Abstract
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 language | English |
|---|---|
| Article number | e14540 |
| Journal | Hydrological Processes |
| Volume | 36 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2022 |
Funding
This research has been supported by the Leverhulme Trust (Where rivers, groundwater and disciplines meet: a hyporheic research network), the UK Natural Environment Research Council (grant no. NE/L003872/1), the European Commission, H2020 Marie Sk?odowska-Curie Actions (HiFreq, grant no. 734317), the U.S. Department of Energy (Pacific Northwest National Lab and DE-SC0019377), the National Science Foundation (grant nos. DEB-1440409, EAR-1652293, EAR-1417603, and EAR-1446328), the University of Birmingham (Institute of Advanced Studies), and with resources from the home institutions of the authors. Ward was supported in part by the Fulbright ? University of Birmingham Scholar program. This research was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Environmental System Science (ESS) program through the Critical Interfaces Science Focus Area at Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725. Data and facilities were provided by the H. J. Andrews Experimental Forest and Long Term Ecological Research program, administered cooperatively by the USDA Forest Service Pacific Northwest Research Station, Oregon State University, and the Willamette National Forest. In lieu of detailed author contributions, we report that this study was conceptualized approximately 10 years ago and has benefited tremendously from discussions with a broad group of friends and collaborators. Work on this manuscript was initiated at the slow freshwater science meeting hold in Santa Maria de Palautordera (Catalonia, NE Spain). The authors of this study each made specific contributions to conceptualization, data collection, analysis, and/or writing and revising the manuscript. The primary data analysed are described by Ward et al. (2019a) and available in Ward?(2019). Results of analyses completed in this study are available in Ward?(2021). The authors declare no conflicts of interest. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors. This research has been supported by the Leverhulme Trust (Where rivers, groundwater and disciplines meet: a hyporheic research network), the UK Natural Environment Research Council (grant no. NE/L003872/1), the European Commission, H2020 Marie Sk\u0142odowska\u2010Curie Actions (HiFreq, grant no. 734317), the U.S. Department of Energy (Pacific Northwest National Lab and DE\u2010SC0019377), the National Science Foundation (grant nos. DEB\u20101440409, EAR\u20101652293, EAR\u20101417603, and EAR\u20101446328), the University of Birmingham (Institute of Advanced Studies), and with resources from the home institutions of the authors. Ward was supported in part by the Fulbright \u2013 University of Birmingham Scholar program. This research was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Environmental System Science (ESS) program through the Critical Interfaces Science Focus Area at Oak Ridge National Laboratory (ORNL). ORNL is managed by UT\u2010Battelle, LLC, for the U.S. Department of Energy under Contract DE\u2010AC05\u201000OR22725. Data and facilities were provided by the H. J. Andrews Experimental Forest and Long Term Ecological Research program, administered cooperatively by the USDA Forest Service Pacific Northwest Research Station, Oregon State University, and the Willamette National Forest. In lieu of detailed author contributions, we report that this study was conceptualized approximately 10\u2009years ago and has benefited tremendously from discussions with a broad group of friends and collaborators. Work on this manuscript was initiated at the slow freshwater science meeting hold in Santa Maria de Palautordera (Catalonia, NE Spain). The authors of this study each made specific contributions to conceptualization, data collection, analysis, and/or writing and revising the manuscript. The primary data analysed are described by Ward et al. ( 2019a ) and available in Ward ( 2019 ). Results of analyses completed in this study are available in Ward ( 2021 ). The authors declare no conflicts of interest. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors.
Keywords
- inductive
- machine learning
- river corridor
- scientific method
- stream corridor