cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.47a190100b254a4993c11c2abced411c
title: WEPPcloud

description: The Water Erosion Prediction Project (WEPP) model is a hydrology and erosion model wildly used by academics and land managers to assess the effects of land management changes on runoff and sediment yield. Even for advanced users, data preparation is a lengthy process that involves downloading various maps and datasets, and processing and saving the data in formats that are readable by the model. 

[WEPPcloud] (https://wepp.cloud) is an online interface for the WEPP model that facilitates input data preparation and hydrologic simulations from any computer connected to the internet. The user can zoom to a location, identify an area of interest, and delineate a watershed. Then based on a series of options that involve selection of soils, managements, and weather information from national or locally-stored databases, the user can quickly get estimates of runoff and soil erosion for their watershed of interest. Results are displayed in the web browser as text files and as geospatial maps and are also downloadable. Most hydrologic simulations can be completed within a few minutes, depending on the size of the watershed. 

WEPPcloud was mainly developed for forestry applications as a joint effort between University of Idaho and Forest Service Rocky Mountain Research Station. Other contributions to the application include USDA ARS, Swansea University, and Michigan Technological University.
DOI: 10.4211/hs.6d479a668f6c40a289d09e6c95ecf250
title: Pressure, Temperature, and Specific Conductivity Measurements from Lakes on San Salvador Island, Bahamas

description: San Salvador Island is located on an isolated carbonate platform situated on the southeastern edge of the Bahamian Archipelago. Over half of the island's small area is covered by hypersaline lakes that expose the island's water table to evaporation.  Many of the island's lakes are connected to the ocean by karst conduits, thereby allowing tidal pumping to drive the exchange of fresh and saltwater during tidal cycles. To investigate the influence of tidal cycles on lake water levels, we monitored water temperature, pressure, and specific conductivity for several lakes located on San Salvador Island, Bahamas. We instrumented lakes with HOBO Onset U20L-04 loggers with a water level accuracy of 0.14 cm. HOBO Onset data loggers were set to record measurements at intervals ranging from 30 seconds to 15 minutes. We chose sampling intervals as to not exceed the HOBO logger's data recording capacity based on our estimated return to the site to download data. For most of the lakes instrumented in this study, we combine multiple timeseries into an individual location file. Accordingly, a single data table may have temporal data gaps and time periods with different sampling intervals. The README.md file included with this dataset contains a table with lake names and locations, sampling rates, and deployment dates.
DOI: 10.4211/hs.31b7c356119241e0a9b1f6f56a9cb44e
title: GRC Weather Station Data, San Salvador Island, Bahamas

description: To monitor meteorologic conditions on San Salvador Island throughout the duration of our well and lake instrumentation campaigns (see associated datasets), we installed an automatic weather station (AWS) at the Gerace Research Centre (GRC) located on the island's northern shore. The GRC weather station was equipped with a HOBO U30 Data Logger that recorded sensor measurements at a 15-minute sampling rate from November 2017 through October 2019. The AWS measured air temperature, and relative humidity with a Temperature/RH Smart Sensor (S-WSB-M003) installed within a solar radiation shield to prevent overheating. Rainfall was measured with a HOBO/Onset Rain Gauge Smart Sensor that using a tipping bucket mechanism mounted on a stainless steel shaft with brass bearings within aluminum housing to monitor rainfall rates up to 12.7 cm per hour. Atmospheric pressure was measured using a Barometric Pressure Smart Sensor within weatherproof housing with an accuracy of +/- 3.0 mbar, a resolution of 1.0 mbar, and a measurement range of 660-1070 mbar. Incoming shortwave solar radiation was measured with a silicon pyranometer (Solar Radiation Smart Sensor) mounted onto the weather station using the Onset Light Sensor Bracket. Data gaps due to sensor failure or proceeding sensor addition to the weather station producing null values are filled with "NaN" (i.e., not a number).
DOI: 10.4211/hs.50e9716922dc487981b71e2e11f3bb5d
title: SUMMA Simulations using CAMELS Datasets on CyberGIS-Jupyter for Water

description: This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the VB study (Van Beusekom et al., 2022) as explained by Maghami et el. (2023). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 60-month actual simulation to demonstrate the capabilities of the notebooks. For even a faster assesment of the capabilities of the notebooks, users are recommended to opt for a shorter simulation period (e.g., 12 months of actual simulation and six months of initialization) and one example CAMELS site. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.
DOI: 10.4211/hs.9d73d61696ee4f6b9c9a11e21cd44e24
title: SUMMA Simulations using CAMELS Datasets for HPC use with CyberGIS-Jupyter for Water

description: This resource, configured for execution in connected JupyterHub compute platforms using the CyberGIS-Jupyter for Water (CJW) environment's supported High-Performance Computing (HPC) resources (Expanse or Virtual ROGER) through CyberGIS-Compute Service, helps the modelers to reproduce and build on the results from the VB study (Van Beusekom et al., 2022) as explained by Maghami et el. (2023). 

For this purpose, four different Jupyter notebooks are developed and included in this resource which explore the paper goal for four example CAMELS site and a pre-selected period of 60-month simulation to demonstrate the capabilities of the notebooks. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook utilizes the CJW environment's supported HPC resource (Expanse or Virtual ROGER) through CyberGIS-Compute Service to executes SUMMA model. This notebook uses the input data from first notebook using original and altered forcing, as per further described in the notebook. The third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). The fourth notebook, only developed for the HPC environment (and only currently working with Expanse HPC), enables transferring large data from HPC to the scientific cloud service (i.e., CJW) using Globus service integrated by CyberGIS-Compute in a reliable, high-performance and fast way. More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these four notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice. As this resource uses HPC, it enables a high-speed running of simulations which makes it suitable for larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month simulation period used in the paper) practical and much faster than when no HPC is used.
DOI: 10.4211/hs.86225d08252747d5a78477c1f74eb158
title: Isotopic composition and major ion concentrations of national and international bottled waters in Costa Rica

description: Global bottled water consumption has largely increased (14.35 billion gallons in 2020) during the last decade since consumers are demanding healthier and safer forms of rehydration. Bottled water sources are normally labeled as mountainous and pristine mineral springs (fed by rainfall and snow/glacier melting processes), deep groundwater wells or industrial purified water. The advent of numerous international and national-based bottled water brands has simultaneously raised a worldwide awareness related to the water source and chemical content traceability. Here, we present the first database of stable isotope compositions and reported chemical concentrations from imported and national-based bottled waters in Costa Rica. In total, 45 bottled waters produced in Costa Rica and 31 imported/produced from USA, Europe, Asia and other countries of Central America were analyzed for δ18O, δ2H, and d-excess. Reported chemical compositions were obtained from available bottle labels. National-based bottle waters ranged from -2.47‰ to -10.65‰ in δ18O and from -10.4‰ to -78.0‰ in δ2H, while d-excess varied from +4.2‰ up to +17.0‰. International bottle waters ranged between -2.21‰ and -11.03‰ in δ18O and from -11.3‰ up to -76.0‰ in δ2H, while d-excess varied from +5.0‰ up to +19.1‰.  In Costa Rica, only 19% of the brands reported chemical parameters such as Na+, K+, Ca+2, Mg+2, Fe+2/+3, F-, Cl-, NO3-, SO4-2, CO3-2, SiO2, dry residue, and pH; whereas 27% of the international products reported similar parameters. The absence of specific geographic coordinates or water source origin limited a spatial analysis to validate bottled water isotope compositions versus available isoscapes in Costa Rica. This information highlights the potential and relevance of the use of water stable isotope compositions to improve the traceability of bottled water sources and the urgent need of more robust legislation in order to provide detailed information (i.e., water source, chemical composition, purification processes) to the final consumers.
DOI: 10.4211/hs.eecd064878cc4eefb3e78967c795d868
title: Spatiotemporal dynamics of nitrous oxide emission hotspots in heterogeneous riparian sediments

description: Supplementary metadata for "Spatiotemporal dynamics of nitrous oxide emission hotspots in heterogeneous riparian sediments", which includes time-series of river stage, water table elevation, groundwater temperature, and groundwater specific conductivity at the Theis Environmental Monitoring and Modeling Site (TEMMS). Chemical data was performed on samples collected at sampling ports throughout the TEMMS floodplain.
DOI: 10.4211/hs.3143b3b1bdff48e0aaebcb4aedf02feb
title: Supporting data and tools for "An Open-source, Semi-supervised Water End Use Disaggregation and Classification Tool"

description: The files provided here are the supporting data and code files for the analyses presented in "An Open-source, Semi-supervised Water End Use Disaggregation and Classification
Tool" a manuscript submitted to the Journal of Water Resource Planning and Management. The data included in this resource were collected using the CIWS-Logger (https://github.com/UCHIC/CIWS-WM-Logger) data logging device. Cyberinfrastructure for Intelligent Water Supply (CIWS) is an open-source, modular, generalized architecture designed to automate the process from data collection to analysis and presentation of high temporal residential water use data. The CIWS-Logger is a low cost device capable of collecting this type of data on existing, magnetically driven water meters. The code included in this resource (CIWS-Disaggregator) demonstrates a new water end use disaggregation and classification tool that builds on existing end use disaggregation studies and addresses the unavailability of code and data used by prior studies. The tool was developed in Python and can be accessed via any current Python programming environment. It was tested on anonymized, high temporal resolution datasets for five homes selected from a larger dataset for 31 homes located in the Cities of Logan and Providence Utah, USA. Results from different meter types and sizes are presented to demonstrate the accuracy of the tool in disaggregating and classifying high temporal resolution data into individual end use events. The results of this paper are reproducible using openly available code and data, representing an accessible platform for advancing end use disaggregation tools. The tool can be adapted to specific research needs.
DOI: 10.4211/hs.bb01ce1e21214f93b197d6855823af53
title: Monitoring Line Hole Well Field, San Salvador Island, Bahamas, 2015-2017

description: San Salvador Island is a small isolated carbonate platform on the southeastern edge of the Bahamian Archipelago. The Line Hole well field is located on an eogenetic karst aquifer on San Salvador Island's northern coast. The island's negative water budget and extensive lake cover have resulted in the upconing of saline water that has fragmented the once continuous freshwater lens. The Line Hole well field consists of several 15-cm diameter wells drilled into the fresh-water lens and arranged in a line perpendicular to the shore. The well field also has two monitoring wells (LH 1, and LH 13), that penetrate approximately 7 m below the water table into higher salinity groundwater. The well field was abandoned in 2016 upon saltwater intrusion to the aquifer. To evaluate the connectivity between the eogenetic karst aquifer monitored by the Line Hole well field and the ocean, we instrumented wells with HOBO U20L-04 loggers to measure pressure and temperature timeseries. We instrumented wells LH4, and LH8, in addition to the monitoring wells LH1 and LH13.
DOI: 10.4211/hs.5949aec47b484e689573beeb004a2917
title: National Water Model HydroLearn Python Notebooks

description: This resource contains Jupyter Python notebooks which are intended to be used to learn about the U.S. National Water Model (NWM). These notebooks explore NWM forecasts in various ways. NWM Notebooks 1,  2, and 3, access NWM forecasts directly from the NOAA NOMADS file sharing system. Notebook 4 accesses NWM forecasts from Google Cloud Platform (GCP) storage in addition to NOMADS. A brief summary of what each notebook does is included below:


Notebook 1 (NWM1_Visualization) focuses on visualization. It includes functions for downloading and extracting time series forecasts for any of the 2.7 million stream reaches of the U.S. NWM. It also demonstrates ways to visualize forecasts using Python packages like matplotlib. 

Notebook 2 (NWM2_Xarray) explores methods for slicing and dicing NWM NetCDF files using the python library, XArray. 

Notebook 3 (NWM3_Subsetting) is focused on subsetting NWM forecasts and NetCDF files for specified reaches and exporting NWM forecast data to CSV files. 

Notebook 4 (NWM4_Hydrotools) uses Hydrotools, a new suite of tools for evaluating NWM data, to retrieve NWM forecasts both from NOMADS and from Google Cloud Platform storage where older NWM forecasts are cached. This notebook also briefly covers visualizing, subsetting, and exporting forecasts retrieved with Hydrotools.

**NOTE: Notebook 4 Requires a newer version of NumPy that is not available on the default CUAHSI JupyterHub instance. Please use the instance "HydroLearn - Intelligent Earth" and ensure to run !pip install hydrotools.nwm_client[gcp].** 

The notebooks are part of a NWM learning module on HydroLearn.org. When the associated learning module is complete, the link to it will be added here. It is recommended that these notebooks be opened through the CUAHSI JupyterHub App on Hydroshare. This can be done via the 'Open With' button at the top of this resource page.
DOI: 10.4211/hs.92999529572340c593b2b8c5b5bcd211
title: Supplement materials for publication: Linking different drought types in a small catchment from a statistical perspective – case study in the Wernersbach catchment, Germany.

description: - Calculated indexes (P,Q,SM)
- Code for each method and picture
- Modelled SM and used BROOK90 parameters
DOI: 10.4211/hs.d98161b9f3fb4d03a4358f6c8b5f2c04
title: MHIA Model  ("Modelo Hidrológico Agregado")

description: MHIA is a continuous hydrological model that computes a balance of the volume of water in the soil, taking into account the following processes: precipitation, infiltration, percolation, evapotranspiration and exfiltration. From these variables, the model evaluates surface and subsurface runoff, generating a hydrograph at the outlet of the modelled catchment. As input data, the model needs to be fed with time series of precipitation and temperature, with whatever time resolution. The model has 14 parameters that must be defined by the user or calibrated from observed discharge time series.
DOI: 10.4211/hs.8f242f0decc3459fb106a57363b96b96
title: California Altered Streamflow Classification

description: In California, riverine ecosystems adapt to a Mediterranean climate: floods in wet winters, snowmelt flows in spring and low flows in summer. Humans have modified the natural river flow patterns in California by storing water during winter and releasing during summer and diverting water from streams. Resulting alterations to the natural flow regimes have degraded riverine ecosystems. Both intense climatic variability and profoundly altered rivers increase the importance of understanding the diversity of streamflow patterns. The present electronic resources quantifies the human alteration on flow regimes in California by categorizing impaired flow regime classes from human alteration. This study is based on the hydrologic classification of altered rivers in California developed by Guitron (2020).  This resources share the predicted hydrologic class for impaired flows in California.
DOI: 10.4211/hs.d001620c2f104a0891dc18825ea2fe31
title: Optical properties of dissolved carbon in a high elevation tropical peatland ecosystem

description: Tropical peatlands are distributed mainly in coastal lowlands; however high elevation regions include a large prevalence of small and fragmented peatlands that are mostly understudied. Anthropogenic pressure to expand cattle farming, agriculture, and urbanization frontiers via artificial drainage of peatlands is increasing carbon losses to the atmosphere and streams. Here we present, the first characterization of dissolved carbon optical properties in ombrotrophic peat bogs of the Talamanca range of Costa Rica, across an altitudinal gradient (2,400-3,100 m asl) during the rainy season. Dissolved organic matter (DOM) sources and decomposition processes were evaluated in the light of dissolved organic and inorganic carbon (DOC and DIC), optical properties, excitation-emission matrices (EEMs), and major water chemistry. DOC concentrations ranged from 0.2 mg/L up to 47.0 mg/L, with a mean value of 12.5 ± 10.2 mg/L. DIC concentrations were below 2 mg/L and δ13CDIC values indicated a mixture between soil organic matter, CO2 in soil water, and in less degree DIC derived from bacterial CO2. Fluorescence intensity of humic-like peaks was 6-7 times greater than fresh-like peaks across all sites. Fluorescence peak ratios coupled with the biological and humification indexes point to a greater relative contribution of recalcitrant soil-derived DOM. EEMs denoted a high prevalence of humic and fulvic acids in the peat bogs, with particular high intensities in soluble microbial by-products-like and aromatic protein regions at three sites. Rainfall variability plays a remarkable role in controlling (acid and anoxic conditions) carbon storage and humification processes. Our data provides a baseline to underpin tropical carbon dynamics across high elevation peatlands.
DOI: 10.4211/hs.316f8ea825aa4915b6c649d36825b1cc
title: NSF-PIRE Food and Water Security Hydrologic Citizen Science Data in the Upper Blue Nile Basin, Ethiopia

description: Citizen science (students and farmers) collected hydrologic data of river stage, depth to groundwater table, soil moisture and soil resistivity in the Upper Blue Nile Basin, Ethiopia over four years. Each site and variable has different time lengths and location files are attached. Soil resistivity sensor was developed specifically for project (Xu 2018). Refer to project website for PI contact and more information: https://pire.engr.uconn.edu
DOI: 10.4211/hs.a6ea89ae20354e39b3c9f1228997e27a
title: Supporting data and tools for "Toward automating post processing of aquatic sensor data"

description: This resource contains the supporting data and code files for the analyses presented in "Toward automating post processing of aquatic sensor data," an article published in the journal Environmental Modelling and Software. This paper describes pyhydroqc, a Python package developed to identify and correct anomalous values in time series data collected by in situ aquatic sensors. For more information on pyhydroqc, see the code repository (https://github.com/AmberSJones/pyhydroqc) and the documentation (https://ambersjones.github.io/pyhydroqc/). The package may be installed from the Python Package Index (more info: https://packaging.python.org/tutorials/installing-packages/).

Included in this resource are input data, Python scripts to run the package on the input data (anomaly detection and correction), results from running the algorithm, and Python scripts for generating the figures in the manuscript. The organization and structure of the files are described in detail in the readme file. The input data were collected as part of the Logan River Observatory (LRO). The data in this resource represent a subset of data available for the LRO and were compiled by querying the LRO’s operational database. All available data for the LRO can be sourced at http://lrodata.usu.edu/tsa/ or on HydroShare: https://www.hydroshare.org/search/?q=logan%20river%20observatory. 

There are two sets of scripts in this resource: 1.) Scripts that reproduce plots for the paper using saved results, and 2.) Code used to generate the complete results for the series in the case study. While all figures can be reproduced, there are challenges to running the code for the complete results (it is computationally intensive, different results will be generated due to the stochastic nature of the models, and the code was developed with an early version of the package), which is why the saved results are included in this resource. For a simple example of running pyhydroqc functions for anomaly detection and correction on a subset of data, see this resource: https://www.hydroshare.org/resource/92f393cbd06b47c398bdd2bbb86887ac/.
DOI: 10.4211/hs.0d456f6dc1484489a5927d244d51bcd5
title: Red Canyon Creek Water Levels, 2017 - 2019

description: Water levels measured with a pressure transducer (PT) downstream of the BDAs in Red Canyon Creek. PT accuracy is +/- 1 cm. See Figure 1 in Davis et al. (2021) for PT installation location.
DOI: 10.4211/hs.7c5e032bdc7648a4a8c863a2c175e5b6
title: DataShare for Han et al., 2021 Ecological Indicators

description: This is the data and code associated with the Publication:
Han, B., Reidy, A., & Li, A. (2021). Modeling nutrient release with compiled data in a typical Midwest watershed. Ecological indicators, 121, 107213.