegetation Surveys in Assateague Island National Seashore, MD, USA (2020-2021)

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 (2020-2021) 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 Assateague Island National Seashore, 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. 
This field work is part of the project “EESLR 2019: Quantifying the benefits of natural and nature-based features in Maryland’s Chesapeake and Atlantic Coastal Bays to inform conservation and management under future sea level rise scenarios” funded by NOAA (Award# NA19NOS4780179). The project is a collaboration between George Mason University, the Maryland Department of Natural Resources (DNR) and The Nature Conservancy (TNC). The overall goal of the project is to quantify the wave attenuation and flood reduction benefits of marshes, SAV and other natural and nature-based features (NNBF) along the shores of Maryland’s Chesapeake and Atlantic Coastal Bays. This project will inform management actions by DNR to maintain or enhance the ecosystem services of marshes and other natural features on state-owned lands; re-evaluate Chesapeake Bay SAV restoration goals; improve existing conservation prioritization tools; and provide relatable, local examples to advance efforts by DNR, TNC, Eastern Shore Land Conservancy (ESLC) and others to promote the use of NNBF in county and municipal adaptation plans.
DOI: 10.4211/hs.08e1b18aa9f240758dd13d9ac875621f
title: HydroDA v1.0

description: Quantifying continental-scale river discharge is essential for understanding the terrestrial water cycle, which is susceptible to errors due to lack of observations or limitations in hydrodynamic modelling. Data assimilation (DA) methods are increasingly utilized to estimate river discharge combined with the emerging amount of river-related remote sensing data (e.g., water surface elevation, water surface slope, river width, flood extent, etc.). However, direct comparison of simulated water surface elevation (WSE) with the satellite altimetry data remains still challenging (i.e., large bias between simulations and observation, uncertainty in parameters, etc.) and can introduce large errors when assimilating satellite observations to hydrodynamic models. We performed several experiments, namely, direct, anomaly, and normalized value assimilations, to investigate the capability of DA to improve the river discharge with the current limitations of hydrodynamic modelling. The hydrological data assimilation was performed using a physically-based empirical localization method in the Amazon Basin. We used satellite altimetry data from ENVISAT, Jason 1 and Jason 2 for this study. The direct DA was used as the baseline of the assimilations, but it was subjected to errors due to the biases in the simulated WSE. As an alternative to direct DA, we used anomaly DA to overcome the errors due to the biases in the simulated WSE. In addition, we found that the modelled WSE distribution and the observed distribution differed considerably (i.e., amplitude differences, seasonal flow variations, distribution skewness due to limitations of hydrodynamic models, etc.). Therefore, a normalized value DA was performed to realize better discharge estimation. River discharge improved in 24%, 38%, and 62% of the stream gauges in the direct, anomaly, and normalized value assimilations compared to simulations without DA. The normalized value assimilation performed better in estimating river discharge given the current limitations of hydrodynamic models. Most of the gauges within the river reaches with satellite observations accurately estimated the river discharge with Nash-Sutcliffe Efficiency (NSE) > 0.6. The amplitudes of WSE were improved in the normalized DA experiment. Furthermore, in the Amazon Basin, normalized assimilation (median NSE=0.47) can improve river discharge estimation over the open-loop simulation with global hydrodynamic modeling (median NSE=0.13). River discharge estimation by direct DA methods can be improved by 7% of NSE by calibrating river bathymetry. Moreover, the direct DA approach outperforms the other DA methods when the runoff is considerably (50%) biased. The uncertainties in hydrodynamic modelling (i.e., river bottom elevation, river width, simplified floodplain dynamics, rectangular cross-section assumption, etc.) should be improved for better estimation of river discharge by assimilating satellite altimetry. This study will contribute to developing a global river discharge reanalysis product that is consistent spatially and temporally.
DOI: 10.4211/hs.c093b1ee56ab426b92219ee0b1068919
title: Dataset for real-time daily ensemble streamflow forecasting across a rainfed river basin using a Bayesian hierarchical model combination framework

description: This dataset contains the files with time series of potential covariates, daily peak monsoon (July-August) streamflow used to post-process daily VIC streamflow forecast across the Narmada River basin network, India, for the period calibration (2003-2018). It also contains a file with basic information (longitude, latitude, and area) for the gauges considered here and observed data and covariates at the Handia gauge for the peak monsoon season 2021. The potential covariates comprise daily VIC forecasted (1- to 10-day lead time) and simulated streamflow from each gauge, and 1, 2, 3, and 4-days accumulated spatial average observed precipitation from the area between the station gauges from 1 to 10-day lead times. The observed streamflow and gridded precipitation data were obtained from the India Water Resources Information System (India-WRIS) and the India Meteorology Department (IMD). Then, data were processed to obtain the dataset presented here.
DOI: 10.4211/hs.d97ce5c11dc347f49a4ec474199dd331
title: Data for: A Systems Approach to Remediating Human Exposure to Arsenic and Fluoride from Over-exploited Aquifers

description: This data set contains anonymous information about 159 water production wells and shallow dug wells (Norias) across the Upper Rio Laja Basin in Guanajuato State, Mexico. The type of information contained includes locations of the wells, well type, depths, surface elevations, static and dynamic water levels, average electricity costs, well outer casing diameter and pump tubing diameter (inner casing), pumping rates which are set to be constant, and miscellaneous notes on the well including whether it is possible to obtain a manual water level and whether the well goes dry occasionally. The well type DWSW and IW means Drinking Water Supply Well and Irrigation Well, respectively. The well IDs (PW1, PW2 etc..) correspond to the same wells of previous datasets from this basin published on HYDROSHARE by Peter Knappett.
DOI: 10.15468/dl.s8ee4x
title: Occurrence Download

description: A dataset containing 1067 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Bactrocera zonata",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 1067 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049972-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.