Raigani Zeinab Mohammadi, Nosrati Kazem, Collins Adrian L
Department of Physical Geography, School of Earth Sciences, Shahid Beheshti University, 1983969411, Tehran, Iran.
Sustainable Agriculture Sciences Department, Rothamsted Research, North Wyke, Okehampton, EX20 2SB, UK.
J Hydrol Reg Stud. 2019 Aug;24:100613. doi: 10.1016/j.ejrh.2019.100613.
The Kamish River catchment (308 km); a mountainous agricultural catchment under dry-land and rangeland farming located in Kermanshah province, in western Iran.
The main objective of this study was to apportion sub-basin spatial source relative contributions to target channel bed sediment samples using a composite fingerprinting procedure including a Bayesian un-mixing model. In total, thirty-four geochemical tracers, eleven elemental ratios and different weathering indices were measured or estimated for 43 tributary sediment samples collected to characterise three sub-basin spatial sediment sources and eleven target bed sediment samples collected at the outlet of the main basin. Statistical analysis was used to select three different composite signatures.
Using a composite signature based on KW-H and DFA, the respective relative contributions (with uncertainty ranges) from tributary sub-basins 1, 2 and 3 were estimated as 54.3% (47.8-62.0), 11.4% (4.2-18.7) and 34.3% (27.6-39.9), compared to 72.0% (61.6-82.7), 13.6% (9.0-18.5) and 14.2% (3.1-25.4) using a combination of KW-H and data mining, and 50.8% (42.8-59.9), 28.7% (20.2-37.3) and 20.3% (12.7-27.2) using a fingerprint selected by KW-H and PCCA. The root mean square difference between these source estimates highlighted sensitivity to the composite signatures. Evaluation of the un-mixing model predictions using virtual mixture tests confirmed agreement between modelled and known source proportions.
卡米什河流域（308平方千米），位于伊朗西部克尔曼沙阿省，是一个以旱地和牧场养殖为主的山区农业流域。
本研究的主要目标是采用包括贝叶斯解混模型在内的综合指纹识别程序，确定子流域空间源对目标河道床沉积物样本的相对贡献。总共对采集的43个支流沉积物样本测量或估算了34种地球化学示踪剂、11种元素比率和不同的风化指数，以表征三个子流域空间沉积物来源，并对在主流域出口采集的11个目标河床沉积物样本进行了表征。采用统计分析来选择三种不同的综合特征。
基于KW-H和DFA的综合特征，支流子流域1、2和3各自的相对贡献（含不确定性范围）估计为54.3%（47.8 - 62.0）、11.4%（4.2 - 18.7）和34.3%（27.6 - 39.9）；相比之下，使用KW-H和数据挖掘相结合的方法得出的结果为72.0%（61.6 - 82.7）、13.6%（9.0 - 18.5）和14.2%（3.1 - 25.4）；使用KW-H和PCCA选择的指纹得出的结果为50.8%（42.8 - 59.9）、28.7%（20.2 - 37.3）和20.3%（12.7 - 2）。这些源估计之间的均方根差异突出了对综合特征的敏感性。使用虚拟混合测试对解混模型预测进行评估，证实了模型与已知源比例之间的一致性。
