Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton EX20 2SB, UK; Isotope Bioscience laboratory-ISOFYS, Ghent University, Coupure Links 653, 9000 Gent, Belgium.
School of Environmental and Rural Science, University of New England, Armidale, Australia; Soil Science Division, Nepal Agricultural Research Council, Nepal.
Sci Total Environ. 2020 Apr 15;713:136703. doi: 10.1016/j.scitotenv.2020.136703. Epub 2020 Jan 15.
Long-chain saturated fatty acid (LCSFA) isotopic composition in tandem with Bayesian isotope mixing models (BIMM) can provide insight into land use-based sediment sources in catchment systems. Apportioning sediment sources robustly, however, requires careful consideration of how additional factors including topography, surface cover and land use practices interact to influence contributions from individual sources. Prior knowledge can be used in BIMM; however, the full capacity of this functionality has not been thoroughly exploited yet in conjunction with sediment fingerprinting. In response, we propose an approach for applying a state-of-the-art BIMM incorporating a sediment connectivity index (SCI) as an informative prior for sediment source apportionment in a highly hydrodynamic catchment in Nepal. A library of LCSFA carbon isotopic composition was constructed for surface soils collected from mixed forest, upland and lowland terraces in the Kharka micro-catchment. δC values of LCSFA of time-integrated suspended bulk (<2 mm) sediment were depleted by 4‰ compared to the fine (<0.063 mm) sediment fraction. Conventional source apportionment for fine sediment samples without the SCI informative prior suggested that 66% of the sediment is derived from forest soils followed by lowland (19%) and upland (15%) terraces. Incorporation of the SCI as an informative prior in BIMM, however, modified the original source apportionment estimates to 90%, 9% and 1% respectively. The lower contributions from agricultural terraces are explained by landscape complexity comprising small levelled terraces that reduce hillslope-to-channel sediment connectivity. This study demonstrates the sensitivity of BIMM posterior distributions to incorporation of an informative prior based on a SCI. Inclusion of SCI linked to land use and management can provide a more physically-grounded approach to estimating sediment source contributions from biogeochemical tracers, and critically one which generates results better reflecting what makes good environmental sense in the context of land management and visual evidence of sediment mobilisation and delivery.
长链饱和脂肪酸 (LCSFA) 的同位素组成与贝叶斯同位素混合模型 (BIMM) 相结合，可以深入了解流域系统中基于土地利用的沉积物来源。然而，要稳健地分配沉积物来源，需要仔细考虑包括地形、地表覆盖和土地利用方式在内的其他因素如何相互作用，从而影响各个来源的贡献。BIMM 可以利用先验知识；然而，这种功能的全部潜力尚未与沉积物指纹图谱结合得到充分利用。有鉴于此，我们提出了一种方法，用于应用最先进的 BIMM，该模型包含一个沉积物连通性指数 (SCI)，作为尼泊尔一个高水动力流域沉积物源分配的信息先验。为 Kharka 小流域的混合林、高地和低地梯田采集的表层土壤构建了 LCSFA 碳同位素组成库。与细颗粒 (<0.063 毫米) 沉积物相比，时间积分悬浮粗颗粒 (<2 毫米) 沉积物中的 LCSFA 的 δC 值减少了 4‰。没有 SCI 信息先验的细颗粒沉积物样本的常规源分配表明，66%的沉积物来自森林土壤，其次是低地 (19%) 和高地 (15%) 梯田。然而，将 SCI 作为信息先验纳入 BIMM 后，对原始源分配估计值进行了修改，分别为 90%、9%和 1%。来自农业梯田的贡献较低，这是由包括小型平整梯田的景观复杂性造成的，这些梯田减少了山坡到河道的沉积物连通性。本研究证明了 BIMM 后验分布对基于 SCI 的信息先验的敏感性。将与土地利用和管理相关的 SCI 纳入其中，可以为使用生物地球化学示踪剂估计沉积物源贡献提供更具物理基础的方法，并且重要的是，该方法可以生成更能反映土地管理背景下合理环境意义和沉积物迁移和输送的直观证据的结果。
