Morovati (2025) Evaluating Use of Multiple Hydrologic Storage Indicators to Enhance Streamflow Forecasting

Identification

• Journal: Utah State Research and Scholarship (Utah State University)
• Year: 2025
• Date: 2025-12-12
• Authors: Reza Morovati
• DOI: 10.26076/c47c-28b8

Research Groups

• Department of Civil and Environmental Engineering, Utah State University.
• Utah Water Research Laboratory (associated with the Committee Chair).

Short Summary

This study evaluates the integration of multiple hydrologic storage indicators—snow water equivalent (SWE), soil moisture, and January baseflow—to improve seasonal streamflow forecasting in the Western United States. The research demonstrates that incorporating these indicators, particularly soil moisture, significantly enhances the accuracy of operational forecasts in mountainous headwater basins.

Objective

• To improve seasonal streamflow forecast accuracy by explicitly incorporating multiple hydrologic storage indicators (SWE, soil moisture, and groundwater/baseflow) into forecasting models for the Upper Colorado River and Great Salt Lake Basins.

Study Configuration

• Spatial Scale: Watersheds within the Upper Colorado River Basin and the Great Salt Lake Basin, focusing on mountainous headwater regions in the interior Western United States.
• Temporal Scale: Seasonal forecasting with a focus on the spring runoff period; January baseflow is utilized as a specific winter-season indicator for groundwater storage.

Methodology and Data

• Models used: Statistical forecasting models comparing various combinations of hydrologic indicators against the National Weather Service (NWS) seasonal water supply forecasts and the Colorado Basin River Forecast Center (CBRFC) "Most Probable" forecast.
• Data sources: Hydrologic storage indicators including Snow Water Equivalent (SWE), January baseflow (serving as a proxy for groundwater storage), and antecedent soil moisture data.

Main Results

• Integrating multiple storage indicators improves forecast accuracy, particularly in undisturbed mountainous headwater areas where natural hydrologic processes dominate.
• Soil moisture was identified as the most consistent indicator for providing incremental improvements to existing NWS seasonal water supply forecasts.
• Soil moisture often yielded the largest gains in forecast skill when added as a standalone predictor to the CBRFC Most Probable forecast.
• The study confirms that antecedent soil moisture conditions are a critical determinant of the efficiency with which rainfall and snowmelt are converted into streamflow during the spring runoff season.
• January baseflow was validated as a useful indicator of groundwater storage, contributing to the overall predictive skill of the models.

Contributions

• Provides a practical framework for water managers to incorporate diverse landscape storage data (beyond traditional snowpack metrics) into operational streamflow forecasting.
• Quantifies the specific value of soil moisture in enhancing the skill of official Colorado Basin River Forecast Center products.
• Offers a methodology to support more sustainable water management, reservoir operations, and drought planning in snow-dominated regions of the Western United States.

Funding

• The provided text does not explicitly list specific grant numbers or funding agencies; however, the research was conducted as part of a Master of Science thesis within the Department of Civil and Environmental Engineering at Utah State University.

Citation

@article{Morovati2025Evaluating,
  author = {Morovati, Reza},
  title = {Evaluating Use of Multiple Hydrologic Storage Indicators to Enhance Streamflow Forecasting},
  journal = {Utah State Research and Scholarship (Utah State University)},
  year = {2025},
  doi = {10.26076/c47c-28b8},
  url = {https://doi.org/10.26076/c47c-28b8}
}

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