Using biomarkers as fingerprint properties to identify sediment sources in a small catchment.

Traditional fingerprinting methods are limited in their ability to identify soil erosion sources where geologic variations are small or where different land uses span geological boundaries. In this study, a new biomarker for fingerprinting, specifically, n-alkanes, was used in a small catchment to identify sediment sources. The n-alkanes were based on land uses, could provide vegetation information, and were relatively resistant to diagenetic modifications and decomposition. This study used a composite fingerprinting method that was based on two types of fingerprint factors (27 biomarker properties and 45 geochemical properties) with 60 source samples (i.e., gully, grassland, forest, and cropland) and nine soil profiles. Genetic algorithm (GA) optimization has been deployed to find the optimal source contribution to sediments. The biomarker results demonstrated that young forest is the main sediment source in this catchment, contributing 50.5%, whereas cropland, grassland and gully contributed 25.6%, 14.4% and 9.5%, respectively; the geochemistry results were similar to the biomarkers. The forest and grassland contributions gradually increased from upstream to downstream, and the sediment contributions of cropland gradually decreased in the direction of the runoff pathway at the check dam. In a comparison of biomarker and geochemical fingerprinting data, the latter may have overestimated the forest inputs to the catchment sediment yields because of a mixed land use history (i.e., forest and grassland). The geochemical fingerprint approach limits its ability to fully discriminate sources based on land management regimes, but the biomarker (individual n-alkanes) displayed the potential to discriminate between a greater number and different types of sediment sources and to provide greater detail regarding sediment sources.