Baseflow plays a critical role in maintaining the aquatic environmental health. However, the driving factors and predictions of baseflow have not been rigorously investigated on a large scale, partly preventing hydrologist from deeply understanding runoff generation. To this end, the Lyne–Hollick digital filter method and the automatic baseflow identification technique were used to estimate the long-term and seasonal baseflow index (BFI) of 619 catchments across Continental United States (CONUS) from 1981 to 2014. Six natural driving factors are selected from the 31 catchment attributes about topography and location, soil, geology, land cover, and climate characteristics. The Random Forest (RF) technique was used to predict the BFI with the selected six driving factors as predictors. Results show that the long-term average BFI was 0.49, and the BFI value was different in four seasons, with the highest value of 0.55 in winter and the lowest value of 0.46 in autumn. The forest fraction, clay proportion and snow fraction were the most powerful factors affecting the long-term average BFI. The RF technique predicts the BFI across the 619 sites in CONUS with a R² of 0.59 after Leave-One-Location cross-validation, which was more satisfactory than the multiple linear regression method. This study can provide a deep insight into the generation and variation of baseflow and guide the annual baseflow prediction for water resources management.

基流在维持水生环境健康方面起着至关重要的作用。然而，基流的驱动因素和预测尚未得到大规模的严格研究，部分阻碍了水文学家深入了解径流的产生。为此，利用 Lyne-Hollick 数字滤波器方法和自动基流识别技术，估计了 1981 年至 2014 年美国大陆 (CONUS) 619 个流域的长期和季节性基流指数 (BFI)。 6 个自然驱动从地形和位置、土壤、地质、土地覆盖和气候特征等 31 个流域属性中选择因子。随机森林 (RF) 技术用于以选定的六个驱动因素作为预测因子来预测 BFI。结果表明，长期平均 BFI 为 0.49，四个季节的BFI值不同，冬季最高值为0.55，秋季最低值为0.46。森林比例、粘土比例和雪比例是影响长期平均 BFI 的最有力因素。RF 技术使用 R 预测 CONUS 619 个站点的 BFILeave-One-Location 交叉验证后的² of 0.59，比多元线性回归方法更令人满意。该研究可以深入了解基流的产生和变化，并指导水资源管理的年度基流预测。