Bridging natural and artificial: Stream reach classification for mixed watersheds

Abstract Heavily managed urban streams, agricultural drainages, and least modified natural streams are rarely analyzed within a single framework, even though modern landscapes include a mix of them. Urbanization, agriculture, deforestation, mining, and other intensive land uses have dramatically altered fluvial systems. Most existing stream classification frameworks were developed for natural streams and rely only on natural geomorphic attributes. The few that address artificial streams focus exclusively on engineered (artificial) characteristics and usually address only a single land-use setting. As a result, these frameworks do not capture the full diversity of stream conditions found in mixed land-use watersheds that are influenced by both natural processes and human interventions. Recognizing this problem, our study proposes a joint geomorphic classification framework that collects and analyzes field data that captures both engineering and geomorphic attributes of streams in mixed land-use watersheds. We implemented this joint framework in a prime example of a mixed land-use watershed—the San Francisco Bay Area, California, USA. We collected a dataset for 164 stream sites. By using hierarchical cluster analysis on 92 variables computed from the field data, we identified 12 regional stream types, including six artificial and six natural ones. Our results demonstrate that this joint framework differentiates unique natural and artificial stream types in urban, agricultural, and wilderness settings. It also captures the subtle internal variability within artificial and natural stream types. Field photograph interpretation corroborates statistical findings that the diverse variables effectively represent real-world conditions of both types. Overall, by blending natural characteristics and the degree of human intervention, our classification provides critical insights that enhance our ability to understand river behavior in heavily managed urbanized, agricultural, and wilderness regions. It holds significant promise for informing targeted restoration and conservation strategies, ultimately contributing to more effective river management and sustainable watershed planning.