Modelling of rainfall partitioning by a deciduous shrub using a variable parameters Gash model

Abstract

Alpine deciduous shrubs play an important role in local water cycle for ecosystems of Qinghai‐Tibet Plateau, but rainfall partitioning of the deciduous shrubs has not been fully understood due to significant variation in spatial coverage and leaf storage capacity, especially the lack of appropriate model to describe relevant processes. To assess these processes, field experiments were performed to determine vegetation and atmospheric parameters for the deciduous Potentilla fruticosa shrub in the growing season of 2012. Based on the experimental data, a variable parameters Gash model specifically for deciduous shrub rainfall partitioning was adapted from the revised Gash model to simulate rainfall partitioning. Results showed that the interception, throughfall, and stemflow accounted for 21.44%, 29.26%, and 49.30% of total rainfall in the shrub patches, respectively. Nearly half of interception (46.1%) was lost through evaporation from saturated leaf canopy during rainfall events. The performance of the original model on deciduous shrub canopy was improved by utilizing a variable parameters Gash model that adopted event‐based changeable meteorological and canopy parameters. The rainfall partitioning pattern in the shrub patch was very sensitive to changes in canopy coverage, evaporation, and rainfall intensity, in which great varieties can add large potential errors to the revised Gash model simulations. To accurately model rainfall partitioning, it is reasonable to adopt variable parameters.

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

“…This indicates that LAI was capable of reflecting the dynamics of S rather than I , with a dominant component of wet evaporation during the growing season. The results of this study therefore suggested that LAI was a more effective canopy parameter than C in terms of mirroring the dynamics of S , owing largely in part to the convenience with which it could be measured; and it was also closely associated with bio‐physical processes in the canopy, such as leaf growth and water retention (Deguchi et al, 2006; Návar, 2019a; Zhang et al, 2018).…”

“…For the Wang model, the simulated I error was relatively low for small rain events because of a lower proportion of wet evaporation to I ; however, it would be risky to employ a constant rate to quantify wet evaporation for heavy rain events (>25 mm), as this could lead to large errors based on the higher proportion of I . Therefore, our results suggest that when the two models are applied, it would be more effective to adopt event‐based and changeable meteorological parameters, such as wet evaporation rate, considering the amount and duration of the rain events (e.g., Návar, 2019b; Zhang et al, 2018).…”

“…In this study, cumulative I for the sparsely canopied larch plantation, as modelled by the revised Gash model, was 229.2 mm, which was 12 mm lower than the measured values, with an RE of 5% over the entire study period. This was similar to the results reported for two coniferous stands in the southern Liupan Mountains (Liu et al, 2017; Shi et al, 2010), and for other forests, such as coniferous (Chen et al, 2013), mixed evergreen and deciduous broad‐leaf (Su et al, 2016), semi‐deciduous (Junqueira Junior et al, 2019), deciduous (Fathizadeh et al, 2018; Herbst et al, 2008; Ma et al, 2019), and shrub stands (Zhang et al, 2018), demonstrating the model's reliability and potential for universal application to various forest types.…”

“…However, S should actually increase with LAI (Pypker et al, 2005), especially for deciduous forests. Zhang et al (2018) reported that deciduous canopy parameters, such as coverage or S , should not be treated as constants during a growing season due to (de)foliation, as this could result in systemic inaccuracy. In this study, the LAI showed a clear parabolic pattern during the growing season, with a maximum in July.…”

“…Recently, some authors have focused on the influence of canopy structural dynamic characteristics on I , as canopy structure could cause direct changes in parameters such as canopy storage capacity ( S ) due to seasonal changes in leaf area—especially with respect to deciduous forests (Muzylo et al, 2009). Previous studies also paid more attention to the effects of canopy surface area (Brauman et al, 2010), thinning (Shinohara et al, 2015), age (Pypker et al, 2005), leafy or leafless status (Deguchi et al, 2006; Fathizadeh et al, 2018; Ma et al, 2019; Zhang et al, 2018), and leaf area index (LAI; Návar, 2019a). For deciduous forests, it has been determined that seasonal LAI patterns representing the dynamic characteristics of canopy structure were important for I simulation—as most annual rainfall and interception events occur in the growing season rather than the dormant season, especially in semi‐arid regions (Ma et al, 2019).…”