cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.92f393cbd06b47c398bdd2bbb86887ac
title: pyhydroqc Sensor Data QC: Single Site Example

description: This resource contains an example script for using the software package pyhydroqc. pyhydroqc was developed to identify and correct anomalous values in time series data collected by in situ aquatic sensors. For more information, 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.

This script applies the functions to data from a single site in the Logan River Observatory, which is included in the repository. The data collected in the Logan River Observatory are sourced at http://lrodata.usu.edu/tsa/ or on HydroShare: https://www.hydroshare.org/search/?q=logan%20river%20observatory.

Anomaly detection methods include ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short Term Memory). These are time series regression methods that detect anomalies by comparing model estimates to sensor observations and labeling points as anomalous when they exceed a threshold. There are multiple possible approaches for applying LSTM for anomaly detection/correction. 
- Vanilla LSTM: uses past values of a single variable to estimate the next value of that variable. 
- Multivariate Vanilla LSTM: uses past values of multiple variables to estimate the next value for all variables. 
- Bidirectional LSTM: uses past and future values of a single variable to estimate a value for that variable at the time step of interest. 
- Multivariate Bidirectional LSTM: uses past and future values of multiple variables to estimate a value for all variables at the time step of interest.

The correction approach uses piecewise ARIMA models. Each group of consecutive anomalous points is considered as a unit to be corrected. Separate ARIMA models are developed for valid points preceding and following the anomalous group. Model estimates are blended to achieve a correction. 

The anomaly detection and correction workflow involves the following steps:
1. Retrieving data
2. Applying rules-based detection to screen data and apply initial corrections
3. Identifying and correcting sensor drift and calibration (if applicable)
4. Developing a model (i.e., ARIMA or LSTM)
5. Applying model to make time series predictions
6. Determining a threshold and detecting anomalies by comparing sensor observations to modeled results
7. Widening the window over which an anomaly is identified
8. Aggregating detections resulting from multiple models
9. Making corrections for anomalous events

Instructions to run the notebook through the CUAHSI JupyterHub:
1. Click "Open with..." at the top of the resource and select the CUAHSI JupyterHub. You may need to sign into CUAHSI JupyterHub using your HydroShare credentials. 
2. Select 'Python 3.8 - Scientific' as the server and click Start.
2. From your JupyterHub directory, click on the ExampleNotebook.ipynb file.
3. Execute each cell in the code by clicking the Run button.
DOI: 10.4211/hs.41a3a9a9d8e54cc68f131b9a9c6c8c54
title: Land Use Changes in The Mississippi River Basin Floodplains: 1941 to 2000 (version 1)

description: This work has been published in the Nature Scientific Data. Suggested citation:
Rajib et al. The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset. Nature Scientific Data 8, 271 (2021). https://doi.org/10.1038/s41597-021-01048-w

Here, we present the first-available dataset that quantifies land use change along the floodplains of the Mississippi River Basin (MRB) covering 60 years (1941-2000) at 250-m resolution. The MRB is the fourth largest river basin in the world (3.3 million sq km) comprising 41% of the United States and draining into the Gulf of Mexico, an area with an annually expanding and contracting hypoxic zone resulting from basin-wide over-enrichment of nutrients. The basin represents one of the most engineered systems in the world, and includes complex web of dams, levees, floodplains, and dikes. This new dataset reveals the heterogenous spatial extent of land use transformations in MRB floodplains. The domination transition of floodplains has been from natural ecosystems (e.g. wetlands or forests) to agricultural use. A steady increase in developed land use within the MRB floodplains was also evident.

To maximize the reuse of this dataset, our contributions also include four unique products: 
(i) a Google Earth Engine interactive map visualization interface: https://gishub.org/mrb-floodplain 
(ii) a Google-based Python code that runs in any internet browser: https://colab.research.google.com/drive/1vmIaUCkL66CoTv4rNRIWpJXYXp4TlAKd?usp=sharing
(iii) an online tutorial with visualizations facilitating classroom application of the code: https://serc.carleton.edu/hydromodules/steps/241489.html
(iv) an instructional video showing how to run the code and partially reproduce the floodplain land use change dataset: https://youtu.be/wH0gif_y15A
DOI: 10.4211/hs.77664f7a07854e17b29557fe308b3dfa
title: Caspar Creek Experimental Watersheds Soil Moisture Dataset

description: The Caspar Creek Experimental Watersheds (CCEW) are a long-term USDA Forest Service research site located near Fort Bragg, California in the Jackson Demonstration State Forest. The CCEW consists of two  experimental watersheds: the North Fork (479 ha) and the South Fork (417 ha). Since 1962, the US Forest Service Pacific Southwest Research Station has been collecting measurements of precipitation, streamflow, and sediment transport; these data are maintained and archived by the USFS. Since their establishment, three timber harvesting experiments have been conducted at the CCEW. 
The first experimental harvest (Phase 1) was a selective cut (66% removal of pre-treatment basal area) conducted in the South Fork watershed using tractor yarding in the early 1970s. Phase 1 of the project includes data from 1962 to 1985. Gauging stations were added in 12 sub-watersheds of the North Fork by 1985 in preparation for the second experimental harvest (Phase 2). Phase 2 of the project includes data from 1985 to 2017. From 1985 to 1992, roughly half of the North Fork watershed was clearcut, mainly using cable yarding, and following the newly-enacted Forest Practice Rules. Two additional gauges were added in North Fork sub-watersheds in 1999 and 2001. Gauging stations were added in 10 sub-watersheds of the South Fork in 2000 in preparation for a third harvesting experiment (Phase 3). Logging for the third harvest occurred in 2017-2019 in the South Fork watershed.  

This data publication contains soil volumetric water content values (cm3 cm-3) at 15, 30, and 100 cm soil depth in three South Fork Caspar Creek sub-watersheds. Measurements were collected from one transect in each subcatchment, which consisted of plots at five hillslope positions—riparian, toeslope, sideslope, shoulder, and ridge (15 sample plots total). Samples were collected at 10 minute intervals using METER EC5 sensors (METER Group, Inc., Pullman, WA, USA; resolution: 0.001 m3 m-3; accuracy +/- 0.03 m3 m-3) vertically into the soil. Measurements start in fall 2015 extend to July 31, 2018. Specifically, the measurements were collected from Treat (TRE; 14 ha); Williams (WIL; 26 ha), and Ziemer (ZIE; 26 ha). 

Vegetation in the South Fork is dominated by third-growth coast redwood (Sequoia sempervirens (D. Don) Endl.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), grand fir (Abies grandis (Doug. ex D. Don) Lindl.), and western hemlock (Tsuga heterophylla (Raf.) Sarg.), with smaller amounts of tanoak (Lithocarpus densiflorus (Fook. and Arn.) Rohn) and red alder (Alnus rubus Bong.). Soils in the sub-basin are predominately well-drained clay-loam Ultisols and Alfisols derived from Franciscan sandstones and shales.
DOI: 10.4211/hs.7ade2a53d8b34aba89b69a91ac8834ee
title: 1 - Channel length survey data

description: This directory includes channel length survey data (outlet discharge and surveyed wetted channel extent for each survey). These data were used, in conjunction with discharge data, to find the scaling factor (α) and scaling exponent (β) for the power function that relates wetted channel extent and discharge (L = αQ^β) reported in the metadata table. Resources associated with channel length include survey data, data ‘thieved’ plots from studies where channel length survey data was reported in plot format.
DOI: 10.4211/hs.a3b06e4eafd1489699933a81226aafce
title: 3 - Watershed metadata

description: Watershed metadata was collected for 14 watersheds from studies where channel length survey data was presented. For variables not found in the publications associated with the channel length surveys, additional sources are referenced. These sources are included in the notes column. Variables without sources were calculated, as described in the Additional Metadata section below. Examples of calculated values include, q_avg_mm_per_day, beta, and l_avg_km. 

For Python packages, modules, and functions used to find calculated values, please see the associated GitHub repository: https://zenodo.org/record/4057320
DOI: 10.4211/hs.76ebc18852cc41e48d4ee83902bc0a7d
title: 3 - Watershed metadata

description: Watershed metadata was collected for 14 watersheds from studies where channel length survey data was presented. For variables not found in the publications associated with the channel length surveys, additional sources are referenced. These sources are included in the notes column. Variables without sources were calculated, as described in the Additional Metadata section below. Examples of calculated values include, q_avg_mm_per_day, beta, and l_avg_km. 

For Python packages, modules, and functions used to find calculated values, please see the associated GitHub repository: https://zenodo.org/record/4057320
DOI: 10.4211/hs.51b088686b1e438ab8da4e5cfc697a23
title: Comprehensive network hydraulic scaling datasets and associated resources (discharge, channel length surveys, watershed metadata, blueline network shapefiles and reference images)

description: Wetted channel networks expand and contract throughout the year. Direct observation of this process can be made by multiple intensive surveys of a catchment throughout the year. Godsey et al. (2014) suggest that the extent of the wetted channel network scales with discharge at the outlet by a power law (L = αQ^β). Using this relationship, we developed a framework to assess variability in the extent of wetted channels as a function of β and the variability in streamflow Q (Lapides et al., In Review, https://eartharxiv.org/mc6np/). This resource constitutes the empirical basis for that study, a comprehensive dataset compiled from literature including: 

1 - Channel length survey data (csv files)
2 - Discharge time series data (csv files)
3 - Watershed metadata (csv file)
4 - Blueline network files (pdf, png, and shp files)

This collection is comprehensive in that it includes all watersheds where at least three channel length surveys have been conducted and where a corresponding discharge time series dataset is available. The requirement of a minimum of three channel length surveys stems from the data requirements to find α and β for the power law relationship between discharge and stream network length for headwater catchments (Godsey et al., 2014). At present, data for 14 watersheds worldwide are included in the collection along with reference maps, watershed metadata, shapefiles and a composite of USGS blueline stream network imagery with terrain for watersheds of interest in the United States. Notably, this collection brings data from a variety of earth science agencies worldwide into a common, clearly labelled format. 

Methods used to process the datasets or create other assets in this collection are included in the abstracts or additional metadata for each of the four resources listed above. Python code used to process data, compute variables, and create graphics is available at: https://zenodo.org/record/4057320
DOI: 10.4211/hs.4382404b935f4fde99c7ff4ada264867
title: Data repository for: A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times

description: The use of hydro-meteorological forecasts in water resources management holds great promise as a soft pathway to improve system performance. Methods for generating synthetic forecasts of hydro-meteorological variables are crucial for robust validation of forecast use, as numerical weather prediction hindcasts are only available for a relatively short period (10-40 years) that is insufficient for assessing risk related to forecast-informed decision-making during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. An empirical copula is used to capture covariance between variables, forecast lead times, and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for 1) streamflow and 2) temperature and precipitation, which are based on hindcasts from the NOAA/NWS Hydrologic Ensemble Forecast System (HEFS) and the NCEP GEFS/R V2 climate model, respectively. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for water resources management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for risk analysis.
DOI: 10.4211/hs.17e9a223ffe64e768e6cb8b2a77c198d
title: Coastal Forest Seawater Exposure Increases Stem Methane Concentration Manuscript Data

description: Measurements of soil and tree stem O2, CO2, and CH4 concentrations were made at five sites representing two coastal eco-regions: the Mediterranean Pacific Northwest and the temperate Atlantic Eastern shore (Fig. 2). In total, 107 trees were sampled, pairing stem and soil gas measurements for CO2, CH4, and O2, and average stem wood density. We identified seawater exposure (exposed or unexposed), tree survival (living or dying), and tree species at each of the five sites. The five sites are Beaver Creek (BC), Goodwin Island (GI), Phillips Creek (PC), Monie Bay (MB), and Moneystump Swamp (MS). Seawater exposure was assigned qualitatively (visual identification of tree in flooded zone) and quantitatively (saline porewaters). The R packages used for data interpretation are freely available in R package version 0.8.3 and R 3.5.2 (RStudio Team, 2017).
DOI: 10.4211/hs.efecc49323cf4a57a8068890889e259f
title: Honolulu King Tide Study: Raw sample dataset

description: Contains all grab sample data collected, including location, date, lat long, salinity, water depth, radon concentration in water, carbamazepine concentrations, caffeine concentrations, fluoroquinolones concentrations, and dissolved nutrient concentrations (including phosphate, nitrate + nitrite, ammonium, total dissolved nitrogen, and total dissolved phosphorus).
DOI: 10.4211/hs.14cc6984035d4fb4aa4e653e1625d0bd
title: Honolulu King Tide Study: Radon Time Series

description: Time series results by study site and sampling date (including king tide vs. spring tide sampling for coastal sites). Data collected include time, water temperature, water salinity, water depth, and radon concentrations in water.
DOI: 10.4211/hs.f9f36a39190e4cc6a7cdc0cd0cc9bdd6
title: Isotopic signals in an agricultural watershed suggest denitrification is locally intensive in riparian areas but extensive in upland soils – data and code

description: The data and R code provided here are the underpinnings of a manuscript in the journal, Biogeochemistry (the manuscript title is parallel to resource title). Nitrogen use efficiency in cultivated agriculture is reduced by denitrification and by leaching of nitrate, which reduces water quality and is subject to denitrification downstream. Denitrification and leaching losses from dryland farming during fallow periods (no crop growing) can play a disproportionately large role in cropping system nitrogen losses. This work combines nitrogen mass balance with δ15N mass balance to estimate denitrification rates in soil relative to groundwater and streams.

Data includes solute concentrations and isotopic composition of nitrate and water in water samples collected from soil, groundwater and surface water.  Soil solution chemistry was characterized in samples from tension lysimeters installed in two non-irrigated fields operated by cooperating farmers. Groundwater and surface water sampling between 2012 and 2017 included two wells, five springs, and three stream sites. Solute concentration and water isotope analysis was conducted in the Montana State University Environmental Analytical Laboratory. Nitrate isotope analyses were conducted at Woods Hole Oceanographic Institution. For detailed analytical methods, see the main manuscript.
DOI: 10.4211/hs.ea4ccadf124b4bed86fe6fc7efd8c779
title: 2 - Discharge time series data

description: This directory includes discharge time series data (q) for 14 headwater stream networks, produced in standard format and common units of mm/day for straightforward hydrograph inter-comparison.
DOI: 10.4211/hs.68290b979a0d4dcbbb03cef36114db47
title: 3 - Watershed metadata

description: Watershed metadata was collected for 14 watersheds from studies where channel length survey data was presented. For variables not found in the publications associated with the channel length surveys, additional sources are referenced. These sources are included in the notes column. Variables without sources were calculated, as described in the Additional Metadata section below. Examples of calculated values include, q_avg_mm_per_day, beta, and l_avg_km. 

For Python packages, modules, and functions used to find calculated values, please see the associated GitHub repository: https://zenodo.org/record/4057320
DOI: 10.4211/hs.1f97ba4f8ea64812b10c14a10071c69f
title: 3 - Watershed metadata

description: Watershed metadata was collected for 14 watersheds from studies where channel length survey data was presented. For variables not found in the publications associated with the channel length surveys, additional sources are referenced. These sources are included in the notes column. Variables without sources were calculated, as described in the Additional Metadata section below. Examples of calculated values include, q_avg_mm_per_day, beta, and l_avg_km. 

For Python packages, modules, and functions used to find calculated values, please see the associated GitHub repository: https://zenodo.org/record/4057320
DOI: 10.4211/hs.f73a4683bf3f45d5a4cb523578ab639d
title: Sage Lot Pond Ra Isotope Data 2018 - 2019

description: This resource contains Ra isotope data from Sage Lot Pond, part of the Waquoit Bay National Estuarine Research Reserve, from the years 2018 - 2019. Data includes marsh porewater and brackish groundwater Ra-224 and dissolved inorganic carbon concentrations, and sediment-bound Ra-224 and Th-228 specific activities. These data have been used to quantify marsh porewater exchange and dissolved inorganic carbon export.
DOI: 10.15468/dl.x38q9x
title: Occurrence Download

description: A dataset containing 144282 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Eupetomena macroura"
 ]
}

 The dataset includes 144282 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049926-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.d04e4b8bf4054a6bac808c5b333559c3
title: 1 - Channel length survey data

description: This directory includes channel length survey data (outlet discharge and surveyed wetted channel extent for each survey). These data were used, in conjunction with discharge data, to find the scaling factor (α) and scaling exponent (β) for the power function that relates wetted channel extent and discharge (L = αQ^β) reported in the metadata table. Resources associated with channel length include survey data, data ‘thieved’ plots from studies where channel length survey data was reported in plot format.
DOI: 10.4211/hs.ca365ffb1a1f49df8b77e393be965fd8
title: Global Hydropower Simulation - Forecast_2022

description: This resource contains the hydropower time series for 735 headwater hydropower dams operating under 3 different schemes – control rules, forecast-informed operations with perfect forecast, and forecast informed operations with deterministic forecast. The deterministic streamflow forecasts depend on seven drivers, that is, four large scale climate drivers— El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO)—and three variables accounting for local processes—lagged inflow, snowfall, and soil moisture.

Start exploring the data by downloading the Rdata together with the open_file.R script. You will be able to find monthly-resolution time series outputs of our simulation, including the hydropower production, storage level, and water releases for each dam.
DOI: 10.4211/hs.f2bfcdae42e74fccb43f17482795d48d
title: Understanding Cooperative Interaction and Barriers to Cross-Boundary Collaboration for Invasive Species Management

description: Conservation plans and invasive species management are generally executed at the scale of independent jurisdictions. However, the important ecological processes and biodiversity to be protected from invasions often occur over large spatial scales and across multiple jurisdictions, creating a need for cooperative management. To understand how entities address cross-boundary management challenges, and which variables allow for success or failure, 20 semi-structured interviews were conducted. Interviewees included employees from federal, county and state agencies, research organizations, nonprofits and local stakeholder groups in two national parks and their surrounding lands in California, USA. Interviews consisted of 26 questions that inquired about the effects of jurisdictional boundaries on non-native invasive species ecology and collaborative management. Participants were selected based on their involvement in non-native invasive species management within two study areas which included Lassen Volcanic, and Sequoia and Kings Canyon National Parks and surrounding lands. Interviews lasting 22-105 minutes were conducted by telephone in August-November 2019. With the consent of interviewees, the interview conversations were recorded on a cell-phone and computer. After the interviews were completed, they were transcribed verbatim. Analysis of the interview data involved generating themes from the interview questions using a process of coding in ATLAS.ti, a qualitative analysis computer software program.