A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes

Abstract

Researchers seek improved models for estimating baseflow in ungauged catchments as the statistical and process-based methods traditionally used are not easily transferable and do not give robust predictions, respectively. In this study, a process-based statistical hybrid model for estimating baseflow, known here as the Budyko-type baseflow regression (BBR) model, was developed by combining the linear storage-discharge relationship with the Budyko function, and applying multiple regression. The performance of the hybrid model was then evaluated using data from 671 near-natural catchments acquired from four large-sample hydrometeorological datasets. The results showed that the BBR model performed well in estimating baseflow on both national and regional scales; the coefficient of determination between the observed and predicted mean annual baseflow varied from 0.85 to 0.94 for the calibration catchments and from 0.71 to 0.96 for the validation catchments. The BBR model has more of a physical basis, is more transferable, and is less likely to produce unrealistic (negative) mean annual baseflows than traditional multiple regression models. It is also less complex than process-based models. The calibrated BBR models were used to estimate the mean annual baseflow and the elasticity of the baseflow to climate and catchment attributes at the national scale for the United States, United Kingdom, Brazil, and Australia. The results showed that climate (precipitation and potential evapotranspiration) had the most control on the baseflow, and that the soil sand content and the Normalized Difference Vegetation Index were also important catchment attributes. The BBR model developed in this study offers scientists a new and improved method for estimating baseflow. The results generated by the model in this study will improve our understanding of how climate and landscape control the baseflow.