Scheftic et al. (2026) Seasonal Predictability of the Land Surface Hydrological Cycle over Two Distinct Regions of the United States within Two Operational Models

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

Identification

• Journal: Journal of Hydrometeorology
• Year: 2026
• Date: 2026-07-20
• Authors: William D. Scheftic, Michael A. Brunke, P. D. Broxton, Amir Ouyed, Xubin Zeng
• DOI: 10.1175/jhm-d-25-0093.1

Research Groups

• National Centers for Environmental Prediction (NCEP)
• European Centre for Medium-Range Weather Forecasts (ECMWF)

Short Summary

This study assesses the predictability of temperature, precipitation, and five additional hydrological variables across two distinct regions in the United States using operational coupled models. The results show that variables with high memory have higher predictability.

Objective

• Investigate the skill of model forecasts against observations for multiple hydrological variables

Study Configuration

• Spatial Scale: Two distinct hydrologic regions (the Missouri basin and California) of the United States
• Temporal Scale: Seasonal forecasting, varying lead times up to several months

Methodology and Data

• Models used: National Centers for Environmental Prediction Climate Forecast System version 2 (NCEP CFSv2), European Centre for Medium-Range Weather Forecasts Seasonal Forecasting System version 5 (ECMWF SEAS5)
• Data sources: Model forecasts, observations

Main Results

• Variables with high memory (e.g., soil moisture, total water storage) have both high perfect-model and actual prediction skill
• Runoff and evapotranspiration have intermediate skill
• High memory variables maintain high skill for longer prediction lead times and exhibit less seasonal variability
• Actual prediction skill is positively related to perfect-model skill across different regions and seasons

Contributions

• Provides new insights into the predictability of hydrological variables beyond temperature and precipitation
• Demonstrates the potential use of perfect-model skill as a proxy for actual prediction skill in ungauged areas

Funding

• Not specified

Citation

@article{Scheftic2026Seasonal,
  author = {Scheftic, William D. and Brunke, Michael A. and Broxton, P. D. and Ouyed, Amir and Zeng, Xubin},
  title = {Seasonal Predictability of the Land Surface Hydrological Cycle over Two Distinct Regions of the United States within Two Operational Models},
  journal = {Journal of Hydrometeorology},
  year = {2026},
  doi = {10.1175/jhm-d-25-0093.1},
  url = {https://doi.org/10.1175/jhm-d-25-0093.1}
}