Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

Adam S. Ward*, Aaron Packman, Susana Bernal, Nicolai Brekenfeld, Jen Drummond, Emily Graham, David M. Hannah, Megan Klaar, Stefan Krause, Marie Kurz, Angang Li, Anna Lupon, Feng Mao, M. Eugènia Martí Roca, Valerie Ouellet, Todd V. Royer, James C. Stegen, Jay P. Zarnetske

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


A unified conceptual framework for river corridors requires synthesis of diverse site-, method- and discipline-specific findings. The river research community has developed a substantial body of observations and process-specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site-, scale-, and mechanism-specific observations begins the process of synthesis. Machine learning is particularly well-suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively-generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.

Original languageEnglish (US)
Article numbere14540
JournalHydrological Processes
Issue number4
StatePublished - Apr 2022


  • inductive
  • machine learning
  • river corridor
  • scientific method
  • stream corridor

ASJC Scopus subject areas

  • Water Science and Technology


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