Nosrati Kazem, Ghasemi Mohammadfarid, Fiener Peter
Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, 1983969411 Tehran, Iran; Water and Soil Resources Research, Institute of Geography, Augsburg University, Germany.
Department of Sedimentary Basins and Petroleum, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran.
Sci Total Environ. 2024 Oct 10;946:174413. doi: 10.1016/j.scitotenv.2024.174413. Epub 2024 Jul 1.
Understanding the origins of sediment within stream networks is critical to developing effective strategies to mitigate sediment delivery and soil erosion in larger drainage basins. Sediment fingerprinting is a widely accepted approach to identifying sediment sources; however, it typically relies on labor-intensive and costly chemical analyses. Recent studies have recognized diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a non-destructive, cost-effective, and efficient alternative for estimating sediment contributions from multiple sources. This study aimed to assess (i) the effects of different particle size fractions on DRIFTS and conservatism tests, (ii) the effects of spectral pre-processing on discriminating sub-catchment spatial sediment sources, (iii) the efficiency of partial least squares regression (PLSR) and support vector machine regression (SVMR) chemometric models across different spectral resolutions and particle size fractions, and (iv) the quantification of sub-catchment spatial sediment source contributions using chemometric models across different particle size fractions. DRIFTS analysis was performed on three particle size fractions (<38 μm, 38-63 μm, and 63-125 μm) using 54 sediment samples from three different sub-catchments and 26 target sediment samples from the Andajrood catchment in Iran. Results showed significant effects of particle size fractions on DRIFTS for both sub-catchment sediment sources and target sediment samples. Conservatism tests indicated that DRIFTS behave conservative for the majority of target sediment samples. Spectral pre-processing techniques including SNV + SGD1 and SGD1 effectively discriminated sources across all particle size fractions and spectral resolutions. However, the optimal combination of pre-processing, spectral resolution, and regression models varied between sub-fractions. Validated model estimates revealed that sub-catchment 1 consistently contributed the most sediment across all particle size fractions, followed by sub-catchments 3 and 2. These results highlight the effectiveness of DRIFTS as a rapid, cost-effective, and precise method for discriminating and apportioning sediment sources within spatial sub-catchments.
