cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.5527ff2d53494455b9a9121158c02dbe
title: San juan del rio basin hydrochemistry

description: Biannual hydrogeologic cycle characterization in a natural semi arid environment, we have applied and compared Craig-Gordon isotopic model with the Penman physical evaporation model. Results presented include rain water, atmospheric water vapor, agricultural pool water,  isotopic evaporation loss and Penman evaporation estimation  Physical and isotopic methodologies are difficult to relate due to basic fundamentals and development conceptualizations. Further work is required to enhance the Craig-Gordon and Gonfiantini evaporation model and its relationship with physical methodologies to obtain accurate estimations in evaporation in natural semiarid environments.
DOI: 10.4211/hs.5f997ec440ea41859bc329ea4a5d7289
title: System for Producing RIver Network Geometry (SPRING) - A conceptual model for generating river bathymetry for entire network in a watershed

description: Modeling riverine processes require accurate representation of topography. However, Digital Elevation Models (DEMs) do not have complete bathymetric representation and need to be augmented with additional bathymetry data. SPRING is a conceptual bathymetry generation tool for creating 3D representation of river channel geometry that can be incorporating into traditional DEMs to develop a complete a more accurate "topo-bathy" DEM. SPRING has an automated framework for processing entire river network in a watershed with minimal user intervention, thereby, enabling it to process large watersheds efficiently. This is a significant advantage over other currently available river bathymetry generation tools which can only process single reaches. Additionally, most of the conceptual bathymetric models currently available to fluvial modelers create symmetric functional surfaces, which do not reflect the anisotropic characteristics of the river channel. SPRING captures the anisotropy in river geometry due to a meandering thalweg, thereby, creating asymmetric river channels that are more representative of natural river systems.

This resource contains an initial release of SPRING. It is available to users as a toolbar in ArcMap, which deploys intuitive Graphic User Interfaces (GUIs) to ensure that no programming (coding) background is required for implementing SPRING. Following files are included with this resource: 
1)	Installation File: This folder contains a zipped file of the SPRING windows installer (.msi) 
2)	SPRING_short_instructions.pdf: As the name suggests, these are concise instructions to set up and get SPRING running for the sample data
3)	SPRING_User_Manual.pdf: These are slightly more detailed instructions about getting SPRING to run on the user’s dataset. It has more background and troubleshooting information on SPRING.
4)	Sample Data: This folder contains a set of sample data. It has a DEM (“sampledem”) and a file geodatabase (“Sample_Data.gdb”). The file geodatabase contains all the input, intermediate and output feature classes that are needed by SPRING.

Please direct all your queries to Sayan Dey (dey6@purdue.edu) and Dr. Venkatesh Merwade (vmerwade@purdue.edu).
DOI: 10.4211/hs.2a2ccab0b4ff4c9f9be97a9aff4f0b27
title: Data from Foster et al. (2021), Effects of large-scale heterogeneity and temporally varying hydrologic processes on estimating immobile pore space: A mesoscale-laboratory experimental and numerical modeling investigation

description: The data presented here are published in Foster, A., Trautz, A.C., Bolster, D., Illangasekare, I.,  and Singha, K. (2021). Effects of large-scale heterogeneity and temporally varying hydrologic processes on estimating immobile pore space: A mesoscale-laboratory experimental and numerical modeling investigation. Journal of Contaminant Hydrology, https://doi.org/10.1016/j.jconhyd.2021.103811.

The advection-dispersion equation (ADE) often fails to predict solute transport, in part due to incomplete mixing in the subsurface, which the development of non-local models has attempted to deal with.  One such model is dual-domain mass transfer (DDMT); one parameter that exists within this model type is called immobile porosity. Here, we explore the complexity of estimating immobile porosity under varying flow rates and density dependencies in a large-scale heterogeneous system. Immobile porosity is estimated experimentally and using numerical models in 3-D flow systems, and is defined by domains of comparatively low advective velocity instead of truly immobile regions at the pore scale. Tracer experiments were conducted in a mesoscale 3-D tank system with embedded large impermeable zones and the generated data were analyzed using a numerical model. The impermeable zones were used to explore how large-scale structure and heterogeneity affect parameter estimation of immobile porosity, assuming a dual-porosity model, and resultant characterization of the aquifer system. Spatially and temporally co-located fluid electrical conductivity (σ_f) and bulk apparent electrical conductivity (σ_b)—using geophysical methods—were measured to estimate immobile porosity, and numerical modeling (i.e., SEAWAT and R3t) was conducted to explore controls of the immobile zones on the experimentally observed flow and transport. Results showed that density-dependent flow increased the hysteresis between measured fluid and bulk electrical conductivity, resulting in larger interpreted immobile pore-space estimates. Increasing the dispersivity in the model simulations decreased the estimated immobile porosity; flow rate had no impact. Overall, the results of this study highlight the difficulty faced in determining immobile porosity values in field settings, where hydrogeologic processes may vary temporally. Our results also highlight that immobile porosity is an effective parameter in an upscaled model whose physical meaning is not necessarily clear and that may not align with intuitive interpretations of a porosity.

Column experiments from Allan Foster's thesis contributing to this work are also included below.
DOI: 10.15468/dl.jrve2d
title: Occurrence Download

description: A dataset containing 3995 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "GadmGid is USA.34_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Agkistrodon contortrix"
 ]
}

 The dataset includes 3995 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049940-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.1a116e5460e24177999c7bd6f8292421
title: SENSEmap-USGLB: Nitrogen and Phosphorus Inputs

description: SENSEmap-USGLB, the Spatially Explicit Nutrient Source Estimate map for the United States Great Lakes Basin, estimates inputs to the landscape from seven sources of nitrogen and six sources of phosphorus at 30 meter resolution for an average year during the 2008-2015 period. SENSEmap uses statistical and machine learning methods to estimate nutrient inputs using remotely sensed data, government records, and literature values. The sources include atmospheric deposition, chemical agricultural fertilizer, chemical nonagricultural fertilizer, manure, septic tanks, nitrogen fixation from legumes, and point sources. This resource includes 30 meter maps of each source along with corresponding watershed summaries at the Hydrologic Unit Code 12 (HUC12) and HUC8 levels, as defined in the USGS 2014 Watershed Boundary Dataset. Watershed summaries include total nitrogen and phosphorus in kg/yr, area normalized watershed inputs in kg/ha/yr, percent contribution of each source individually, and the percent contributions of combined agricultural sources and non-agricultural sources. Single-year per crop estimates of total nitrogen fixation inputs are also included. The values provided represent an average nutrient input in kg/ha/yr over the 2008-2015 period, generated from a single model realization. SENSEmap may be used to quantify nutrient inputs within nutrient budgets, process-based models, and water quality health indicators. SENSEmap estimates are not loads to groundwater, streams, or lakes and do not include nutrient exports due to harvest, denitrification, or other processes. SENSEmap is a regional-scale estimate, produced at fine resolution, and includes stochastic processes for certain inputs. As such, it should not be used for field level assessments, or where precise knowledge of local inputs is of key concern. SENSEmap-USGLB is described in full detail in the manuscript and supporting information of Hamlin et al.'s (2020) “Spatially Explicit Nutrient Source Estimate Map (SENSEmap): Quantifying Landscape Nutrient Inputs With Spatially Explicit Nutrient Source Estimate Maps in Journal of Geophysics: Biogeosciences (https://doi.org/10.1029/2019JG005134).
DOI: 10.4211/hs.e314ed52a83a46dd9e575304c299bd83
title: Data for "A Computationally Efficient and Physically Based Approach for Urban Flood Modeling Using a Flexible Spatiotemporal Structure"

description: Hurricane Harvey maximum inundation depth and extents produced using the Interconnected Channel and Pond Routing Model. The full paper can be accessed at https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019WR025769
DOI: 10.4211/hs.8ce4c0bf59fa42a1829c98e8e993872e
title: Borehole Lithological Data of SW Bengal Basin

description: Borehole data showing lithology as a function of depth in 1452 locations. The data have been compiled from the sources mentioned in the data source section.
DOI: 10.4211/hs.ea30176c717d4b7baeb85c16427f1d6f
title: Calculating Runoff using TOPMODEL (M6)

description: This resource contains data inputs and an iPython Jupyter Notebook used to simulate semi-distributed variable source area runoff generation in a tributary to the Logan River. This resource is part of the HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about.

In this activity, the student learns how to (1) calculate the topographic wetness index using digital elevation models (DEMs) following up on a previous module on DEMs and GIS in Hydrology; (2) apply TOPMODEL concepts and equations to estimate soil moisture deficit and runoff generation across a watershed given necessary watershed and storm characteristics; and (3) critically assess concepts and assumptions to determine if and why TOPMODEL is an appropriate tool given information about a specific watershed.

Please note that this exercise sets up the data needed to estimate runoff in the Spawn Creek watershed using TOPMODEL. Spawn Creek is a tributary of the Logan River, Utah. This exercise uses some of the same data as the Logan River Exercise in Digital Elevation Models and GIS in Hydrology at https://www.hydroshare.org/resource/9c4a6e2090924d97955a197fea67fd72/. If running the TOPMODEL for other study sites, you need to prepare a DEM TIF file and an outlet shapefile for the area of interest.
- There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects).
- If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS.
DOI: 10.4211/hs.54375615d258461086125d5fc85a4c32
title: Simulation of Lake Erken water temperature (1961-2017) with GOTM: Model configuration, and calibration, input data, output data and observed water temperature - Update

description: The physical hydrodynamic model GOTM was used to reconstruct the past water temperature of Lake Erken (Sweden) in order to investigate possible changes in its thermal structure during the period 1961-2017. To run the model, seven climatic parameters were used as forcing data: Wind Speed (m/s), Air Temperature (°C), Air Pressure (hPa), Relative Humidity (%), Cloud Cover (dimensionless value between 0-1), Precipitation (mm/day) and Shortwave Solar Radiation (W/m-2). This resource contains the model configuration file, input data of the model, the observed water temperature used to calibrate the model, and the output data.

The file 'erken.xml' is the model configuration file. The file 'config_acpy.xml' is the model calibration configuration file

The input data of the model are:
- 'Erken_DailyPrec_1960-2017.dat': Lake Erken Daily Precipitation
- 'Erken_MetFile_1960-2017.dat': hourly dataset that contains Lake Erken Wind Speed, Air Temperature, Air Pressure, Relative Humidity and Cloud Cover
- 'Erken_HourlySWR_1960-2017.dat': hourly dataset of Lake Erken Shortwave Solar radiation


The file 'ErkenObsWTemp_1961-2017.obs' contains the real daily water temperature data of Lake Erken and it was used to calibrate the model.

The output data of the model are:
- 'Mod_temp_hr_z.txt': contains the depth profile of lake Erken
- 'Mod_temp_hr_temp.txt': contains the calibrated modeled water temperature profile on daily time-step between 1960-2017.

The input data used to run the model were available between 1961-2017. To avoid that the initial state of the model could increase the errors during calibration, a 1-year simulation spin-up was used to minimize calibration errors. To make full use of the available input data, a copy of the 1961 meteorological data was appended at the beginning of the input files 'Erken_DailyPrec_1960-2017.dat', 'Erken_MetFile_1960-2017.dat' and 'Erken_HourlySWR_1960-2017.dat'. The data referred to the year 1960 are therefore only a copy of the 1961 data and they were used as spin-up year. For this reason, the data referred to the year 1960 in the output files should be discarded.

This resource is an update of the resource in Hydroshare https://doi.org/10.4211/hs.7e5ec8c0e2b245199ab13cc9ae08b841. In this current resource, the file 'ErkenObsTemp_1961-2017.obs' was updated with water temperature data of April and November that were missing in the previous resource. Moreover, the calibration file 'config_acpy.xml' has been added here.
DOI: 10.4211/hs.f25ceec3b5e141e690051cc1ec1f4acf
title: Hydrogeological Data of SW Bengal Basin

description: This resource contains various hydrogeological data from the SW Bengal Basin in India and Bangladesh. These data have been used in a cross border groundwater studies aiming to understand the sources of high arsenic concentration in deep (>150m) groundwater. All of the groundwater radiocarbon, d13C, 3H, arsenic data, and sediment age data indicating the source as 'this study' have been collected by a team of researchers lead by this author. Other data such as pumptest data, sediment ages, and borehole lithology have been compiled by the author from various published sources mentioned in the source column of each data, detail citation of each of those sources are provided in the source section. Borehole data showing lithology as a function of depth in 1452 locations. The radiocarbon, d13C, 3H, arsenic data are available for 33 locations along two latitudinal transects one paralleling 23.22 and the other 22.87 degree latitudes. Pump test data is available in 18 locations. There are a total of 18 sediment age data.
DOI: 10.4211/hs.aa567b589f3d47998c83dc1698162a6f
title: Coping with flood by using the Geographic Information Systems (GIS) as state-of-the-art tools: A Report

description: A report to provide complete knowledge of flood modeling with essential hands-on training. The Water & Environment Division under the Department of Civil Engineering, National Institute of Technology Warangal had organized a one week Global Initiative of Academic Networks (GIAN) Course under the supervision of Minister of Human Resource Development (MHRD), Government of India on “Geographic Information Systems (GIS) Methods for Flood Risk Management” from 25th July to 1st August, 2018.
DOI: 10.4211/hs.b3ca27267d0c4a23b7f459917e3067ec
title: High Park Fire hillslope erosion data and watershed simulations

description: This resource includes two datasets for High Park Fire sediment yield and sediment load from June-October 2013. 

The first file, HPF_hillslope_observed.csv contains hillslope sediment yield data that were originally reported in Schmeer et al. (2018). 

The column values are: 
"Site" = site ID
Area_ha = field-delineated hillslope area
SY_Mg_ha = measured hillslope sediment yield in Mg per ha
P_mm = total precipitation depth measured at the nearest rain gauge 

The second file, HPF_watershed_simulated, contains simulated total watershed sediment loads for multiple models discretized at varying target hillslope resolutions. 

The column values are:
"area_ha" = target area for the hillslopes
"watershed" = name of the watershed simulated, either Hill Gulch or Skin Gulch
"model" = name of the model used,
"Mg" = watershed total sediment load in Mg
"fraction" = fraction increase in watershed total sediment load relative to the sediment load simulated for 0.5 ha hillslope resolution
DOI: 10.17026/ar/9keu9i
title: PAN-00181340 - composite brooch with high catch-plate series 2

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00181340&lt;/p&gt;
description: Modified: 2025-10-21T08:57:52+02:00
DOI: 10.4211/hs.7507a464b96f49139a5c446c827ee9d3
title: SUMMA sopron and pySUMMA 1.0.0 version Singularity

description: This resource included a singularity image for SUMMA sopron and pySUMMA 1.0.0 version. Detail description is in definition file.
DOI: 10.4211/hs.c647e1fab5874f29bf2e92f56f3e1f94
title: SWMM Model of the Milandre Cave (Switzerland)

description: This dataset contains the input and output files of a hydraulic model (EPA Storm Water Management Model (SWMM)) of the downstream part of the Milandre Cave (Switzerland).  The aim of the model is to simulate sediment transport processes in the karst conduits, which is done as a post-processing step.

The model is based on the cave survey and calibrated on the basis of hydraulic head, flow rate and tracer test data. The software version is SWMM5.0.019 for Linux. The simulation results (flow rates, hydraulic heads, flow velocities) are used to compute the mean boundary shear stress and the shear velocity in the conduits in order to assess sediment transport processes.
DOI: 10.4211/hs.63f44659faee4d859c59992784f4b27c
title: Tables of Synthesized Results for "Stormwater control impacts on runoff volume and peak flow: A meta-analysis"

description: This resource contain flat text files of data in support of the manuscript "Stormwater control impacts on runoff volume and peak flow: A meta-analysis" currently under review in Hydrological Processes.  The data files contain (1) a list of studies from which results were extracted to perform the meta-anlaysis, (2) a list of sites where the studies were performed (see figure 3 in the manuscript), (3) the extracted results used for runoff analysis (see Table 2 in the manuscript for a description), and (4) the extracted results used for peak flows analysis (see Table 2 in the manuscript for a description).
DOI: 10.4211/hs.992d96e406fd4c67b90c016f1c187ac5
title: Wildfires lead to decreased carbon and increased nitrogen concentrations in upland arctic streams

description: The Central Siberian Plateau (CSP) is undergoing rapid climate change that has resulted in increased frequency of forest fires and subsequent alteration of watershed carbon and nutrient dynamics. Across a watershed chronosequence (3 to >100 years since wildfire) we quantified the effects of fire on quantity and composition of dissolved organic matter composition (DOM), stream water nutrients concentrations, as well as in-stream nutrient uptake. Wildfires increased concentrations of nitrate for a decade, while decreasing concentrations of dissolved organic carbon and nitrogen (DOC and DON) and aliphatic DOM contribution for five decades. These post-wildfire changes in stream DOM result in lower uptake efficiency of in-stream nitrate in recently burned watersheds. Nitrate uptake (as uptake velocity) is strongly dependent on DOM quality (e.g. polyphenolics), ambient dissolved inorganic nitrogen (DIN), and DOC to DIN ratios. Our observations and experiments suggest that a decade-long pulse of inorganic nitrogen and a reduction of DOC export occur following wildfires in streams draining the CSP. Increased fire frequency in the region is thus likely to both decrease DOM and increase nitrate delivery to the main stem Yenisei River, and ultimately the Arctic Ocean, in the coming decades.
DOI: 10.4211/hs.a4e4a5914725470494e1eee16907bf86
title: BCCZO -- Soil Moisture, Water Potential -- Gully Site, Soil Pit (BT_Gully_SLmstWRPtn) -- Betasso -- (2010-2020)

description: ** THESE SENSORS START FAILING SEPTEMBER 2019 AND ALL SENSORS REMOVED 2020. **

Decagon Devices EC-5 soil moisture sensors and MPS-1 soil water potential sensors placed at various depths in soil pits.

3 Decagon Devices, Inc. EC-5 soil moisture sensors, 3 Decagon Devices, Inc. MPS-1 soil water potential sensors. Soil sensors placed at 15, 40, and 70 cm depth from surface; 70cm depth sensors placed into competent saprolite. 



Sensor group IDs and descriptions-

BT_Gully_EC5_15, Soil Moisture, Decagon EC-5 soil moisture sensors

BT_Gully_EC5_40, Soil Moisture, Decagon EC-5 soil moisture sensors

BT_Gully_EC5_70, Soil Moisture, Decagon EC-5 soil moisture sensors

BT_Gully_MPS1_15, Soil Water Potential, Decagon MPS-1 soil water potential sensors

BT_Gully_MPS1_40, Soil Water Potential, Decagon MPS-1 soil water potential sensors

BT_Gully_MPS1_70, Soil Water Potential, Decagon MPS-1 soil water potential sensors



Also see related datasets
DOI: 10.4211/hs.3477aeb91a39447e90c981b98816e9fe
title: BCCZO -- Soil Moisture, Water Potential -- BT_Borrow_SLmstwrptn -- Betasso -- (2010-2020)

description: ** THESE SENSORS START FAILING SEPTEMBER 2019 AND ALL SENSORS REMOVED 2020.**

Decagon Devices EC-5 soil moisture sensors and MPS-1 soil water potential sensors placed at various depths in soil pits.

3 Decagon Devices, Inc. EC-5 soil moisture sensors, 3 Decagon Devices, Inc. MPS-1 soil water potential sensors. Soil sensors placed at 15, 40, and 70 cm depth from surface; 70cm depth sensors placed into competent saprolite. 



Sensor group IDs and descriptions-

BT_Borrow_EC5_100, Soil Moisture, Decagon EC-5 soil moisture sensors

BT_Borrow_EC5_130, Soil Moisture, Decagon EC-5 soil moisture sensors

BT_Borrow_EC5_70, Soil Moisture Decagon EC-5 soil moisture sensors

BT_Borrow_MPS1_100, Soil Water Potential , Decagon MPS-1 soil water potential sensors

BT_Borrow_MPS1_130, Soil Water Potential , Decagon MPS-1 soil water potential sensors

BT_Borrow_MPS1_70, Soil Water Potential, Decagon MPS-1 soil water potential sensors



Also see related datasets