cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.eddb06e91a914618a89a63bb2c2774e0
title: Code and Data for CAMELS-Chem Concentration-Discharge Analysis

description: Here we provide the data and R scripts to complete the analyses and create the figures presented in the manuscript titled, “Solute export patterns across the contiguous United States” by Kincaid et al. 2024 at Hydrological Processes. Importantly, this resource contains paired solute concentration (C) and discharge (Q) data for 11 solutes from CAMELS-Chem (Sterle et al. 2024; https://doi.org/10.5194/hess-28-611-2024). This relational database was built upon the CAMELS dataset (https://doi.org/10.5194/hess-21-5293-2017), an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous United States. The version of CAMELS-Chem provided here has US Geological Survey (USGS) National Water Information System (NWIS) C and Q data for 506 catchments. C and Q measurements span from 1898 to 2020 with the first paired C-Q sample occurring in 1924. Solutes include aluminum (Al), calcium (Ca), chloride (Cl), dissolved organic C and N (DOC, DON), magnesium (Mg), nitrate (NO3), potassium (K), silica (Si), sodium (Na), and sulfate (SO4). Of note, a shorter version of the CAMELS-Chem database that spans from 1980 to 2018, but includes data for more stream water quality constituents and atmospheric deposition data is described in CAMELS-Chem (Sterle et al. 2024; https://doi.org/10.5194/hess-28-611-2024) and available for download via Hydroshare (http://www.hydroshare.org/resource/841f5e85085c423f889ac809c1bed4ac).

The R scripts and data files provided in this resource are intended to allow users to replicate the tables and figures in the Kincaid et al. manuscript. Specifically, we provide all files to complete the analyses coded in in the R script 9_analyses_figures_for_manuscript.R. However, other R scripts and data files provided should allow users to replicate intermediate steps in the analyses as well. See the README file for more details, but analyses provided in the R scripts include: modeling C-Q relationships with the power-law function using data-driven Bayesian segmented regression; conducting hierarchical clustering to group catchments based on catchment attributes; building random forest models to select catchment attribute correlates of C-Q metrics; conducting flow-duration exceedance probability analyses; and general code for figures, tables, and other statistics presented in the Kincaid et al. manuscript.

The metadata for the CAMELS-Chem dataset (camels_chem_all_2022-02-25.csv) is available in camels_chem_metadata.csv
DOI: 10.4211/hs.44cb63d8a7b14fe98bdd15a4fd05e9c7
title: Projected changes in annual maximum discharge for Iowa communities

description: Understanding the projected changes in annual maximum peak discharge is important to improve resiliency in water resource planning and design at the community level. Currently, much of the literature on climate change impacts focuses on analyses at the regional scale. In this study we use a hydrologic model to evaluate the projected changes in annual maximum peak discharge at the community-level across Iowa under two emission scenarios, Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5). We utilize climate forcings from global climate models part of the Coupled Model Intercomparison Projected Phase 5 (CMIP5) from 1950 to 2100. Our simulations show a detectable increase in annual maximum discharge for 27% of Iowa’s communities under RCP8.5. However, under RCP4.5 none of the communities are projected to face an increase in annual maximum discharge by the end of the 21st century; furthermore, precipitation intensity under this scenario is not projected to increase in the latter half of the 21st century. Our results point to a larger increase in precipitation intensity and annual maximum discharge under RCP8.5 compared to RCP4.5, especially in the second half of this century. The projected flood peak distributions tend to become statistically different from the historical ones later in the 21st century under RCP8.5 than under RCP4.5. This study provides a basis for analyzing climate change impacts at a local decision-making scale. 

This resource provides the codes and some data used for this analysis.
DOI: 10.4211/hs.01ad081983234d85b7cbf1f10ac961c1
title: Data supporting the manuscript ‘Water isotopic composition traces source and dynamics of water supply in a semi-arid agricultural landscape'

description: Data were collected at variable frequencies between 2012 and 2022. Rain water was collected from 2013 to 2016 near the towns of Stanford, Moccasin, and Moore, MT using precipitation collectors and a mineral oil coating to limit evaporation (Product Alternatives, Inc.; Fergus Falls, MN). Snow water was collected in 2014 near Moore, MT and from 2019 to 2022 near Moccasin, MT using a 10 cm-diameter metal core inserted vertically in the snowpack to sample the entire snow column. Snow samples were sealed in Ziploc bags, weighed, and allowed to melt before thorough mixing and pouring an aliquot into 20 mL scintillation vials. A monthly precipitation composite sample was collected from 2021-2022 near Moccasin, MT. Terrace soil water was collected from 2013 to 2016 in a privately owned field near Moore, MT using porous cup tension lysimeters (PTFE/silica; Prenart Equipment; Frederiksberg, Denmark). Terrace groundwater was collected from 2013 to 2022 from wells and springs across the Moccasin terrace landform. Riparian groundwater and surface water were collected from 2020 to 2022 from three stream corridors on the Moccasin terrace. Stream water was collected from 2012 to 2022 from streams on the Moccasin terrace. All groundwater and surface water were sampled using a peristaltic pump (Geotech Environmental Equipment; Denver, CO) and pumped through a 0.45-μm capsule filter (Geotech Environmental Equipment; Denver, CO) into 20 mL glass scintillation vials. Data were collected to assess water isotopic compositions of precipitation and landscape water to understand soil, aquifer, and stream hydrological processes and integration of source waters in a semi-arid, non-irrigated agricultural landscape.
DOI: 10.4211/hs.f7d6db8f8677402d808531924bbcf60c
title: CUAHSI-SI23 Theme-1 Nextgen-aridity: CFE config & outputs

description: This repository contains the CUAHSI Summer Institute 2023 results, Theme-1 Nextgen-aridity team. For each CAMELS US basin, we provide the initial parameter configurations (refer to the "config" folder) generated by Dr. Luciana Cunha, and the best-calibrated parameters and streamflow outputs from CFE-classic and CFE-ODE run (refer to the "outputs" folder) generated by the Nextgen-aridity team. The CFE was calibrated for 2000-2009 WY with a 1-year of spin-up using Dynamically Dimensioned Search optimization algorithm (N=500).

Please refer to the following resources for further details:
- Short summary (Chapter 2): https://www.hydroshare.org/resource/709de9e2bc5640b281c7f022cecc593d/
- Long version of the report can be found here: https://github.com/NWC-CUAHSI-Summer-Institute/ngen-aridity
- GitHub Repository: https://github.com/NWC-CUAHSI-Summer-Institute/calibrate-cfe

*** If you use this resource, please provide appropriate citation for this work (see "Citation" section below). If you make any modifications to the original materials, please indicate those changes in your work. ***

Known issue: there were fewer than 10 cases where the baseflow did not converge after a warm-up period of 1 year. We plan to rerun the calibration with an extended warming-up period.
DOI: 10.4211/hs.21aaf93ef6894b26b184713e6134273d
title: Spatiotemporal Evolution of Riparian Redox Zonation in Response to River Stage Fluctuation and Dynamic Biofilm Growth

description: Riparian zones are one of the most complex redox-dynamic systems, where sequential redox zones of dissolved oxygen, nitrate, manganese dioxide, ferric hydroxide and sulfate reduction commonly form. River stage dynamics and microbes are two key controls on nutrient transformation in the riparian zone. Microbial growth, on one hand, increases the biogeochemical reaction rate by altering the microbial population. On the other hand, decreases the permeability of the media due to clogging effects, thereby increasing the residence time of solutes. However, previous studies have often concentrated on steady-state flow systems and overlooked the impacts of river stage fluctuations and dynamic microbial growth on redox zonation. In this study, we investigated the interactions among river fluctuations, nutrient supplies, microbial metabolisms and sediment physical properties in a dynamic riparian system. A column experiment was conducted, and a one-dimensional modeling framework that coupled flow, reactive solute transport, dynamic microbial growth and bioclogging processes was developed to study the spatiotemporal evolution of redox zonation in response to river stage fluctuations and microbial growth. Our results showed that two redox zonation patterns, including a typical sequence and an interlaced sequence, could form in the dynamic riparian zone. Increasing exogenous DOC concentration facilitates the occurrence of anomalous sequence redox zonation. Modeling results also indicated that ignoring microbial growth leads to a significant difference in the spatiotemporal characterization of the redox zonation, especially for a more permeable sediment. Our results implicate the occurrence of more complicated redox zonation in a dynamic riparian system than previously reported.
DOI: 10.4211/hs.00baad14d1a546499eb39cdebde479ba
title: Peru Watershed Data for the Arequipa Region

description: Surface water quality in southern Peru has been monitored for over a century. However, the information is distributed among different organizations, in different formats, and is available with considerable delay after official request. A substantial portion of the data has not been quality assured. Much of the data is available in difficult-to-access formats such as scanned PDFs. The diverse heterogeneity of the data and format make it very difficult to use it to conduct meaningful water quality analysis. In this remarkable effort, we gathered all available surface water quality information from five main watersheds in the Arequipa Region of southern Peru: Camaná, Chili, Ocoña, Tambo, and Yauca. The effort required manual entry of more than 130 thousand records (distributed among 29 general chemistry and nutrient parameters, 49 metals, 20 bacterium types, and 77 organic types) from 276 stations (1905-2021, the majority being generated in the last two decades) that were merged into one master Excel database to be available to the general public for further analysis. With this new Excel database, surface water quality information is now available in an organized and easy-to-work-with format, contributing to better research and further decision making for a better environment.
DOI: 10.4211/hs.8697e34af0224f61baebf0478d6e0ee3
title: Dataset for "Modelling macroplastic fluxes in the Imus catchment: Impacts of long-term accumulation and extreme events" (Clayer et al.)

description: This dataset is a part of the manuscript "Modelling macroplastic fluxes in the Imus catchment: Impacts of long-term accumulation and extreme events" by Clayer et al.
The dataset was used with INCA-Macroplastics, a spatiotemporally explicit model for mismanaged plastic mobilization and transport from land to sea from the INtegrated CAtchment (INCA) family. INCA-Macroplastics encompasses all components of the catchment, is driven by available data (weather, population counts, solid waste production rates and composition) and enables calibration and validation against a range of observations (e.g., river monitoring, household surveys). The tool encompasses a litter source module driven by waste generation data as well as weather, land use and population data. It was applied to the Imus River, Cavite, Philippines, estimated to be among the top 50 most polluting rivers in the world. We modelled macroplastic transport, retention and export following two calibrations ("LandAcc" and "RiverAcc") and two emission scenarios ("default" and "Remediation2006", note that "Remediation2006" is referred to as "RA 9003 enforcement" in the manuscript) covering 1990-2020. Model outputs include water discharge, plastic stocks in various catchment and river compartments, as well as plastic exports to sea.

This repository includes 5 compressed folders (*.7z) with data files with outputs of a Monte Carlo sensitivity analyses performed for each  calibration ("LandAcc" and "RiverAcc") and plastic emission scenario("default" and "Remediation2006") as described in the folder names. Variable names and units are given in the file names:
- "dates.dat" are the datetime array over 1990-2020
- "Water_Discharge_m3_s.dat" is the water discharge at the river outlet in m3/s. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020.
- "Total_Plastics_on_land_Free_and_Buried_Kg.dat" is the total (buried and free) weight of plastic litter on land in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns)  over 1990-2020.
- "Buried_Plastics_on_land_Kg.dat" is the weight of buried plastic litter on land in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020.
- "Plastics_on_RiverBank_Kg.dat" is the weight of plastic litter on riverbanks in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020.
- "Plastics_in_RiverVegetation_Kg.dat" is the weight of plastic litter stuck in river vegetation in kg. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020.
- "Plastics_exported_to_Sea_KgPerDay.dat" is the weight of plastic litter exported each day to the sea in kg/day. Each column represents a run from the Monte Carlo run (up to 3000 columns) over 1990-2020.

In addition, there are two excel files with data from the household survey and from monitoring of macroplastic litter in the Imus River.
DOI: 10.4211/hs.1a7d73ae3ac044969740f985b56d031c
title: Visualizing the Results of the Calibration of the Distributed Hydrological Model Iber+ with the Surrogate-Assisted Evolutionary Algorithm

description: This data represents outcomes from experiments focused on optimizing a distributed hydrological model via a Surrogate-Assisted Evolutionary Algorithm (SAEA). Two scripts perform data analysis, visualization, and strategy comparison. The first script imports pertinent datasets, visualizes the model's performance, and applies the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to create a confidence band for predictions. The second script manages data for multiple generations and compares Monte Carlo (MC) and Evolutionary Algorithm with Surrogate Modeling (EA-SM) strategies, visualized in a scatter plot. Together, these scripts provide comprehensive insights into the model's performance, efficiency, and potential enhancements.
DOI: 10.4211/hs.56bd02c71ebe447a844758898e675bad
title: SWE Estimation Application of National Snow Model. Python script and estimations for the Upper Colorado River Basin Water Year 2022

description: Directory of files to perform historical and near-to-date regional snow water equivalent (SWE) estimates across the Western United States. Estimates are performed by the large-scale, machine-learning National Snow Model (https://github.com/AlabamaWaterInstitute/National-Snow-Model), which was trained on historical in-situ snow observations from 2013-2019. 
This resource contains Python scripts to produce SWE estimates for a region defined by a user-input shapefile at 1-km resolution. The model is ready to run "out-of-the-box" upon download provided the directory structure is maintained; the user must only provide a shapefile of the region of interest. Estimates are saved in the submission_format_DATE.csv and may be joined with geographical location information in ..._Geo_df.csv. Detailed descriptions of function arguments can be found in the Region_SWE.py file. Note that pre-processing and estimation of large regions is computationally and memory intensive and high-performance computing is recommended for such areas. Smaller regions may easily be executed on a personal machine. 

This resource also contains weekly SWE simulations of water year 2021-22 for the Upper Colorado River Basin. This is a preliminary estimate that has not been cross-validated. It is provided as an example of model application. 
 
***This is a preliminary research product and its results should not be used for operational purposes. The National Snow Model is undergoing constant performance and functionality improvement and validation testing.***
 
Funding for this project was provided by the National Oceanic and Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research to Operations in Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama, NA22NWS4320003.
DOI: 10.4211/hs.97d410cbf838450b9194b4410f9e0f5e
title: Pilot Study on Tracing Fugitive Dust Using from the Parleys Canyon Kilgore Quarry, Utah.

description: Strontium (Sr) isotopes, particularly the 87Sr/86Sr ratio, are increasingly utilized in dust studies for identifying sources and tracking transport pathways. This pilot study conducted in Millcreek City, Utah, USA, leverages Sr isotopes as a tracer to quantify the contribution of dust from the Kilgore Quarry, near the mouth of Parley's Canyon, to local dust deposition. A dust sample was collected from the exterior wall of a utility shed in the Canyon Rim neighborhood, where it has been accumulating dust transported by canyon winds over several years. Analysis of the dust sample was performed in the University of Utah's Geology and Geophysics Department's ICPMS laboratory.

Comparison with baseline Sr isotope ratios from dust samples collected in the Salt Lake City area in 2017-2018 and bedrock samples from the TwinCreek Limestone were undertaken. The data indicated that the Canyon Rim dust sample had a significantly different Sr isotope ratio compared to the baseline, suggesting a distinct dust source. Utilizing a mass fractionation estimate, it was determined that approximately 60.35% of the dust sample was sourced from the Kilgore Quarry. Wind data collected at the quarry site by UDOT since 2008 further corroborated this finding, indicating prevailing winds that align with Parleys Canyon's axis, thereby facilitating dust transport from the quarry to the Salt Lake Valley.

This study underscores the effectiveness of Sr isotopes in dust source identification, demonstrating that a significant portion of dust in the study area can be attributed to the Kilgore Quarry. The results underline the importance of dust management at the quarry to minimize environmental and potential health impacts. The study also sets a precedent for further investigations that could provide additional insights into dust sources and transport pathways, aiding in environmental protection and public health initiatives.
DOI: 10.4211/hs.727e01d85a08419aaa3a8dee2856f005
title: SUTRA input files for simulations of aufeis formation

description: This resource contains input files for SUTRA simulations used to replicate the conceptual model of the Kuparuk aufeis field developed by Terry et al. (2020) and to investigate the sensitivity of the aufeis volume of various parameters. The results of the simulations are presented and discussed in Lainis, A., Neupauer, R.M., Koch, J.C., and Gooseff, M.N. (2024). Seasonal and decadal subsurface thaw dynamics of an aufeis feature investigated through numerical simulations, Hydrological Processes, https://doi.org/10.1002/hyp.15106. Reference: Terry, N., Grunewald, E., Briggs, M., Gooseff, M., Huryn, A. D., Kass, M. A., Tape, K. D., Hendrickson, P., & Lane, J. J. W. (2020). Seasonal subsurface thaw dynamics of an aufeis feature inferred from geophysical methods. Journal of Geophysical Research: Earth Surface, 125(3):e2019JF005345. https://doi.org/10.1029/2019JF005345.
DOI: 10.48597/r69x-ytw7
title: Measurement of POPs (persistent organic pollutants) at De Zilk

description: Measurement of POPs (persistent organic pollutants) at De Zilk
DOI: 10.4211/hs.312ffdbe4cbb453aafd7444ca8453d27
title: Subsurface Response to an Extreme Precipitation Event, Front Range, Colorado

description: North‐central Colorado experienced an extreme precipitation event (EPE) in September 2013, during which the equivalent of 80% of the region's annual average precipitation fell in a few days. Widespread flooding occurred above ground, but the short‐ and long‐term subsurface response remains unclear. The objective of the study is to better understand the dynamic subsurface response, namely how the water table and soil water storage responded to a large amount of infiltration in a short period of time and how the hydrologic properties of the subsurface influence the response. Better understanding of subsurface response to EPEs is expected to increase with the advent of more intense and frequent EPEs in the coming decades. A one‐dimensional subsurface flow model using HYDRUS‐1D, was built to simulate and examine infiltration of an EPE at a site in the Boulder Creek Watershed, Colorado. Model calibration was conducted with local field data to fit site observations over a 6-yr period. A rapid water table depth response in field observations was observed, with the modeled subsurface storing water for 18 months acting as a hydro-buffer during recovery. To examine influence on model results, a sensitivity study of soil hydraulic parameters was conducted. The sensitivity study found that changes in n, an empirical parameter related to pore-size distribution, most significantly affects water table depth. The implications are that one-dimensional models may provide useful estimates of water table fluctuations and subsurface hydro-buffer capacities in response to EPEs, which could be of use to regions preparing for EPE effect on water resources.

Link to journal article: https://acsess.onlinelibrary.wiley.com/doi/pdf/10.1002/vzj2.20189
DOI: 10.48597/temx-dvux
title: Measurement of POPs (persistent organic pollutants) at De Zilk

description: Measurement of POPs (persistent organic pollutants) at De Zilk
DOI: 10.48597/cjus-nhgf
title: Measurement of POPs (persistent organic pollutants) at De Zilk

description: Measurement of POPs (persistent organic pollutants) at De Zilk
DOI: 10.4211/hs.d36f090fd5a04ecda53a1109f1db180e
title: Water-table Response to Extreme Precipitation Events

description: Extreme precipitation events (EPEs) will play a significant role in influencing soil–water and groundwater storage worldwide. We examined water-table depth (WTD) response to EPEs for 17 cases representative of soils and climate settings across the United States. Precipitation data from NOAA’s Precipitation Frequency Data Server were used for each case to characterize 1-day extreme precipitation events (EPEs) with annual exceedance probabilities of 0.1 % over an average baseline date range of 1981–2011. The inverse solution in the HYDRUS-1D modeling software was used to obtain the soil–water retention curve for each case. Non-EPE and EPE scenarios were modeled and compared to examine water-table displacement (ΔWTD) and recession time (trec). The ΔWTD ranged from 0.6 to 2.4 m across cases and were not directly controlled by EPE amount; instead, ΔWTD was inversely related to available … 

Link to journal article: https://www.sciencedirect.com/science/article/pii/S0022169423000823
DOI: 10.48597/cn5j-nk9b
title: Measurement of POPs (persistent organic pollutants) at De Zilk

description: Measurement of POPs (persistent organic pollutants) at De Zilk
DOI: 10.48597/f5wc-8yq9
title: Measurement of POPs (persistent organic pollutants) at De Zilk

description: Measurement of POPs (persistent organic pollutants) at De Zilk