Soltani et al. (2026) Predicting runoff in data-sparse drylands: a case study evaluating regionalization success for two parsimonious loss models

Identification

• Journal: Frontiers in Water
• Year: 2026
• Date: 2026-09-22
• Authors: Farzaneh Soltani, Sara Rassa, Gerhard Schoener
• DOI: 10.3389/frwa.2026.1959714

Research Groups

• Gerald May Department of Civil, Construction and Environmental Engineering, University of New Mexico, Albuquerque, NM, United States
• Southern Sandoval County Arroyo Flood Control Authority, Rio Rancho, NM, United States

Short Summary

This study evaluates the success of regionalization for two parsimonious loss models in predicting runoff in data-sparse drylands. The linear-constant model outperformed the curve number method for large storm events.

Objective

• Evaluate the effectiveness of regionalization for predicting runoff in arid and semi-arid regions using two simple loss models.
• Compare the performance of the linear-constant and curve number methods under different precipitation conditions.

Study Configuration

• Spatial Scale: Regional (Walnut Gulch Experimental Watershed, Arizona, USA) to local scale (Star Heights test watershed, New Mexico, USA)
• Temporal Scale: 2000-2025 for Walnut Gulch data; 2025 for Star Heights data

Methodology and Data

• Models used: HEC-HMS with SCS curve number (CN) and linear-constant (LC) methods
• Data sources: High-resolution precipitation and runoff records from the Southwest Watershed Research Center Data Access Project, Next Generation Weather Radar (NEXRAD) data

Main Results

• The linear-constant model outperformed the curve number method for large storm events in both Walnut Gulch and Star Heights watersheds.
• For small rainfall events, the curve number 0.05 scenario performed better than most other scenarios.

Contributions

• This study provides actionable guidance for practicing engineers and hydrologists facing model selection and parameterization decisions in data-sparse dryland watersheds.
• The results highlight the importance of selecting an appropriate loss model when regionalizing parameters from a donor basin to a target watershed with limited data.

Funding

• Not specified

Citation

@article{Soltani2026Predicting,
  author = {Soltani, Farzaneh and Rassa, Sara and Schoener, Gerhard},
  title = {Predicting runoff in data-sparse drylands: a case study evaluating regionalization success for two parsimonious loss models},
  journal = {Frontiers in Water},
  year = {2026},
  doi = {10.3389/frwa.2026.1959714},
  url = {https://doi.org/10.3389/frwa.2026.1959714}
}