cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.650ba9303d5942c2b36fa5beafbf612a
title: BEREV data 2017-2018

description: This dataset is supplementary information to: Schilling et al. (2021): Quantifying groundwater recharge dynamics and unsaturated zone processes in snow‐dominated catchments via on‐site dissolved gas analysis. Water Resour. Res., e2020WR028479. doi: 10.1029/2020WR028479

The data was used to develop a novel tracer method for the quantification of groundwater recharge from snowmelt and includes hydraulic, snow, meteorologic and tracer measurements covering the period of Nov-2017 to Dec-2018. The data was recorded in the experimental boral headwater catchment 'Bassin Expérimental du Ruisseau des Eaux-Volées' (BEREV) of Université Laval, located in the Forêt Montmorency, Québec, Canada.
DOI: 10.4211/hs.43280a7de6ef48b4b800ab5c12ae58cb
title: Accompanying Data to Hampton & Basu (2022) "A novel Budyko-based approach to quantify post-forest-fire streamflow response and recovery timescales"

description: Recent increases in the incidences of wildfires have necessitated the development of methodologies to quantify the effect of these fires on streamflows. Climate variability has been cited as a major challenge in revealing the true contribution of disturbance to streamflow changes. To address this, we developed an annual Budyko “decomposition” method for (1) statistical change detection of hydrologic signatures post-fire, (2) separating climate-driven and fire-driven changes in streamflow, and (3) estimating hydrologic recovery timescales after fire. We demonstrate the use of this methodology for 17 watersheds in Southern California with high interannual variability in precipitation. We show that while traditional metrics like changes in flow or runoff ratio might not detect a disturbance effect due to confounding climate signals, the Budyko framework can be used successfully for statistical change detection. The Budyko approach was also found to be robust in detecting changes in 5 highly burned catchments (>40% burned area ratio), while changes in less burned (2) and unburned catchments (10) were insignificant. We further used the Budyko approach to quantify the contribution of fire-driven versus climate driven changes in streamflow and found that fire contributed to an average increase in streamflow on the order of 80 mm yr-1, though the effect varied greatly between years.  Finally, we estimated hydrologic recovery timescales that varied between 5 to 45 years for four burned catchments. We found a significant linear relationship between recovery time and burned area at medium and high severity for our study catchments, with about 4 years of recovery time per 10% of the watershed burned.
DOI: 10.4211/hs.13ddcc71f7bb45329028e7ed93d8ef95
title: Glose Dissertation Data

description: This resource includes all of the numerical modeling files, data sets, and subsequent post-processing files that were used in the completion of my dissertation. Data sets include all COMSOL model files for each study, all .csv outputs for temperature and flux for each study, and all post-processing routines as .m files.
DOI: 10.15468/dl.racvkk
title: Occurrence Download

description: A dataset containing 1033 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tremarctos ornatus"
 ]
}

 The dataset includes 1033 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049938-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.2f9643bb5d85436ba997a466ba8ed653
title: Ward et al., Climate Change Causes River Network Contraction and Disconnection in the H.J. Andrews Experimental Forest, Oregon, USA - Tabular Data

description: Tabular model output data in support of the publication:

Ward AS, Wondzell SM, Schmadel NM and Herzog SP (2020) Climate Change Causes River Network Contraction and Disconnection in the H.J. Andrews Experimental Forest, Oregon, USA. Front. Water 2:7. doi: 10.3389/frwa.2020.00007
DOI: 10.4211/hs.2dead0d54fe74ee7943e89ae4159aab1
title: Lake Erken High Frequency Monitoring data used for EU Water JPI project PROGNOS

description: The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models  http://prognoswater.org/).  PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive the the HF monitoring data sets that were used by PROGNOS project Partner Uppsala Universtiy to calibrate and verify the performance of the GOTM (https://gotm.net/)and SELMA models that are coupled by the frame work for aquatic biogeochemical models (https://github.com/fabm-model).  All data were collected from Lake Erken the site of the Uppsala University Limnology field station (http://www.ieg.uu.se/erken-laboratory/).  HF data is from 2015, 2016, 2017, and 2018, years when there was good coverage of the three main categories of data that are needed for water quality modeling:  1) meteorological data; 2) water temperature data; and 3)lake biogeochemical data. These data are in the format routinely collected,and can contain additional measurements that are not actually used in the model simulations.
DOI: 10.4211/hs.949dcc1647e144aab5e1ffdaaa535b87
title: HIC Lightning talk: Model and Code Sharing via CUAHSI-hosted MATLAB Online

description: Are there analysis tools that can work with my data? Have other researchers developed code that I can reuse? How can I find these code examples and ramp up quickly so that I can apply them to my project? 

With MATLAB Online hosted directly on cuahsi.org using Hydroshare resources, researchers, educations and students can access the relevant data and shared models more easily. This talk will demonstrate the use of MATLAB Online to work with hydrological data using new geospatial data access, data analytics and visualization techniques. We’ll also cover how to share work as notebooks, complete with embedded graphics, equations, and publication-quality formatting, using the new MATLAB Live Editor, enabling more transparent research and improved teaching and learning of water data science and more.
DOI: 10.15468/dl.etfk7j
title: Occurrence Download

description: A dataset containing 710 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-3.657991 41.114292, -3.659410 41.154924, -3.713126 41.154924, -3.711686 41.114292, -3.657991 41.114292))",
 "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 710 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049939-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.deeacf38f730458d87957b8aeeb6f52f
title: Abundance, Distribution, and Geometry of Naturally Occurring Streambank Soil Pipes

description: This is the data repository for the journal article entitled "Abundance, Distribution, and Geometry of Naturally Occurring Streambank Soil Pipes" published in Freshwater Science in 2019 or 2020 by Amiana M. McEwen and Erich T. Hester.  The data themselves, as well as information about the data, can be found in several locations:

1) Many data are in the journal article, which are available at the journal website or can be requested by emailing Erich Hester at ehester@vt.edu
2) Many data are available in the files associated with this Hydroshare resource, which are described in the readme.txt file
3) Any questions that are not answered by the above methods can be directed to Erich Hester at ehester@vt.edu
DOI: 10.4211/hs.e69cdabd12114a79ade60033c814d0db
title: Stream monitoring data & case sudy data (catchment U8) in Sweden - use of turbidity as a surrogate for suspended solids and phosphorus

description: This resource contains water quality data (suspended solids and phosphorus concentrations, turbidity, as well as other chemistry parameters and constituents) at 108 monitoring stations in Sweden between the years 2010 and 2012. The monitoring stations are included in national and regional monitoring programs, research projects and the "Coordinated recipient monitoring" program. Data is available from the website http://miljodata.slu.se/mvm/. 
The resource also contains data from the agricultural catchment U8 which served as a case study for the use of a turbidity sensor to estimate total phosphorus loads. The catchment is part of the national monitoring program for agricultural catchments. More information and the whole dataset regarding this program can be found in  http://jordbruksvatten.slu.se/. The current dataset for U8 contains flow data, turbidity sensor data, manual and flow proportional concentrations of suspended solids, total phosphorus, particulate phosphorus, dissolved phosphorus and other constituents concentrations such as nitrogen and TOC.
DOI: 10.4211/hs.eace2a19e3134ba8a442d75a344d4885
title: Example Model Simulation for Lake Erken from the EU Water JPI project PROGNOS

description: The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models  http://prognoswater.org/).  PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive that demonstrates model simulations that were run on Lake Erken as part of the PROGNOS project. Simulations were run using the GOTM (https://gotm.net/)and SELMA models that are coupled by the frame work for biogeochemical models (https://github.com/fabm-model). The lake model was calibrated using the program acpy (https://bolding-bruggeman.com/portfolio/acpy/) The measured data used for calibration in the format used by acpy are also included in this archive

All data were collected from Lake Erken the site of the Uppsala University Limnology field station (http://www.ieg.uu.se/erken-laboratory/). The simulation period is between 2004-2018, and we used a 5 year (1999-2003) model spin-up period prior to simulation and calibration.  Model forcing data are obtained from meteorological stations at the Erken laboratory, and from Erken's routine monitoring program.  To calibrate the model data sets of HF water temperature, chlorophyll fluorescence and dissolved oxygen are combined with laboratory measurement from the routine monitoring program.  Calibration data for nutrients are only available from the Erken laboratories routine monitoring program All data files are formated as required by the lake models.  Files that define the model parameterization (xml and yaml) are also included
DOI: 10.4211/hs.7235a0d6a18343078b2028085b7d8018
title: Data For Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method

description: This resource contains the data and scripts used for:  Garousi-Nejad, I., D. G. Tarboton, M. Aboutalebi and A. F. Torres-Rua, (2019), "Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method," Water Resources Research, http://doi.org/10.1029/2019WR024837.

Abstract from the paper:
Flood inundation remains challenging to map, model, and forecast because it requires detailed representations of hydrologic and hydraulic processes. Recently, Continental‐Scale Flood Inundation Mapping (CFIM), an empirical approach with fewer data demands, has been suggested. This approach uses National Water Model forecast discharge with Height Above Nearest Drainage (HAND) calculated from a digital elevation model to approximate reach‐averaged hydraulic properties, estimate a synthetic rating curve, and map near real‐time flood inundation from stage. In 2017, rapid snowmelt resulted in a record flood on the Bear River in Utah, USA. In this study, we evaluated the CFIM method over the river section where this flooding occurred. We compared modeled flood inundation with the flood inundation observed in high‐resolution Planet RapidEye satellite imagery. Differences were attributed to discrepancies between observed and forecast discharges but also notably due to shortcomings in the derivation of HAND from National Elevation Dataset as implemented in CFIM, and possibly due to sub optimal hydraulic roughness parameter. Examining these differences highlights limitations in the HAND terrain analysis methodology. We present a set of improvements developed to overcome some limitations and advance CFIM outcomes. These include conditioning the topography using high‐resolution hydrography, dispersing nodes used to subdivide the river into reaches and catchments, and using a high‐resolution digital elevation model. We also suggest an approach to obtain a reach specific Manning's n from observed inundation and validated improvements for the flood of March 2019 in the Ocheyedan River, Iowa. The methods developed have the potential to improve CFIM.

The file Readme.md describes the contents and steps for reproducing the analyses in the paper.
DOI: 10.4211/hs.5f685bb517eb4343b1b293a921f14639
title: Data analysis of snow water equivalent for test watershed in the Colorado River Basin in 2009

description: This resource includes the data analysis code and results using a subset of the model simulation of snow water equivalent for the watershed of Dolores River above McPhee reservoir in the Colorado River Basin from 1988 to 2010. The model used is the Utah Energy Balance model which is a physically based snow melt model. 

The data analysis code used NetCDF Operator commands (http://nco.sourceforge.net). It first subsets the data from January to May, 2009 to identify the maximum snow water equivalent for each grid cell within this period and write the result to a new NetCDF file (max.nc). It then subsets the data for April 1st and 15th, 2009 (april_1.nc, april_15.nc) and evaluates the snow water equivalent difference between the two dates to create a new NetCDF file (diff.nc). This provides the analysis result for accumulation (increase) or ablation (decrease) during this period. Water managers often track such snow water equivalent changes in water supply forecasts
DOI: 10.4211/hs.38ac7dd90c7d4353bb492604981782f0
title: Global distribution of hydrologic controls on forest growth

description: Vegetation provides key ecosystem services and is an important component in the hydrological cycle. Traditionally, the global distribution of vegetation is explained through climatic water availability. Locally, however, groundwater can aid growth by providing an extra water source (e.g. oases) or hinder growth by presenting a barrier to root expansion (e.g. swamps). In this study we analyse the global correlation between humidity (expressing climate driven water- and energy availability), groundwater and forest growth, approximated by the fraction of absorbed photosynthetically active radiation, and link this to climate and landscape position. The results show that at the continental scale, climate is the main driver of forest productivity; climates with higher water availability support higher energy absorption and consequentially more growth. Within all climate zones, however, landscape position substantially alters the growth patterns, both positively and negatively. The influence of the landscape on vegetation growth varies over climate, displaying the importance of analysing vegetation growth in a climate-landscape continuum.
DOI: 10.4211/hs.05481f58200b49d4a0f4d542d4e15da1
title: Sky Lake water level data, 2016-2018, Humphreys County, Mississippi

description: Lake stage, wetland stage, and groundwater levels in 11 wells collected by Michael C. Gratzer at the University of Mississippi Department of Geology & Geological Engineering. Data used in the following with abstract below:  Gratzer, M.C., Davidson, G.R., O'Reilly, A.M., and Rigby, J.R., (in  press 12/2019), Groundwater recharge from an oxbow lake-wetland system in the Mississippi Alluvial Plain, Hydrological Processes, https://doi.org/10.1002/hyp.13680

The Mississippi River Valley Alluvial Aquifer ranks among the most over-drafted aquifers in the United States due to intensive irrigation. Concern over declining water levels has increased focus on understanding the sources of recharge. Numerous oxbow lakes overlie the aquifer which are often considered hydraulically disconnected from the groundwater system due to fine-grained bottom sediments. In the current study, groundwater levels in and around a 445-ha oxbow lake-wetland in Mississippi were monitored for a two-year period that included an unusually long low-water condition in the lake (>17 months), followed by a high-water event lasting over four months before returning to earlier low water levels. The high water pulse (>4 m rise) provided a unique opportunity to track the impact in the underlying alluvial aquifer. During low-water conditions, groundwater flowed westward beneath the lake. Following the lake rise, groundwater beneath and near the perimeter responded as quickly as the same day, with more delayed responses moving away from the lake. Within two months, a groundwater mound formed near the center of the oxbow (>3 m increase), with a reversal in the local hydraulic gradient toward the east. Flow returned to a westward gradient when the lake level dropped back below 0.3 m. Analysis of precipitation and nearby river stage could not account for the observed behavior. Recharge to the aquifer is attributed to rising water levels spreading over point bar deposits and into surrounding forested wetlands where preferential flow pathways are likely to exist due to buried and decomposing tree remains. An earlier study in the wetland demonstrated increasing redox potential in isolated zones, consistent with the existence of preferential flow pathways through the bottom sediments (Lahiri & Davidson, this issue). Retaining high water levels in oxbow lakes could be a relatively low-cost water-management practice for enhancing aquifer recharge.
DOI: 10.4211/hs.53bd0030da8d4a0b9286c91069d545c4
title: Lake Langtjern High-Frequency Monitoring data used for the EU Water JPI project PROGNOS

description: The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models http://prognoswater.org/). PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive the the HF monitoring data sets that were used by PROGNOS project Partner NIVA (Norwegian Institute for Water Research) to calibrate and verify the performance of the GOTM (https://gotm.net/) and SELMA models that are coupled by the frame work for aquatic biogeochemical models (https://github.com/fabm-model).

Data were collected from two sources:
a) NIVA's Langtjern monitoring site (http://aquamonitor.no)
b) Publicly available data from the Norwegian Meteorological office (https://thredds.met.no/thredds/catalog.html)

HF frequency data encompasses, at least, from August 2014  to August 2017, except for the carbon concentration at the outlet of the lake where only data until August 2016 was available.
DOI: 10.4211/hs.a371fd69d41b4232806d81e17fe4efcb
title: Annual Irrigation Maps - High Plains Aquifer (AIM-HPA, Deines et al. 2019)

description: This resource is a repository of the map products for the Annual Irrigation Maps - High Plains Aquifer (AIM-HPA) dataset produced from Landsat satellite data in Deines et al. 2019. The maps cover a 608,260 km2 area across the High Plains Aquifer in the United States. AIM-HPA provides annual irrigation maps for 34 years (1984-2017). Please see Deines et al. 2019 for full details. If needed, copies can be requested from the author at jillian.deines@gmail.com.

Preferred citation: 
Deines, J.M., A.D. Kendall, M.A. Crowley, J. Rapp, J.A. Cardille, and D.W. Hyndman. 2019. Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine. Remote Sensing of Environment 233:111400. DOI: 10.1016/j.rse.2019.111400

Map Metadata:
Map products are projected in EPSG:5070 - CONUS Albers Equal Area, NAD83
Resolution: 30 m
Raster value key: 
0 = NoData, outside of study boundary
1 = Irrigated
2 = Not irrigated

Corresponding author: Jillian Deines, jillian.deines@gmail.com

Disclaimer: Irrigation maps are produced for research purposes and have an associated classification accuracy estimated to be ~91%. Overall, they are able to capture about 85% of the variation in county irrigated area statistics provided by USDA NASS. Please see Deines et al. 2019 for further detail on the methods underlying map production and estimated accuracies across years.

Note:
AIM-HPA can also be accessed directly from Google Earth Engine via the following publicly shared asset ID: "projects/h2yo/IrrigationMaps/AIM/AIM-HPA/AIM-HPA_Deines_etal_RSE_v01_1984-2017"
Example: var aimhpa = ee.Image("projects/h2yo/IrrigationMaps/AIM/AIM-HPA/AIM-HPA_Deines_etal_RSE_v01_1984-2017");
Example GEE code editor script for exporting maps from GEE: https://code.earthengine.google.com/e7623e04f410879b5d16b724dee94d0c
DOI: 10.4211/hs.4ff4b2391fe54bd2b6052b53500fb7fb
title: HYDRUS-1D model output for study of rainfall and snowmelt partitioning by climate and soil type

description: This compilation of data serves as the data repository for Hammond et al., 2019. Included are four spreadsheets of data containing HYDRUS 1-D model simulation outputs at event and annual time scales. Simulations were run for historical periods, historical periods where all snow was converted to rainfall, multiple different soil profiles depth and texture alterations, and for artificial concentrated and intermittent input scenarios. For more, see the citation below:

Hammond, J. C., Harpold, A. A., Weiss, S., and Kampf, S. K.: Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-98, 2019.
DOI: 10.4211/hs.7b81ceca6a5b426dac9905541fe27d95
title: Sandy Run, PA, baseflow data

description: Files related to the publication Ledford, SH and L Toran. In revision. Downstream evolution of wastewater treatment plant nutrient signals using high-temporal monitoring. Hydrological Processes. Funded by the William Penn Foundation.  Questions: sledford@gsu.edu.  Data collected on Sandy Run, a tributary to Wissahickon Creek, in Montgomery County, PA. Contains three files: (1) Ledford2019_SR_temporal_data is QA/QCed logger data collected at hourly intervals at the two main sites and at 15 minute intervals below the wastewater treatment plants.  Data include depth, temperature, specific conductivity, dissolved oxygen, nitrate, and phosphate, although all parameters were not collected at all sites. (2) Ledford2019_SR_Modeling_results includes StreamMetabolizer (Appling et al. 2018) modeling results for GPP and ER.  (3) Ledford2019_SR_modeling_fits includes modeled DO from StreamMetabolizer.
DOI: 10.5067/caliop/calipso/l3_ice_cloud-standard-v2-00
title: CALIPSO Lidar Level 3 Ice Cloud Data, Standard V2-00