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| Created: | Jul 09, 2025 at 1:46 a.m. (UTC) | |
| Last updated: | Jul 13, 2025 at 3:23 a.m. (UTC) | |
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Abstract
Canopy interception remains one of the most uncertain components of the hydrometeorological water balance, particularly in land surface models that rely on simplified representations of vegetation structure and storm dynamics. This study benchmarks event-scale interception loss estimates from three widely used operational land surface models (Mosaic, Noah, and VIC) against direct observations from 22 forested sites in the National Ecological Observatory Network. Using 1,787 storm events, we evaluate model performance in predicting interception loss as a total depth (IL) and as a percent (I%) of precipitation amount, with and without a precipitation agreement filter. All three models systematically underestimate IL magnitude and variability across sites, with mean bias values ranging from −6% to −18% and low R², even when rainfall inputs closely match observations. Precipitation amount, wind speed, and potential evaporation emerged as the strongest predictors of IL error, suggesting that model limitations—such as restricted canopy storage and simplified energy balance treatments—limit responsiveness to storm intensity. Errors in I% were more strongly influenced by energy-related variables and showed greater variability. Site-level factors like vegetation class and soil texture contributed minimally. These findings suggest that model performance may improve through a) expanding the meteorological variables used to drive wet-canopy evaporation and b) implementing multi-layer canopy storage schemes to better capture within-grid heterogeneity. Both would represent steps toward a more physically realistic framework for simulating interception in forested environments.
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This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| NASA Shared Services Center | GEDI | 80NSSC21K0198 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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