Abstract Empirical baseflow filters are widely used for baseflow separation. These filters rely on ad‐hoc parameters that introduce significant uncertainties in the calculation. A recent study by Mei et al. (2024, https://doi.org/10.1029/2023wr036386 ) optimized these parameters using environmental tracer data for 1,100 catchments across the Contiguous United States (CONUS). However, optimized parameters are unavailable for most CONUS catchments lacking tracer data. To address this gap, we developed regionalization models for the filter parameters using the random forest (RF) algorithm and 82 catchment‐scale characteristics, including geomorphology, climate, soil properties, and human activities. We demonstrated this approach for the block length parameter of the smooth minima baseflow filter, one of the optimized filters in Mei et al.’s study, across 855 catchments. Our results show that the prediction of achieves an of 0.80. Predictor importance analysis identified catchment area as the most influential factor, followed by climate, hydrology, soil, and water usage characteristics. Using the RF‐predicted in baseflow separation improves daily baseflow accuracy, with the median Kling‐Gupta Efficiency increasing from 0.62 to 0.80 compared to the literature‐suggested area‐based power function. This study enhances the accuracy of baseflow separation, providing a robust foundation for understanding streamflow partitioning and supporting improved hydrological modeling.
Lin et al. (Sun,) studied this question.