The elemental composition of sediments in rivers is the product of physical and chemical erosion of rocks, which is then transported across drainage networks. A corollary is that fluvial sedimentary geochemistry can be used to understand geologic, climatic, and geomorphic processes. Here, we predict elemental compositions of river sediments using drainage networks extracted from digital elevation data and erosional models. The Geochemical Baseline Survey of the Environment was used to quantify substrate (i.e., source region) chemistry. Sedimentary compositions in rivers downstream are predicted by formally integrating eroding substrates with respect to distance downstream. Different erosional models, including the Stream Power model and uniform incision rates, are tested. Predictions are tested using a new suite of compositions obtained from fine grained (<150 μm) sediments at 67 sites along the Spey, Dee, Don, Deveron, and Tay rivers, Cairngorms, UK. Results show that sedimentary geochemistry can be predicted using simple models that include the topography of drainage networks and substrate compositions as input. The concentration of numerous elements including Magnesium, Rubidium, Uranium, Potassium, Calcium, Strontium, and Beryllium can be accurately predicted using this simple approach. Predictions are insensitive to the choice of erosional model, which we suggest is a consequence of broadly homogeneous rates of erosion throughout the study area. Principal component analysis of the river geochemical data suggests that the composition of most Cairngorms river sediments can be explained by mafic/felsic provenance and conservative mixing downstream. These results suggest that the elemental composition of river sediments can be accurately predicted using simple erosional models and digital elevation data.

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河流沉积物地球化学作为源区的保守混合物：来自英国凯恩戈姆斯的观测和预测

河流中沉积物的元素组成是岩石物理和化学侵蚀的产物，然后通过排水网络传输。一个必然的结论是，河流沉积地球化学可以用来理解地质，气候和地貌过程。在这里，我们使用从数字高程数据和侵蚀模型中提取的排水网络来预测河流沉积物的元素组成。环境的地球化学基准调查用于量化基质（即源区域）的化学成分。下游河流中的沉积物成分是通过相对于下游距离将侵蚀基质正式整合而预测的。测试了不同的侵蚀模型，包括流功率模型和均匀切口速率。使用一套新的组合物对预测进行测试，这些组合物是从英国凯恩戈姆斯河，迪河，唐河，德弗龙河和泰河的67个地点的细颗粒（<150μm）沉积物中获得的。结果表明，可以使用简单的模型来预测沉积地球化学，这些模型包括排水网络的地形和底物成分作为输入。使用这种简单的方法可以准确地预测包括镁，Rub，铀，钾，钙，锶和铍在内的多种元素的浓度。预测对侵蚀模型的选择不敏感，我们认为这是整个研究区域侵蚀率大致均匀的结果。河流地球化学数据的主成分分析表明，大多数凯恩戈姆斯河沉积物的成分可以用镁铁质/长英质物源和下游的保守混合来解释。这些结果表明，使用简单的侵蚀模型和数字高程数据可以准确地预测河流沉积物的元素组成。