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

Abstract

Model optimization is essential for accurately simulating rainfall interception loss (I) in artificial forests and for quantifying the influence of canopy structure, rainfall regime, and meteorological variables on I. Herein, the revised Gash (addressing physical mechanisms) and Wang (addressing canopy structural impacts) models were used for comparative I simulations using a larch plantation in NW China. We used throughfall (Tf) and stemflow (Sf) measurements from the growing seasons of three consecutive years and found that from the total rainfall measured (1371.6 mm) in 59 selected rain events, Tf, Sf, and I accounted for 80.8%, 1.6%, and 17.6%, respectively. Surprisingly, the two tested models showed similar behaviours in their I simulations, with their performances classified as “very good,” exhibiting 5% and 2.3% cumulative I relative errors for the revised Gash and Wang models, respectively. We noted that, although I was well simulated for light–moderate rain events (<25 mm), absolute errors increased significantly as rainfall amount and duration increased, owing to the constant evaporation rate applied in the models. Leaf area index (LAI) explained 40% of the water detention variation in the canopy, rather than I, which only reflected canopy storage capacity dynamics during the growing season. Collectively, our results revealed that models only addressing the independent impact of wet evaporation or structural dynamics are inadequate for accurately simulating I during heavy rain events (>25 mm). Therefore, future models should account for the potential errors arising from evaporation estimation and consider the effects of LAI.

Dynamics of the Net Precipitation in China from 2001 to 2020

“…Excluding the scale gap, the saturation phenomenon of the remote sensing-based vegetation index also leads to inaccuracies in estimating the vegetation growth status in densely vegetated areas [71,72]. Additionally, site-based studies underscore the importance of various canopy structure variables, including leaf shape, canopy thickness, size, and forms, in influencing rainfall redistribution processes [9,73,74]. Neglecting these structural properties of vegetation could potentially limit the accuracy of contributors for regional NP [17,39,73].…”

Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model

“…From Southeast to Northwest, precipitation significantly decreased with the effect of the monsoon climate [57]. In addition, the vegetation type changed from tropical or subtropical forest to temperate steppe or temperate desert (Figure 1), which are characterized by a sparser canopy structure that can retain less rainwater [58]. A similar spatial pattern was also detected in the ratio of interception to precipitation (Figure 3b), except for the cold temperate coniferous forest, which exhibited a relatively higher interception ratio but lower canopy interception.…”

“…Such increases might facilitate the growth of vegetation canopy and enhance canopy interception of rainwater [70]. Although the NDVI could be used as a good indicator of regional vegetation changes, many other canopy structure variables still influence the rainfall interception process, including leaf shape, canopy thickness, canopy surface area, and canopy forms [11,52,58,71]. Ignoring these variables would limit the accuracy of the model results for regional canopy interception, therefore they should be considered when modeling regional canopy interception in future studies.…”