水土保持学报2026，Vol.40Issue(1)：67-77,11.DOI:10.13870/j.cnki.stbcxb.2026.01.015
多种复合指纹法示踪高寒区流域风水作用产沙来源
Tracing Sediment Sources from Hydrological and Aeolian Processes in Alpine Watersheds Using Multiple Composite Fingerprint Methods
摘要
Abstract
[Objective]To accurately trace sediment sources of water conservancy facilities such as rivers and reservoirs in alpine regions under combined wind-water erosion environments by using multiple composite fingerprint methods.[Methods]In the Shagou River Basin,a tributary of the Longyangxia Reservoir on the Yellow River,soil samples were collected from aeolian sand and fluvial sediment source areas,along with fresh sediment samples at the basin outlet.Forty elemental fingerprint factors were analyzed using X-ray fluorescence spectroscopy.Three fingerprint methods were used to analyze sediment sources,including the multi-group fingerprint factor method,the machine learning optimal composite fingerprint method,and the Walling⋅C optimal composite fingerprint method.[Results]For fingerprint factor screening,the CT-KW-DFA method achieved a cumulative discrimination rate of 82.40%using discriminant function analysis(DFA),while the CT-RF-DFA method reached 100%,demonstrating a 17.60%improvement in discrimination capacity over the CT-KW-DFA method.The CT-RF-DFA method better distinguished sediment source regions.The multi-group fingerprint factor method indicated that aeolian sediment contributed 53.40%while fluvial sediment contributed 46.60%.The machine learning optimal composite fingerprint method revealed that aeolian sediment contributed 63.00%,and fluvial sediment contributed 37.00%.The Walling⋅C optimal composite fingerprint method revealed that aeolian sediment contributed 50.11%and fluvial sediment contributed 49.89%.The average contribution rates across the three methods were 55.50%for aeolian sediment and 44.50%for fluvial sediment.The sediment sources revealed by the multi-group fingerprint factor method were closest to the average of the three methods.In the machine learning optimal composite fingerprint method,the Bayesian model demonstrated good convergence and excellent fitting performance.In the Walling⋅C optimal composite fingerprint method,the goodness-of-fit of the Walling⋅C multivariate mixing model was 94.50%.[Conclusion]The computational processes of all three composite fingerprint methods perform well in tracing sediment sources in alpine river regions.All three methods indicate that in the Shagou River Basin,aeolian processes contribute a higher proportion of sediment than fluvial processes.The combined effects of seasonal aeolian activities and changes in river ice conditions are the dominant factors controlling sediment transport.This study is important for revealing sediment sources under combined wind-water erosion in alpine regions,and provides technical support for the erosion prevention and control of water conservancy facilities such as rivers and reservoirs in alpine regions.关键词
泥沙来源/复合指纹法/风水侵蚀区/青藏高原Key words
sediment sources/composite fingerprinting method/wind-water erosion area/Qinghai-Xizang Plateau分类
农业科技引用本文复制引用
穆开放,方海燕,陈琼,周强,柳本立,牛百成..多种复合指纹法示踪高寒区流域风水作用产沙来源[J].水土保持学报,2026,40(1):67-77,11.基金项目
国家自然科学基金项目(42107372,42330502) （42107372,42330502）
青海省基础研究计划项目(2022-ZJ-942Q) （2022-ZJ-942Q）
