cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.d750278db868447dbd252a8c5431affd
title: MC-LSTM papers, model runs

description: Runs from two papers exploring the use of mass conserving LSTM. Model results used in the papers are 1) model_outputs_for_analysis_extreme_events_paper.tar.gz, and 2) model_outputs_for_analysis_mass_balance_paper.tar.gz. 

The models here are trained/calibrated on three different time periods. Standard Time Split (time split 1): test period(1989-1999) is the same period used by previous studies which allows us to confirm that the deep learning models (LSTM andMC-LSTM) trained for this project perform as expected relative to prior work. NWM Time Split (time split 2): The second test period (1995-2014) allows us to benchmark against the NWM-Rv2, which does not provide data prior to 1995. Return period split: The third test period (based on return periods) allows us to benchmark only on water years that contain streamflow events that are larger (per basin) than anything seen in the training data (<= 5-year return periods in training and > 5-year return periods in testing). 

Also included are an ensemble of model runs for LSTM, MC-LSTM for the "standard" training period and two forcing products. These files are provided in the format "<model_type>_<forcing_product>_standard_training.tar.gz". Note that these individual ensemble member runs we used to produce the runs in the files "model_outputs_for_analysis_<*>_paper.tar.gz".

IMPORTANT NOTE: This python environment should be used to extract and load the data: https://github.com/jmframe/mclstm_2021_extrapolate/blob/main/python_environment.yml, as the pickle files serialized the data with specific versions of python libraries. Specifically, the pickle serialization was done with xarray=0.16.1.

Code to interpret these runs can be found here:
https://github.com/jmframe/mclstm_2021_extrapolate
https://github.com/jmframe/mclstm_2021_mass_balance

Papers are available here:
https://hess.copernicus.org/preprints/hess-2021-423/
DOI: 10.4211/hs.d31bf19dbd5042b5960e374e9c9b2e94
title: Digital Hydroconnectivity of Reservoirs and Streamgages across the coterminous United States using RICON algorithm

description: This dataset presents the digital hydroconnectivity between streamgages and reservoirs as a concise edge list for the coterminous United States (CONUS) using the River and Infrastructure Connectivity Network (RICON) algorithm. The dependency between streamgages and reservoirs across CONUS provides (1) a complete list of edges for the unidirectional network connecting streamgages, reservoirs, and NHDPlusflowline features across different watershed regions, and (2) attributes of all nodes (reservoirs and streamgages), including their geospatial locations, unique identifiers, immediately upstream and downstream nodes, etc. The CONUS RICON data archive available at Hydroshare contains the dataset tables in comma-separated values (CSV) format. The combined information of all nodes and NHDflowline information are provided as ".rds" files, NHDPlusv2 attributes used for each drainage are provided in ".RData" format. For each drainage area, final output hydroconnectivity presented as individual edge lists are saved as 'Edge_List.csv' in each subfolder. All the codes for data development and additional vignettes are available on Github.
DOI: 10.4211/hs.cf9090408d1f4b8193e8ce2b7e8ed313
title: Hydraulic head data for Second Creek, Minnesota, USA, Summer 2017

description: This dataset includes daily hydraulic head for surface water and shallow groundwater measured at Second Creek, a riparian wetland and stream system in northeast Minnesota, USA. These head observations were made over June to October, 2017 using Schlumberger Baro or Diver pressure transducers deployed in a manually deployed stream gauge and three shallow piezometers. These data were collected with the purpose of determining vertical head gradients to characterize hyporheic flux direction and magnitude. Additional details are included the readme.md file within the resource.
DOI: 10.4211/hs.219501de7ee648e3bb0855ef9265c608
title: Data for La Follette et al (MCA on rock moisture and streamflow), updated 22 January 2022

description: This resource contains the all relevant data for its associated manuscript by La Follette et al. on the topic of multiple criteria analysis on rock moisture and streamflow in a lumped conceptual hydrologic model. Specifically, this resource includes outputs of the Elder Creek model, as well as calibration and forcing data. See the readme file for a detailed description. Note that two older versions of these data exist, which do not correspond to any manuscript.
DOI: 10.4211/hs.4b940b8593a4416e954a47bbbc58c568
title: April 1 SWE spatial percentiles using SNODAS for the contiguous USA

description: April 1 SWE is used across the western USA as a predictor of spring streamflow. Here, we use SNODAS data (https://nsidc.org/data/g02158) to map 10, 25, 50, 75, and 90th percentiles of April 1 SWE across the contiguous USA. This data is part of the data supplement for Lapides et al., 20XX.
DOI: 10.4211/hs.366ed14bfd154ee3b6a528d3488717fe
title: Data to Supplement Evaluating the Accuracy of Reclamation’s 24-Month Study Lake Powell Projections

description: This collection is supplementary data and code referenced in the white paper titled Evaluating the Accuracy of Reclamation’s 24-Month Study Lake Powell Projections from the Future of the Colorado River Project in Center for Colorado River Studies. The study analyzed the accuracy of Lake Powell inflow and elevation projections reported by Reclamation’s 24-Month Study. This collection is to preserve and provide access to data used in the study in the interest of transparency and reproducibility of this work.
DOI: 10.4211/hs.df161b19c39e4160ac4743bc2b5f4103
title: Sage Lot Pond Gas Flux

description: This is the raw data for JGR-B paper Carey et al. (2022) "Higher Temperature Sensitivity of Ecosystem Respiration in Low Marsh Compared to High Elevation Marsh Ecosystems". Gas data in units of umol/m2/s.
DOI: 10.4211/hs.d4f4b7601c694667bdf62a7826cad1a6
title: Predictive_model_Assam

description: The resources contain grid averaged arsenic concentration (mean concentrations) and predictor variables in Jorhat and Golaghat districts of Assam. GPS location has included error terms for privacy. Basic workflow random forest model in python environment is also provided. Final model was determined through random 10-fold cross-validation. Final model was used in the prediction of arsenic probability in unknown locations. We have also checked spatial cross-validation. The results were found to be consistent and confirmed the overall distribution of high/moderate/low-risk zones for arsenic in groundwater.

Some of the original point data can be downloaded from: https://www.hydroshare.org/resource/bbe23dfacab647568a18dc338114d6d7/
reference: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2017WR022485
DOI: 10.4211/hs.56b96a380f4e4e32ba9ede927d40806e
title: Plant Physiological and Hydraulic Data for Subalpine Fir Infested with Balsam Woolly Adelgid, Idaho and Utah

description: Since the 1950’s balsam woolly adelgid (Adelges piceae) has been infesting true fir species (Abies sp.) within the Northwestern United States. Balsam woolly adelgid infestations within subalpine fir (Abies lasiocarpa) stands have led to the deterioration of tree stands throughout the northern Intermountain Northwest. Currently, there are only a few studies quantifying the physiological impacts of balsam woolly adelgid infestation on subalpine fir trees. Thus it is unclear how subalpine fir morphology and physiology are altered when infested by balsam woolly adelgid, especially at drier and/or warmer edges of subalpine fir’s range. Here, we quantified the impacts of balsam woolly adelgid infestation on whole-tree water relations in subalpine fir, at cellular to whole-tree scales. We hypothesized that tree morphology would be significantly altered in infested trees, and scale with the intensity of infestation. Additionally, we hypothesized that water stress in infested trees would be evident at cellular to whole-tree scales. Finally, we predicted that hydraulic efficiency would decrease as infestation-level increased. We found that morphological traits such as gouting and crown area were not different between trees of varying infestation levels, nor were branch-tip water potentials. However, cell water relations parameters such as osmotic water potential and turgor loss point were more negative compared to values from non-infested trees in the literature. Finally, sapwood- and leaf area-specific hydraulic conductivity in branches in all infested trees were orders of magnitude lower than conductivities reported for healthy trees. Collectively, our findings demonstrate that balsam woolly adelgid has a strong impact on subalpine fir morphology and whole-tree water relations, and provide insights into the mechanisms causing mortality in infested trees.
DOI: 10.4211/hs.ea5d0091789043e8a8091ae43d314554
title: CESEP Brine Leakage Control Experiment

description: The Center for Experimental Study of Subsurface Environmental Processes (CESEP) conducted an intermediate-scale laboratory experiment to validate a developed approach for designing control systems for the potential brine leakage from CO2 storage zones. The developed approach applies the technique of deep brine extraction to control the leakage, thus it incorporates the global optimizer of Genetic Algorithm (GA) and a FEFLOW-based transport model to find the best extraction locations in the storage zone that minimizes the needed amount of extracted brine. In an ~8m long soil tank, a brine leakage plume was controlled using the extraction system designed based on the GA results. Collected data was then used to first make sure that the GA results and boundary conditions were accurately applied in the experiment and second to evaluate whether the observed plume concentrations in the shallow aquifer met the predefined constraining limits in the optimization problem. Acquired data during the experiment included transient measurements of the injection and extraction flow rates as well as plume concentrations. The conducted experiment and the testing system are described in detail in a research article developed by the dataset authors and entitled "Monitoring Brine Leakage from Deep Geologic Formations Storing Carbon Dioxide: Design Framework Validation Using Intermediate-Scale Experiment". For any questions, users are referred to the data owners.
DOI: 10.4211/hs.f380f42ac86f4c899d74e5818ae76eb8
title: Investigating Nutrient Input from Rivers/Streams and Wastewater Treatment Plants to Utah Lake

description: Harmful algal blooms (HABs) are a common problem for water bodies that affects aquatic life, community health, and recreation. Excessive phosphorus (P) and nitrogen (N) input to aquatic ecosystems often cause HABs. Utah Lake, one of largest freshwater lakes in the western United States, experiences seasonal HABs. Utah Lake is considered hypereutrophic due to nutrient input from agricultural and stormwater runoff, atmospheric deposition (precipitation and dust), effluent from wastewater treatment plants (WWTPs), etc. This research focused on the nutrient loads from eighteen sites (both upstream and downstream) of ten Utah Lake tributaries and seven WWTPs. Water samples were tested over a six-week period using a CheMetrics V-2000 Photometer to determine the concentrations (mg/L) of four inorganic compounds: orthophosphate (PO43-), nitrate (NO3-), nitrite (NO2-), and ammonia (NH3). A YSIDSS Pro water quality meter was used to measure other water parameters (pH, chlorophyll a, phycocyanin, etc.). Our data showed temporal and spatial variations in nutrient concentrations. Downstream river sites had higher 6-week average concentrations (NH3: 0.87, NO3-: 3.28, NO2-: 0.15, and PO43-: 1.41) than the upstream sites (NH3: 0.09, NO3-: 0.62, NO2- :0.04, and PO43-: 0.23) for all tributaries. The WWTPs had much higher average concentrations when compared to the tributaries (NH3:2.10, NO3-: 42.11, NO2-: 0.33, and PO43-: 6.50). A limit for P was set at 1 mg/L for the WWTPs on January 1st, 2020, by the Utah Division of Water Quality. Based on our data five of the seven WWTPs exceeded this limit but were allowed because of individual extensions. Each WWTP has their own limit for NH3 which they all complied to. However, there is no limit for NO3- and NO2- at the WWTPs. Our results indicated that the failure to abide by the P limit and a lack of limits on total inorganic nitrogen could have resulted in much higher nutrient input to Utah Lake from the WWTPs than previously thought. To minimize HABs, a stricter nutrient standard should be placed on the effluent from the WWTPs, which could be achieved by investment in wastewater treatment technology. Also, improved farming practices (such as crop rotation and efficient irrigation techniques) could help decrease nutrient runoff from agricultural land into the tributaries and the lake.
DOI: 10.15468/dl.et5hpv
title: Occurrence Download

description: A dataset containing 594 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Russian Federation, China, Japan, Korea, Republic of)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Populus maximowiczii"
 ]
}

 The dataset includes 594 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049968-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.df5fa2f5d1b74be4bf0a049e1e59889c
title: Data Component Use Case for Landslide Susceptibility Calculation

description: This resource includes a Jupyter Notebook to demonstrate how to use several CSDMS Data Components (https://csdms.colorado.edu/wiki/DataComponents) to download topography and soil datasets to calculate the hourly landslide susceptibility for a study area in Puerto Rico when Hurricane Maria hit the island on September 20th, 2017. 

HydroShare users can run the Jupyter Notebook (landslide_puertorico.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub"
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If you encounter "Kernel Restarting" error when running this notebook on CUAHSI JupyterHub, select "Kernel" -> “Shut Down All Kernels" -> "Restart Kernel and Clear All Outputs" and rerun this notebook.

Please go to https://github.com/gantian127/landslide_usecase to learn how to run this notebook on local PC or CSDMS JupyterHub.
DOI: 10.4211/hs.7f6295a88f2743a58e3447db650df0d2
title: Introductory groundwater laboratory exercise for in-person and online teaching modes

description: This resource includes parallel online and in-person versions of an introductory laboratory exercise focused on groundwater. A pre-lab activity introduces students to the concept of groundwater as a resource and its flow. In the lab exercise, students make either in-person or virtual measurements of groundwater levels in a network of wells and create a contour map of the water table. Students will determine how the lake is connected to groundwater, what the differences are between lake water and groundwater quality, and whether the groundwater is likely to be potable. Specific educational goals of the exercise are to introduce students to the concept of groundwater and how it flows, gain confidence collecting environmental data, and learn to make and interpret a contour map. The exercise is geographically situated in the Mirror Lake Water Science Learning Laboratory, an outdoor laboratory space in the heart of The Ohio State University’s Main Campus in Columbus, Ohio (USA). Materials can be adapted for other observational well networks. The content is appropriate for general education or introductory classes in earth or environmental science, geology, and environmental engineering.
DOI: 10.4211/hs.2a07b89d97334654859c62f178cdeb0d
title: Biosphere 2 Landscape Evolution Observatory miniLEO PERTH Experiment Dataset

description: This data set was collected in miniLEO located in Landscape Evolution Observatory (LEO), Biosphere 2, University of Arizona, USA. This data was collected during 08/252014 - 09/22/2014 and was utilized to estimate the transit time distribution (TTD) and the StorAge Selection (SAS) function.
DOI: 10.4211/hs.e80005f70e974e729db02b25945ded8d
title: Structural hillslope connectivity is driven by tectonics more than climate and modulates hydrologic extremes and benefits

description: This data product is related to a journal article that has been accepted for publication in Geophysical Research Letters (July, 2022). 

This resources includes the Python scripts to calculate Index of Connectivity maps and MATLAB scripts for generating the plots used in the manuscript. This resource also includes the following Word/PDF files: (1) the text file of the manuscript, (2) the figures file, and (3) the supplemental information file. These files describe the process the authors undertook to create a structural connectivity map of the contiguous United States (CONUS). The exact methods are described in the text file. To download connectivity raster maps, visit the following link: https://apps.cuahsi.org/connectivity-map.

The plain language summary for the manuscript is shown below: 

Hillslopes are critical landscape features that intercept, store, and route water, from its source as rainfall to its fate as river discharge. The strength of this routing is a function of climatic and tectonic forces, but their relative importance to hillslope connectivity is uncertain. We simulated the Index of Connectivity, a topographic analogue for structural connectivity, for 75 billion locations in CONUS, across a range of climatic and tectonic settings. At the CONUS-scale, we found that hillslope connectivity is largely driven by tectonic forces, including uplift and seismic activity, and that highly connected hillslopes are more susceptible to landslides while poorly connected hillslopes promote wetland development. We provide a web data portal to serve as a tool for stakeholders to visualize and leverage structural connectivity data in their respective study areas.
DOI: 10.4211/hs.4871ac7e869d40d8ad05cf02ae545cd5
title: Calculating streamwater age using StorAge Selection functions at Dry Creek, CA

description: Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Lapides et al. (2021) combined field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is an intensively monitored catchment in the northern California Coast Ranges with a Mediterranean climate and thin subsurface critical zone. Due to limited storage capacity, runoff response is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and runoff thresholds. Even though SAS modeling reveals that streamflow is younger at higher wetness states, flow is still typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer than anticipated, with implications for contaminant delivery and biogeochemical reactions. Our findings have implications for the assumptions built into classic hydrograph separation inferences, namely, whether overland flow consists of new water.

For this work, we translated SAS modeling code in Matlab from Benettin and Bertuzzo (2018) to Python and provide here a set of code for SAS modeling in Python and example data for Dry Creek, CA produced for the SAS modeling publication by Lapides et al. (2021).
DOI: 10.4211/hs.15b1a61f47724a6e8deb100789353df2
title: Hydroinformatics and Water Data Science Instructor Interviews and Surveys

description: This resource contains the results of interviews and surveys of instructors of hydroinformatics and water data science courses in the United States conducted in Fall 2021. Potential participants were initially identified via investigator connections, review of relevant literature, and information on institutional and personal websites discovered by Internet searches. Target participants were selected based on their experience teaching hydroinformatics, water data science, or related subject matter at an institution of higher education. We used email to invite contacts to participate, and participants elected to respond to questions either via online survey or recorded interview. During each interview or survey, participants were asked to identify any additional instructors who might be a good fit for the project. The survey was composed using Qualtrics software and administered with links personalized for each participant. Interviews were conducted over Zoom, recorded, and subsequently transcribed. Each interview lasted approximately 45-60 minutes. Procedures were approved by the Utah State University Institutional Review Board for Human Subjects Research with participation limited to instructors within the United States. 

This resource contains the list of questions asked to each participant, interview transcripts, and survey responses. Participant names and institutions have been removed from the files.

This resource contains supporting data for the paper Jones AS, Horsburgh JS, Bastidas Pacheco CJ, Flint CG and Lane BA (2022) Advancing Hydroinformatics and Water Data Science Instruction: Community Perspectives and Online Learning Resources. Front. Water 4:901393. doi: 10.3389/frwa.2022.901393.