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Published on: April 19, 2018
Particle-size-coupled tracing (PSCT): A source-size classification framework for sediment source apportionment under
Lingshan Ni1, Haobang Niu2, Jintian Zhang2
1State Key Laboratory of Soil and Water Conservation and Desertification Control, College of Soil and Water Conservation Sciences and Engineering (Institute of Soil and Water Conservation), Northwest A&F University, 26 Xinong Road, Yangling, Shaanxi Province, 712100, PR China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xinong Road, Yangling, Shaanxi Province, 712100, PR China.
None:
Reliable apportionment of sediment sources is an important prerequisite for understanding sediment dynamics and can support future assessment of sediment-associated non-point source pollution and catchment water-quality management. However, hydrodynamic particle sorting during erosion and transport disrupts tracer conservativeness, thereby reducing sediment source fingerprinting accuracy. To address this issue, we developed and evaluated a particle-size-coupled tracing (PSCT) framework that explicitly embeds particle-size classes into source classification to reconstruct source-sediment comparability under sorting stress. Using a representative headwater catchment on the Loess Plateau, we evaluated the PSCT framework with four-class (FC) and six-class (SC) schemes via artificial mixtures and rainfall simulations with known source contributions. Its performance was compared against the traditional classification (TC) scheme using geochemical elements (GE) and mid-infrared spectroscopy (MIR) as tracers. Results indicated that the TC scheme failed to account for source particle-size heterogeneity, yielding mean absolute errors (MAEs) of 29.3% for GE and 18.4% for MIR in artificial mixtures. In contrast, the PSCT framework substantially reduced sorting-induced bias, with the SC scheme performing best. It reduced MAEs to 6.8% for GE and 4.5% for MIR in artificial mixtures. Under rainfall simulations, the SC scheme maintained average MAE values below 10%, indicating strong robustness under near-natural hydrodynamic conditions. These findings demonstrate that explicitly coupling particle size with source classification effectively enhances tracer conservativeness, reduces sorting-induced bias, and improves sediment source apportionment reliability under dynamic erosion and transport conditions. The proposed framework provides a mechanistically grounded basis for more reliable sediment source apportionment and may support future applications aimed at quantifying sediment-associated pollutant pathways.
