cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.a2661d94a39e449bad34663c32cf485c
title: Tank water extent in the Arkavathy watershed 1973-2010

description: The Arkavathy watershed in southern India has exhibited considerable changes in inflow to major reservoirs, but explanations for these changes have been hampered by limited hydrological records in the watershed. In order to understand long-term hydrological change in this heavily managed watershed, we developed a spatially distributed dataset of surface water by generating time series of water extent in nearly 1700 man-made lakes, or tanks, over a 40-year period. Using an automated classification approach with subpixel unmixing, we classified water extent in each of the tanks in 40 Landsat images from 1973 to 2010, including 16 end-of-monsoon-season (December or January) images which characterized tank status after the rainy season.  An additional 8 images in 2013 and 2014 were classified to analyze dry-season behavior and for validation. The classification results compared well with a reference dataset of water extent of tanks (R-squared=0.95).  We also evaluated hydrological change in the watershed in 42 clusters of tanks by modeling water extent in the cluster on hydrological covariates and time. Based on a water balance argument, we inferred that any statistically significant results for the coefficient on the time covariate were indicative of long-term hydrological change. The results of this remote sensing classification for each of the tanks in the Arkavathy watershed are contained in this resource.  The covariates and results from the statistical model are also included.
DOI: 10.4211/hs.3dfee662698b4da6bc9c455bbffc35d4
title: Spectral analysis of continuous redox data reveals geochemical dynamics near the stream-aquifer interface: Supplemental Data

description: Changes in streamflow and water table elevation influence oxidation–reduction (redox) conditions near river–aquifer interfaces, with potentially important consequences for solute fluxes and biogeochemical reaction rates. Although continuous measurements of groundwater chemistry can be arduous, in situ sensors reveal chemistry dynamics across a wide range of timescales. We monitored redox potential in an aquifer adjacent to a tidal river and used spectral and wavelet analyses to link redox responses to hydrologic perturbations within the bed and banks. Storms perturb redox potential within both the bed and banks over timescales of days to weeks. Tides drive semidiurnal oscillations in redox potential within the streambed that are absent in the banks. Wavelet analysis shows that tidal redox oscillations in the bed are greatest during late summer (wavelet magnitude of 5.62 mV) when river stage fluctuations are on the order of 70 cm and microbial activity is relatively high. Tidal redox oscillations diminish during the winter (wavelet magnitude of 2.73 mV) when river stage fluctuations are smaller (on the order of 50 cm) and microbial activity is presumably low. Although traditional geochemical observations are often limited to summer baseflow conditions, in situ redox sensing provides continuous, high-resolution chemical characterization of the subsurface, revealing transport and reaction processes across spatial and temporal scales in aquifers.
DOI: 10.4211/hs.54df00b15c02458685fa3b622f2ecc7b
title: Output from data-driven model of flood severity in Norfolk, VA 

description: This is tabular output data from two data-driven models used to predict flood severity, Poisson regression and Random Forest regression. Both outputs from the training and testing phases of the modeling are included in the resource. Additionally, results indicating the relative importance of each predictor variable in the Random Forest model are provided in the "rf_impo_out.csv" file. This work is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.4211/hs.ff8be5aea3224c15b262bfddd5fb6033
title: Input data for flood severity modeling in Norfolk, VA

description: This is tabular input data originally used in two data-driven models (Poisson regression and Random Forest) for predicting flood severity. The inputs to the model (or predictor variables) are environmental conditions such as cumulative rainfall, high and low tides, etc. The outputs (or target variable) of the model is the number of flood reports per storm event. This data was used in work that is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.4211/hs.712cd2ce8f604c8f824d6836ee3fcb53
title: Data-driven model script for flood severity modeling in Norfolk, VA

description: This is a script written in the R programming language. The script is used to train and apply two data-driven models, Random Forest and Poisson regression. The target variable is the number of flood reports per storm event in Norfolk, VA USA. The input variables for the models are environmental conditions on an event time scale (or daily if no flood reports were made for an event). This script was used to produce results published in a paper in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.
---
Original run configurations:
R version = 3.3.3
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Packages  used:
'randomForest' (version 4.6-12)
'caret' (version 6.0-73)
DOI: 10.4211/hs.9badb941303d4813b3c398cdab038368
title: Supporting Information: Estimating Discharge in Low-Order Rivers with High-Resolution Aerial Imagery

description: Introduction 
This document provides an overview of the accompanying data files used in the production the accompanying manuscript. 
Text S1.
For each virtual gauging station, we have included:
1.	NIR orthorectified mosaics for each flight
2.	Low flow RGB orthorectified mosaic and DSMs
3.	Shapefiles of the hydraulic model domains
4.	HEC-RAS models
5.	Total station channel surveys
6.	In situ observations of wetted width and wetted widths extracted from imagery

WRR_GIS:
All of the imagery, digital surface models, and shapefiles for the hydraulic model domains (items 1-3 above) are included in the WRR_GIS folder.   To view these data, launch the included ArcMap Map Document: KingEtAl_WRR_2017.mxd.  Data reference paths are relative for inter computer fidelity.  This map document was produced with ArcMap 10.3.  The hydraulic domain shapefile data were used to produce the geometry files used in HEC-RAS with the HEC-GeoRAS toolkit.

WRR_HEC-RAS:
The HEC-RAS models (item 4 above), produced in HEC-RAS 5.0.3 are provided in the folder WRR_HEC-RAS.  The project files for each virtual gauging station are located in WRR_HEC-RAS \ VGS# \ Projects where VGS# is virtual gauging station number of interest.  These project files contain the information to run the open channel hydraulic models.  Opening these files will prompt a warning message that some files were not found.  These files were test runs that are not germane to the final results and were therefore not included in an attempt to minimize confusion.  Note that HEC-RAS models follow our local naming convention and map onto the virtual gauging station naming convention as follows: VGS1 = Kup8US, VGS2 = Kup7DS, VGS3 = Kup5uus.

WRR_GroundTruthing:
The ground truthing data (items 5 and 6 above) are included in csv files within the WRR_GroundTruthing folder.  Total station surveys are provided along with corresponding elevations extracted from the Digital Surface Models in csv files located at WRR_GroundTruthing \ ChannelSurveys \ VGS#TransectCompare.csv where VGS# is the virtual gauging station of interest.  Observed wetted width and the corresponding wetted widths extracted from the NIR mosaics are provided in the csv file: WRR_GroundTruthing \ WettedWidths \ WettedWidthComparison.csv.
DOI: 10.4211/hs.77314eb264c84bb6833cf19e8ade03a9
title: Raw street flood report data from Norfolk, VA 2010-2016

description: Street flooding reports made by mostly City of Norfolk staff from 2010-2016. The coordinate system used for the X and Y coordinates is "Virginia state plane, south zone, feet (NAD83)."  These data were processed and used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.4211/hs.38a4ce62960942b4ad8398ee58a777cf
title: Script for processing street flood reports

description: Script and accompanying notebook written in Python 2.7 for processing street flood reports made by City of Norfolk staff. The output data from this script were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.4211/hs.fb2775104b554f4da0dc1045192caf6f
title: Processed street flood data from Norfolk, VA 2010-2016

description: Processed street flooding data from street flood reports made by City of Norfolk, VA staff 2010-2016. These data were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.4211/hs.5db7884111fb4662a13f64707c0c6890
title: Script for combining street flood severity model input, output

description: Script and accompanying notebook written in Python 2.7 for combining flood report data (output) and environmental data (input) into a format suitable for a data-driven model. These data used as target values for street data-driven flood prediction severity modeling for Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.4211/hs.6c9e9239f70a421b8c49ae642a7b9291
title: Daily environmental observations Norfolk, VA 2010-2016

description: Daily observations data for rainfall, wind, tide, and water table levels. These variables are more fully defined in the raw source data. These data are used as input for data-driven prediction of street flood severity in Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.5281/zenodo.17915521
title: Mexican Fishing Vessel Monitoring System Database (2011-2024)

description: A comprehensive database of Mexican fishing vessel activity spanning 2011-2024, derived from satellite-based Vessel Monitoring System (VMS) data obtained from CONAPESCA (Comisión Nacional de Acuacultura y Pesca).
The database contains 95.8 million georeferenced observations from 2,621 unique fishing vessels operating across Mexican territorial waters in the Pacific Ocean, Gulf of Mexico, and Caribbean Sea.
Each observation includes vessel identification, spatiotemporal coordinates, movement metrics (speed, heading, distance traveled, turning angles), and behaviorally-classified fishing activity. The dataset integrates environmental data (sea surface temperature with temporal lags), catch landing reports, and spatial intersection with Marine Protected Areas (MPAs) and Exclusive Economic Zone (EEZ) boundaries.
description: This dataset accompanies the publication: 'A comprehensive database of Mexican fishing vessel activity derived from satellite monitoring (2011-2024)' submitted to Scientific Data.
DOI: 10.5281/zenodo.17915522
title: Mexican Fishing Vessel Monitoring System Database (2011-2024)

description: A comprehensive database of Mexican fishing vessel activity spanning 2011-2024, derived from satellite-based Vessel Monitoring System (VMS) data obtained from CONAPESCA (Comisión Nacional de Acuacultura y Pesca).
The database contains 95.8 million georeferenced observations from 2,621 unique fishing vessels operating across Mexican territorial waters in the Pacific Ocean, Gulf of Mexico, and Caribbean Sea.
Each observation includes vessel identification, spatiotemporal coordinates, movement metrics (speed, heading, distance traveled, turning angles), and behaviorally-classified fishing activity. The dataset integrates environmental data (sea surface temperature with temporal lags), catch landing reports, and spatial intersection with Marine Protected Areas (MPAs) and Exclusive Economic Zone (EEZ) boundaries.
description: This dataset accompanies the publication: 'A comprehensive database of Mexican fishing vessel activity derived from satellite monitoring (2011-2024)' submitted to Scientific Data.
DOI: 10.4211/hs.41c8d8f8788c4ba0b0bfbb924fe1d403
title: Script for aggregating Norfolk, VA environmental data to daily time scale

description: Script and accompanying ipython notebook written in Python 2.7 for aggregating sub-daily environmental data (rainfall, tide, wind, groundwater) to a daily timescale. The input data are from Norfolk, Virginia. Several different methods of aggregation are used including averages and maximums.  The processed/aggregated data are combined with street flood report data to be used in data-driven, predictive modeling. The script in this resource was used in the analysis described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.
DOI: 10.4211/hs.546fa3feeaf242fc8aabf9fe05ab454c
title: NOAA NHC - Irma Storm Track - Advisories Track

description: NOAA publishes advisory bulletins with named storm conditions and expectations, see [http://www.nhc.noaa.gov/archive/2017/IRMA.shtml]. Information from these bulletins was extracted using an R Code in "Generate Hurricane Track.ipynb" to produce the file Irma.csv. This was then converted to a vector shapefile using ArcGIS. 

See also "NOAA NHC Irma 2017 Storm Track" page for other related data  [https://www.hydroshare.org/resource/aa5c9982a4694a19be2fa9299b78e5ca/]
DOI: 10.4211/hs.8217eab0997d493782ff321ca5f95f28
title: New Hampshire EPSCoR Intensive Aquatic Network continuous Discharge, Nitrate, fDOM, Temperature, and Specific Conductance Data

description: Continuous data is collected at 9 sites throughout New Hampshire. At each site data is collected every 15 minutes by the datalogger from a HOBO Stage logger (or site is paired with USGS site), Satlantic SUNA and YSI EXO2. Data is collected and transmitted to UNH by cell telemetry once a day where it is stored on the NH EPSCoR data server. The data that is collected from the SUNA are Nitrate in mg/L and is corrected by grab sample NO3 analyzed by IC. Data that is collected by the YSI are Stream Temperature, Specific Conductance, and fDOM in QSU. The fDOM is corrected by temperature, turbidity (not included), and absorbance. 

This data set was used to analyze the high-frequency time series of stream solutes to characterize the timing and magnitude of nutrient and organic matter transport over event, seasonal, and annual timescales as well as to assess to whether nitrate (NO3-) and dissolved organic carbon (DOC) transport are coupled in watersheds. Our dataset includes in situ observations spanning 2 – 4 years in 10 streams and rivers across New Hampshire, including observations of nearly 700 individual hydrologic events.  These events are identified in the files. 

Methods and findings are described in the associated WRR manuscript.
DOI: 10.4211/hs.ee0bd6dc2c224a7eaf7b01e931c26021
title: Evaluation of Nitrate, fDOM, and Turbidity Sensors in New Hampshire Streams

description: A state-of-the-art network of water quality sensors was established in 2012 to gather year-round high temporal frequency hydrochemical data in streams and rivers throughout the state of New Hampshire through the NH EPSCoR project.  This spatially-extensive network includes eight headwater stream and two main-stem river monitoring sites, spanning a variety of stream orders and land uses.  We evaluated the performance of nitrate, fluorescent dissolved organic matter (fDOM), and turbidity sensors included in the sensor network and the data is shared here.  

Nitrate sensors were first evaluated in the laboratory for interference by different forms of dissolved organic carbon (DOC), and then for accuracy in the field across a range of hydrochemical conditions.  Turbidity sensors were assessed for their effectiveness as a proxy for concentrations of total suspended solids (TSS) and total particulate C and N, and fDOM as a proxy for concentrations of dissolved organic matter.  Overall sensor platform performance was also examined by estimating percentage of data loss due to sensor failures or related malfunctions.  Although laboratory sensor trials show that DOC can affect optical nitrate measurements, our validations with grab samples showed that the optical nitrate sensors provide a reliable measurement of NO3 concentrations across a wide range of conditions.  Results showed that fDOM is a good proxy for DOC concentration (r2=0.82) but is a less effective proxy for dissolved organic nitrogen (r2=0.41).  Turbidity measurements from sensors correlated well with TSS (r2=0.78), PC (r2=0.53) and PN (r2=0.51).
DOI: 10.4211/hs.b92ad2ea2d434d54a7e82e7379b02dcf
title: Christina River Basin - Stream Suspended Sediment (1993-2012)

description: Total suspended solids (TSS) and Volatile Suspended Solids (VSS) from White Clay Creek near the Stroud Water Research Center, Avondale, PA, USA. The purpose is to quantify export of inorganic and organic particulate matter from the 725-hectare watershed. Samples consist of those taken at monthly intervals, normally the first Wednesday of each month regardless of weather or flow conditions and those taken after precipitation events. The monthly samples are manual grab samples collected in 5-L polyethylene “space saver” bottles from a few centimeters below the surface and without disturbance to the stream bed. The event samples were collected in response to precipitation of 20 mm or more using an ISCO automated sampler which collected 1-L samples s in polyethylene bottles at hourly intervals through an intake approximately 20 cm above the bed. Each of approximately four events per year are represented by approximately 10 samples selected from the hourly series to characterize the rise, peak, and falling limb of the hydrograph. Additional events are represented by the three samples nearest peak flow.

Variables:  Solids_ total suspended, carbon to nitrogen molar ratio, carbon_ particulate organic, nitrogen_ particulate organic, nitrogen-15 stable isotope ratio delta

Date Range:  (1993-2012)

Dataset Creators/Authors:  Aufdenkampe, A.K.; Newbold, J.D.; Anderson, B. A.; Richardson, D.; Damiano, S.G.

Contact:  Sara G. Damiano, Stroud Water Research Center, 970 Spencer Road, Avondale, PA 19311, sdamiano@stroudcenter.org

Field Area:   Boulton Run | Christina River Basin | Forest Endmember: Spring Brook | White Clay Creek @ SWRC | Construction Endmember: White Clay Creek below landfill | Lower White Clay Creek | Agricultural Endmember: South Branch Doe Run

Copied from:
Aufdenkampe, A.K.; Newbold, J.D.; Anderson, B. A.; Richardson, D.; Damiano, S.G. (2012). "CZO Dataset: Christina River Basin - Stream Suspended Sediment (1993-2012)." Retrieved 21 Jan 2018, from http://criticalzone.org/christina/data/dataset/2474/.

For guidance on how to parse these CZODisplayFiles, see  example Jupyter Notebooks in Python at https://github.com/aufdenkampe/EnviroDataScripts/tree/master/CZODisplayParsePlot.
DOI: 10.4211/hs.ff7435cd22d94914ad3a674c40b229e9
title: Third-order White Clay Creek and Boulton Run - Climate, Stable Isotopes, Stream Water Chemistry (2011-2012)

description: Deuterium and Oxygen-18 measured on stream water samples collected during baseflow and stormflow conditions.

Variables:  oxygen-18 (d18O), deuterium (dD)

Date Range:  (2011-2012)

Dataset Creators/Authors:  Diana L. Karwan

Contact:  Diana L. Karwan, Department of Forest Resources, University of Minnesota, St. Paul, MN 55108

Field Area:   White Clay Creek @ SWRC

Copied from: Diana L. Karwan (2012). "CZO Dataset: Third-order White Clay Creek and Boulton Run - Climate, Stable Isotopes, Stream Water Chemistry (2011-2012)." Retrieved 21 Jan 2018, from http://criticalzone.org/christina/data/dataset/3365/

For guidance on how to parse these CZODisplayFiles, see example Jupyter Notebooks in Python at https://github.com/aufdenkampe/EnviroDataScripts/tree/master/CZODisplayParsePlot.
DOI: 10.4211/hs.27d34fc967be4ee6bc1f1ae92657bf2b
title: A hydroclimatological approach to predicting regional landslide probability using Landlab

description: This resource supports the work published in Strauch et al., (2018) "A hydroclimatological approach to predicting regional landslide probability using Landlab", Earth Surf. Dynam., 6, 1-26 .  It demonstrates a hydroclimatological approach to modeling of regional shallow landslide initiation based on the infinite slope stability model coupled with a steady-state subsurface flow representation. The model component is available as the LandslideProbability component in Landlab, an open-source, Python-based landscape earth systems modeling environment described in Hobley et al. (2017, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017).  The model operates on a digital elevation model (DEM) grid to which local field parameters, such as cohesion and soil depth, are attached.  A Monte Carlo approach is used to account for parameter uncertainty and calculate probability of shallow landsliding as well as the probability of soil saturation based on annual maximum recharge. The model is demonstrated in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2.   

This resource contains a 1) User Manual that describes the Landlab LandslideProbability Component design, parameters, and step-by-step guidance on using the component in a model, and 2) two Landlab driver codes (notebooks)  and customized component code to run Landlab's LandslideProbability component for 2a) synthetic recharge and 2b) modeled recharge published in Strauch et al., (2018).   The Jupyter Notebooks use HydroShare code libraries to import data located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/.  

The Synthetic Recharge Jupyter Notebook <Synthetic_recharge_LandlabLandslide.ipynb>  demonstrates the use of the Landlab LandslideProbability Component on a synthetic grid with synthetic data with four options for parameterizing recharge.  This notebook was used to verify and validated the theoretical application and digital representation of Landslide processes. 

The Modeled Recharge Jupyter Notebook <NOCA_runPaper_LandlabLandslide.ipynb>  models annual landslide probability in the North Cascades National Park Complex, and was used to verify that model results in Strauch et al., (2018) could be reproduced online.
DOI: 10.4211/hs.eb20c1fa74ad44a2a51834985dbf4481
title: Wet–dry cycles impact DOM retention in subsurface soils

description: Research data for Y. Olshansky et al.:Wet–dry cycles impact DOM retention in subsurface soils. Biogeosciences
DOI: 10.15468/dl.zhfw7b
title: Occurrence Download

description: A dataset containing 1782 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-4.942984 41.489938, -4.944739 41.526611, -4.993290 41.526611, -4.991519 41.489938, -4.942984 41.489938))",
 "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 1782 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049931-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.50c26ff32a1f461090a36eb0099dad76
title: Resource workflow diagram for street flood severity modeling in Norfolk, VA 2010-2016

description: Diagram depicting the relationship between 10 different HydroShare resources used to produce results for data-driven street flood severity modeling done for Norfolk, VA for 2010-2016. The analysis is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.