Data Sheet 1_Predicting runoff in data-sparse drylands: a case study evaluating regionalization success for two parsimonious loss models.pdf

Optimized parameters derived from this donor basin were then transferred to the target basin, a partially urbanized watershed in New Mexico with a dense network of rain and flow gaging stations. The target basin has a limited observational record and therefore serves as a suitable test case for regionalization under data-limited conditions.</p>Results<p>Validation results from the donor basin demonstrate that the LC model outperformed the CN model for large storm events across four performance metrics.

Performance for small events was not significantly different. When parameters were transferred to the target basin and tested for a rare, high-intensity storm event recorded in 2025, the LC model again performed better for most metrics.</p>Discussion<p>While regionalization can be a viable strategy to support stormwater infrastructure design in data-scarce, semi-arid urban watersheds, this study indicates that its success depends on model selection.</p>