Meteorological and Vegetation Structure Controls on Liquid Throughfall in US Forests and Shrublands

Abstract Throughfall is the dominant input of water to most terrestrial ecosystems and is primarily driven by precipitation quantity, although the relationship varies among sites. A wide range of meteorological and site‐based properties also influence throughfall and may explain this variability, but their importance for accurately predicting throughfall quantities across differing sites remains unknown. Here I develop models to predict daily throughfall quantities at ∼1 m 2 resolution based on up to 19 environmental parameters using multi‐year data from sites throughout the US. Three random forest models of varying complexity were trained to predict throughfall: a simple model (RF‐1) driven solely by precipitation quantity, and more complex models that incorporated an additional eight (RF‐9) and eighteen (RF‐19) variables. RF‐1 was able to predict throughfall quantities (±28%) and accuracy was modestly improved by including additional model parameters (±24–26%). Improvements in model performance were most apparent for smaller precipitation events (<10 mm), which are less likely to fully saturate the canopy (22% improvement in prediction accuracy for the RF‐19 model). Precipitation quantity, maximum intensity, and duration were consistently identified as the most important drivers of throughfall, whereas variables relating to evaporative potential and canopy water storage capacity were identified as moderately important. These models allow the impacts of environmental changes (e.g., forest regrowth after clearcutting or increased precipitation intensity) to be evaluated, as well as inform decisions about which parameters to include in field‐ and model‐based studies of throughfall and its converse, interception, when resources are limited.