ABSTRACT:

Since the closing of Glen Canyon Dam, the clear waters of the Colorado River have stripped sediment from beaches and sandbars in the Grand Canyon. In an attempt to distribute sand to rebuild beaches, high flow experiments (HFE) have been conducted wherein large releases from Glen Canyon Dam are made over several days. The HFE events are timed to follow the summer/fall monsoon season when sand delivery from the Paria River is typically high given that the Paria is the primary source of sand to the Colorado River in Marble Canyon. Unrelated reservoir operating rules coordinate annual releases from Lake Powell so that the storage contents of Lakes Powell and Mead are equalized. If these “equalization flows” are released when there is relatively little sand supplied from the Paria River, they are likely to erode downstream sandbars, including those created by HFEs. Currently, there is no connection between the operations for reservoir equalization and for implementation of HFEs. Our analysis examines potential changes to the equalization protocols to explore whether equalization flows can be delayed to avoid releases that cause sandbar depletion. Results indicate that delaying equalization in favor of sediment supply results in some inequity for Lakes Powell and Mead, but the imbalance is less than anticipated and less than with no equalization present. Jointly considering sediment supply and equalization could help retain sediment within the Grand Canyon, however, even in years of sand load that meets the threshold for HFE experiments, the sediment supply may not be sufficient to balance out the volumes of equalization flows.

This data resource consists of the files used to support this work. The word document and the power point presentation present the results of this work. The folder CRSS contains two other folders. One folder, 'model' contains a saved version of the Colorado River Simulation System - a model that may be implemented in Riverware. This saved model includes slots corresponding to estimated sediment and slots generated by the implemented ruleset to govern equalization (Sediment Equalization Trigger, Years Without Sediment, 1-yr, 2-yr, 3-yr Equalization Delay). The 'ruleset' folder contains rulesets used in this analysis. There are four rulesets - each corresponding to scenarios run. The folder Data contains R code for running statistical analysis on input sediment data and flow data. The raw input files to run the code are included that correspond to natural flow inputs(obtained from the Bureau of Reclamation) and sand load from the Paria River (obtained from the Grand Canyon Monitoring and Research Center). The Results folder includes 1. a table of Estimated Summer Sandload and 2. a spreadsheet of CRSS results for the various scenarios run along with plots for comparing between them.

ABSTRACT:

This project used Budyko-based methods to determine the elasticity and sensitivity of 29 subbasins in the Colorado River Basin. Elasticity and sensitivity are metrics used to determine the relative expected changes in runoff given changes in precipitation and temperature, respectively. We used publicly available data to determine long term averages for temperature, precipitation, and runoff for principal Colorado River subbasins. Given those data, we used Budyko-based methods to estimate the elasticity and sensitivity of each subbasin to changes in temperature and precipitation. We determined the aridity index of each subbasin and Budyko parameter (w), which aggregates watershed storage characteristics. Subcatchments located in the Upper Basin, driven mostly by snowmelt, have a lower aridity index and higher w value than those in the Lower Basin, driven by monsoonal storm events. The Paria and the Little Colorado River subbasins are particularly sensitive to changes in precipitation and temperature. To identify the initialization of direct human impacts, we used a double mass curve break point analysis on a single subcatchment. Two breakpoints were identified, 1963 and 1988, corresponding to human impact and climate change, respectively.

This data resource includes a document and power point reporting the key findings of this work. We include the code, input, and output files used to perform analyses, all of which are described in the readme.

ABSTRACT:

Due to changes in the climate and increased human consumptive uses, determining the availability of future water in the Colorado River Basin is critical. Availability of water can be estimated based on river discharge, precipitation, and air temperatures. Many research projects in the Colorado River Basin depend on these data. In order to simplify the data collection process for many projects, a single database with precipitation, temperature, and flow data from various sources was created. Discharge data were obtained from three independent sources while precipitation and temperature data were obtained from two independent sources. Using scripts in R, all the data was formatted and imported into a SQLite database named the Observation Data Model (ODM). The ODM provides researchers a vast amount of hydrologic data in a single database that can be easily downloaded. This product provides researchers the ability to spend less time gathering data and more time analysing the data.To determine the level of effort required to interact with the ODM, some minor analysis and plots were created in R. These plots revealed the disadvantage of using ODM rather than a gridded database. Extracting or querying the desired data is slightly more involved. To help mitigate this disadvantage, the process of extracting data is described in this report. The ODM and all associated input data and scripts were uploaded to public Hydroshare resource. This enables the ODM to be downloaded and used by anyone.

ABSTRACT:

Dissolved Oxygen (DO) and Water Temperature (T) are two essential parameters in assessing water quality because of their significant role in aquatic life and habitats. DO is a measure of the amount of oxygen available for aquatic organisms to breath and survive. The concentration of DO depends on several factors such as temperature, pressure, and salinity (Wetzel, 2001). In the Logan River, Utah, the temperature has the most influence on DO. The aim of this study is to assess the relationship between DO and T at one of the sites of the Logan River Observatory named “Logan River at Main Street (Highway 89/91) Bridge” in a reproducible way. At the end, meeting the required quality standards by them is also investigated.

ABSTRACT:

This dataset holds streamflow sequences for each of three drought scenarios developed to characterize plausible future drought conditions in the Colorado River Basin. These sequences were produced using the methods described in Center for Colorado River Studies Future of the Colorado River Project white paper 4 entitled “The Future Hydrology of the Colorado River Basin” by Salehabadi, Tarboton et al. (2020) and paper Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061. This study defined three drought scenarios: (1) Millennium Drought, (2) Mid 20th Century Drought and (3) Paleo Tree Ring Severe Drought. The first two were defined using the US Bureau of Reclamation Natural flows from 2000-2018 and 1953-1977 respectively. The last was defined using the years 1576-1600 from the Meko et al., 2017 tree ring reconstruction of streamflow at Lees Ferry. 100 streamflow traces, each 42 years long were produced for each scenario by resampling years with replacement. Resampling from identified past drought scenarios, provides test droughts based on past flows that are more severe, due to the variety in the sampling, than any past droughts that have actually occurred. They are nevertheless plausible, since they are derived from past records. We used a nonparametric resampling approach referred to as “Water Year Block Disaggregation” to split the simulated annual flow at Lees Ferry into monthly flow at each of the 29 Colorado River Simulation System (CRSS) natural inflow sites. For the first two scenarios where there are historic natural flows at the 29 CRSS sites, this selects the entire water year block of monthly flows across sites for the corresponding drought year. For the paleo scenario, where there are not flows at each of the sites, the historic natural flow year with annual flow at Lees Ferry closest to the paleo flow is selected, and then flows across the sites and months adjusted by the ratio of paleo flow to closest historic flow.

ABSTRACT:

This dataset holds scripts for Duration-Severity and Cumulative Deficit analyses developed to examine the severity of sustained droughts that have impact on storage and water supply in the Colorado River Basin. These analyses were performed using the methods described in Salehabadi, Tarboton et al. (2022; 2020). These studies analyzed the US Bureau of Reclamation Natural flow and Tree Ring Reconstructed flow from Meko et.al., 2017, both at Lees Ferry, using the Duration-Severity and Cumulative Deficit plots, which show the mean flow and cumulative magnitude of departure from average conditions, or “deficit”, for different durations. These plots presented by Salehabadi, Tarboton et al. (2020; 2022) characterize the severity of past droughts that have occurred in the Colorado River Basin. Based on examination of these plots, this study defined three drought scenarios: (1) Millennium Drought, (2) Mid-20th Century Drought, and (3) Paleo Tree Ring Drought. The first two were defined using the US Bureau of Reclamation Natural flows from 2000-2018 and 1953-1977 respectively. The last was defined using the years 1576-1600 from the Meko et al., 2017 tree ring reconstruction of streamflow at Lees Ferry.

- Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061.
- Salehabadi, H., D. G. Tarboton, E. Kuhn, B. Udall, K. G. Wheeler, D. E. Rosenberg, S. A. Goeking and J. C. Schmidt, (2020), "The Future Hydrology of the Colorado River Basin," White Paper 4, Future of the Colorado River Project, Center for Colorado River Studies, Utah State University, 71 p., https://qcnr.usu.edu/coloradoriver/files/WhitePaper4.pdf.

ABSTRACT:

This collection holds the data and analysis scripts for Salehabadi, Tarboton et al. (2022; 2020). These studies examined historical natural flow, tree-ring flow reconstruction, and projected streamflow from climate change models to generate plausible severe drought scenarios to consider during planning in the Colorado River Basin. This collection has been developed to provide access to and preserve the data used in these studies, in the interests of transparency and reproducibility of this work.

- Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061.
- Salehabadi, H., D. G. Tarboton, E. Kuhn, B. Udall, K. G. Wheeler, D. E. Rosenberg, S. A. Goeking and J. C. Schmidt, (2020), "The Future Hydrology of the Colorado River Basin," White Paper 4, Future of the Colorado River Project, Center for Colorado River Studies, Utah State University, 71 p., https://qcnr.usu.edu/coloradoriver/files/WhitePaper4.pdf.

ABSTRACT:

This dataset holds CRSS-ready flow sequences adjusted for temperature increases in the 21st century (2018 to 2100) as well as temperature increases that occurred during the natural flow period from 1906 to 2017. These flow traces were produced using the methods described in the file memo by Udall (2020). This study developed six different temperature-adjusted natural flow datasets based on considering three different temperature sensitivities (-3%/°C, -6.5%/°C, and -10%/°C) times two different temperature projections (RCP4.5 and RCP8.5). The flows were generated by modifying the existing Reclamation natural flow dataset (USBR, 2019) at each of the 29 Colorado River Simulation System (CRSS) natural inflow sites. Two main steps of temperature adjustment process in this study were: 1- Create a set of natural flows that would have occurred were temperatures constant from 1906 to 2017 assuming 2017 temperatures, and 2- Further adjust downward the constant 2017 temperature flows to account for expected losses due to warming in the 21st century. The flow data are provided in an CRSS-ready Indexed Sequential Method (ISM) series with a starting year of 2018 and an ending year of 2100.

ABSTRACT:

The Colorado River is facing an unprecedented water supply crisis due to a 20% reduction of streamflow compared to the 20th century average and to policies that have allowed 21st century consumptive water use to exceed water supplies. To continue to meet demands, storage in the two largest reservoirs in the United States, Lakes Mead and Powell, have fallen from nearly full in 2000 to a projected level of 25% full by the end of the year. Existing drought management policies have thus far been unable to arrest this decline. If the current drought were to continue, substantially greater reductions in consumptive use will be necessary to avoid the loss of hydropower and avoid unpredictable delivery reductions to water users. To address the imbalance between supply and consumption, we identify combinations of limits on Upper Basin consumptive use alongside reduced deliveries to the Lower Basin and Mexico. These adaptation measures need to be applied swiftly to avoid further decline if the current drought persists.

This collection is supplementary data and code referenced in the journal article titled "What will it take to stabilize the Colorado River? ". This collection is to preserve and provide access to data used in the study in the interest of transparency and reproducibility of this work.

ABSTRACT:

This paper summarizes the current understanding of future hydrology from the perspective of how that understanding can be incorporated into the Colorado River Simulation System and other river planning models. We also provide scenarios that characterize and estimate plausible future drought conditions, based on the record of past droughts in historic and tree ring-estimated natural flow. Scenarios described in this report, although sometimes of low probability, are based on flows that have occurred in the past or can be reconstructed from the past record of streamflow. If such conditions have happened in the past, they might occur in the future, and these scenarios should be considered in future planning.

ABSTRACT:

This resource is an updated version of the following resource:
Salehabadi, H., D. Tarboton (2022). Hydrology scenarios that characterize plausible future drought conditions in the Colorado River Basin, HydroShare, https://doi.org/10.4211/hs.ca2e152c9fca4b2aa7c3294a388c522d

The previous dataset was updated using the most recent version (last updated on 12/15/2022) of the US Bureau of Reclamation Natural Flow database, covering the time period from 1906 to 2020. In addition to this update, the dataset now provides CRSS-ready-to-use input files including:
• Flow inputs at 29 CRSS sites
• MWD_ICS.SacWYType (Sacramento Water Year Type)
• TMD_East_Slope_Supply.St_Vrain_Annual_Flow (St Vrain Annual Flows)
• HydrologyParameters.SupplyScenario
• HydrologyParameters.TraceNumber
• MeadFloodControlData.hydrologyIncrement

This dataset holds streamflow sequences for three drought scenarios developed to characterize plausible future drought conditions in the Colorado River Basin. These sequences were produced using the methods described in Center for Colorado River Studies Future of the Colorado River Project white paper 4 entitled “The Future Hydrology of the Colorado River Basin” by Salehabadi, Tarboton et al. (2020) and paper Salehabadi, H., D. G. Tarboton, B. H. Udall, K. G. Wheeler and J. C. Schmidt, (2022), "An Assessment of Potential Severe Droughts in the Colorado River Basin," JAWRA Journal of the American Water Resources Association, https://doi.org/10.1111/1752-1688.13061. The three defined drought scenarios are as follows: (1) Millennium Drought, (2) Mid 20th Century Drought, and (3) Paleo Tree Ring Drought. The first two droughts were defined using the US Bureau of Reclamation Natural flows from 2000-2020 and 1953-1977, respectively. The last was defined using the years 1576-1600 from the Meko et al., 2017 tree ring reconstruction of streamflow at Lees Ferry. 100 streamflow traces, each 50 years long were produced for each scenario by resampling years with replacement. Resampling from identified past drought scenarios, provides test droughts based on past flows that are more severe, due to the variety in the sampling, than any past droughts that have actually occurred. They are nevertheless plausible, since they are derived from past records. We used a nonparametric resampling approach referred to as “Water Year Block Disaggregation” to split the simulated annual flow at Lees Ferry into monthly flows at each of the 29 Colorado River Simulation System (CRSS) natural inflow sites. For the first two scenarios where there are historical natural flows at the 29 CRSS sites, this selects the entire water year block of monthly flows across sites for the corresponding drought year. For the paleo scenario, where there are no flows at the sites, the historical natural flow year with the annual flow at Lees Ferry closest to the paleo flow is selected and then flows across the sites and months adjusted by the ratio of paleo flow to closest historical flow.

ABSTRACT:

This resource holds streamflow sequences (or traces or time series) for the adjusted paleo-conditioned ensemble developed to represent increasing variability around a declining mean storyline in the Colorado River Basin as described by Salehabadi et al. (2024).

This resource holds the data generated in:
Salehabadi, H., Tarboton, D. G., Wheeler, K. G., Prairie, J., Smith, R., & Baker, S. (2024). Developing Storylines of Plausible Future Streamflow and Generating a New Warming-Driven Declining Streamflow Ensemble: Colorado River Case Study. ESS Open Archive. https://doi.org/10.22541/essoar.172469147.74897971/v1

ABSTRACT:

This resource contains the R Scripts developed to characterize and assess annual streamflow ensembles using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree-based classification approach to categorize both ensembles and metrics. The results, also included here, provide a way to visualize and interpret differences between streamflow ensembles. The presented metrics and their classification provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non-stationarity, and contributing to better planning in river basins.

This resource contains the data and scripts used in
Salehabadi, H., Tarboton, D. G., Wheeler, K. G., Smith, R., & Baker, S. (2024). Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence-Based Analysis: Colorado River Case Study. Water resources research, 60, e2024WR037225. https://doi.org/10.1029/2024WR037225

ABSTRACT:

This HydroShare resource provides Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA Analysis of Record for Calibration (AORC) Dataset. There are two Jupyter Notebooks
1. AORC_Point_Data_Retrieval.ipynb
2. AORC_Zone_Data_Retrieval.ipynb
The first retrieves data for a point in the area of the US covered, specified using geographic coordinates. The second retrieves data for areas specified via an uploaded polygon shapefile.
These notebooks programmatically retrieve the data from Amazon Web Services (https://registry.opendata.aws/noaa-nws-aorc/), and in the case of shapefile data retrieval average the data over the shapes in the given shapefile.
The notebooks provided are coded to retrieve data from AORC version 1.1 released in ZARR format in December 2023.
The Analysis Of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the continental United States and Alaska and their hydrologically contributing areas (https://registry.opendata.aws/noaa-nws-aorc/). It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 across the Continental U.S. (CONUS) and from 1981 across Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the original NOAA process generated AORC data in netCDF format, the data has been post-processed to create a cloud optimized Zarr formatted equivalent that NOAA also disseminates.

ABSTRACT:

This HydroShare resource provides Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA National Water Model CONUS Retrospective Dataset. There are two Jupyter Notebooks
1. NWM_output_variable_retrieval_with_FeatureID.ipynb
2. NWM_output_variable_retrieval_with_shapefile.ipynb
The first retrieves data for one point (feature ID). The second retrieves data for areas specified interactively or via an uploaded shapefile.
These notebooks programmatically retrieve the data from Amazon Web Services (https://registry.opendata.aws/nwm-archive/), and in the case of Zone data retrieval average the data over the zones specified.
The notebooks provided are coded to retrieve data from NWM retrospective analysis version 3.0 released in ZARR format in December 2023.
The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations (https://registry.opendata.aws/nwm-archive/ ). These simulations used meteorological input from retrospective data. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations