Experimental study on the effect of simulated grass and stem coverage on resistance coefficient of overland flow

Abstract

AbstractVegetation plays a significant role in preventing desertification, conservation of soil and water. However, nowadays, there is still uncertainty regarding the mechanisms of flow resistance caused by dissimilar vegetation covers. To address this gap, a series of these was conducted in this study to investigate the influence of synthetic grass and stems on the variation of resistance on overland flows. Thirty vegetation configurations were selected (combining five synthetic grass coverage options and six synthetic stem coverage ones), and tested against five unit discharges (flow range = 0.28–2.22 L m−1 s−1) and four slope gradients (3.49%–20.78%). The results obtained showed that the unit discharge is the key driving factor for the transition from laminar to transitional flow. Furthermore, the relationship between the resistance coefficient (f) and the Reynolds number (Re) was not monotonically increasing or decreasing, but behaviours observed were specifically linked to each vegetation coverage. However, a critical coverage threshold was identified, and it corresponded to 2.72% when the slope gradient tested was 3.49%. This threshold decreased with the increase of the slope gradient. In addition, when the vegetation coverage was less than the critical threshold, the f was negatively correlated with Re, otherwise if the vegetation coverage was higher than the critical threshold identified the f–Re relation was positively correlated. The total flow resistance under synthetic grass and stem cover was unequal to the linear superposition of grain resistance and form resistance caused by synthetic stem and grass, which meant that the linear superposition approach is not applicable to overland flows. Finally, a model was developed to predict the flow resistance by applying the π‐theorem and the multiple nonlinear regression analysis and it has been validated against the experimental results confirming its accuracy and high performance (adj.R2 = 0.99, NSE = 0.94).