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<p>Reservoir sedimentation in arid, high-sediment basins reflects coupled fluvial transport and shoreline aeolian reworking, yet the relative contributions of upstream versus shoreline sources remain poorly constrained. Using Danghe Reservoir in the Hexi Corridor, northwestern China, we developed a dual-pathway, dual-fingerprint, three-endmember framework to apportion inlet deposits among upstream bed load, upstream suspended load, and reservoir-bank aeolian sand. We sampled 16 along-channel and 6 shoreline sites and combined grain-size distributions with nine-element EDXRF fingerprints. Grain-size mixing was solved by NNLS fitting within a shared size window, and geochemical mixing was performed using CLR-transformed, covariance-weighted NNLS. Bed sediment D50 ranged from 37 to 364&#xa0;μm, with a median of 147&#xa0;μm, whereas suspended sediment D<sub>50</sub> ranged from 7.07 to 81.63&#xa0;μm, with a median of 10.02&#xa0;μm. Shoreline aeolian sand showed D<sub>50</sub> values of approximately 86–217&#xa0;μm across banks. The deterministic NNLS solution gave central estimates of 26% aeolian input and 74% upstream-derived material, with the upstream component further resolved into 51% bed-load and 23% suspended-load contributions. Bootstrap-based uncertainty propagation and sensitivity analyses were used to account for within-source variability, endmember overlap, and non-uniqueness in the mixing solution. The revised apportionment is therefore reported as central estimates with 95% uncertainty intervals: aeolian sand 0.26 [0.00–0.44], upstream bed load 0.51 [0.00–0.74], and upstream suspended load 0.23 [0.00–0.74]. These results indicate that upstream-derived material is the dominant source group, while the internal partitioning between bed-load and suspended-load contributions remains uncertain because of partial tracer-space overlap. The proposed grain-size-prioritized and geochemically calibrated framework provides a quantitative basis for source-focused mitigation in arid, high-sediment reservoirs.</p>

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Grain-size-prioritized and geochemically calibrated fingerprinting of sediment sources in a high-sediment reservoir of the Hexi Corridor

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Hao Wang,
  • Yu Wang,
  • Miao Tian,
  • Ying Zhang,
  • Kaiqing Liu,
  • Tian-feng Luo,
  • Weilong Ren,
  • Ejia Qinni

摘要

Reservoir sedimentation in arid, high-sediment basins reflects coupled fluvial transport and shoreline aeolian reworking, yet the relative contributions of upstream versus shoreline sources remain poorly constrained. Using Danghe Reservoir in the Hexi Corridor, northwestern China, we developed a dual-pathway, dual-fingerprint, three-endmember framework to apportion inlet deposits among upstream bed load, upstream suspended load, and reservoir-bank aeolian sand. We sampled 16 along-channel and 6 shoreline sites and combined grain-size distributions with nine-element EDXRF fingerprints. Grain-size mixing was solved by NNLS fitting within a shared size window, and geochemical mixing was performed using CLR-transformed, covariance-weighted NNLS. Bed sediment D50 ranged from 37 to 364 μm, with a median of 147 μm, whereas suspended sediment D50 ranged from 7.07 to 81.63 μm, with a median of 10.02 μm. Shoreline aeolian sand showed D50 values of approximately 86–217 μm across banks. The deterministic NNLS solution gave central estimates of 26% aeolian input and 74% upstream-derived material, with the upstream component further resolved into 51% bed-load and 23% suspended-load contributions. Bootstrap-based uncertainty propagation and sensitivity analyses were used to account for within-source variability, endmember overlap, and non-uniqueness in the mixing solution. The revised apportionment is therefore reported as central estimates with 95% uncertainty intervals: aeolian sand 0.26 [0.00–0.44], upstream bed load 0.51 [0.00–0.74], and upstream suspended load 0.23 [0.00–0.74]. These results indicate that upstream-derived material is the dominant source group, while the internal partitioning between bed-load and suspended-load contributions remains uncertain because of partial tracer-space overlap. The proposed grain-size-prioritized and geochemically calibrated framework provides a quantitative basis for source-focused mitigation in arid, high-sediment reservoirs.