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Evaluating the use of Soil Moisture, January Baseflow, and Snow Water Equivalent storage indicators to enhance Colorado Basin River Forecast Center water supply forecasts
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| Type: | Resource | |
| Storage: | The size of this resource is 32.1 MB | |
| Created: | Jul 16, 2025 at 4:59 p.m. (UTC) | |
| Last updated: | Nov 17, 2025 at 5:55 a.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content |
| Sharing Status: | Public |
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| Views: | 807 |
| Downloads: | 145 |
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Abstract
This resource provides the dataset and Python workflows used to evaluate improved water supply forecasting for the Upper Colorado River Basin and the Great Salt Lake Basin areas served by the Colorado Basin River Forecast Center (CBRFC). The study focuses on enhancing April–July runoff volume predictions by explicitly incorporating three key hydrologic storage indicators—January baseflow, soil moisture, and snow water equivalent (SWE)—alongside the official CBRFC Most Probable (MP) water supply forecast. These indicators represent antecedent conditions that help explain variability in spring snowmelt-driven streamflow across snow-dominated watersheds.
Data and Python code used to implement the multiple linear regression (MLR) models, station data processing, and spatial analysis are included here. The research found that combining multiple storage indicators with the CBRFC forecast leads to gains in predictive skill, particularly in headwater basins where natural hydrologic processes are less influenced by regulation. Among the variables evaluated, soil moisture contributed the largest improvements when added to the model.
This resource holds data and code used to compute the results reported in the MS thesis: Morovati, R., (2025), "Evaluating Use Of Multiple Hydrologic Storage Indicators To Enhance Streamflow Forecasting " MS Thesis, Civil and Environmental Engineering, Utah State University.
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| This resource is described by | Morovati, R., (2025), "Evaluating Use Of Multiple Hydrologic Storage Indicators To Enhance Streamflow Forecasting " MS Thesis, Civil and Environmental Engineering, Utah State University. |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | HDR Institute: Geospatial Understanding through an Integrative Discovery Environment | 2118329 |
| Utah Water Research Laboratory | Graduate Research Assistantship | None |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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