Work

Low-flow regionalization in a data-scarce Brazilian semi-arid basin using gridded climate data

Year: 2026

Type: article

Abstract: Estimating low flows is essential for water-resources management, especially in semi-arid regions with sparse hydrometric monitoring and high drought vulnerability. This study advances low-flow regionalization in a Brazilian semi-arid basin by integrating spatial climate data, precipitation and actual evapotranspiration from TerraClimate, into regional regression models. Of fourteen compiled gauges, ten met data-quality criteria and were used for calibration and leave-one-station-out cross-validation (LOSO-CV). The streamflow-equivalent water balance computed from TerraClimate fields ( $$\:{\text{WBeq}}_{\text{TC\_TC}}$$ ) showed the best univariate predictive performance, outperforming models based on drainage area and on precipitation alone. Spatialization applied a WBeq > 0 domain-of-applicability mask and an empirical envelope check based on the observed station range. Uncertainty was quantified by residual bootstrap in log space, and scenario perturbations ( P - 10%, AET + 10%) indicated strong sensitivity of low-flow estimates in the driest reaches. Overall, the results show that freely available gridded climate datasets can improve low-flow regionalization in data-scarce basins and support water management in semi-arid environments.

Source: Modeling Earth Systems and Environment

DOI: 10.1007/s40808-026-02806-8

Authors: Arthur Kolling Neto, Rayssa Balieiro Ribeiro

Institution: Universidade Federal de Viçosa

Language: English

Topic: Hydrology and Watershed Management Studies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation

Open Access status: hybrid

Estimated APC paid: $3,190