cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.2a72bbd6f80d4045b5763b01650f1894
title: Crow Creek Watershed 2025: Hydraulic Conductivity & Soil Texture

description: The Crow Creek Study Watershed sits at ~2,500 masl in elevation near Laramie, WY and is used as a teaching and research laboratory for the Mountain Hydrology Research Group at the University of Wyoming. The watershed is a small headwater tributary to crow creek adjacent to National Forest System Road 707. There is a Crow Creek SNOTEL site within the watershed for meteorological time series data with a median peak snow water equivalent of ~170 mm that occurs in late March. There is a strong north and south aspect within the delineated watershed area of approximately 0.3 km^2. Site plots A, B, C, D, and E were each approximately 100 m^2 along a transect of north to south facing aspect respectively. Average slopes of the north aspect were approximately 15 degrees, while slopes of the south aspect were approximately 16 degrees.
DOI: 10.4211/hs.a99546d93aea49bd9e405cb6b3911b72
title: Probable Maximum Precipitation Estimation Using Generalized Extreme Value Distribution and Regionalization Analysis

description: This dataset is used as input for implementing a new methodology to estimate Probable Maximum Precipitation (PMP), which is based on the Generalized Extreme Value (GEV) distribution and regionalization analysis. It consists of 428 time series of annual maximum daily precipitation (AMDP) recorded across Chile, along with the corresponding metadata.
DOI: 10.4211/hs.bdc496c09e394875accc732e94daab3b
title: Knowledge-Guided Machine Learning for Operational Flood Forecasting

description: We present a knowledge-guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main components: inverse and forward models. The inverse model uses observed precipitation, temperature, and streamflow data to generate a representation of the current underlying catchment state. The forward model predicts streamflow using the learned catchment state. The FHNN architecture is designed to model multi-scale processes and capture their interactions, a critical ability for flood modeling. FHNN also improves forecasts based on real-time data through an inference-based data integration approach using inverse modeling. FHNN’s data integration approach improves forecasts in response to observed data more efficiently than data assimilation methods that require computationally intensive optimization. To show the performance of FHNN, we compare the FHNN to a leading deep learning alternative (autoregressive LSTM) on the large-sample CAMELS-US dataset, and operational flood forecast data from the US National Weather Service (NWS). Official NWS flood forecasts are generated by expert human forecasters using a physics-based model, in a human-in-the-loop process. Thus, we assess the flood forecast ability of FHNN by directly comparing its performance against these NWS expert-derived forecasts. The human forecaster creates a more accurate forecast within the first 18 hours of a forecast’s issuance, but FHNN has significantly better predictions thereafter. This research lays the groundwork for leveraging the predictive performance of AI-based models with the expertise in forecasting agencies to produce better river forecasts.
DOI: 10.4211/hs.719a8d719907435dade9102aa9a0fa8d
title: An Internal Validation Assessment of Scale Across Social Vulnerability Index Model Structures (Uncertainty and Global Sensitivity Analysis)

description: Proactive and equitable planning for natural hazards is vital, as these events can cause mass destruction and severely impact livelihoods. To aid hazard preparedness decision-making, organizations can utilize tools like a social vulnerability index (SVI), developed to identify vulnerable populations to ensure that those with inherent social inequities are considered in planning. However, SVI construction involves various approaches that introduce epistemic uncertainty, potentially affecting resulting decisions. While progress has been made in understanding how construction processes affect index results, the spatial elements of SVI models are underexplored, with conflicting views on the influence of scale selection. This study addresses this gap by evaluating how changes in the selection of scalar properties (areal units and geographic boundaries) and indicator selection impact SVI ranks for two indices, the Centers for Disease Control SVI and the Hazards Vulnerability and Resilience Institute SVI. We examine these changes across three model structures: hierarchical with z-score standardization, hierarchical with percentile ranking normalization, and inductive with z-score standardization, employing an uncertainty and sensitivity analysis. When altering scalar and indicator properties, we found the inductive model less robust than hierarchical models. We also observed indicator selection as the primary driver of variability in SVI ranks across all model structures. However, we found significant yet mixed effects of scale selection and interaction effects on variability in SVI ranks. Our findings highlight the role of scale selection in shaping index outcomes and underscore the need for critical evaluation in SVI creation to advance equitable hazard management.
DOI: 10.4211/hs.9b2b865acc51481babc74599470a3bfc
title: ChemLotUS: A Benchmark Dataset of Lotic Chemistry across US River Networks

description: ChemLotUS contains water quality information in rivers and streams carefully curated and linked to a high resolution stream network. The dataset covers the contiguous United States (CONUS) with nearly 35 million records in about 290,000 sites. 11 water constituents are included so far: Calcium (Ca), Electrical Conductivity (EC), pH, Total Suspended Solids (TSS), Turbidity (Tu), Total Organic Carbon (TOC), Dissolved Oxygen (DO), Chlorophyll a (Chl-a), Nitrate (NO3), Soluble Reactive P (SRP), and Total P (TP). Raw water quality data comes from the Water Quality Portal (WQP - https://www.waterqualitydata.us/), and network information comes from the National Hydrography Dataset High Resolution (NHD-HR - https://www.usgs.gov/national-hydrography/nhdplus-high-resolution).

The repository contains the ChemLotUS dataset, as well as the code developed to obtain it, following the structure described on the Readme1 file.
The Readme2 file contains the definitions of the columns contained in each of the csv files comprised in the dataset.

This version is preferred over the two previous ones, considering the following differences:

1. With DOI: 10.4211/hs.595e2ba483524feba22273e4dfbed190 (Apr 10, 2025): During a workshop conducted in May 5 - 9, 2025 at UF using this previous version, we identified potential outliers not identified as such in the csv files. The new version solves this issue.
2. With DOI: 10.4211/hs.266099a29929423e9cec8bdb886b6a21 (Aug 26, 2024): Noting that this is the version originally submitted to peer review, the subsequent versions follow the recommendations made by the reviewers. 

More details about the ChemLotUS dataset, including the methods employed to obtain it and several example use cases, are presented in the homonymous paper "ChemLotUS: A Benchmark Dataset of Lotic Chemistry across US River Networks", published on May 2025 in Water Resources Research (https://doi.org/10.1029/2024WR039355).
DOI: 10.4211/hs.84911fe3496f44eebbce512a6e8d0db3
title: Supporting Information datasets for article: "Explicit simulation of reactive microbial transport with a dual-permeability, two-site kinetic deposition formulation using the integrated surface-subsurface hydrological model HydroGeoSphere", EGUsphere [preprint], 2025

description: This repository contains the Supporting Information Datasets DS1, DS2 and DS3 for Currle, F., Therrien, R., and Schilling, O. S.: Explicit simulation of reactive microbial transport with a dual-permeability, two-site kinetic deposition formulation using the integrated surface-subsurface hydrological model HydroGeoSphere, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-372, 2025. At the moment of publication of this repository, the manuscript was under review for publication in Hydrol. Earth Syst. Sci.
DOI: 10.17632/btt4vbgdr5
title: Dataset of measured runoff and sediment in the Shuiwanggou and Heifangou catchments

description: This dataset contains measured runoff and sediment data from two small agricultural catchments on the Loess Plateau: Shuiwanggou (without soil and water conservation measures) and Heifangou (with such measures). It was collected at the catchment outlets during individual runoff events, with corresponding hydrological and sediment variables used to derive flow‑energy indicators.
DOI: 10.4211/hs.fafd9b02ee3b4496830511342dab64a3
title: Coastal Groundwater Dynamics in Lahaina Beaches, Hawaiʻi, and Implications for the Transport of Wildfire-Derived Contaminants

description: The August 2023 wildfire in Lahaina, Maui, resulted in extensive environmental damage, including the release of untreated wastewater and combustion-derived pollutants such as nutrients and polycyclic aromatic hydrocarbons (PAHs) into the environment. This study investigates the seasonal dynamics of groundwater flow and solute transport within a tidally influenced beach aquifer affected by the wildfire. Utilizing field observations and a two-dimensional, density-dependent, variably saturated groundwater model calibrated with year-long data, we simulated groundwater flow and salinity distributions. Lagrangian particle tracking was subsequently employed to assess solute pathways and transit times under contrasting seasonal conditions. Results indicate that during the summer, the inland groundwater table in the beach is relatively higher, likely due to a delayed inland recharge signal, leading to predominantly seaward groundwater flow with rapid solute transport to the shoreline. Conversely, winter conditions, marked by enhanced tidal effects and wave activity, induce greater seawater infiltration, deeper recirculation cells, and a net inland-directed flow, leading to landward movement and retention of solutes within the aquifer. Extended transit times under these conditions enhance the potential for in-situ transformations, including sorption, microbial redox reactions, and mineral precipitation/dissolution, particularly affecting redox-sensitive species like phosphate, ammonium, and iron-bound contaminants. Spatial variability in nutrient concentrations across the beach subsurface underscores the role of tidal modulation in influencing solute export. These findings highlight the necessity of incorporating seasonal and tidal variability into coastal contaminant fate assessment and inform time-sensitive management strategies for post-wildfire recovery and the protection of nearshore ecosystems in Hawaiʻi and similar coastal regions.
DOI: 10.17632/btt4vbgdr5.1
title: Dataset of measured runoff and sediment in the Shuiwanggou and Heifangou catchments

description: This dataset contains measured runoff and sediment data from two small agricultural catchments on the Loess Plateau: Shuiwanggou (without soil and water conservation measures) and Heifangou (with such measures). It was collected at the catchment outlets during individual runoff events, with corresponding hydrological and sediment variables used to derive flow‑energy indicators.
DOI: 10.4211/hs.85ab212b18f543b0bd3c7b749cfeef66
title: Long-Term Monthly Baseflow Index Signatures

description: This resource presents data used in the study by van der Heijden et al. (under review, 2025). The data contains monthly mean baseflow indices (BFI) for 1,432 United Stated Geological Survey (USGS) streamflow gages across the Continental United States (CONUS).

The streamflow gages used are those considered of reference-quality by the Geospatial Attributes of Gages for Evaluating Streamflow, version II (GAGES-II) dataset (Falcone, 2011) that contain at least 20 years of continuous daily streamflow measurements.

Baseflow separation was conducted using a single parameter recursive digital low-pass filter based on Lyne and Hollick, (1979) for baseflow separation. The filter, implemented in the R package EcoHydRology (Fuka et al., 2014), was converted to Python (Hamshaw et al., 2020) and as part of this research is now available through the Baseflow Python package developed by Brigham Young University Hydroinformatics group (https://github.com/BYU-Hydroinformatics). The filter parameter was tuned to the long-term flow history of each gage individually using the 10th non-exceedance percentile of streamflow.

References:
Ryan van der Heijden, Ali Dadkhah, Scott Hamshaw, Mandar Dewoolkar, Ehsan Ghazanfari, Norm Jones, Gustavious Williams, Prabhakar Clement, Donna M. Rizzo. “Identifying and interpreting physical processes and NWM prediction bias associated with baseflow regimes across the CONUS.” Water Resources Research (under review, 2025).

Falcone, James A. 2011. “GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow.” Report. Reston, VA. USGS Publications Warehouse. https://doi.org/10.3133/70046617.

Lyne, V. D. and Hollick, M.: Stochastic time-variable rainfall runoff modelling, Hydrology and Water Resources Symposium, Institution of Engineers, Australia, Perth, 10–12 September 1979, 89–92, 1979.

Hamshaw, Scott D., Donna M. Rizzo, Ali Javed, and Linh Nguyen. "Watershed data science at the event scale: Revealing insights in watershed function through analysis of concentration-discharge relationships." In AGU Fall Meeting Abstracts, vol. 2020, pp. H077-08. 2020.

Fuka DR, Walter MT, Archibald JA, Steenhuis, TS, and Easton ZM. “EcoHydRology, a community modeling foundation for Eco-Hydrology.” 2014.
DOI: 10.4211/hs.25546b4a45044b0ea80ac45f782cabcd
title: Synthesized data for E. coli drivers in Arizona

description: Data used to (1) analyze all in-stream E. coli observations with respect to the type of river and antecedent rainfall conditions and (2) through the aggregation of E. coli data to the watershed level (10-digit Hydrologic Unit Codes (HUCs)) and then examining the relationship to different watershed characteristics using a linear regression model, in Arizona, United States of America, for the period 2010 - 2019.
DOI: 10.34740/kaggle/dsv/14132625
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.4211/hs.1859c5d39c7b41a2bf296d2e0e08f52b
title: Data associated with Elliott et al. Geochemical and sediment dynamics during an experimental high flow pulse event on the Allegheny River: Lessons for river system management

description: High flow and flood events are important components of natural river flow regimes, as they maintain healthy aquatic habitats and riparian ecosystems. However, human alteration of rivers for flood control, including dam construction, modifies and homogenizes downstream hydrological patterns by moderating extreme high and low flows. In this study, we evaluate the biogeochemical fluxes stemming from the first experimental spring flood (i.e., pulse event) on the Allegheny River (Pennsylvania, USA) conducted by the United States Army Corps of Engineers as part of the Sustainable Rivers Project. The pulse event was initiated on March 30, 2023, by a sustained release of 451 cms from the Kinzua Dam for 21 hours. We hypothesized that this experimental spring pulse would initially lead to the release of organic-rich sediment and nutrients stored behind the Kinzua Dam. We also expected the "geochemical fingerprint" of downstream water to change over the duration of the pulse event due to the released flood water interacting with hyporheic water, scouring the channel and riparian areas, and then ultimately receding. To quantify the impact of the pulse on downstream biogeochemistry, we collected hourly water grab samples over a 48-hour period that spanned pre- and post-pulse conditions from multiple locations downstream of the Kinzua Dam. We evaluated the water chemistry of grab samples for multiple parameters, including concentrations of dissolved metals, nutrients, total suspended solids, and nitrate isotopes. Our results indicate that variable water chemistries can be linked to the timing and flushing of different portions of the river channel and floodplain across the span of the pulse event. These results demonstrate the value of downstream monitoring during pulse events for tracing flow dynamics, quantifying material fluxes, and documenting the impact of similar experimental spring floods on downstream river ecosystems.

This resource includes the data files of: environmental measurements (including but not limited to water temperature, pH, conductivity); hourly grab sample concentrations of dissolved metals, nutrients, total suspended solids, and nitrate isotopes; United States Geological Survey gage streamflow during the pulse event; and code associated with organizing data and calculations.
DOI: 10.4211/hs.26f08ef7fe344fcb9d3da45160b697fe
title: Hydrology, rather than wildfire burn extent, determines post-fire organic and black carbon export from mountain rivers in central coastal California [Dataset]

description: Coastal mountain rivers export disproportionately high quantities of terrestrial organic carbon directly to the ocean, feeding microbial communities and altering coastal ecology. To better predict and mitigate the effects of wildfires on aquatic ecosystems and resources, we must evaluate the relationships between fire, hydrology, and carbon export, particularly in the fire-prone western United States. This study examined the spatiotemporal export of particulate and dissolved organic carbon (POC and DOC, respectively) and particulate and dissolved black carbon (PBC and DBC, respectively) from five coastal mountain watersheds following the 2020 CZU Lightning Complex Fires (California, USA). Despite high variability in watershed burn extent (20-100%), annual POC, DOC, PBC, and DBC concentrations remained relatively stable among the different watersheds. Instead, they correlated significantly with watershed discharge. Our findings indicate that hydrology, rather than burn extent, is a primary driver of post-fire carbon export in coastal mountain watersheds.
DOI: 10.4211/hs.2b810b96206847f0993f2de21e77ce79
title: Water isotope compositions during Tropical Storm Alberto (2024)

description: Tropical cyclones can play a key role in alleviating drought in semi-arid regions, yet their isotopic impacts remain poorly understood. This database explores the hydrological and isotopic effects of Tropical Storm Alberto (June 2024) in northeastern Mexico. Using rainfall and isotope data from multiple stations, the analysis reveals extreme precipitation contributions and pronounced isotopic variability. The storm left a distinct isotopic imprint in surface waters, highlighting its role in water resource recovery and its value for reconstructing hydroclimatic variability in drought-prone areas.
DOI: 10.4211/hs.a46a305cb4b4430e9643178fe11f7cf3
title: 4-State Hidden Markov Model Using PMDI and Temperature for Climate-Informed Scenario Generation

description: This repository contains R scripts for implementing a computationally efficient 4-state Hidden Markov Model (HMM) that uses temperature as a covariate to generate ensembles of plausible Palmer Modified Drought Index (PMDI) scenarios across the Western U.S. The model uses paleo PMDI data, which spans from 1500 to 1980 with a matrix grid of 1823 x 481 (e.g., 1823 grid-cells and 481 years). Similarly, paleo temperature data covers the same period, arranged in a matrix grid of 1637 grid cells by 481 years. To address the high dimensionality of the datasets, Principal Component Analysis (PCA) is applied to each variable, and the first six principal components (PCs) from both PMDI and temperature are retained as input to the HMM. The trained HMM is then used to simulate future PMDI scenarios by leveraging bias-corrected CMIP6 temperature projections under the Shared Socioeconomic Pathway (SSP) 2–4.5 scenario. The HMM framework is designed to capture the spatiotemporal variability and regime-shifting behavior of hydroclimatic patterns. It provides critical insights into the spatial correlation of wet and dry conditions across the Western U.S., supporting regional drought risk assessment and long-term water resource planning.

For a more detailed description of the model, please refer to the following paper:

Tezcan, B., & Garcia, M. (2025). Training a hidden Markov model with PMDI and temperature to create climate informed scenarios. 
Frontiers in Water, 7, Article 1472695. https://doi.org/10.3389/frwa.2025.1472695
DOI: 10.4211/hs.3283314667ac4c2daff3effbc5b633ca
title: Open Science Contribution Links for David Tarboton

description: A listing of several open science contribution from Dr. David Tarboton, including Open Software, a Data-sharing platform, Online Education Resources, and Research Community Coordination and Leadership roles and efforts that supports open science.
DOI: 10.4211/hs.cf7fda39073543efbf3b8de7ae17d3f4
title: Water Depth Measurements for Rangeland Ponds in the Ferlo Region of Senegal

description: The Ferlo region of Senegal is a rangeland area where ephemeral ponds are an important source of water for watering livestock.  Staff gauges were installed in 12 ponds in the Ferlo region of Senegal.  The gauges were installed at the approximate deepest point of each pond. Data enumerators collected pond depth measurements over the 2022 and 2023 rainy season, until each pond dried up. Data were collected approximately every two weeks from July to December 2022 and approximately every six days from June to December 2023. These data consist of a csv file containing: date that measurement was collected ("date", YYYY-mm-dd), measured water depth ("depth", meters), pond name ("pond"), latitude of staff gauge ("latitude", degree north WGS84), and longitude of staff gauge ("longitude", degree east WGS84).

Data collection was supported by the following funding sources:
- NASA Applied Sciences Program under the Water Resource Program, grant # 80NSSC22K0930 entitled “Earth Observation-Based Monitoring and Forecasting of Rangeland Water Resources” and NASA Harvest Consortium, grant # 80NSSC17K0625 entitled “NASA Harvest: NASA's Global Food Security and Agriculture Consortium.”
- Azione contro la Fame Italia foundation, through the project "Western Sahelian Pastoral Early Warning System - PEWS - A tool towards resilience to climate change and climate variability.”
DOI: 10.17632/xc9tjwkd3s.1
title: Associations of Skin Cancer, Dermatitis, and Risk Factors for Skin Conditions with Epigenetic Aging Biomarkers Among a Representative Sample of U.S. Adults: NHANES 1999-2002 - Supplementary Material

description: This document contains supplemental information regarding the methodology and results of this study, including more details about statistical analysis and regression results.