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Enhancing Streamflow Reanalysis Across the Conterminous US Leveraging Multiple Gridded Precipitation Data Sets
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Enhancing Streamflow Reanalysis Across the Conterminous US Leveraging Multiple Gridded Precipitation Data Sets
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Journal Article
Enhancing Streamflow Reanalysis Across the Conterminous US Leveraging Multiple Gridded Precipitation Data Sets
2025
Overview
Streamflow observations, essential for various water resource applications, are often unavailable at critical locations in need. Although different models have been proposed to enhance streamflow predictability at ungauged locations, the challenge extends beyond model fidelity. Differences in meteorologic forcing data sets, precipitation in particular, can significantly affect the accuracy of hydrologic predictions. This challenge intensifies across regions characterized by diverse hydro‐climatological and geographical conditions, such as in the conterminous US (CONUS) where a single precipitation product struggles to consistently replicate observed hydrographs, particularly peak flow dynamics. To enhance streamflow predictions, we utilize a VIC‐RAPID hydrologic modeling framework driven by multiple commonly used meteorological forcing data sets, such as Daymet, PRISM, ST4, AORC, and their hybrids and create multiple sets of 40‐year (1980–2019) hourly, daily, and monthly streamflow reanalysis, Dayflow Version 2, for 2.7 million river reaches across the CONUS. Most forcings lead to skillful streamflow performance, except for ST4 in the mountainous west, where severe radar blockage adversely affects the accuracy. The evaluation using over 6,000 hourly stream gauges shows that hourly AORC and ST4 lead to improved annual peak flow performance over Daymet—driven streamflow (Dayflow V1), particularly in smaller basins, highlighting the value of high temporal resolution forcings in hydrologic predictions. Compared with other benchmark data sets like National Water Model V3.0, AORC‐driven VIC‐RAPID exhibits improved regional streamflow performance, with comparable peak flow representation. We envision that multi‐forcing streamflow reanalysis data can inform regions in need of forcing data enhancement, diagnose hydrologic model performance, and benefit diverse water resource applications. Plain Language Summary Accurate prediction of streamflow is challenging in areas where direct observations are lacking. Though existing models aim to improve predictions at ungauged rivers, streamflow predictability is not dependent on the model alone. The quality of meteorological data sets, mainly related to precipitation, significantly influences hydrologic predictions. For regions like conterminous US with diverse hydro‐climatological and geographical conditions, a single forcing data set might not work well for all water resources applications. To overcome these challenges, we use a large‐scale hydrologic model driven by multiple widely used meteorological data sets to produce a 40‐year (1980–2019) high‐resolution streamflow reanalysis, Dayflow Version 2 (https://doi.org/10.13139/OLCF/2222888), for 2.7 million river reaches across the conterminous US. Most of these reaches demonstrate skillful streamflow performance with some regional patterns. The study shows that multi‐forcing streamflow reanalysis data can be valuable for enhancing forcing data in data‐scarce regions, evaluating hydrologic model performance, and supporting various water resource applications. Key Points CONUS‐wide high‐resolution streamflow reanalysis is presented for 1980–2019 across multiple forcings at 2.7 million river reaches Multiple forcings offer distinct advantages for various water resource applications The AORC forcing captures peak flow dynamics better, especially in smaller basins
