cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.64f6af3dcea4475688cac0d6b4917d8d
title: Preserving a Flood of Data: Hurricane Harvey 2017 Data Archive

description: The 2017 Atlantic hurricane season was among the busiest on record, producing 18 tropical depressions, 18 tropical storms, 10 hurricanes that occurred in succession, and 23 separate landfalls by Atlantic named storms. Six of the ten hurricanes further  strengthened into major hurricanes. Three of these were Harvey (TX-LA-MS), Irma (FL-GA-NC), and Maria (Puerto Rico and other Caribbean islands). These three were so devastating, and so close to each other in time, that the National Science Foundation (NSF) urged the research community to apply for NSF RAPID grants to study these hurricanes and their impacts, to better understand how these occurred. A team led by David Tarboton, David Maidment, Jerad Bales, and Ray Idaszak received funds [1] to build a storm and flood data archive for Harvey and Irma, to be housed on HydroShare, and managed by CUAHSI.  The project period is from October 2017 to September 2018.  This presentation summarizes work to date (April 2018) on the Harvey part of the collection. 

The downloadable datasets are accessed via HydroShare.  Some large datasets are hosted on RENCI iRODS and the Univ of Texas Advanced Computing Center (TACC) facilities. 
   - Use the Hurricane Harvey 2017 Story Map [2] for contextual access to the data, via links in the side panel on each tab. 
   - For file browser access to the archive, visit the Hurricanes data collection on HydroShare [3]. 
   - Visit the CUAHSI project page [4] to learn more about the project, and for access to Irma and Maria data.

[1] NSF RAPID Grant [https://nsf.gov/awardsearch/showAward?AWD_ID=1761673] 
[2] Hurricane Harvey 2017 Story Map [http://arcg.is/001jje] 
[3 Hurricane collection root on HydroShare [https://www.hydroshare.org/resource/2836494ee75e43a9bfb647b37260e461/]
[4] CUAHSI project landing page [https://www.cuahsi.org/projects/hurricanes-2017-data-archive]
DOI: 10.4211/hs.221fdfd6988d457c85bc40d4531b98f6
title: Water quality data Dinkel catchment

description: This dataset holds all the data measured for the MSc Hydrology fieldwork from 15 till 21st of April 2018. It includes measurements of EC, pH, Alkalinity, Hardness, Phosphorus, Nitrate and the coordinates. It is the collection of data of 5 groups on 5 days.
DOI: 10.4211/hs.7fdb51e5aa4445678fac8c6f67546425
title: Are Higher Concentrations of Pesticides Expected in Areas of Higher Agricultural Pesticide Use Intensity?

description: The Florida Department of Environmental Protection (FDEP) conducts annual probabilistic surveys of Florida’s freshwater resources (flowing waters consisting of rivers, streams, and canals, lakes, and unconfined aquifers).  FDEP incorporated organo-nitrogen and organo-phosphorous pesticides, their degradants, and the neonicotinoid imidacloprid into their probabilistic sample surveys for unconfined aquifers in 2015, flowing waters in 2016, and lakes in 2017.  Pesticide use intensities (kilograms per hectare) were derived for three classes of pesticides (herbicides, fungicides & insecticides) by creating chloropleth maps of estimated usage versus total agricultural area within 29 drainage basins.  Total fresh water concentrations of the most frequently detected pesticides were derived by dissolving data at the sampling locations and aggregating their values.  Two graduated symbol maps present these results graphically, one map depicts total number of detections for each sampling location and the other map shows total concentrations of the most frequently detected compounds.  The investigation shows the occurrence of these pesticides and their degradants within and among Florida’s freshwater resources and its relationship to pesticide use.
DOI: 10.4211/hs.9e888ababe6648ad95d07b6ec5ed7cb0
title: HydroShare User Experience Redesign - Olin SCOPE Final Project

description: The CUAHSI-SCOPE team conducted user-based research to evaluate and design an improved user experience for HydroShare. The user-oriented project focused on identifying key users and workflows, defining current limitations of the system, and developing a comprehensive document of design recommendations.
DOI: 10.4211/hs.041671fbc8a544cd8a979af6c2227f92
title: SUMMA 2.0.0 Sopron version (lubuntu-16.04.4)

description: SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

This version updated for the sopron workshop in Hungary(15~18 April, 2018)
DOI: 10.4211/hs.656024e6f2cd404c93250c2c5d2193be
title: Global snow season length and trends in snow season length using MOD10A2, US snow season and trends using MOD10A1

description: Snow season length (SS), reported in days, is the length of time that snow is present on the ground on an annual basis. It is determined by finding the first and last snow occurrence for any given water year (Oct 1 - Sep 30 NH, Jan 1 - Dec 31 SH), and then finding the difference between these two dates. SS was calculated on a pixel by pixel basis using MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Collection 6 obtained from the National Snow and Ice Data Center (NSIDC) for global calculation and MOD10A1 for US calculation. Spatial coverage is for MODIS tiles h08v04, h08v05, h09v04, h09v05, and h10v04 for water years 2001 - 2015. Files are provided in the "USA Contiguous Albers Equal Area Conic USGS" projection. Funding provided by NSF grant EAR-1446870.
DOI: 10.4211/hs.55c30255450e4f19825e9a5eee0e89ab
title: srtm 90 meter

description: Flooding analysis on Nakhon si thammarat area in THAILAND
DOI: 10.57967/hf/7231
title: jewish-museum-and-tolerance-center_collection
DOI: 10.4211/hs.adb14c9c073e4eee8be82fadb21a0a93
title: Harvey Basemap - Hydrology Map Data

description: This resource contains medium-resolution (1:100k) National Hydrography Dataset (NHDPlus) [1] map data for a region of 39 Hydrologic Unit Code (HUC) 6-digit (HUC6) basins around the Hurricane Harvey impact zone across Texas, Louisiana, Mississippi and Arkansas. This includes 5978 subwatersheds, 190,192 catchments, and 192,267 flowlines.

USGS active stream gages (924) were downloaded from the USGS National Water Information System (NWIS) [2] and augmented with each gage's HUC2, HUC4, HUC6, HUC8, HUC10 & HUC12 basin identifiers, and COMID of the NHD stream reach for the containing catchment. This allows the user to easily aggregate gages by various watershed boundaries.

NOAA Advanced Hydrologic Prediction System (AHPS) [3] has 362 river forecast points in the Harvey study area. Many of these are co-located with USGS NWIS gages to leverage authoritative observation data.

A shapefile of Texas dams (7290) was directly received from the Texas Commission for Environmental Quality (TCEQ) [4].  They suggest if you have any questions about data, to make an Open Records Request [5].

References
[1] NHDPlus Version 2 [http://www.horizon-systems.com/NHDPlus/V2NationalData.php]
[2] USGS NWIS [https://waterdata.usgs.gov/nwis]
[3] NOAA AHPS [https://water.weather.gov/ahps/forecasts.php]
[4] TCEQ Data and Records [https://www.tceq.texas.gov/agency/data]
[5] TCEQ Open Records Request [https://www.tceq.texas.gov/agency/data/records-services/reqinfo.html]
DOI: 10.4211/hs.0af044f25ebb4cc7b15f54f025378832
title: Texas Basemap - Lidar DEM

description: This resource contains Lidar-DEM collection status shapefiles from the Texas Natural Resources  Information System (TNRIS) [http://tnris.org]. 
November 2023 updates: this year, TNRIS changed its name to Texas Geographic Information Office (TxGIO). The domain name hasn't changed yet, but the data hub is continually evolving. See [1], [2] for current downloadable data. 

For purposes of Hurricane Harvey studies, the 1-m DEM for Harris County (2008) has also been uploaded here as a set of 4 zipfiles containing the DEM in tiff files. See [1] for a link to the current elevation status map and downloadable DEMs.  
    Project name:  H-GAC 2008 1m 
    Datasets: 1m Point Cloud,  1M Hydro-Enforced DEM, 3D Breaklines, 1ft and 5ft Contours
    Points per sq meter: 1
    Total area:  3678.56 sq miles
    Source: Houston-Galveston Area Council (H-GAC)
    Acquired by:  Merrick, QA/QC:  Merrick
    Catalog:  houston-galveston-area-council-h-gac-2008-lidar

References: 
[1] TNRIS/TxGIO StratMap elevation data [https://tnris.org/stratmap/elevation-lidar/]
[2] TNRIS/TxGIO DataHub [https://data.tnris.org/]
DOI: 10.4211/hs.73c4f3dcff884a6da2c0982df769987c
title: FEMA - Harvey Damage Assessments and Claims

description: This resource contains shapefiles for FEMA Damage Assessments, Auto Claims, and Property Claims, publicly available here [1].  
Damage  assessments are organized in daily map layers. These appear to be cumulative, but some days' records do not include all the previous days' records. 
   - Aug 27, 2017:  Coastal damage assessments (26,027 records)
   - Aug 28: Damage assessments (78,218)
   - Aug 29: Damage assessments (115,412)
   - Aug 30: Damage assessments (137,754)
   - Aug 31: Damage assessments (161,366)
   - Sep 02: Damage assessments (156,099)
A document is provided that explains the damage assessment methodology. 

Auto and Property Claims are each in a single shapefile, containing all records from Aug 25-Sep 08: 
   - Auto claims, Aug 25-Sep 08 (203, 312 records)
   - Property claims, Aug 25-Sep 08 (226,167)
These identify location, date and type of loss. These are all claims submitted during this period, which may include damages not caused by Hurricane Harvey. 

Other damage assessments and inundation depth grids are available at the FEMA Natural Hazard Risk Assessment Program (NHRAP) ftp site [2]. These include:
- Windfield contours [3]
- PDC Hazus Wind Adv26 (Hurrevac) [4]

References
[1] FEMA Damage Assessments ftp [https://data.femadata.com/NationalDisasters/HurricaneHarvey/Data/DamageAssessments/]
[2] FEMA Natural Hazard Risk Assessment Program (NHRAP) ftp [https://data.femadata.com/FIMA/NHRAP/Harvey/]
[3] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harvey_WindSpeedContours.zip]
[4] [https://data.femadata.com/FIMA/NHRAP/Harvey/PDC_HAZUS_Damage_Loss_Assessment_ADV26_26AUG17_2100UTC.PDF]
DOI: 10.4211/hs.9d510596780b4d6a871078adc7be79c4
title: Texas Addresses Basemap Collection

description: This collection contains Texas statewide map data often used as base layers for hydrologic and geographic analysis, organized by these categories: 
- Addresses and Boundaries (Texas address points, counties, Councils of Government boundaries, Texas Dept of Public Safety districts and regions)
- Hydrology (streams, gages, dams, catchments, watersheds)
- Transportation (Texas roads, railways, bridges, low water crossings)

These data layers generally date to 2015-2016.

The Texas Address and Base Layers Story Map referenced here [1] is an interactive web app supported by Esri ArcGIS Online, that provides visualization and access to specific data layers for Texas only.

References
[1] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/]
DOI: 10.4211/hs.54062b4895964b778302bcf425594093
title: Sixth Water/Diamond Fork data repository

description: Datasets generated during Jabari Jones' Master's thesis at Utah  State University, focused on channel change of Sixth Water Creek and Diamond Fork River, Utah, USA (Jones, J.C.,  2018. Historical channel change caused by a century of flow alteration on Sixth Water Creek and Diamond Fork River, UT. Master's thesis, Utah State University). This resource includes data collected in the field as well as data generated in GIS. Field data include cross-section surveys, RTK GPS surveys, sediment transport measurements, bed grain size analysis, and unmanned aerial vehicle (drone) photography. GIS data include shapefiles generated from aerial imagery and digital elevation models. Data were collected and generated between July 2016 and May 2018  All data, metadata and related materials meet the quality standards relative to the purpose for which they were collected and generated.
DOI: 10.4211/hs.cf97755bf8ba43b8b67cf5b376eca7dc
title: Hydroshare, JupyterHub, and strategies for collaborative and cloud based data sharing, modeling and analysis

description: Advances in many domains of earth science increasingly require integration of information from multiple sources, reuse and repurposing of data, and collaboration.  HydroShare is a web based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI).  HydroShare includes a repository for users to share and publish data and models in a variety of formats, and to make this information available in a citable, shareable, and discoverable manner.  HydroShare also includes tools (web apps) that can act on content in HydroShare, providing users with a gateway to high performance computing and computing in the cloud.  Jupyter notebooks, and associated code and data are an effective way to document and make a research analysis or modeling procedure reproducible.  This presentation will describe how a Jupyter notebook in a HydroShare resource can be opened from a JupyterHub app using the HydroShare web app resource and API capabilities that enable linking a web app to HydroShare, reading of data from HydroShare and writing of results back to the HydroShare repository in a way that results can be shared among HydroShare users and groups to support research collaboration.  This interoperability between HydroShare and other cyberinfrastructure elements serves as an example for how EarthCube cyberinfrastructure may integrate. Base functionality within JupyterHub supports data organization, simple scripting and visualization, while Docker containers are used to encapsulate models that have specific dependency requirements.  This presentation will describe the strategy for, and challenges of using models in Docker containers, as well as using Geotrust software to package computational experiments as 'geounits', which are reproducible research objects that describe and package computational experiments.

Presentation at EarthCube all hands meeting, June 6-8, 2018, Washington, DC https://www.earthcube.org/ECAHM2018
DOI: 10.4211/hs.85c5f592e347452a84f552f17a9a05c1
title: Civil Air Patrol - Harvey Oblique Aerial Photos

description: The Civil Air Patrol is routinely tasked by FEMA and local public safety officials with taking aerial photographs. This collection comprises nearly 30,000 photos taken over the Hurricane Harvey study area, between  August 19, 2017 and June 2, 2018. The majority of this collection were taken over southeast Texas from August 10 to September 2, 2017. These were originally uploaded to the web using the GeoPlatform.gov imageUploader capability, and hosted as a web map layer [1].  For this Harvey collection, I exported the dataset of photo location points to a local computer, subset it to the Harvey event,  and created a shapefile, which is downloadable below. The photos and thumbnails were not included in this archive, but are attribute-linked to the FEMA-Civil Air Patrol image library on Amazon cloud [2].  

The primary resource for these photos is the University of Texas at Austin Center for Space Research (UT CSR), hosted at the Texas Advanced Computational Center (TACC) [3]. These photos are organized by collection date, and each date folder has photo metadata in Javascript (js) and json format files. UT CSR has published a separate web app for browsing these photos [4], as well as several other flood imagery sources.  

Note: The cameras used by the Civil Air Patrol do not have an electronic compass with their GPS to record the viewing direction. The easiest way to determine the general angle is to look at consecutive frame counterpoints to establish the flightpath direction at nadir and adjust for the photographer's position behind the pilot looking out the window hatch on the port (left) side of the aircraft. The altitude above ground level is typically between 1000-1500 feet, so it's easy to locate features in reference orthoimages.

Another source of aerial imagery is from the NOAA National Geodetic Survey (NGS) [5]. This imagery was acquired by the NOAA Remote Sensing Division to support NOAA homeland security and emergency response requirements. 

References
[1] US federal GeoPlatform.gov Image Uploader map service (ArcGIS Server)  [https://imageryuploader.geoplatform.gov/arcgis/rest/services/ImageEvents/MapServer]
[2] FEMA-Civil Air Patrol image library on Amazon cloud [https://fema-cap-imagery.s3.amazonaws.com]
[3] UT CSR primary archive for Harvey photos on TACC [https://web.corral.tacc.utexas.edu/CSR/Public/17harvey/TxCAP/]
[4] UT CSR web app for browsing CAP photos [http://magic.csr.utexas.edu/hurricaneharvey/public/]
[5] NOAA NGS Hurricane Harvey Imagery [https://storms.ngs.noaa.gov/storms/harvey/index.html#7/28.400/-96.690]
DOI: 10.4211/hs.a6921eef1cbf4968b271d972bd997ab3
title: Economic Water Demand Functions to Value Urban Water Scarcity along Utah's Wasatch Front

description: Representing urban water demands economically is useful to understand how anticipated changes like population growth, conservation, water development, climate change, and environmental water demands may affect water deliveries and scarcity.  Utah is the second driest state in the nation, while per capita water use is near the highest in the nation, averaging 167 gallons per person per day.  This implies that creative water management will be ongoing in Utah’s future.  Urban economic loss functions are estimated using residential demand functions for Utah’s Wasatch Front Metropolitan Area, which includes Logan, Salt Lake City, Ogden, Layton, Provo, and Orem urban regions.  Water price, volume of water applied at that price, urban population, and price elasticity data are presented.  Results show seasonal residential water demand functions and seasonal urban (residential, industrial, institutional, and commercial) economic loss functions for Logan, Ogden, Salt Lake City, and Provo metropolitan areas.  Limitations to this method are outlined and discussion focuses on estimating urban water demand functions and potential economic losses input into hydro-economic models and ecological-economic models to evaluate promising solutions to Utah’s persistent water problems.
DOI: 10.4211/hs.c2df2a80b9d6490788704a24854f4879
title: CDC Social Vulnerability Index 2014

description: This is the Social Vulnerability Index (SVI) developed by the U.S. Centers for Disease Control (CDC) [1]. This is often used by the emergency response community to anticipate areas where social support systems are weaker, and residents may be more likely to need help. A map viewer for the national database can be found here [2]. 

November 2023 updates: at the time of Hurricane Harvey, the latest SVI was based on 2014 census data. The CDC SVI website and feature services have since changed. See the current (updated) links for more details. 

Subsets of CDC's 2014 SVI for the Hurricane Harvey and Hurricane Irma hydrologic study areas can be downloaded from the contents list below. 

[1] SVI web site [https://www.atsdr.cdc.gov/placeandhealth/svi/index.html
[2] SVI interactive map [https://www.atsdr.cdc.gov/placeandhealth/svi/interactive_map.html]]
DOI: 10.4211/hs.5af38c0096014e3c96adecb92bc6e26d
title: Differential effects of biomass burning on carbon and nutrient dynamics in Arctic fluvial ecosystems

description: The Central Siberia Plateau (CSP) is undergoing rapid climate change resulting in increasing frequency of forest fires, which have uncertain effects on organic matter and nutrient delivery from headwater streams to downstream ecosystems. Across a fire chronosequence (3 to >100 years) underlain by continuous permafrost, we quantified the effects of wildfire on quantity and quality of dissolved organic matter (DOM) and inorganic nutrients in streams. Wildfire decreased DOM concentrations for about 50 years, but elevated nitrate (NO3-) concentrations lasted only 10 years; ammonium and phosphate concentrations were unchanged. This increase in NO3- and decrease in dissolved organic carbon (DOC) results in a wide range of DOC:NO3-, a ratio that is known to regulate NO3- uptake and denitrification in streams. Ultrahigh-resolution mass spectrometry and DOM optical properties showed that the composition of stream DOM changes after fire, with decreased abundance of polyphenols and aliphatic forms of DOM that are typically more biolabile than other forms of OM. Increasing wildfire frequency is thus likely to have major shifts in the metabolism, carbon flux, and nutrient balance of Arctic fluvial systems.