Besso et al. (2026) Mapping 1 April SWE in the Western US Using Standardized Anomalies and Quantiles From SWE Reanalysis and In Situ Stations

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-01-01
• Authors: Hannah Besso, Ross Mower, Justin M. Pflug, Jessica D. Lundquist
• DOI: 10.1029/2025wr040902

Research Groups

Not specified in abstract.

Short Summary

This study introduces new methods for real-time distributed 1 April Snow Water Equivalent (SWE) estimation in the Western US by combining historical SWE reanalysis products with current in situ point measurements, achieving a median R² of 0.64 and a root mean squared error of 0.13 m.

Objective

• To develop and test new methods for real-time distributed 1 April Snow Water Equivalent (SWE) estimation in the Western US, particularly in the Upper Colorado River Basin, by leveraging historical SWE reanalysis products and current in situ point measurements.

Study Configuration

• Spatial Scale: Western US, with specific testing in the Upper Colorado River Basin (UCRB). Distributed (gridded) estimates.
• Temporal Scale: Historical data from 1990–2021 (32 years) for reanalysis. Real-time estimates focused on 1 April.

Methodology and Data

• Models used: Clustering algorithm (for regional SWE anomaly patterns), parametric and nonparametric distribution assumptions for SWE estimation.
• Data sources:
  • Historical (1990–2021) 1 April SWE from a reanalysis product.
  • Real-time point measurements from in situ snow stations.

Main Results

• A clustering algorithm successfully identified regions in the Western US with historically similar SWE anomalies.
• The most accurate method for estimating distributed 1 April SWE combined a parametric distribution assumption with current-year observations from the collection of stations within the same cluster as each grid cell.
• This optimal method produced distributed 1 April SWE estimates with a median R² of 0.64, a percent bias of 0.49%, and a root mean squared error of 0.13 m when compared to SWE reanalysis data in withheld years.
• The developed methods are generalizable and applicable wherever historical gridded data and real-time point measurements are available.

Contributions

• Introduces novel methods for real-time distributed 1 April SWE estimation by effectively integrating high-accuracy historical reanalysis products with real-time point measurements.
• Demonstrates the utility of regional SWE anomaly patterns (derived from clustering) for significantly improving the accuracy of distributed SWE estimates.
• Provides a robust and generalizable framework that can be applied to enhance operational spring runoff forecasting in snow-dominated basins globally.

Funding

Not specified in abstract.

Citation

@article{Besso2026Mapping,
  author = {Besso, Hannah and Mower, Ross and Pflug, Justin M. and Lundquist, Jessica D.},
  title = {Mapping 1 April SWE in the Western US Using Standardized Anomalies and Quantiles From SWE Reanalysis and In Situ Stations},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2025wr040902},
  url = {https://doi.org/10.1029/2025wr040902}
}