Big data show idiosyncratic patterns and rates of geomorphic river mobility

Richard J. Boothroyd (), Richard D. Williams, Trevor B. Hoey, Gary J. Brierley, Pamela L. M. Tolentino, Esmael L. Guardian, Juan C. M. O. Reyes, Cathrine J. Sabillo, Laura Quick, John E. G. Perez and Carlos P. C. David
Additional contact information
Richard J. Boothroyd: University of Glasgow
Richard D. Williams: University of Glasgow
Trevor B. Hoey: Brunel University London
Gary J. Brierley: University of Auckland
Pamela L. M. Tolentino: University of Glasgow
Esmael L. Guardian: University of the Philippines
Juan C. M. O. Reyes: University of the Philippines
Cathrine J. Sabillo: University of the Philippines
Laura Quick: University of Glasgow
John E. G. Perez: University of the Philippines
Carlos P. C. David: University of the Philippines

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Big data present unprecedented opportunities to test long-standing theories regarding patterns and rates of geomorphic river adjustments. Here, we use locational probabilities derived from Landsat imagery (1988-2019) to quantify the dynamics of 600 km2 of riverbed in 10 Philippine catchments. Analysis of lateral adjustments reveals spatially non-uniform variability in along-valley patterns of geomorphic river mobility, with zones of relative stability interspersed with zones of relative instability. Hotspots of mobility vary in magnitude, size and location between catchments. We could not identify monotonic relationships between local factors (active channel width, valley floor width and confinement ratio) and mobility. No relation between the channel pattern type and rates of adjustment was evident. We contend that satellite-derived locational probabilities provide a spatially continuous dynamic metric that can help unravel and contextualise forms and rates of geomorphic river adjustment, thereby helping to derive insights into idiosyncrasies of river behaviour in dynamic landscapes.

Date: 2025
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DOI: 10.1038/s41467-025-58427-9

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