多种复合指纹法示踪高寒区流域风水作用产沙来源OA
Tracing Sediment Sources from Hydrological and Aeolian Processes in Alpine Watersheds Using Multiple Composite Fingerprint Methods
[目的]为运用多种复合指纹法精准示踪高寒区河流、水库等水利设施在风、水复合侵蚀环境下的泥沙来源.[方法]在黄河龙羊峡水库支流沙沟河流域,采集风沙和水沙泥沙来源区的土壤样本,以及流域出口的新鲜泥沙样本.利用X射线荧光光谱仪测试40种元素指纹因子,采用多组指纹因子法、机器学习最佳复合指纹法、Walling⋅C最佳复合指纹法3种指纹法解析泥沙来源.[结果]在指纹因子筛选方面,CT-KW-DFA指纹因子筛选方法的DFA累计判别率为82.40%,CT-RF-DFA指纹因子筛选方法DFA累计判别率为100%,CT-RF-DFA指纹因子筛选方法较CT-KW-DFA指纹因子筛选方法累计判别率提高17.60%,后者能更好地区分泥沙源区.多组指纹因子法显示风沙贡献为53.40%,水沙贡献为46.60%.机器学习最佳复合指纹法显示风沙贡献为 63.00%,水沙贡献为 37.00%.Walling⋅C最佳复合指纹法显示风沙贡献为50.11%,水沙贡献为49.89%.3种方法风沙、水沙贡献率平均值分别为55.50%、44.50%.多组指纹因子法揭示的泥沙来源最接近3种方法的平均值,机器学习最佳复合指纹法中贝叶斯模型计算结果收敛性良好、拟合度优异,Walling⋅C最佳复合指纹法中Walling⋅C多元混合模型拟合优度为94.50%.[结论]3种复合指纹法示踪高寒区河流泥沙来源的计算过程表现良好.3种方法均表明,沙沟河风力作用产沙贡献率高于水力作用产沙,季节性风沙活动与河面冰情变化对泥沙输移的共同作用是主导因素.研究结果对揭示高寒区域风水复合侵蚀泥沙来源具有重要作用,可为高寒区河流、水库等水利设施的侵蚀防治提供技术支撑.
[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.
穆开放;方海燕;陈琼;周强;柳本立;牛百成
青海师范大学地理科学学院,西宁 810008||青海省自然地理与环境过程重点实验室,西宁 810008中国科学院地理科学与资源研究所陆地水循环及地表过程重点实验室,北京 100101青海师范大学地理科学学院,西宁 810008||青海省自然地理与环境过程重点实验室,西宁 810008||青海师范大学国家安全与应急管理学院,西宁 810008青海师范大学地理科学学院,西宁 810008||青海省自然地理与环境过程重点实验室,西宁 810008||青海师范大学国家安全与应急管理学院,西宁 810008中国科学院西北生态环境资源研究院干旱区生态安全与可持续发展全国重点实验室,敦煌戈壁荒漠生态与环境研究站,兰州 730000青海师范大学地理科学学院,西宁 810008||青海省自然地理与环境过程重点实验室,西宁 810008||中国科学院、水利部成都山地灾害与环境研究所山地灾害与地表过程重点实验室,成都 610041
农业科技
泥沙来源复合指纹法风水侵蚀区青藏高原
sediment sourcescomposite fingerprinting methodwind-water erosion areaQinghai-Xizang Plateau
《水土保持学报》 2026 (1)
67-77,11
国家自然科学基金项目(42107372,42330502)青海省基础研究计划项目(2022-ZJ-942Q)
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