A new approach to modeling tree rainfall interception

Abstract

Abstract. A three-dimensional physically based stochastic model was developed to describe canopy rainfall interception processes at desired spatial and temporal resolutions. Such model development is important to understand these processes because forest canopy interception may exceed 59% of annual precipitation in old growth trees. The model describes the interception process from a single leaf, to a branch segment, and then up to the individual tree level. It takes into account rainfall, meteorology, and canopy architecture factors as explicit variables. Leaf and stem surface roughness, architecture, and geometric shape control both leaf drip and stemflow. Model predictions were evaluated using actual interception data collected for two mature open grown trees, a 9-year-old broadleaf deciduous pear tree (Pyrus calleryana "Bradford" or Callery pear) and an 8-year-old broadleaf evergreen oak tree (Quercus suber or cork oak). When simulating 18 rainfall events for the oak tree and 16 rainfall events for the pear tree, the model over estimated interception loss by 4.5% and 3.0%, respectively, while stemflow was under estimated by 0.8% and 3.3%, and throughfall was under estimated by 3.7% for the oak tree and over estimated by 0.3% for the pear tree. A model sensitivity analysis indicates that canopy surface storage capacity had the greatest influence on interception, and interception losses were sensitive to leaf and stem surface area indices. Among rainfall factors, interception losses relative to gross precipitation were most sensitive •o rainfall amount. Rainfall incident angle had a significant effect on total precipitation intercepting the projected surface area. Stemflow was sensitive to stem segment and leaf zenith angle distributions. Enhanced understanding of interception loss dynamics should lead to improved urban forest ecosystem management.

A stochastic model of throughfall for extreme events

“…Deterministic approaches to modelling evaporation by these processes have dominated the canopy interception literature (e.g. Rutter et al, 1971;Gash, 1979;Xiao et al, 2000a), and these models could serve as a way to use stochastic weather data to predict evaporation. The objectives of this research are only to estimate vegetation effects on precipitation in the statistical sense.…”

The Urban Environment Can Modify Drought Stress of Small-Leaved Lime (Tilia cordata Mill.) and Black Locust (Robinia pseudoacacia L.)

“…Furthermore, urban trees store carbon [6], reduce rainwater runoff [7], and filter pollutants [8]. The moderation of microclimates and improvement of environmental conditions by urban trees has been analyzed by several studies [9][10][11][12][13], with results highlighting the importance of trees for cities and the city climate. Therefore, urban forests can also become a key component to the adaptation of cities to climate change [14].…”

Spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception

“…Although remote sensing may reduce the amount of field work needed, it cannot be excluded completely without a detrimental impact and high levels of uncertainty on the outcome of the task. It is important to accurately estimate LAI as Xiao et al (1998) found that modelling canopy interception was most sensitive to storage capacity and LAI as the storage capacity is directly related to the LAI. Similarly, Limousin et al (2008) found that a 25% reduction in storage capacity reduces interception loss by 8.6%…”