cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.8aeaaa19cecc4a72994910942d687a11
title: Boyne River computational model

description: SWAT model for the Boyne River, located in northern Michigan, USA. The model uses citizen science data from CrowdHydrology <http://www.crowdhydrology.com/>. The model was calibrated using the following stations: MI1023. MI124, MI1025, MI1026. The SWAT model was built using ArcSWAT Version 2012.10_4.19. 

We acknowledge the Friends of the Boyne River <https://boyneriver.org/> and Michigan Trout Unlimited <http://www.michigantu.org/> for their role in community engagement, support during field visits, and maintenance of the CrowdHydrology network. The authors gratefully acknowledge the active participation of citizens near the Boyne River.
DOI: 10.4211/hs.d353ce4a7dc643c8a566504bfd578a57
title: Ward et al., 2019, Solute Transport and Transformation in an Intermittent, Headwater Mountain Stream with Diurnal Discharge Fluctuations

description: Data accompany the peer-reviewed manuscript:

Ward, A.S.; Kurz, M.J.; Schmadel, N.M.; Knapp, J.L.; Blaen, P.J.; Harman, C.J.; Drummond, J.D.; Hannah, D.M.; Krause, S.; Li, A.; Marti, E.; Milner, A.; Miller, M.; Neil, K.; Plont, S.; Packman, A.I.; Wisnoski, N.I.; Wondzell, S.M.; Zarnetske, J.P. Solute Transport and Transformation in an Intermittent, Headwater Mountain Stream with Diurnal Discharge Fluctuations. Water 2019, 11, 2208.
DOI: 10.4211/hs.f7614f0b1c784584af3cd4cdd7675a5a
title: Eco-hydrologic and water supply management modeling products for the Research Triangle, North Carolina, USA

description: This dataset consists of multiple products derived from eco-hydrologic, statistical, and water supply routing models used to determine water supply vulnerability to hydrologic change within the Research Triangle of North Carolina, USA. The products are as follows: (1) eco-hydrologic modeling (SWAT and RHESSys models) products providing water availability estimates at the outlets of watersheds in the greater Research Triangle region, as well as precipitation and evaporation estimates for regional water supply reservoirs, projecting from 2010-2069, which were used to develop synthetic stochastic hydrology for further water supply modeling; (2) products of stochastic hydrology development to generate synthetic reservoir inflows based on eco-hydrologic modeling results for regional reservoirs, timeseries from 2015-2060, which are split into .tar repositories of (a) demands (b) inflows and (c) evaporation; (3) results from water supply management modeling used for results and visualization in related research publications.
DOI: 10.4211/hs.4a414f6284b749b1852145c93fda86ab
title: Pumping saline groundwater field and model data

description: This is model output of pumping saline groundwater from the coastal aquifer of Almeria and field data.
DOI: 10.4211/hs.9c4a6e2090924d97955a197fea67fd72
title: Digital Elevation Models and GIS in Hydrology (M2)

description: This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. 
  - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). 
- If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. 
- You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.
DOI: 10.4211/hs.6c604c94779a49b4aea67868f4308364
title: Model output data for "Whale burial and buried organic matter impacts on biogeochemical cycling in beach aquifers and leachate fluxes to the nearshore zone"

description: This dataset contains the model output data shown in Figures 2-11 in the manuscript "Whale burial and buried organic matter impacts on biogeochemical cycling in beach aquifers and leachate fluxes to the nearshore zone." The data for Figures 2-8 and figure 11 is in .mat file format, which requires MATLAB to open.
DOI: 10.4211/hs.275f3521cc834467a34c051a716a3b30
title: Gilkey Glacier mass balance point observations 2013-2015, Southeast Alaska

description: Field point glacier mass balance observations (i.e. snow water equivalent and snow/ice ablation) collected on the Gilkey Glacier, Southeast Alaska, between 2012 and 2015. For snow water equivalent (SWE) observations, density measurements are made using full-snowpack-depth core samples, and depth measurements are to the previous year's summer surface. For melt observations, height changes are recorded using drilled (i.e. fixed in ice/firn) ablation stakes or wires, with ice density assumed at 850 kg/m3.
Columns include:
A-C: Internal database identifiers
D: Name of stake/site
E: Elevation, in meters above sea level
F: Water equivalent balance (positive indicates gain over listed time span, negative indicates loss) in meters water equivalent
G: Error (currently N/A)
H: Season (winter, summer, or annual net balances)
I:  Strata (N/A)
J: Start date (initial observation date)
K: End date (final observation date)
L: Source, i.e. who took the measurement
M: Any additional notes 
N: Geometry (N/A)
O: Latitude of observation
P: Longitude of observation
DOI: 10.4211/hs.b2920694abd64462b2d9e742b3c80ad8
title: Geng and Michael, 2019

description: This includes model files processing codes for nearshore pumping simulations in coastal volcanic aquifers.
DOI: 10.4211/hs.e24fdc11692a44368018a790b4d86b27
title: Parameters of Global Surface Soil Moisture Drydown using SMAP

description: We provide the pathways and parameters of surface soil moisture (SM) drydown using global observations from NASA's Soil Moisture Active Passive (SMAP) at 36 KM spatial resolution. Globally dominant canonical shapes of SM drydowns are identified using a non-parametric approach. A pixel-wise fitting of the selected canonical forms using a non-linear least-squares approach provide the pathways and parameters of SM drydown. The data generated from this study can be used for diverse applications including (and not limited to) identification of dominant soil hydrologic regimes, understanding land-surface coupling strength, and estimating effective soil water retention parameters at remote-sensing footprint scale etc. 
Details can be found in our paper: Sehgal, V., Gaur, N., & Mohanty, B. P. (2020). Global Surface Soil Moisture Drydown Patterns. Water Resources Research, 56, e2020WR027588. https://doi.org/10.1029/2020WR027588
DOI: 10.4211/hs.aae061ed6ab345818ceacd61fa644679
title: Mean annual climate and watershed properties for 121 reference USGS watersheds

description: This data release provides the underlying data for Kampf et al., in review: "Rethinking the role of the water balance in hydrologic research." Mean annual climatic variables based on the Northern Hemisphere water year (October 1 to September 30) and several watershed properties are provided for 121 USGS reference watersheds smaller than 1,000 square kilometers. For each climatic variable, mean annual values were derived from watershed average annual values.

The columns of the dataset are as follows:

SP- 	watershed averaged January 1 to July 1 snow persistence as in Hammond et al., 2018
P_mm - watershed averaged total water year precipitation from PRISM, Daly, 2013
Q_mm - total water year water yield from USGS NWIS
QdivP -  runoff ratio, total water year water yield divided by total water year precipitation
PET - watershed averaged total water year potential evapotranspiration from gridMET - Abatzoglou, 2013
PdivPET	- the ratio of total water year precipitation to total water year potential evapotranspiration from the sources above.
Elev_mean_m - GAGES-II, Falcone, 2011
Area_km2 - GAGES-II, Falcone, 2011

Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131.

Daly, C. (2013). Descriptions of PRISM spatial climate datasets for the conterminous United States (PRISM Doc., 14 p.). Corvallis, OR: PRISM Climate Group, Oregon State University.

Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow (Digit. Spat. Data set). Reston, VA: U.S. Geological
Survey.

Hammond, J. C., Saavedra, F. A., & Kampf, S. K. (2018). How does snow persistence relate to annual streamflow in mountain watersheds of the Western U.S. with wet maritime and dry continental climates? Water Resources Research, 54, 2605–2623. https://doi.org/10.1002/ 2017WR021899
DOI: 10.4211/hs.e2d9afd7d6d74be8a4843af5d8fd86e1
title: White et al., 2019. HESS

description: The contents of this resource are associated with the following paper. 
White, A., Moravec, B., McIntosh, J., Olshansky, Y., Paras, B., Sanchez, R. A., Ferre, T. P. A., Meixner, T., & Chorover, J. (2019). Distinct stores and routing of water in the deep critical zone of a snow-dominated volcanic catchment. Hydrology and Earth System Sciences
DOI: 10.4211/hs.6af3a8cc235d43a6a5be13298aee0af2
title: Agreement and uncertainty among climate change impact models: A synthesis of sagebrush steppe vegetation predictions

description: Ecologists have built numerous models to predict how climate change will impact vegetation, but these predictions are difficult to validate, making their utility for land management planning unclear. In the absence of direct validation, researchers can ask whether predictions from varying models are consistent. Here, we analyzed 43 models of climate change impacts on sagebrush (Artemisia tridentata Nutt.), cheatgrass (Bromus tectorum L.), pinyon-juniper (Pinus spp. and Juniperus spp.), and forage production on Bureau of Land Management (BLM) lands in the United States Intermountain West. These models consistently projected pinyon-juniper declines, forage production increases, and the potential for sagebrush increases in some regions of the Intermountain West. In contrast, models of cheatgrass did not predict consistent changes, making cheatgrass projections uncertain. While differences in emission scenarios had little influence on model projections, predictions from different modeling approaches were inconsistent in some cases. This model-choice uncertainty emphasizes the importance of comparisons such as this.
The projected vegetation changes have important management implications for agencies such as the BLM. Pinyon-juniper declines would reduce the BLM’s need to control pinyon-juniper encroachment, and increases in forage production could benefit livestock and wildlife populations in some regions. Sagebrush habitat may benefit where sagebrush is predicted to increase, but sagebrush conservation and restoration projects will be challenged in areas where climate may not remain hospitable. Projected vegetation changes may also interact with increasing future wildfire risk, potentially impacting vegetation and increasing management challenges related to fire.
DOI: 10.4211/hs.3b420b738128411e8e1e11b38b83b5f1
title: Agreement and uncertainty among climate change impact models: A synthesis of sagebrush steppe vegetation predictions

description: Ecologists have built numerous models to predict how climate change will impact vegetation, but these predictions are difficult to validate, making their utility for land management planning unclear. In the absence of direct validation, researchers can ask whether predictions from varying models are consistent. Here, we analyzed 43 models of climate change impacts on sagebrush (Artemisia tridentata Nutt.), cheatgrass (Bromus tectorum L.), pinyon-juniper (Pinus spp. and Juniperus spp.), and forage production on Bureau of Land Management (BLM) lands in the United States Intermountain West. These models consistently projected pinyon-juniper declines, forage production increases, and the potential for sagebrush increases in some regions of the Intermountain West. In contrast, models of cheatgrass did not predict consistent changes, making cheatgrass projections uncertain. While differences in emission scenarios had little influence on model projections, predictions from different modeling approaches were inconsistent in some cases. This model-choice uncertainty emphasizes the importance of comparisons such as this.

The projected vegetation changes have important management implications for agencies such as the BLM. Pinyon-juniper declines would reduce the BLM’s need to control pinyon-juniper encroachment, and increases in forage production could benefit livestock and wildlife populations in some regions. Sagebrush habitat may benefit where sagebrush is predicted to increase, but sagebrush conservation and restoration projects will be challenged in areas where climate may not remain hospitable. Projected vegetation changes may also interact with increasing future wildfire risk, potentially impacting vegetation and increasing management challenges related to fire.

Included in this page are the data and code used to complete this analysis and visualize results. This includes the original images of model results used in our analysis, and the code used to process and analyze these images to produce our final results.
DOI: 10.4211/hs.76e60d8b9ead4c33898c14dd677c6e06
title: Ranking forest effects on snow storage: a decision tool for forest management

description: Forests modify snow accumulation and ablation rates, and overall snow storage amounts and durations, with multiple processes acting simultaneously and often in different directions. To synthesize complex forest-snow relations and help guide near-term management decisions, we present a decision tree model based on a hypothesized hierarchy of processes and associated variables that predict forest effects on snow storage. In locations with high wind speeds, forests enhance snow storage magnitude and duration relative to open areas. Where wind speeds are low, and winter and spring air temperatures are colder, forests diminish snow storage magnitude but enhance duration. Where air temperatures are warmer, forests diminish both magnitude and duration. Forest structure and aspect are secondary influences that shift the net effect of forest on snow storage. We apply the model to map the influence of forests on snow storage under historic and warming climate conditions across the western United States, but this model is applicable in any region with forests and snow. The decision tree model provides practitioners a first-step evaluation to guide management decisions that consider where and how forests can be managed to optimize in-situ water storage alongside other objectives, such as reducing wildfire fuels. This framework also articulates geospatial hypotheses, in order of anticipated importance, to be tested in future investigations of forest-snow-climate relations.

The data and code included herein are described in Dickerson-Lange, et al. 2021, Ranking forest effects on snow storage: a decision tool for forest management, Water Resources Research.  The repository contains all input data, model code, and results.
DOI: 10.4211/hs.39d9f1263d744c4c98325a80e8fca51a
title: Hydrogeologic controls of surface water-groundwater nitrogen dynamics within a tidal freshwater zone

description: Microbial processing of reactive nitrogen in stream sediments and connected aquifers can remove and transform nitrogen prior to its discharge into coastal waters, decreasing the likelihood of harmful algal blooms and low oxygen levels in estuaries. Canonical wisdom points to the decreased capacity of rivers to retain nitrogen as they flow towards the coast. However, how tidal freshwater zones, which often extend hundreds of kilometers inland, process and remove nitrogen remains unknown. Using geochemical measurements and numerical models, we show that tidal pumping results in the rapid cycling of nitrogen within distinct zones throughout the riparian aquifer. Near the fluctuating water table nitrification dominates, with high nitrate concentrations (>10 mg N L-1) and consistent isotopic composition. Beneath this zone, isotopes reveal that nitrate is both denitrified and added over the tidal cycle, maintaining nitrate concentrations >3-4 mg N L-1. In most of the riparian aquifer and streambed, nitrate concentrations are <0.5 mg N L-1, suggesting denitrification dominates. Model results reveal that oxygen delivery to groundwater from the overlying unsaturated soil fuels mineralization and nitrification, with subsequent denitrification in low oxygen, high organic matter regions. Depending on flow paths, tidal freshwater zones could be sources of nitrate in regions with permeable sediment and low organic matter content.
DOI: 10.4211/hs.5b968dd6115a40ff9740ea88215d7719
title: Data from Wieting et al. (2017), Quantifying soil hydraulic property changes with fire severity by laboratory burning

description: Data from Wieting, C., Ebel, B., and Singha, K. (2017). Quantifying the effects of wildfire on changes in soil properties by surface burning of soils from the Boulder Creek Critical Zone Observatory. Journal of Hydrology-Regional Studies, http://dx.doi.org/10.1016/j.ejrh.2017.07.006, 43-57.

Infiltration processes are not well understood in fire-affected soils because soil hydraulic properties and soil-water content are altered by the heat. This study uses intact soil cores, which should maintain preferential flow paths, that were collected in the field to explore the impacts of fire on soil properties and infiltration processes during rainfall. Three soil scenarios are presented here: unburned control soils, and low- and high-severity burned soils. Fire severity was simulated in the laboratory using a heating gun, and established based on temperature and duration of heating. Soil properties pre- and post-burn were measured using laboratory techniques including: Mini Disk Infiltrometer tests, Water Drop Penetration Time (WDPT) Tests, and measurements of dry bulk density and total organic carbon (TOC). Soil moisture and temperature were recorded at approximately 2.5 cm and 7.5 cm in soil cores as was the cumulative volume of water exiting the core during rainfall simulations. Mini Disk infiltration experiments suggest a decrease in both cumulative infiltration and infiltration rates from unburned to low-severity burned soils. High-severity burned soils saw an increase in cumulative infiltration. We interpret these changes as a result of the burning off of organic materials, enabling water to infiltrate more instead of being stored in the organics. The field saturated hydraulic conductivity did not vary from unburned to low-severity burned soils, but increased in high-severity burned soils due to the lack of organics that help inhibit water movement. During rainfall simulations, soil-water storage decreased from when soils were burned, likely because of the inability to store water within organic materials since they were burned. Vulnerability to raindrop impact also increased with fire severity. Together, these results indicate that fire-induced changes from low-severity wildfires were not as drastic as high-severity wildfires, and that high-severity burned soils can infiltrate more water, but not necessarily store it. Quantifying soil properties affected by wildfire, which can be gained through controlled laboratory simulations like this study, will aid in predicting post-wildfire behavior on the watershed scale.
DOI: 10.4211/hs.d2481d34e4414494a24a0be0f14bd3a1
title: Data from Singha and Gorelick (2005), Saline tracer visualized with electrical resistivity tomography: field scale spatial moment analysis

description: Data from Singha, K. and Gorelick, S.M. (2005). Saline tracer visualized with electrical resistivity tomography: field scale spatial moment analysis. Water Resources Research, 41, W05023, https://doi.org/10.1029/2004WR003460, 17 p.

Cross-well electrical resistivity tomography (ERT) was used to monitor the migration of a saline tracer in a two-well pumping-injection experiment conducted at the Massachusetts Military Reservation in Cape Cod, Massachusetts. After injecting 2200 mg/Lof sodium chloride for 9 hours, ERT data sets were collected from four wells every 6 hours for 20 days. More than 180,000 resistance measurements were collected during the tracer test. Each ERT data set was inverted to produce a sequence of 3-D snapshot maps that track the plume. In addition to the ERT experiment a pumping test and an infiltration test were conducted to estimate horizontal and vertical hydraulic conductivity values. Using modified moment analysis of the electrical conductivity tomograms, the mass, center of mass, and spatial variance of the imaged tracer plume were estimated.Although the tomograms provide valuable insights into field-scale tracer migration behavior and aquifer heterogeneity, standard tomographic inversion and application of Archie’s law to convert electrical conductivities to solute concentration results in underestimation of tracer mass. Such underestimation is attributed to (1) reduced measurement sensitivity to electrical conductivity values with distance from the electrodes and (2) spatial smoothing (regularization) from tomographic inversion. The center of mass estimated from the ERT inversions coincided with that given by migration of the tracer plume using 3-D advective-dispersion simulation. The 3-D plumes seen using ERT exhibit greater apparent dispersion than the simulated plumes and greater temporal spreading than observed in field data of concentration breakthrough at the pumping well.