A comparison of five forest interception models using global sensitivity and uncertainty analysis

Author(s): Linhoss, A.; Siegert, C.

Interception by the forest canopy plays a critical role in the hydrologic cycle by removing a significant portion of incoming precipitation from the terrestrial component. While there are a number of existing physical models of forest interception, few studies have summarized or compared these models. The objective of this work is to use global uncertainty and sensitivity analysis to compare five mechanistic interception models including the Rutter, Rutter Sparse, Gash, Sparse Gash, and Liu models. Using a one-hour continuous rainfall simulation and input probability distribution functions of values from the literature, our results show that gross precipitation [PG], the free throughfall coefficient [p], canopy cover [Cc], canopy storage capacity [S], and trunk storage capacity [St] are the most important model inputs. On the other hand, the climatic variables that determine evaporation have relatively low levels of importance in modeling interception based on our rainfall simulation scheme. As such, future modeling efforts should aim to breakdown inputs that are the most influential in determining model outputs into easily measurable physical components. Additionally, low, medium, and high one-hour rainfall scenarios were simulated to determine the sensitivity of input parameters to variable rainfall conditions. Under the low rainfall scenario [PG] was the most important parameter across all five models. Under medium and high rainfall scenarios, parameters that described canopy storage ([S] and [St]) and canopy heterogeneity ([p] and [Cc]) became more important. Because this study compares models, the choices regarding the input probability distribution functions are applied across models, which enables a more definitive ranking of model uncertainty.

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