Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique
Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique
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DOI：
10.1007/s00477-021-02057-2
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发表时间：
2021-06
影响因子：
4.2
通讯作者：
Shanshan Huang;Q. Dong;Xu Zhang;Weishan Deng
中科院分区：
文献类型：
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作者：
Shanshan Huang;Q. Dong;Xu Zhang;Weishan Deng
基流在维持水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严格的调查，在一定程度上阻碍了水文学家深入了解径流的产生。为此，使用Lyne-Hollick数字滤波方法和基流自动识别技术，估算了1981 - 2014年美国大陆（CONUS）619个流域的长期和季节基流指数（BFI）。从31个流域属性中选择了6个自然驱动因子，包括地形和位置、土壤、地质、土地覆盖和气候特征。随机森林（RF）技术被用来预测的BFI与选定的六个驱动因素作为预测。结果表明，该地区多年平均BFI值为0.49，四季BFI值不同，冬季最高，为0.55，秋季最低，为0.46。森林覆盖率、粘粒含量和积雪覆盖率是影响长期平均BFI的最强因子。RF技术预测了美国大陆619个研究中心的BFI，在Leave-One-Location交叉验证后R2为0.59，比多元线性回归方法更令人满意。该研究可深入了解基流的产生和变化规律，为水资源管理提供年基流预测指导。
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 R2of 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.