Characteristics of canopy interception and its simulation with a revised Gash model for a larch plantation in the Liupan Mountains, China

Integrated effects of rainfall regime and canopy structure on interception loss: A comparative modelling analysis for an artificial larch forest

“…This result was in good agreement with the results for a deciduous Robinia pseudoacacia forest (17.6%) reported by Ma et al (2019). Although the result also agrees well with the findings from another larch stand of different tree structures in the south of the Liupan Mountains (17.4%) (Liu et al, 2017), this could have been the result of the offset of lower T f and higher S f compared to the stand in this study (see below). For the intergrowing seasons, the I values were 58.9 mm (20.6% of I / P G ) for 2011, 82.1 mm (14.9% of I / P G ) for 2012, and 100.2 mm (18.7% of I / P G ) for 2013.…”

“…On each measurement day, one above‐canopy measurement and 15 below‐canopy measurements were taken at fixed points close to sunrise to ensure near‐uniform sky illumination, with the process repeated in triplicate to reduce measurement errors. The daily plot LAI was estimated through interpolation, using a fitted quadratic polynomial function for LAI on the day of the year (Liu et al, 2017; Tian et al, 2019; Xiong et al, 2015).…”

“…A canopy interception model is an effective tool for understanding, describing, estimating, and predicting forest interception, which allows experimental results to be extrapolated, both in time and space, and provides insights into the interception process (Liu et al, 2017; Muzylo et al, 2009). Of the variety of existing forest interception models (empirical, probabilistic, etc.…”

“…In one study, the I of a larch plantation in the southern Liupan Mountains (which experience a semi‐humid climate) was modelled using the revised Gash model, with the results indicating that the model was satisfactory for simulating total I , but was less accurate for certain individual events within the growing season (Liu et al, 2017). However, for the northern Liupan Mountains, where there is a semi‐arid climate, it remains unknown whether the model would be sufficiently flexible to simulate I for a larch plantation with a sparser canopy (i.e., trees that exhibit smaller stem diameter at breast height [DBH], tree height, and crown statistics), compared with the above stand.…”

The effects of tree characteristics on rainfall interception in urban areas

“…This indicates the importance of the spatial distribution of a tree canopy when estimating its total rainfall interception. Traditionally, rainfall interception by a tree canopy is estimated by using the average LAI of the whole tree canopy (Fathizadeh et al 2018;Liu et al 2018), even though the saturation of certain leaves or a canopy section can affect spatial and total rainfall interception rates. By using the recently developed TLS and point cloud techniques (Hosoi and Omasa 2006), it is possible to estimate the spatial distribution of LAI in a tree canopy and the effect of this on urban ecology at a finer scale.…”