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

Abstract. Although intermittent rivers exist naturally, global changes have a direct influence on streamflow permanence. Measurements and modelling in temporary rivers are still scarce and yet, essential for prediction and understanding of scarcity scenarios. Thus, this work aims to map and model the spatio-temporal dynamics of an intermittent river. The study area is the Umbuzeiro River in the Brazilian Semiarid (∼100 km), whose spatially coherent streamflow occurs exclusively in the wettest months during the rainy season. We conducted twelve UAV surveys between March and November 2022 in selected river reaches. With the imagery from UAV surveys, we classified river reaches into "Wet", "Transition", "Dry" or "Not Determined" with visual inspection of 1.0 m reaches. In order to explain the observed patterns, we analysed 40 candidate predictors based on static and dynamic landscape attributes. Among these, altitude, drainage area, distance from dams, and dynamic predictors proved to be most informative in Random Forest models. We selected three Random Forest models based on the different dynamic predictors. The models differ in the source and type of dynamic predictor used to capture the temporal dynamics: (a) series of Sentinel MNDWI; (b) series of Planetscope NDVI; and (c) accumulated precipitation (30 days). All model variants successfully mimicked river intermittency with an accuracy of around 80 % for both test and training. Models (a) and (b) captured the temporal dynamics in model extrapolation to the whole river. When analysing the spatial distribution of intermittency, models (a) and (c) better identified areas more prone to "Wet" or "Transition" classes. This way, model (a) was identified as the most successful in simulating intermittency both temporally and spatially. The use of Sentinel MNDWI in model (a) aggregates enough spatial information, so the model can better simulate water occurrence classes. The findings presented here emphasize the possibility of using this index even in narrow temporary rivers. The results provide insight into the hydrological diversity of semi-arid rivers and are, therefore, important to understand their role in water availability.