Analysis of reach-scale sediment process domains in glacially-conditioned catchments using self-organizing maps

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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Given limited resources for managing erosion hazards and water quality along rivers, stakeholders in water resource management could benefit from tools to identify river reaches prone to sediment loading. The Self-Organizing Map (SOM) is a useful tool for clustering multivariate observations and analyzing complex, nonlinear river systems. Through multiple stages of SOM application, we identified seven sediment regimes in river reaches based on stream geomorphic assessment data.