Articles | Volume 30, issue 3
https://doi.org/10.5194/hess-30-849-2026
https://doi.org/10.5194/hess-30-849-2026
Research article
 | 
13 Feb 2026
Research article |  | 13 Feb 2026

River intermittency: mapping and upscaling of water occurrence using unmanned aerial vehicle, Random Forest and remote sensing landscape attributes

Nazaré Suziane Soares, Carlos Alexandre Gomes Costa, Till Francke, Christian Mohr, Wolfgang Schwanghart, and Pedro Henrique Augusto Medeiros

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Short summary
We use drone surveys to map river intermittency in reaches and classify them into "Wet", "Transition", "Dry" or "Not Determined". We train Random Forest models with 40 candidate predictors, and select altitude, drainage area, distance from dams and dynamic predictors. We separate different models based on dynamic predictors: satellite indices (a) and (b); or (c) antecedent precipitation index (30 days). Model (a) is the most successful in simulating intermittency both temporally and spatially.
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