Given the limited resources available for managing erosion hazards and addressing water quality impairment along rivers, stakeholders engaged in water resource management would benefit from tools to identify those river reaches most prone to adjustment and which disproportionately load sediment to receiving waters. The extent and rate of vertical and lateral channel adjustments in response to natural and human disturbances vary considerably across space and time; and this complexity and nonlinearity introduce challenges for classification or modeling of river reaches using conventional statistical techniques. The Self-Organizing Map (SOM) is a data-driven computational tool with advantages for clustering or classifying multivariate observations and for exploratory data analysis and visualization of complex, nonlinear systems. We applied a SOM to cluster multivariate stream geomorphic assessment data into reach-scale sediment process domains for 193 river reaches in glacially-conditioned catchments of northeastern US using field- and GIS-derived hydraulic and geomorphic parameters. The reaches comprised a range of channel types from confined to unconfined, steep- to shallow-gradient, mid-to-high order, and bedrock to alluvial channels. Fifteen variables were identified that meaningfully separated reaches into seven sediment regimes, following a two-stage application of the SOM. A coarse-tune SOM identified sediment regime classes at the supply-limited and transport-limited extremes of a continuum, including bedrock channels and confined, steep-gradient reaches as well as braided, depositional channels at alluvial fan or delta settings. A second-stage, fine-tune SOM nuanced differences in sediment production and transport for unconfined reaches with varying degrees of floodplain disconnection resulting from natural or human stressors. This classification framework is transferable to other hydroclimatic regions, with consideration of additional or alternate independent variables unique to those regions, and can provide valuable insights for river management to promote flood resiliency, restore water quality and improve instream and riparian habitats.

鉴于可用于管理河流侵蚀危害和解决水质损害的资源有限，从事水资源管理的利益相关者将受益于识别那些最容易调整的河流以及将沉积物过多地装载到接受水域的工具。响应自然和人为干扰，垂直和横向通道调整的程度和速率在空间和时间上有很大差异；这种复杂性和非线性为使用常规统计技术对河段进行分类或建模提出了挑战。自组织图（SOM）是一种数据驱动的计算工具，具有对多变量观测值进行聚类或分类以及对复杂的非线性系统进行探索性数据分析和可视化的优势。我们使用现场和GIS得出的水力和地貌参数，将SOM应用于将多变量河流地貌评估数据聚类到美国东北部冰川环境流域193个河段的河床规模沉积物过程域中。这些河段包括各种类型的河道，从受限到无限制，从陡坡到浅坡，从中到高阶，从基岩到冲积河道。在SOM的两个阶段应用之后，确定了15个变量，这些变量有意义地分离为7个沉积物区域。粗略的SOM在连续体的供应受限和运输受限的极端情况下确定了沉积物类型，包括基岩河道和狭窄的陡坡河段以及冲积扇或三角洲设置的辫状沉积河道。第二阶段 在自然界或人为压力源导致洪泛平原断开程度不同的情况下，微调SOM对无限制河段的沉积物生产和输运的细微差别。该分类框架可以转移到其他水文气候地区，同时考虑到这些地区特有的其他或替代的独立变量，并且可以为河流管理提供宝贵的见解，以提高洪水抗灾力，恢复水质并改善河流和河岸生境。