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.

ABSTRACT:

This study presents a comprehensive comparison of gridded datasets for the Great Salt Lake (GSL) basin, focusing on precipitation and temperature as the main inputs for hydrological balances. The evaluated gridded datasets include PRISM, DAYMET, GRIDMET, NLDAS-2, and CONUS404, with in-situ data used for assessing alignment and accuracy. Key metrics such as Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) were employed to evaluate gridded dataset performance. Spatial and temporal accuracy analyses were conducted across different GSL basin regions to understand variations in accuracy. DAYMET emerged as the leading dataset for precipitation across most metrics, demonstrating consistent performance. For temperature, GRIDMET and PRISM ranked higher, indicating better representation of temperature patterns in the GSL basin. Spatial analysis revealed variability in accuracy for both temperature and precipitation data, emphasizing the importance of selecting suitable datasets for different regions to enhance overall accuracy. The insights from this study can inform environmental forecasting and water resource management in the GSL basin, assisting researchers and decision-makers in choosing reliable gridded datasets for hydrological studies.

ABSTRACT:

This study investigates the water storage dynamics in the Weber River Basin, one of the subbasins within the Great Salt Lake Basin, using historical reservoir data sourced from the United States Bureau of Reclamation (USBR) and the United States Department of Agriculture (USDA). The collected data provides a detailed look at reservoir storage capacities and fluctuations, which could serve as a valuable resource for conducting water balance analyses.

The consolidation of USBR and USDA data presents a unique opportunity for researchers interested in identifying water storage deficiencies and potential losses in the subbasin. Such analyses are essential for developing effective water management strategies and ensuring the sustainable use of water resources in the region. This study not only sheds light on current water storage trends but also establishes a critical resource base that can be utilized by other researchers to further explore water balance dynamics in the Weber River Basin.

ABSTRACT:

This resource aggregates data on the reservoirs and time series of reservoir storage within the Great Salt Lake Basin. It has been assembled by combining information from the United States Department of Agriculture (USDA) and the United States Bureau of Reclamation (USBR).