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

JOURNAL OF HYDROLOGY

卷:538

Article

Linhoss, Anna C.¹  Siegert, Courtney M.² 

[1] Mississippi State Univ, Dept Agr & Biol Engn, Starkville, MS 39762 USA

[2] Mississippi State Univ, Dept Forestry, Starkville, MS 39762 USA

关键词: Interception;    Sensitivity analysis;    Uncertainty analysis;    Canopy storage capacity;   

DOI  :  10.1016/j.jhydrol.2016.04.011

来源: Elsevier

【 摘 要 】

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 sensitivity and uncertainty analysis to compare five mechanistic interception models including the Rutter, Rutter Sparse, Gash, Sparse Gash, and Liu models. Using parameter probability distribution functions of values from the literature, our results show that on average storm duration [Dur], gross precipitation [PG], canopy storage [S] and solar radiation [Rn] are the most important model parameters. On the other hand, empirical parameters used in calculating evaporation and drip (i.e. trunk evaporation as a proportion of evaporation from the saturated canopy [e], the empirical drainage parameter [b], the drainage partitioning coefficient [pd], and the rate of water dripping from the canopy when canopy storage has been reached [Ds]) have relatively low levels of importance in interception modeling. As such, future modeling efforts should aim to decompose parameters that are the most influential in determining model outputs into easily measurable physical components. Because this study compares models, the choices regarding the parameter probability distribution functions are applied across models, which enables a more definitive ranking of model uncertainty. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free