Thota et al. (2026) Semi‐Bayesian Space‐Time Hierarchical Framework for Modeling and Prediction of Seasonal and Extreme Precipitation in the U.S. Northern Great Plains

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Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-07-01
• Authors: Samba Siva Sai Prasad Thota, Balaji Rajagopalan, Imtiaz Rangwala, Álvaro Ossandón
• DOI: 10.1029/2025wr041467

Research Groups

• University of Colorado Boulder (Department of Atmospheric and Oceanic Sciences)
• National Center for Atmospheric Research (NCAR)

Short Summary

This study adapts a semi-Bayesian hierarchical modeling framework to jointly characterize space-time variability in seasonal precipitation totals and extremes across the Northern Great Plains, providing probabilistic forecasts under climate variability and change conditions.

Objective

• Investigate the joint space-time variability of seasonal precipitation totals and extremes using a semi-Bayesian hierarchical modeling framework

Study Configuration

• Spatial Scale: Regional (Northern Great Plains)
• Temporal Scale: 1951-2019, with contemporaneous (0-month), 1-month, and 2-month lead times

Methodology and Data

• Models used: Semi-Bayesian hierarchical modeling framework, combining Gamma and Generalized Extreme Value distributions for precipitation totals and extremes, respectively
• Data sources: Precipitation data from 60 NGP stations

Main Results

• Strong performance in capturing historical spatial and temporal variability in precipitation across all lead times, with contemporaneous models showing the highest skill
• Conditional posterior distributions provide probabilistic forecasts of seasonal precipitation totals and extremes

Contributions

• Original value lies in developing a semi-Bayesian hierarchical modeling framework for joint space-time analysis of precipitation totals and extremes, offering critical information for climate variability and change conditions

Funding

• National Science Foundation (NSF) Grant # [insert grant number]
• National Oceanic and Atmospheric Administration (NOAA) Grant # [insert grant number]

Citation

@article{Thota2026SemiBayesian,
  author = {Thota, Samba Siva Sai Prasad and Rajagopalan, Balaji and Rangwala, Imtiaz and Ossandón, Álvaro},
  title = {Semi‐Bayesian Space‐Time Hierarchical Framework for Modeling and Prediction of Seasonal and Extreme Precipitation in the U.S. Northern Great Plains},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2025wr041467},
  url = {https://doi.org/10.1029/2025wr041467}
}