利用两种指纹因子判别小流域泥沙来源

为精确识别黄土高原丘陵沟壑区典型小流域泥沙来源，分析了流域内“源-汇”地区土壤的理化性质及生物标志物（正构烷烃）作为潜在指纹识别因子，并建立了复合指纹模型。结果显示:单独的土壤理化指纹及正构烷烃均不能有效识别泥沙来源；土壤理化指纹和正构烷烃分别在林地、农地及沟道的辨别上显示出其局限性。多元化的复合指纹（碳优势指数CPI、Ca、TP、C₂₀、C₂₉、Fe）则能辨别90.5%的泥沙来源。模型结果显示沟道是该流域泥沙的主要来源，占60.8%，其次为农地占20.7%，林地占11.3%，草地占7.2%。研究表明，结合生物标志物的复合指纹法能更精确地反映泥沙来源，适用于各泥沙源头的地质条件差异较小的流域，对黄土高原小流域水土流失治理具有指导意义。

Using two kinds of fingerprint factors to identify sediment sources in a small catchment

In order to accurately identify sediment sources in the gully and hilly zones of the Loess Plateau, this study was used a composite fingerprinting method that was based on two types of fingerprint factors (14 physicochemical properties and 23 biomarker properties (n-alkanes)) with source samples and sediments derived from check dam. The results showed that, neither the individual physicochemical properties nor biomarker properties cannot effectively identify sediment sources. The physicochemical properties were showing the limitation in identifying forest and cropland, and the n-alkanes were showing the limitation in identifying the gully. However, the optimum composite fingerprint consisted of three physicochemical properties and three biomarker properties (CPI, Ca, TP, C₂₀, C₂₉ and Fe) could correctly distinguish 90.5% of the samples. The results showed that in the study area, gully was the main sediment source in this catchment, reaching 60.8%, while the cropland contributed 20.7% of the sediment, and forest and grassland contributed 11.3% and 7.1%, respectively. This research demonstrates that using the composite fingerprint consists of traditional fingerprint properties and biomarkers to identify the sediment source are more accurate, it would be suitable for the area where the geologic variations of a study area are small. The study is meaningful for designing sediment management and soil erosion control strategies in Loess Plateau.