An end-member algorithm for deciphering modern detrital processes from lake sediments of Lake Donggi Cona, NE Tibetan Plateau, China

Deciphering significant sedimentological processes from a set of sediment samples is an important step in reconstructing environmental changes. One approach going beyond classical methods is the unmixing of grain-size distributions. This paper presents a flexible end-member modelling algorithm that is based on eigenspace analysis and considers inherent uncertainties. It has been applied to the detrital grain-size components of lacustrine surface sediment samples of Lake Donggi Cona, Qinghai Province, China. It allows up to five grain-size end-members to be characterised and quantified in an optimal model. An end-member with a major mode in the clay domain accounts for 34% of variance within the grain-size data set. It may represent sedimentation of suspension load from linear and laminar runoff during heavy precipitation events in summer. Three end-members in the fine sand to medium silt domains make up 60% of lacustrine sedimentation. They may represent local to remote aeolian processes that peak in wintertime. A multimodal end-member explaining the remaining 6% of variance may represent further fluvial and littoral dynamics or random fluctuations and measurement errors. Several model runs of different scaling and numbers of end-members provided a suitable way to determine uncertainties inherent to the model. A comparison of 12 different model runs and their respective uncertainties yielded a distinct model of robust end-members. The clay and medium silt end-members are robust features of detrital sedimentation within Lake Donggi Cona. They alone explain 54.4% of total variance in the data. However, no spatial pattern or relation to water depth is found for any of the grain-size end-members. Thus, when past detrital sedimentation at Lake Donggi Cona is reconstructed, a special focus should be on the robust features attributed to aeolian and suspension-related sedimentation processes as well as on effective sediment mixing processes impeding a distinct correlation between grain size and spatial attributes. Further applications of the end-member modelling algorithm to other depositional environments are encouraged to demonstrate its universal applicability.