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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Surface Water Quality Data from Beaver-Impacted Streams; Trail Creek and East River, Colorado 2025

This data package contains surface water chemistry measurements collected in 2025 to evaluate how beaver damming and low-tech process-based stream restoration influence water quality and metal mobility in mountainous headwater systems of the Upper Colorado River Basin. Sampling was conducted at Trail Creek (Taylor Park watershed, Colorado), a tributary undergoing restoration through installation of low-tech process-based structures (i.e., beaver dam analogs), and at off-channel beaver ponds within the East River floodplain (East River watershed, Colorado). Samples were collected along longitudinal transects spanning upstream control reaches, beaver-influenced ponded reaches, and downstream segments. Additional samples were collected from near-surface pore waters within a beaver dam seepage face. The dataset includes concentrations of major and trace elements measured by inductively coupled plasma–mass spectrometry (ICP-MS) and inductively coupled plasma–optical emission spectrometry (ICP-OES), major anions measured by ion chromatography (IC), and dissolved organic carbon (DOC; reported as non-purgeable organic carbon, NPOC). Samples were size-fractionated at 0.45 micrometers (µm), 0.22 µm, and 0.02 µm to distinguish particulate (>0.45 µm), colloidal (0.22–0.02 µm), and dissolved (<0.02 µm) fractions. The data package consists of comma-separated value (.csv) files containing tabulated chemical concentration data, sample metadata (site identifiers, geographic coordinates, sampling dates, fraction type), and quality control flags. All files are provided in open, non-proprietary formats that can be accessed using standard data analysis software such as Microsoft Excel, R, Python, MATLAB, or other programs capable of reading .csv files. Units, detection limits, and analytical methods are documented in accompanying metadata files. The dataset is designed to support analyses of (1) how beaver impoundment and restoration structures alter elemental partitioning and transport, (2) the role of iron and organic carbon in mediating trace metal mobility, and (3) reach-scale changes in water quality across restoration gradients. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Anions↗
DOE OSTI2021

2017 Meander C and O floodplain groundwater chemistry from the East River, Colorado

This dataset includes groundwater geochemistry data from floodplain piezometers collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of hyporheic exchange and other river corridor processes on riverine export of solutes. Floodplain piezometers in the Pumphouse vicinity (Meander C: MCP, MCB; Meander O: MOP) were sampled on daily to weekly time scales during summer-fall 2017. Data includes in-field measurements (pH, electrical conductivity [EC], oxidation reduction potential [ORP], dissolved oxygen [DO], Fe(II), and groundwater level) along with laboratory measurements (dissolved inorganic carbon [DIC], dissolved organic carbon [DOC], metals and major cations, anions [chloride, sulfate, nitrate], and dissolved ammonium). The chemistry data is contained in a csv file, sampling protocol in a pdf document, and sample locations in both a kmz file which can be opened in Google Earth and a csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Geochemistry of East River, Colorado floodplain sediments from Meander Z and Meander Y collected in 2017.

This dataset includes sediment geochemical characterization data from floodplain sediments collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of biogeochemical cycling and other river corridor processes on riverine export of solutes. Sediment cores were collected from Meander Z (MZA, MZB) and Meander Y (MYP) just upstream of the confluence with Brush Creek in September 2017 to depths of approximately 40-95 cm in 15-20 cm intervals. Analyses include moisture content, particle size analysis, total inorganic and total organic carbon, and extractions by 1 M KCl (for adsorbed NH4), anoxic HCl (for Fe(II) content), ammonium oxalate (for amorphous Fe-oxide content), and dithionite (for crystalline Fe-oxide content). Sample locations are included in both a kmz file, which can be opened in Google Earth, and a csv file, and methods are included as a text document. All characterization data is summarized in a Microsoft Excel file, and is also included as individual .csv files for each extraction (ammonium oxalate, anoxic HCl, dithionite, and KCl) and for bulk sediment data (moisture content, particle size analysis, TIC, and TOC).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sulfur x-ray absorption spectroscopy data from bedrock shale, soil, and floodplain sediment from the East River, Colorado watershed

This dataset includes sulfur x-ray absorption near edge spectroscopy (XANES) data collected on solid samples as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the speciation of sulfur and degree of shale weathering in solid samples across the watershed and their impact on riverine export of sulfate. Solid samples, including shale, hillslope soil, and floodplain sediment samples collected in 2016-2018, were analyzed by bulk XANES. Density fractions of hillslope soil, including a light fraction representing particulate organic matter and a heavy fraction representing minerals are also analyzed by bulk XANES. Micro-focused XANES on a weathered shale sample (PLM6, 2.7 m) was also performed. Sample locations are included in the 'sample meta data.csv' file, and XANES data for each sample type are included in csv files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

2017 Meander C sediment characterization from the East River, Colorado

This dataset includes characterization data collected on sediment samples from Meander C as part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected to investigate the nature of mineral-organic associations across the meander transect and with depth. Samples were collected in 2017, and analyzed using sequential extractions (water, pyrophosphate, HCl, and dithionite) and density fractionation. Data collected include: texture, pH, root biomass, and metals (Fe, Al, Ca, Si) of bulk sediment samples; C and N of heavy fraction; and extractable OC, Fe, and Al, and organic matter characterization using high-resolution mass spectrometry (FT-ICR-MS) of sequential extractions. Sample locations are included in the ‘sample_metadata.xlsx” file. Sediment characterization data is included in the accompanying csv files: bulk and density fractions (MCB2017_density-and-bulk-fractions.csv), sequential extractions (MCB2017_sequential-extractions.csv), and FT-ICR-MS data from sequential extractions in three files to be read by the R package ftmsRanalysis (MCB2017_FTICRMS-sequential-edata.csv, MCB2017_FTICRMS-sequential-emeta.csv, and MCB2017_FTICRMS-sequential-fdata.csv). Sediment was collected in triplicate; ID’s refer to individual samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Groundwater and Surface Water Flow (GSFLOW) model files for the East River, Colorado

The data package contains model input files and executables for the East River, Colorado (750 km2) located in the headwaters of the Upper Colorado River Basin. The code applied is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution to represent topographic complexity and a daily timestep accounts for energy and water partitioning between the snowpack, vegetation, soil zone and bedrock. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model accounts for nine stratigraphic units and extends 400 m below land surface. Using this modeling framework we explore historical water budgets (water year 1987-2022) and the influence of seasonal warming and associated mechanisms driving groundwater declines and streamflow loss. A Readme_062424.txt file provides instructions on how to download all files. Input and output files are provided for the historical simulation representative of water years 1987 to 2022 (i.e. baseline) and the all-year +4C warming scenario. Instructions are provided to run the seasonal warming scenarios with warming applied only to the autumn, winter, spring or summer months. Modeled output used in figures for Carroll et al., 2024 are also provided with metadata describing where these data were obtained. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Updated on 06-24-2024: The dataset was updated to include model outputs (updated base.zip and 4C.zip files), figure source files (Figure_Source_Files.zip), an updated Readme file (Readme_062424.txt), climate input files (climate_input_files.zip), and the adoption of ESS-DIVE File Level Metadata and CSV reporting formats (inclusion of flmd.csv and dd.csv files).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CRiSPPy: An advanced hydropower scheduling tool for the Colorado River Storage Project

The Western Area Power Administration (WAPA) plays a vital role in delivering reliable and cost-effective hydroelectric power to millions of customers across the western United States. The Colorado River Storage Project (CRSP) carries out WAPA’s mission in Arizona, Utah, Colorado, New Mexico, Nevada, Wyoming and Texas. Achieving this mission requires effective management of the Colorado River system, and depends on the use of advanced analytical tools and modeling methodologies. For many years, CRSP has relied on the Generation and Transmission Maximization Superlite (GTMax SL) model for its mid-term and long-term hydroscheduling needs. However, the evolving energy market, power system operations, environmental rules, and hydrology conditions, coupled with advancements in computational capabilities, have necessitated the development of a more modern and robust solution. This report introduces the Colorado River Storage Project Python-based (CRiSPPy) model, a new, advanced hydropower scheduling tool developed to address CRSP ever-evolving challenges. CRiSPPy represents a significant leap forward in our ability to model and optimize the operation of the Colorado River system. It incorporates state-of-the-art optimization algorithms, enhanced data management capabilities, and an advanced graphical user interface, providing WAPA CRSP personnel with unprecedented insights and decision-making support. This document details the development, capabilities, and implementation of CRiSPPy. It is intended to serve as a comprehensive resource for WAPA staff, stakeholders, and anyone interested in the future of hydropower scheduling in the Colorado River Basin. We are confident that CRiSPPy will enhance WAPA's mission while adapting to the challenges of a dynamic and increasingly complex environment. The version of CRiSPPy described in this report is the version 2.3. New versions of CRiSPPy will be developed as the tool keeps evolving to address CRSP challenges.

13 HYDRO ENERGY↗
DOE OSTI2022

Investigating uncertainties in human adaptation and their impacts on water scarcity in the Colorado river Basin, United States

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer’s water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers’ adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. Here, by conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers’ adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.

Agent-based modeling↗
NASA NTRS2014

Groundwater Depletion During Drought Threatens Future Water Security of the Colorado River Basin

Streamflow of the Colorado River Basin is the most overallocated in the world. Recent assessment indicates that demand for this renewable resource will soon outstrip supply, suggesting that limited groundwater reserves will play an increasingly important role in meeting future water needs. Here we analyze 9 years (December 2004 to November 2013) of observations from the NASA Gravity Recovery and Climate Experiment mission and find that during this period of sustained drought, groundwater accounted for 50.1 cu km of the total 64.8 cu km of freshwater loss. The rapid rate of depletion of groundwater storage (5.6 +/- 0.4 cu km/yr) far exceeded the rate of depletion of Lake Powell and Lake Mead. Results indicate that groundwater may comprise a far greater fraction of Basin water use than previously recognized, in particular during drought, and that its disappearance may threaten the long-term ability to meet future allocations to the seven Basin states.

water security↗
DOE OSTI2025

Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"

This data package contains the associated data and scripts for Nagamoto, E., Ombadi, M., Ciulla, F. et al. Widespread drought-driven declines in streamflows and water quality in the Upper Colorado River Basin during 1998-2022. Commun Earth Environ 7, 734 (2026). https://doi.org/10.1038/s43247-026-03890-5. This purpose of this study was to investigate the impact of the 21st century drought on water quantity and quality at catchments throughout the Upper Colorado River Basin (UCRB). We used stream flow, water temperature, specific conductance, air temperature, precipitation, and catchment attribute data for over 200 sites in the UCRB, collected from the National Water Information System using Basin3D (Varadharajan, 2023), GAGESII (Falcone, 2010), and the Google Earth Engine. We identified years of severe drought between 1998 and 2022 using the Standardized Precipitation Evaporation Index (SPEI), then calculated the relative change percentage of the stream flow, water temperature, and specific conductance from drought versus non-drought years. We used the attribute information from GAGESII to investigate what physical traits of catchments are associated streamflow vulnerability (greater relative change) or resilience to drought. We used land cover data from the National Land Cover Database (USGS, 2024) to assess any changes to physical attributes that may not be represented in the static attributes information in GAGESII. To increase data availability, we modeled stream temperature using methods from Willard, 2023. While the study period is water years 1998 to 2022, the raw water quantity and quality data extends to 1950 and the meteorological data extends to 1980. The data and code can be downloaded via the UCRB_drought.zip. Within the zip, the files are organized as follows: - INPUTS: Contains all input data used in UCRB_Drought_Workflow.ipynb - OUTPUTS: Contains all intermediate data created from UCRB_Drought_Workflow.ipynb as well as final products including the calculated Standardized Evapotranspiration Index (SPEI) - climatic_variables: The code used to collect meteorologic data from Google Earth Engine - feature_importance: The code used for the catchment attributes analysis - preprocessing: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - pyeto: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - calculations: Code used in UCRB_Drought_Workflow_Impacts.ipynb - plotting: Code used in UCRB_Drought_Workflow_Impacts.ipynb - README.md - UCRB_Drought_Workflow_Preprocessing.ipynb: The code used to prep raw data for the analysis - UCRB_Drought_Workflow_Impact.ipynb: The code which uses the prepped raw data for analysis, and plots all figures - requirements_ucrb-drought_v2.yml: The requirements file to create a virtual environment and Jupyter Lab kernel to run the code The INPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_RAW" folder contains raw data for streamflow, water temperature, and specific conductance in a ".h5" file. The "NLCD_RAW" folder contains ".csv" files with annual land cover percentages for counties within the UCRB. The "MET_RAW" folder contains a ".csv" file with monthly meteorological data (air temperature and precipitation) for the sites in the UCRB which was obtained from code in the climatic_variables folder. The "GAGESII" folder contains ".csv" files with physical catchment attribute variables for catchments across the country. The "WT_LSTM_data" folder contains ".csv" files with calculated WT (Willard, 2023) and the associated RMSEs. The "Upper_Colorado_River_Basin_Boundary" folder contains geographic data including a shapefile for plotting in the UCRB_Drought_Workflow.ipynb. The "RESERVOIRS_RAW" folder contains ".csv" files for each reservoir in the UCRB with daily reservoir storage. There are also two files in the INPUTS folder that have combined reservoir storage data and reservoir metadata. The OUTPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_data" folder contains a folder "Water_year" with the associated cleaned data, metadata, and data availability information in ".csv" files, a folder "Median_Relchange" with the relative change comparing drought to non-drought years in ".csv" files, and a folder "Peak95_Min5_Relchange" that has ".csv" files for the relative change in peak (95th %) and minimum (5th %) variables. The "NLCD_data" folder contains the difference in land cover from the beginning to end of the study period and the percentage of the county that is within UCRB bounds can be found in Nagamoto et al (2025)). The "MET_data" folder contains separated monthly air temperature and precipitation data and the calculated PET in ".csv" files. The "SPEI_data" folder contains ".csv" files with calculated SPEI values (one restricted to the study period and the other with information from the entire MET data period). The "Paper_Tables" folder contains two ".csv" files containing site information and data availability and information about the GAGESII trait aggregated categories. The base directory includes the file “flmd.csv” for a list and description of all files and the file “dd.csv” for data dictionaries. Scripts for preprocessing, analysis, and figure generation are located in the associated GitHub repository found at [https://github.com/iNAIADS/drought-impacts/tree/develop/UCRB-drought]. UPDATE 1: Title and code file updated to match submitted manuscript 10-15-2025. UPDATE 2: Code and data files updated to match revised manuscript 3-4-2026. UPDATE 3: Code and data files updated to match revised manuscript 6-7-2026. ** NOTE: DD and FLMD have not been updated yet. UPDATE 4: Added associated Manuscript information and DD and FLMD have been updated. To cite this code, please use the following BibTeX: @misc{nagamoto2025drought, author = {Emily Nagamoto and Fabio Ciulla and Mohammad Ombadi and Jared Willard and Rosemary Carroll and Charuleka Varadharajan}, title = {Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"}, year = {2025}, doi = {10.15485/2551894}, publisher = {ESS-DIVE Repository}, url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2551894} }

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation

Abstract Colorado River streamflow has decreased 19% since 2000. Spring (March‐April‐May) weather strongly influences Upper Colorado River streamflow because it controls not only water input but also when snow melts and how much energy is available for evaporation when soils are wettest. Since 2000, spring precipitation decreased by 14% on average across 26 unregulated headwater basins, but this decrease did not fully account for the reduced streamflow. In drier springs, increases in energy from reduced cloud cover, and lowered surface albedo from earlier snow disappearance, coincided with potential evapotranspiration (PET) increases of up to 10%. Combining spring precipitation decreases with PET increases accounted for 67% of the variance in post‐2000 streamflow deficits. Streamflow deficits were most substantial in lower elevation basins (<2,950 m), where snowmelt occurred earliest, and precipitation declines were largest. Refining seasonal spring precipitation forecasts is imperative for future water availability predictions in this snow‐dominated water resource region.

Geology↗
DOE OSTI2025

Post-2026 Environmental Impact Statement Rate Analysis for the Colorado River Storage Project

The Glen Canyon Dam (GCD) is a principal power-generating asset within the Colorado River Storage Project (CRSP), accounting for approximately 70–80% of CRSP power production over the past two decades. In June 2023, the U.S. Bureau of Reclamation (Reclamation) issued a Notice of Intent to prepare an Environmental Impact Statement (EIS) outlining operational guidelines and strategies for Colorado River Basin reservoirs after 2026 (Reclamation, 2023). Power generation is among CRSP’s statutory purposes under the Colorado River Storage Project Act of 1956 (U.S. Congress, 1956). Assessing how alternative policy frameworks affect CRSP power production and the resulting electricity rates for U.S. customers is therefore essential to inform decision-making. This report evaluates projected electricity rates and the market value of electricity from the Western Area Power Administration (WAPA) CRSP GCD under multiple post-2026 policy scenarios to support Reclamation’s EIS development. Results from advanced econometric and machine learning models indicate that the Enhanced Coordination alternative (EnhanCoor), Maximum Operational Flexibility alternative (CCA), and Supply Driven - 55 alternative (SD55) alternatives yield more favorable hydropower generation and capacity outcomes, which are objectives outlined in Reclamation’s documentations (Reclamation, 2007; Reclamation, 2016). Specifically, these scenarios are associated with higher electricity production, lower projected rate trajectories, and greater economic value to the U.S. power system from CRSP generation. The remaining five scenarios generally produce lower generation, higher rate trajectories, and reduced long-term market values.

13 HYDRO ENERGY↗
DOE OSTI2022

The Press and Pulse of Climate Change: Extreme Events in the Colorado River Basin

Extremes in temperature and precipitation are associated with damaging floods, prolonged drought, destructive wildfires, agricultural challenges, compromised human health, vulnerable infrastructure, and threatened ecosystems and species. Often, the steady and progressive trends (or presses) of rising global temperature are the central focus in how climate impacts are described. However, observations of extreme weather events (or pulses) increasingly show that the intensity, duration and/or frequency of acute events are also changing, resulting in greater impacts on communities and the environment. Describing how the influence of extreme events may shape water management in the Colorado River Basin in clear terms is critical to sound future planning and efforts to manage risk. Three scenario planning workshops in 2019 and 2020 were held as part of a Colorado River Conversations series, identifying potential impacts from multiple intersecting extreme events. Water managers identified climate-related events of concern in the Colorado River Basin that necessitate greater attention and adaptive responses. To support efforts to include consideration of climate-change-driven extremes in water management and planning, we explore the current state of knowledge at the confluence of long-term climate shifts and extreme weather in the Colorado River Basin related to the events of concern that were identified by scenario planning participants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Observationally-based relations among water cycle parameters during snowfall events in the Upper Colorado River Basin

Cold-season precipitation predictability within the complex terrain of the Upper Colorado River Basin is vital for water resource management across many western states, as the Colorado River serves as a primary source of water for over 40 million people in the Western United States and Mexico. This study uses the remote sensing measurements of such water cycle parameters as the liquid equivalent snowfall rate, S, and accumulation, A, vertically integrated amounts of supercooled cloud liquid and ice expressed as liquid water path(LWP) and ice water path (IWP), respectively, and vertically integrated water vapor IWV.

Matrosov, Sergey [CIRES University of Colorado Bou↗
DOE OSTI2020

Resolution matters when modeling climate change in headwaters of the Colorado River

The continued growth of Southwestern cities depends on reliable water export from Rocky Mountain headwaters, which provide ~85% of Colorado River Basin (CRB) streamflow. Despite being more sensitive to warming temperatures, alpine systems are simplified in the regional-scale models currently in use to plan for future water supply. We used an integrated hydrologic model that couples groundwater and surface water with snow and vegetation processes to examine the effect of topographic simplifications as a result of grid coarsening in a representative CRB headwater basin. High-resolution (100 m) simulations predicted headwater streamflow losses of 16% by 2050 while coarse-resolution (1 km) simulations predict only 12%, suggesting that regional-scale models (coarser than 1 km) likely overestimate future Colorado River Basin water supplies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hyporheic, Floodplain, and Surface Water (on Floodplain and River) Geochemical Datasets, and Shapefiles on Meander C at the East River, Colorado

The purpose of this dataset was to sample surface water and groundwater geochemistry at random locations in the East River, Colorado to provide a broad spatial dataset of geochemistry. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. We conducted this random sampling at multiple time points including during the snowmelt flooding stage, during peak flood, during the falling limb of flood, and during baseflow. This datasets contain many datafiles. We include flat text files of surface water geochemistry including surface water on the floodplain and surface water in the river, geochemistry of floodplain pore-water samples, and geochemistry of hyporheic pore-water samples. All datasets are also included as shapefiles so location geochemistry is co-located with sampling locations and sampling depths. This datasets also has the locations of small channels, and hollows that are on top of floodplain Meander C. These small channels funnel surface water from the hillslope through the floodplain and out to the river. All text datasets can be analyzed with software such as Excel or R. All shapefiles can be viewed with freely available R packages such as Leaflet.

54 ENVIRONMENTAL SCIENCES↗