cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.5300b40ffcbe411e918770ec1044bd4b
title: GRACE-based Standardized Drought Indices over East Africa for 2002-2016

description: This dataset contains standardized drought indexes (SIs) over East Africa derived from total water storage anomaly (TWSA) observations from the Gravity Recovery and Climate Experiment (GRACE) mission.  We obtained the RL05M.1 CRI Filtered Version 2 (Wiese et al., 2016, Watkins et al., 2015) monthly mass grids of the NASA JPL global mascon solution from the JPL’s GRACE TELLUS site.  TWSAs for 40 mascons covering East Africa were extracted from the dataset for May, 2002 through August, 2016.  Observations collected between the 10th and 20th day of the month were retained as the SI dataset.  The resulting SI dataset did not contain data for several months due to missing observations in the global mascon solution dataset or because the observation for the month was not between the 10th and 20th day of the month.  The missing values were interpolated and the final SI dataset contains a TWSA value for each mascon for each month from May, 2002 through August, 2016.  1-month, 3-month, and 6-month standardized drought indexes were calculated following the nonparametric approach described by Farahman and AghaKouchak (2015).  This methodology derives the SI using the empirical Gringorten plotting position (Gringorten, 1963).

The SI dataset contains the following fields:
•	ID: mascon ID assigned by NASA JPL
•	Year: year of the SI
•	Month: month of the SI
•	SI1Mo: one-month SI
•	SI3Mo: three-month SI
•	SI6Mo: six-month SI

This dataset was created on April 20, 2017.  Figures mapping the drought indices by year are presented with the dataset.

Farahmand, A., & AghaKouchak, A. (2015). A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76, 140–145. https://doi.org/10.1016/j.advwatres.2014.11.012
Gringorten, I. I. (1963). A plotting rule for extreme probability paper. Journal of Geophysical Research, 68(3), 813–814. https://doi.org/10.1029/JZ068i003p00813
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., & Landerer, F. W. (2015). Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons: Improved Gravity Observations from GRACE. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671. https://doi.org/10.1002/2014JB011547
D. N. Wiese, D.-N. Yuan, C. Boening, F. W. Landerer, M. M. Watkins. 2016. JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2. Ver. 2. PO.DAAC, USA. Dataset accessed [2017-02-07] at http://dx.doi.org/10.5067/TEMSC-2LCR5.
DOI: 10.4211/hs.1b799717b3df40d7946f13ca8b7bcb6f
title: WRSS

description: Materials for WRSS package
DOI: 10.4211/hs.f64ccf5250274c0f9c099d97e24dd6c2
title: Supporting Data for Buahin et al. 2019: Parallel multi-objective calibration of a component-based river temperature model

description: The files provided here are input files for the river temperature modeling components created for the calibration exercise presented in Buahin et al. 2019, "Parallel multi-objective calibration of a component-based river temperature model" in Environmental Modeling and Software (https://doi.org/10.1016/j.envsoft.2019.02.012). Input files are organized into different folders for the different components as follows:

1. Composition: Contains the coupled model composition files (i.e., *.hcp) used in the HydroCoupleComposer graphical user interface. It describes the components that have been coupled as well as the data exchange connections nodes between them.

2. CalibrationComponent: Contains the input files for the calibration component specifying the calibration objectives, decision variables, and optimization algorithm to use for calibration.

2. CSHComponent: Contains input files for the channel solute and heat transport component.

3. HTSComponent: Contains input files for the hyporheic transient storage component.

4. ObjectiveFunctionComponent: Contains input files used to specify the objectives to be minimized by the CalibrationComponent.

5. RHEComponent: Contains input files used in the radiative heat exchange component.

6. SWMMComponent: Contains input files used in the Stormwater Management Model (SWMM) component.

7. TimeSeriesProviderComponent: Contains input files used to prescribe time varying input data to other components.

The windows version of the HydroCoupleComposer executable needed to run this composition can be downloaded on Github (https://github.com/HydroCouple/HydroCoupleComposer/releases/download/v1.2.2/HydroCoupleComposer.msi)
DOI: 10.4211/hs.f8420c1447fe440eb93e656b2db0b64d
title: LCZO-Stream Water Chemistry, Streamflow / Discharge, Hysteretic response of solutes and turbidity at the event scale across forested tropical montane watersheds - Luquillo Experimental Forest (2016-2017)

description: Concentration-discharge relationships are a key tool for understanding the sourcing and transport of material from watersheds to fluvial networks. Storm events in particular provide insight into variability in the sources of solutes and sediment within watersheds, and the hydrologic pathways that connect hillslope to stream channel. Here we examine high-frequency sensor-based specific conductance and turbidity data from multiple storm events across two watersheds (Quebrada Sonadora and Rio Icacos) with different lithology in the Luquillo Mountains of Puerto Rico, a forested tropical ecosystem. Our analyses include Hurricane Maria, a category 5 hurricane. To analyze hysteresis, we used a recently developed set of metrics to describe and quantify storm events including the hysteresis index (HI), which describes the directionality of hysteresis loops, and the flushing index (FI), which describes whether the mobilization of material is source or transport limited. We also examine the role of antecedent discharge to predict hysteretic behavior during storms. Overall, specific conductance and turbidity showed contrasting responses to storms. The hysteretic behavior of specific conductance was very similar across sites, displaying clockwise hysteresis and a negative flushing index indicating proximal sources of solutes and consistent source limitation. In contrast, the directionality of turbidity hysteresis was significantly different between watersheds, although both had strong flushing behavior indicative of transport limitation. Overall, models that included antecedent discharge did not perform any better than models with peak discharge alone, suggesting that the magnitude and trajectory of an individual event was the strongest driver of material flux and hysteretic behavior. Hurricane Maria produced unique hysteresis metrics within both watersheds, indicating a distinctive response to this major hydrological event. The similarity in response of specific conductance to storms suggests that solute sources and pathways are similar in the two watersheds. The divergence in behavior for turbidity suggests that sources and pathways of particulate matter vary between the two watersheds. The use of high-frequency sensor data allows the quantification of storm events while index-based metrics of hysteresis allow for the direct comparison of complex storm events across a heterogeneous landscape and variable flow conditions.

Additional scripts for hysteresis analysis are available here in the 'python scripts for analysis' folder and at https://github.com/miguelcleon/HysteresisAnalysis/
DOI: 10.17026/ar/dljibf
title: PAN-00085312 - wall fragment (metal vessel)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00085312&lt;/p&gt;
description: Modified: 2025-07-18T16:14:50+02:00
DOI: 10.4211/hs.1f0242ee442945ca87de348ad2dd4315
title: Data for Particle Seeded Grains to Identify Highly Irregular Solid Boundaries and Simplify PIV measurements

description: Particle image velocimetry, PIV, is a non-invasive technique for measuring velocity fields. It is especially powerful when coupled with refractive index-matching (RIM) to map velocity fields around solid objects. The solid objects are typically removed from the flow field with a masking approach before performing the PIV analysis and mapping the velocity field. However, imasking required a-priory information on solid location and their geometric shape which is difficult in to select them when PIV is done with RIM. Here we report and store the data used in the contribution "Particle Seeded Grains to Identify Highly Irregular Solid Boundaries and Simplify PIV measurements" by Basham, Budwig and Tonina, 2019, in Frontiers in Earth Science, doi: 10.3389/feart.2019.00195.
DOI: 10.4211/hs.9985b3cb38c94cee872b28f6dcdef739
title: Waterhackweek2019 Data Access and Time-series Statistics Cyberseminar

description: Data about water are found in many types of formats distributed by many different sources and depicting different spatial representations such as points, polygons and grids. How do we find and explore the data we need for our specific research or application? This seminar will present common challenges and strategies for finding and accessing relevant datasets, focusing on time series data from sites commonly represented as fixed geographical points. This type of data may come from automated monitoring stations such as river gauges and weather stations, from repeated in-person field observations and samples, or from model output and processed data products. We will present and explore useful data catalogs, including the CUAHSI HIS catalog accessible via HydroClient, CUAHSI HydroShare, the EarthCube Data Discovery Studio, Google Dataset search, and agency-specific catalogs. We will also discuss programmatic data access approaches and tools in Python, particularly the ulmo data access package, touching on the role of community standards for data formats and data access protocols. Once we have accessed datasets we are interested in, the next steps are typically exploratory, focusing on visualization and statistical summaries. This seminar will illustrate useful approaches and Python libraries used for processing and exploring time series data, with an emphasis on the distinctive needs posed by temporal data. Core Python packages used include Pandas, GeoPandas, Matplotlib and the geospatial visualization tools introduced at the last seminar. Approaches presented can be applied to other data types that can be summarized as single time series, such as averages over a watershed or data extracts from a single cell in a gridded dataset – the topic for the next seminar.

Cyberseminar recording is available on Youtube at https://youtu.be/uQXuS1AB2M0
DOI: 10.4211/hs.7bd7b3f4c91741b9bf22bfff717ec286
title: Dynamic Water-Balance/Nonlinear-Reservoir GW-SW interaction model

description: Model developed and documented in:  O’Reilly, A.M., Holt, R.M., Davidson, G.R., Patton, A., Rigby, J.R., 2020. A dynamic water-balance/nonlinear-reservoir model of a perched phreatic aquifer–river system with hydrogeologic threshold effects: Water Resources Research 56(6): e2019WR025382. https://doi.org/10.1029/2019WR025382

Heterogeneity in the hyporheic zone or near-field geology can impart a threshold effect on groundwater-surface water (GW-SW) exchange. Variations in the texture of riverbed sediments and lithologic variations in adjacent and underlying geology are examples of common heterogeneities. Hydrologic interaction with these heterogeneities leads to distinct types of “behavior” that “switch” when surface-water or groundwater levels rise above or fall below the interface of the layers of differing lithology. A dynamic water-balance/nonlinear-reservoir model incorporating threshold effects was developed for a perched phreatic aquifer–river system. Four conceptualizations of the system were modeled, each of which simulates a perched aquifer as a dynamical system that receives recharge from the riverbank and loses water to an underlying regional aquifer, using combinations of zero, one, or two thresholds representing layered heterogeneity in riverbank and/or aquifer lithology. Application of the model code was demonstrated at a location in the Lower Mississippi River Valley, USA. Models were run using hourly river-gage measurements, calibrated to a 382-day period of corresponding measurements in a nearby well, and further assessed for a 3.5-year period representing varied hydrologic conditions. The best performance was demonstrated by the model incorporating threshold effects, which elucidated four modes of GW-SW system behavior controlled by both riverbank (riverbed hydraulic conductivity) and aquifer (hydraulic conductivity and storage coefficient) properties. The dynamical system modeling approach incorporates the salient hydrologic processes of a GW-SW system with layered heterogeneity. Based upon fundamental mass-conservation concepts, the simple dynamic water-balance/linear-reservoir model has broad applicability to many hydrogeologic settings.
DOI: 10.4211/hs.01de2a97d51042d7aeece1f28ff4c8cc
title: WRSS

description: The data belong to a manuscript submitted to the Journal of Water Resources Management, Springer.
DOI: 10.4211/hs.e2cca000bbd14538b02c79358bb6e1d6
title: Error Assessment for Height Above the Nearest Drainage Inundation Mapping

description: This is the repository for the paper "Error Assessment for Height Above the Nearest Drainage Inundation Mapping" by Lukas Godbout, Jeff Y. Zheng, Sayan Dey, Damilola Eyelade, David Maidment, and Paola Passalacqua. Please direct all communication to LukasGodbout@utexas.edu.

Real time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage (HAND) method can be used in conjunction with Synthetic Rating Curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic modeling calibrated against observed stage heights. We find that SRCs are accurate enough for large scale approximate inundation mapping while not accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe that reach length and slope predict divergence between the two types of rating curves, and that SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.
DOI: 10.4211/hs.39abb2d1a3614b88afc02b22b77021a2
title: A model analysis of the tidal engine that drives nitrogen cycling in coastal riparian aquifers

description: In coastal rivers, tides facilitate surface water-groundwater exchange and strongly coupled nitrification-denitrification near the fluctuating water table. We used numerical fluid flow and reactive transport models to explore hydrogeologic and biogeochemical controls on nitrogen transport along an idealized tidal freshwater zone based on field observations from White Clay Creek, Delaware, USA. The capacity of the riparian aquifer to remove nitrate depends largely on nitrate transport rates, which initially increase with increasing tidal range but then decline as sediments become muddier and permeability decreases. Over the entire model reach, local nitrification provides a similar amount of nitrate as surface and groundwater contributions combined. More than half (~66%) of nitrate removed via denitrification is produced in-situ, while the vast majority of remaining nitrate removed comes from groundwater sources. In contrast, average nitrate removal from surface water due to tidal pumping amounts to only ~1% of the average daily in-channel riverine nitrate load or 1.77 kg of nitrate along the reach each day. As a result, tidal bank storage zones may not be major sinks for nitrate in coastal rivers but can act as effective sinks for groundwater nitrate. By extension, tidal bank storage zones provide a critical ecosystem service, reducing contributions of groundwater nitrate, which is often derived from septic tanks and fertilizers, to coastal rivers.
DOI: 10.4211/hs.cf0e4c7d6a41457ebcea2ffc397b712d
title: Machine Learning Uganda Borehole Failure

description: Do water borehole failures, in Uganda, correlate with geographic details, population, or political reasons? Is there a trend based on which orgnization oversaw the installation or raised the capital? Can we create an ML model to determine if correlations exist?
DOI: 10.15468/dl.7uw38p
title: Occurrence Download

description: A dataset containing 10537 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-4.049779 40.671139, -4.051230 40.709273, -4.101557 40.709273, -4.100088 40.671139, -4.049779 40.671139))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 10537 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049937-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.8010620a2ddb459492f118fdf57b59c8
title: Laboratory Bed Form Hysteresis Experiments

description: Recirculating flume experiments tracking the evolution of sand bed forms through repeat 2d sonar scans.  Experiments track change in bed form geometries across abrupt and gradual water discharge changes.
DOI: 10.4211/hs.e3c159631acd470cbeef5fa1abe0142e
title: sample data for hess-2019-205 submission

description: Sample data for HESS-2019-205 submission

Description: This file contains the event magnitudes and spacing for Cases 1 & 3 presented in the submitted manuscript to HESS titled "Recession analysis 42 years later - work yet to be done".  

CVS File: This file is an ordered set of the normalized event magnitude [-] and the start date fo the event (Time/Timescale [-])

Matlab File: The file is presented is in a .mat file extension created in Matlab. The data is divided into 3 columns: mag, value, and start_locs. The column of "Mag" defines the event magnitudes, which are log-normally distributed with a mean 1 of a standard deviation of 1. The column of "value" defines the event duration which has a mean of 2.5 and a standard deviation of 1. The "start_locs" column as the cumulative event durations that identify the start time of each event. Below is the associated Matlab code used to create the file:
%% Matlab Code %%
mag= lognrnd(1,1[number_of_events,1]); %create log-normally distributed dataset of event magnitudes for a defined number of events
mag(mag<0)=1; %remove any negative magnitudes 
value=round(lognrnd(2.5,1,[number_of_events,1])); %create log-normally distributed dataset of event durations for a defined number of events
value(value<=0)=1; %remove any negative durations
start_locs=[2;cumsum(value)]; %create cumulative event start time-series
DOI: 10.4211/hs.a2b87a2f25d046958ac604e522f449c0
title: Animas watershed snowmelt modeling in the 2010 water year (use case 2)

description: This resource contains the use case results of web-based simulation for snowmelt modeling research. The model input files were created by executing the Python script (ueb_setup.py) in CUAHSI JupyterHub web app, which made web requests to HydroDS modeling web services (https://github.com/CI-WATER/Hydro-DS) for inputs preparation. The model output files were created by using the model input files and the UEB web app (https://appsdev.hydroshare.org/apps/ueb-app/). A JupyterHub Notebook file (Data_analysis_code.ipynb) includes the data analysis code to compare the model output created by this use case and another use case (https://doi.org/10.4211/hs.1be4d7902c87481d85b93daad99cf471) with different model grid resolutions (600 m vs 1200 m).
DOI: 10.4211/hs.66e45a2c6b724b9da50f036bf3216a19
title: SISS model data and script

description: This resource is linked to the following manuscript: 
Lutz et al.: How Important is Denitrification in Riparian Zones? Combining Endmember Mixing and Isotope Modeling to Quantify Nitrogen Removal Processes, in review for Water Resources Research

This resource provides concentration and isotope data in a groundwater well field along a 2 km stream section in central Germany. We developed a mathematical model combining endmember mixing and isotope modeling to account for mixing of river water and groundwater, and quantify nitrate transformation at the study site (i.e., the SISS model). This enabled us to explicitly determine the extent of denitrification (as permanent nitrate removal process) and nitrate removal by additional processes associated with negligible isotope fractionation (e.g., plant uptake, microbial assimilation and dissimilatory nitrate reduction to ammonium) in the riparian system.

Content:
*the SISS model code 
*chloride and nitrate concentration data from the field site
*nitrate and stable water isotope data from the field site
DOI: 10.4211/hs.7e5ec8c0e2b245199ab13cc9ae08b841
title: Simulation of Lake Erken water temperature (1961-2017) with GOTM: Model configuration, input data, output data and observed water temperature

description: The physical hydrodynamic model GOTM was used to reconstruct the past water temperature of Lake Erken (Sweden) in order to investigate possible changes in its thermal structure during the period 1961-2017. To run the model, seven climatic parameters were used as forcing data: Wind Speed (m/s), Air Temperature (°C), Air Pressure (hPa), Relative Humidity (%), Cloud Cover (dimensionless value between 0-1), Precipitation (mm/day) and Shortwave Solar Radiation (W/m-2). This resource contains the model configuration file, input data of the model,  the observed water temperature used to calibrate the model, and the output data.

The file 'erken.xml' is the model configuration file. 

The input data  of the model are:
- 'Erken_DailyPrec_1960-2017.dat': Lake Erken Daily Precipitation  
- 'Erken_MetFile_1960-2017.dat': hourly dataset that contains Lake Erken Wind Speed, Air Temperature, Air Pressure, Relative Humidity and Cloud Cover 
- 'Erken_HourlySWR_1960-2017.dat': hourly dataset of Lake Erken Shortwave Solar radiation
- 'ErkenObsTemp_1961-2017.dat': real daily water temperature data of Lake Erken 

The file 'ErkenObsTemp_1961-2017.obs' contains the real daily water temperature data of Lake Erken and it was used to calibrate the model.

The output data of the model are:
- 'Mod_temp_hr_z.txt': contains the depth profile of lake Erken
- 'Mod_temp_hr_temp.txt': contains the calibrated modeled water temperature profile on daily time-step between 1960-2017.

The input data used to run the model were available between 1961-2017. To avoid that the initial state of the model could increase the errors during calibration, a 1-year simulation spin-up was used to minimize calibration errors. To make full use of the available input data, a copy of the 1961 meteorological data  was appended at the beginning of the input files 'Erken_DailyPrec_1960-2017.dat', 'Erken_MetFile_1960-2017.dat' and 'Erken_HourlySWR_1960-2017.dat'. The data referred to the year 1960 are therefore only a copy of the 1961 data and they were used as spin-up year. For this reason,  the data referred to the year 1960 in the output files should be discarded.