Parameterisation of aerodynamic roughness over boreal, cool- and warm-temperate forests

Abstract

1Roughness length and zero-plane displacement over boreal, cool-and warm-temperate 2 forests were observed and parameterised using forest structure data. Previous mod-3 els for roughness length and zero-plane displacement using leaf area index and 4 frontal area index did not describe intersite differences, and the model for zero-5 plane displacement did not express seasonal variations with the change of leaf area 6 that was smaller in dense forest than in sparse forest. The observed results show 7 that intersite differences of normalised zero-plane displacement were related to stand 8 density, and seasonal variations were related to leaf area index at each forest, with 9 the degree depending on stand density. From these observations, a new concept is 10 proposed for normalised zero-plane displacement: the basal part is primarily de-11 termined by the density of stems and branches (stand density), while the seasonal 12 variation depends on the density of leaves (leaf area index), which is limited to

Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

“…Additional studies [43,44,47] have indicated that vegetation vertical parameters (i.e., the height and FAI) are critical for estimates of d and z 0m obtained via remote sensing-based roughness models [42][43][44]. However, few studies have focused on the long term parameterization scheme of d and z 0m while considering the variation of vertical vegetation information over time.…”

“…Here, dynamic forest vertical information for height and the FAI were obtained from field calibrated regression models and interannual forest AGB variations in order to drive the SD model. Instead of the arithmetical mean height, the Lorey's height was derived due to the fact that it is a better expression for forest stand status, as well as for d and z 0m [44,47]. Although the R 2 values (0.52 and 0.36) of both fittings were not high, the RMSEs (2.35 m and 0.35 were relatively low.…”

“…For d and z 0m , experimental methods based on measurements of turbulent fluxes can provide estimates but are only locally valid and cannot be scaled to larger areas. Alternatively, remotely sensed methods have been developed for these parameters using the functions of vegetation structural parameters (i.e., LAI, [41], frontal area index (FAI) [42], both LAI and FAI [43], stand density (SD) (trees¨ha´1), and stem-branch-leaf distributions [44]). For the kB´1 model, some studies have improved parameterization [38,45,46].…”

Seasonal, Diurnal and Wind-Direction-Dependent Variations of the Aerodynamic Roughness Length in Two Typical Forest Ecosystems of China

“…Therefore, the relationship between z om and the LAI needs further study. For forests, z om /h is a function not only of the LAI, but also of a density parameter, such as the frontal area index (Schaudt and Dickinson 2000) or stand density (Nakai et al 2008a). However, data on the frontal area index and stand density were not easy to obtain thus far.…”

“…Van Dijk et al (2004) found that z om /h is 0.03 for a rain-fed cropping system with maize and cassava rotations. Some researchers have parameterized z om as a function of h, frontal area index or leaf area index (LAI) (Schaudt and Dickinson 2000;Nakai et al 2008a). However, empirical estimates of z om are still used in many models.…”