cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.bcbbc43ca978423b941c6e58e39b0688
title: Borehole, Surface and Airborne Geophysical data from UW I-80 Hydrogeophysical Project

description: Here we present the Surface, Borehole, and Airborne Geophysical datasets for the UW - I-80 Hydrogeophysical Study from 2019 - 2022. From July 2019 through July 2020, the University of Wyoming Dept. of Geology and Geophysics (UW G&G) was tasked to collect, analyze and process an Airborne Electromagnetic Geophysical Survey (AEM) along the Interstate 80 (I-80) corridor from the bottom of Telephone Canyon to the summit of I-80 within Albany County, WY (SE of Laramie, WY).  The survey extended south to encompass the City of Laramie municipal wellfields (i.e. Pope and Soldier Springs) and the UW G&G groundwater recharge study site wells within Government Gulch (near the I-80 summit). During this first year of the study, the AEM data were acquired and 3D geophysical/geologic and hydrogeophysical databases are being analyzed for information about groundwater recharge into the eastern Laramie Basin and the subsurface geologic structures that influence groundwater movement from the base of I-80 toward the City of Laramie municipal wellfields. The aim is to identify where any subsurface contaminant threats from the I-80 corridor will travel and at what depths these pathways occur (i.e. toward Laramie or the Pope/Soldier municipal wellfields at the water table level or deeper).
DOI: 10.4211/hs.4ab43e1b507b496b9b42749701daed5c
title: Water Quality Dataset for water quality prediction tool

description: Water is the most precious and essential resource among all-natural resources. Some organism survives without oxygen and food such as Tardigrades. But no one can survive without water. The increase in the development of industries and human activities over the previous century is having an overwhelming impact on our environment. Most cities in the world have started to implement the aqua management system. The development of cloud computing, artificial intelligence, remote sensing, big data and the Internet of Things provide new opening and move toward the improvement and application of aqua resource monitoring system. For predicting water quality of rivers, dams and lakes in India, water quality parameter dataset is created. The name of the data set is Aquaattributes. Completely 1360 samples are presented in the Aquaattributes. The data set size is 190 KB. Attributes of the dataset location name along with its longitude and latitude values and water quality parameters.
DOI: 10.4211/hs.f487c02857ce4ebfadbbdd7fd268391a
title: Storm surges, Waves and Vegetation Surveys in Chesapeake Bay Environmental Center, MD, USA (2021-2022)

description: These datasets include measurements of wave conditions, and vegetation bio-mechanic characteristics (biomass, stem height, diameter, and density) during the period of (2021-2023) that were measured in the field during extreme events, regular tidal cycles, and over different seasons. This dataset provides the information for the campaigns in Chesapeake Bay Environmental Center, Maryland, USA. Measurements were carried out with RBR D-wave sensors; vegetation surveys included the measurements of vegetation height, diameter and stem spacing using randomly placed 0.25 m2 quadrats on the ground surface. The sensors, and vegetation measurement locations are approximate. The team measured 1) the percent cover of each individual species, 2) canopy height, stem diameter and density, and 3) waves.
This fieldwork is part of the project “EESLR 2019: Quantifying the benefits of natural and nature-based features in Maryland’s Chesapeake and Atlantic Coastal Bays to inform conservation and management under future sea level rise scenarios” funded by NOAA (Award# NA19NOS4780179). The project is a collaboration between George Mason University, the Maryland Department of Natural Resources (DNR) and The Nature Conservancy (TNC). The overall goal of the project is to quantify the wave attenuation and flood reduction benefits of marshes, SAV and other natural and nature-based features (NNBF) along the shores of Maryland’s Chesapeake and Atlantic Coastal Bays. This project will inform management actions by DNR to maintain or enhance the ecosystem services of marshes and other natural features on state-owned lands; re-evaluate Chesapeake Bay SAV restoration goals; improve existing conservation prioritization tools; and provide relatable, local examples to advance efforts by DNR, TNC, Eastern Shore Land Conservancy (ESLC) and others to promote the use of NNBF in the county and municipal adaptation plans.
DOI: 10.4211/hs.c4ebe3515a894446bf26aba0aedd7fdd
title: Data Component Use Case for Permafrost Thaw and Hillslope Diffusion

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, snow, and temperature data to calculate the permafrost active layer thickness and simulate the hillslope diffusion process for a study area in Alaska.

HydroShare users can test and run the Jupyter Notebook (permafrost_alaska.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/permafrost_usecase to learn how to run this notebook on local PC.
DOI: 10.4211/hs.0f830b9de0af413cb45ef8906aa34544
title: Daily resolution spatial and temporal stream carbon dioxide data

description: This dataset includes four streams with daily CO2 data at five sites labeled daily data. There is an additional data set labeled LTM sites that has daily CO2 and stream chemistry at 4 sites. Finally, there is a file labeled reach characteristics that has the number of seeps, the slope, and other reach related measurements.
DOI: 10.4211/hs.0181f4621d184a89a625ee16dd9858a6
title: Forest and Ground Cover classification, DEM, and Beryllium-10 data for the Luquillo Experimental Forest

description: Topography is commonly viewed as a passive backdrop on which vegetation grows. Yet, in certain circumstances, a bidirectional feedback may develop between the control of topography and the spatial distribution of vegetation and landform development, because vegetation modulates the erosion of the land surface. Therefore, if reinforcing feedbacks are established between erosion and land cover distribution over timescales relevant to landform development, then the interactions between vegetation and topography may create distinctive landforms, shaped by vegetation. We expose here a strong correlation between the spatial distribution of vegetation, erosion rates, and topography at a characteristic length scale of 102-103m (mesoscale topography) in the Luquillo Experimental forest (LEF) of Puerto Rico. We use high-resolution LiDAR topography to characterize landforms, satellite images to classify the vegetation into forest types, and in-situ produced cosmogenic 10Be in the quartz extracted from soils and stream sediments to document spatial variations in soil erosion. The data document a strong correlation between forest type and topographic position (hilltop vs. valleys), and a correlation between topographic position and 10Be-derived erosion rates over 103-104 years. Erosion is faster in valleys, which are mostly covered by monocot Palm Forest, and slower on surrounding hills mostly covered by the dicot Palo Colorado Forest. Transition from one forest type to the next occurs across a break-in-slope that separates shallowly convex hilltops from deeply concave valleys (coves). The break-in-slope is the consequence of a longer-lasting erosional imbalance whereby coves erode faster than hills over landscape-shaping timescales. Such a deepening of the coves is usually spurred by external drivers, but such drivers are here absent. This implies that cove erosion is driven by a process originating within the coves themselves. We propose that vegetation is the primary driver of this imbalance, soil erosion being faster under Palm forest than under Palo Colorado forest. Concentration of the Palm forest in the deepening coves is reinforced by the better adaptation of Palm trees to the erosive processes that take place in the coves, once these develop steep slopes. At the current rate of landscape development, we find that the imbalance started within the past 0.1-1.5 My.  The initiation of the process could correspond to time of settlement of these mountain slopes by the Palm and Palo Colorado forests.

S2-Shapefile1- Study Area
S2-Shapefile2 - Cove And Ridge Tops On Quartz Diorite 
S2-Shapefile3 - Ground Proofing Tracks 

S1-Grid1.tif - Elevation 
S1-Grid2.tif - Elevation in Study Area
S2-Grid1.tif- Forest Classification; Sierra Palm is classified with #2 on each raster cell, Palo Colorado is classified with #3 in each grid cell.
DOI: 10.4211/hs.40f0b7c1d6c74de58663a1bbee350326
title: Deposition and River paired timeseries

description: The data provided here is used in the submitted manuscript "Synchrony of nitrogen wet deposition inputs and watershed exports using information theory" by Desneiges S. Murray1, Edom Moges2, Laurel Larsen2, Michelle D. Shattuck1, William H. McDowell1, and Adam S. Wymore1 (1Department of Natural Resources and the Environment, University of New Hampshire, Durham NH, USA; 2Department of Geography, University of California, Berkeley California, USA. Corresponding author: Desneiges Murray (desneiges.murray@unh.edu)

Weekly, year-round, paired (e.g., < 1 km apart) wet deposition and river nitrogen (TDN, NO3, NH4, DON) time series from 2003 to 2020 from the Lamprey River Hydrological Observatory in New Hampshire, USA. Discharge data from the paired USGS site with river chemistry sampling site is also provided. This data is input to the associated Python Script, and is cleaned, paired, and weekly anomalies are calculated for input to information theory (IT) algorithms. IT code can be provided upon request.
DOI: 10.4211/hs.59786af26dd745fd8e07750d128b14ee
title: Water Chemistry and FDOM Data from North-Central Florida Springs and Wells

description: This resource includes data for various water chemistry parameters collected from a river sink-rise system, 12 springs, and 13 wells in North-Central Florida from 2018 to 2022.  Parameters include water temperature, pH, DO, SpC, ORP, CH4, CO2, major ions, DOC, DIC, nutrients (NO3- and NH4+), FDOM characteristics (FI, HIX, BIX, spectral slope ratio, and absorption coefficients at 254, 355, and 440 nm), trace metals (As, Sr, Fe, Mn, Al), and δ13C values for CH4, CO2, and DIC. Also included are corresponding samples of atmospheric CH4 and CO2 concentrations and δ13C values.
DOI: 10.4211/hs.fc8455652d1044218f3046b7dd56e5ea
title: Data for "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing"

description: This archive includes data used in Zhang et al.'s WRR paper "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing", which is under review currently. The archive contains 1) raw data (daily-scale CAMELS streamflow data and watershed attributes) and 2) MATLAB scripts used to perform data-driven sparse sensing and generate sample figures. The streamflow data used in this study was retrieved from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (https://ral.ucar.edu/solutions/products/camels). The MATLAB code used for data-driven sparse sensing was retrieved from the Github repository by Krithika Manohar (https://github.com/kmanohar/SSPOR_pub) and customized for this study.
DOI: 10.4211/hs.8e35713f7ac04f2a8e628fa58b54da44
title: Kaolinite Deposition Dynamics and Streambed Clogging During Bedform Migration under Losing and Gaining Flow Conditions

description: Clogging of streambeds due to clay deposition influences the stream-subsurface exchange flux and thus directly modulates hyporheic ecological and biogeochemical processes. Clogging of sandy streambeds has previously been studied under losing and gaining flows and during streambed movement, but not when these two flow conditions coincided. We conducted flume experiments to quantify the combined effect of moving bedforms and losing or gaining flows on kaolinite deposition and streambed clogging. The experiments were conducted by adding pulses of kaolinite in a flume packed with sand under a streamwater velocity of 25 cm/s. We measured the deposition rates, dynamics of hyporheic exchange flux (HEF) and vertical hydraulic conductivity (Kv), and the vertical distribution of kaolinite at the end of the experiments under two losing and two gaining flows (Darcy velocity of 10 and 20 cm/day). Kaolinite deposition led to clogging and reduction in Kv and HEF under all flow conditions. Deposition occurred faster under losing flow conditions than under gaining flow conditions. However, the changes in Kv and HEF were similar under losing and gaining flow conditions for similar kaolinite concentrations in the bed. Our results indicate that the deposition patterns of kaolinite were more influenced by bedform movement than by losing or gaining flow conditions. This implies that bedform morphodynamics control local-scale clogging of sandy streambeds and should be accounted for when studying the hydrology of catchments at larger scales.
DOI: 10.4211/hs.1b0db50d8e4c40199ba841d8640c568a
title: Source code for: Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data

description: This is the source code and data for: Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data.

The source code: Umbrales_share.R was develop in R language.. 
The source data: Landslides Catalog of SENAMHI-Peru (LCS) was compiled by the team of the hydrology department (DHI) of Peruvian National Service of Meteorology and Hydrology (SENAMHI).

More information at https://github.com/caemillan/Rainfall_thresholds_for_shallow_landslide.git.
DOI: 10.4211/hs.46b98820b7214850aadf6fa60c9bc1d1
title: Great Salt Lake Mineral Extraction Water Withdrawals

description: Great Salt Lake water withdrawals for use in mineral extraction evaporation ponds.  This data is part of  a study "The Salinity of the Great  Salt Lake and its Deep Brine Layer." This study analyzes the lake’s historical salinity and level data record. Its purpose is to better understand the movement and changes of salt in time and space within the lake and evaluate the occurrence and extent of the deep brine layer. Mineral extraction water withdrawals contribute to changes in the salinity of the lake.
DOI: 10.4211/hs.a79d513a08064ecd85f781bb9dfb642d
title: Data for Measuring porous media velocity fields and grain bed architecture with a quantitative PLIF-based technique

description: Porous media flows are common in both natural and anthropogenic systems. Mapping these flows in a laboratory setting is challenging and often requires non-intrusive measurement techniques, such as particle image velocimetry (PIV) coupled with refractive index matching (RIM). RIM-coupled PIV allows the mapping of velocity fields around transparent solids by analyzing the movement of neutrally buoyant micron-sized seeding particles. The use of this technique in a porous medium can be problematic because seeding particles adhere to grains, which causes the grain bed to lose transparency and can obstruct pore flows. Another non-intrusive optical technique, planar laser-induced fluorescence (PLIF), can be paired with RIM and does not have this limitation because fluorescent dye is used instead of particles, but it has been chiefly used for qualitative flow visualization. Here, we propose a quantitative PLIF-based methodology to map both porous media flow fields and porous media architecture. Velocity fields are obtained by tracking the advection-dominated movement of the fluorescent dye plume front within a porous medium. We also propose an automatic tracking algorithm that quantifies 2D velocity components as the plume moves through space in both an Eulerian and a Lagrangian framework. We apply this algorithm to three data sets: a synthetic data set and two laboratory experiments. Performance of this algorithm is reported by the mean (bias error, B) and standard deviation (random error, SD) of the residuals between its results and the reference data. For the synthetic data, the algorithm produces maximum errors of B & SD = 32% & 23% in the Eulerian framework, respectively, and B & SD = −0.04% & 3.9% in the Lagrangian framework. The small-scale laboratory experimental data requires the Eulerian framework and produce errors of B & SD = −0.5% & 33%. The Lagrangian framework is used on the large-scale laboratory experimental data and produces errors of B & SD = 5% & 44%. Mapping the porous media architecture shows negligible error for reconstructing calibration grains of known dimensions. Article DOI: 10.1088/1361-6501/acfb2b
DOI: 10.4211/hs.879c870655a743cbb436d59a26d4b297
title: Seasonal and Morphological Controls on Nitrate Retention in Arctic Deltas: Dataset

description: The Arctic Ocean is the smallest and shallowest of the world’s oceans but receives the largest riverine input per basin volume. Thus, the flux of nitrate from land to sea through rivers in the circumpolar Arctic can have large impacts on coastal life and global biogeochemical cycles. To assess the impact of terrestrially derived nitrate on Arctic Ocean chemistry under current and future scenarios, accurate accounting of nitrate loading is needed. Deltas act as filters that reduce nitrate loads, but current estimates of flux exclude the effects of Arctic deltas on nitrate export. We use a novel approach based on satellite imagery to estimate the potential impact of deltas on nitrate retention on five Arctic deltas, representing a range of morphologies and sizes. Our analysis shows seasonal and morphological differences in nitrate processing rates in each delta with retention rates ranging from 2.9-15 percent of incoming load and most retention occurring during winter months. Under future climate scenarios, the efficiency of nitrate retention decreases by up to 10 percent. Our models suggest that Arctic deltas alter the magnitude of nitrate entering the ocean and future predictions of loading or current earth system models could be improved by incorporating their effect.  The dataset includes input parameters and model results for nitrate retention on the Kolyma, Lena, Mackenzie, Yenisei and Yukon deltas.
DOI: 10.4211/hs.b55bb9282db5471889987a164e0e1a4e
title: Gibson Jack 2022 ecohydrology data

description: This resource contains ecohydrologic data collected within the North Fork of the Gibson Jack watershed, located in Pocatello, Idaho, USA in the northern Rocky Mountains. These datasets were collected to look at interactions between stream water and ecologic riparian processes. This ecohydrologic dataset includes stream water level,  soil moisture, sap flow, and vapor pressure deficit (VPD) for three study sites that span a range of observed groundwater conditions. Water level and water elevation were measured at three paired fully-screened stream well and partially screened piezometers using Onset U-20 water level loggers and corrected for barometric pressure. Water elevations in the wells and piezometers were used to calculate vertical head gradients (VHG) at each stream site using the methods outlined in Baxter et al., 2003. Soil moisture was measured 20 cm below ground level using METER GS3 soil moisture sensors. Sap Flow is the site-average sap flow from the three Dynamax Dynagage Heat Balance sensors deployed at each site.  A U24 Onset Relative Humidity Sensor was used to record the relative humidity and temperature needed to calculate VPD.

This dataset was used to look at causal interactions between the ecohydrologic variables described above using Convergent Cross-Mapping (CCM). We ran the CCM analysis in R using the rEDM package (Park et al., 2022 -- https://cran.r-project.org/web/packages/rEDM/rEDM.pdf). We have included an R script (EcoHydroCCM.R) that gives an example of the parameters we used, as well as the workflow for running CCM with both the seasonality testing and lagged versions of CCM. For simplicity in this script, we provided the workflow by focusing on one interaction -- the interaction between sap flow and stream water level.
DOI: 10.4211/hs.2892ca1822774f3f99332b7b7a3bdca0
title: Multiscale ecological niche modeling exhibits varying climate change impacts on habitat suitability of Madrean Pine-Oak trees

description: This resource contains the code and data used in this publication.

Publication Abstract:
Anthropogenic climate change and increasing greenhouse gas emissions are expected to globally impact the biological function, community structure, and spatial distribution of biodiversity. Many existing studies explore the effect of climate change on biodiversity, generally at a single spatial scale. This study explores the potential effects of climate change on the habitat suitability of seven tree species at two distinct spatial scales: the Coronado National Forest (CNF), a local management area, and the Sierra Madre Occidental (SMO), an ecoregional extent. Habitat suitability was determined by extrapolating Ecological Niche Models (ENMs) based on citizen-science tree occurrence records into future climatic conditions using projected 30-year normals for two anthropogenic emissions scenarios through the end of the century. These ENMs, examined at a spatial resolution of 1 km2, are constructed using a mean average ensemble of three commonly used machine learning algorithms. The results show that habitat suitability is expected to decrease for all seven tree species at varying degrees. Results also show that climate-forcing scenario choice appears to be far less important for understanding changes in species habitat suitability than the spatial scale of modeling extent. Additionally, we observed non-linear changes in tree species habitat suitability within the SMO and CNF dependent on forest community type, latitude, and elevational gradient. The paper concludes with a discussion of the necessary steps to verify the estimated alters of these tree species under climate change. Most importantly, provides a framework for characterizing habitat suitability across spatial scales.
DOI: 10.4211/hs.28af99c09ee4423dbffef28bf32837e0
title: CSDMS Data Components Use Cases

description: This collection includes HydroShare resources for the use case Jupyter Notebooks of the CSDMS Data Components (https://csdms.colorado.edu/wiki/DataComponents ). These use cases cover a variety of topics, including landslide susceptibility mapping, modeling of overland flow in a wildfire-impacted catchment, permafrost landscape processes, and wave power. Each use case is designed to demonstrate the application and the capabilities of the CSDMS Data Components. 

Please click on each HydroShare resource link in this collection to access the corresponding Jupyter Notebook and learn how to run it on the CUAHSI JupyterHub.
DOI: 10.4211/hs.a08e6289b9ac4e4c8b2fef900bd34c5c
title: Nitrate removal efficiency in hyporheic zone when stream-borne POC serves as dominant carbon source

description: The content of this resource serves as the data for the paper titled "Effect of Particulate Organic Carbon Deposition on Nitrate Reduction in Hyporheic Zone" that submitted to Water Resources Research. Thses data were output from the validated simulation and the baseline numerical simulation.