Qingfu Xiao

Winter rainfall interception by two mature open‐grown trees in Davis, California

A rainfall interception measuring system was developed and tested for open-grown trees. The system includes direct measurements of gross precipitation, throughfall and stemflow, as well as continuous collection of micrometeorological data. The data were sampled every second and collected at 30-s time steps using pressure transducers monitoring water depth in collection containers coupled to Campbell CR10 dataloggers. The system was tested on a 9-year-old broadleaf deciduous tree (pear, Pyrus calleryana ‘Bradford’) and an 8-year-old broadleaf evergreen tree (cork oak, Quercus suber) representing trees having divergent canopy distributions of foliage and stems. Partitioning of gross precipitation into throughfall, stemflow and canopy interception is presented for these two mature open-grown trees during the 1996–1998 rainy seasons. Interception losses accounted for about 15% of gross precipitation for the pear tree and 27% for the oak tree. The fraction of gross precipitation reaching the ground included 8% by stemflow and 77% by throughfall for the pear tree, as compared with 15% and 58%, respectively, for the oak tree. The analysis of temporal patterns in interception indicates that it was greatest at the beginning of each rainfall event. Rainfall frequency is more significant than rainfall rate and duration in determining interception losses. Both stemflow and throughfall varied with rainfall intensity and wind speed. Increasing precipitation rates and wind speed increased stemflow but reduced throughfall. Analysis of rainfall interception processes at different time-scales indicates that canopy interception varied from 100% at the beginning of the rain event to about 3% at the maximum rain intensity for the oak tree. These values reflected the canopy surface water storage changes during the rain event. The winter domain precipitation at our study site in the Central Valley of California limited our opportunities to collect interception data during non-winter seasons. This precipitation pattern makes the results more specific to the Mediterranean climate region. Copyright

Rainfall interception by Santa Monica's municipal urban forest

Effects of urban forests on rainfall interception and runoff reduction have been conceptualized, but not well quantified. In this study rainfall interception by street and park trees in Santa Monica, California is simulated. A mass and energy balance rainfall interception model is used to simulate rainfall interception processes (e.g., gross precipitation, free throughfall, canopy drip, stemflow, and evaporation). Annual rainfall interception by the 29,299 street and park trees was 193,168 m3 (6.6 m3/tree), or 1.6% of total precipitation. The annual value of avoided stormwater treatment and flood control costs associated with reduced runoff was

A new approach to modeling tree rainfall interception

110,890 (

Using AVIRIS data and multiple-masking techniques to map urban forest tree species

3.60/tree). Interception rate varied with tree species and sizes. Rainfall interception ranged from 15.3% (0.8 m3/tree) for a small Jacaranda mimosifolia (3.5 cm diameter at breast height) to 66.5% (20.8 m3/tree) for a mature Tristania conferta (38.1 cm). In a 25-year storm, interception by all street and park trees was 12,139.5 m3 (0.4%), each tree yielding

Surface water storage capacity of twenty tree species in Davis, California.

0.60 (0.4 m3/tree) in avoided flood control costs. Rainfall interception varied seasonally, averaging 14.8% during a 21.7 mm winter storm and 79.5% during a 20.3 mm summer storm for a large, deciduous Platanus acerifolia tree. Effects of differences in temporal precipitation patterns, tree population traits, and pruning practices on interception in Santa Monica, Modesto, and Sacramento, California are described.

Performance of engineered soil and trees in a parking lot bioswale

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 to 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.

Tree health mapping with multispectral remote sensing data at UC Davis, California

Tree type and species information are critical parameters for urban forest management, benefit cost analysis and urban planning. However, traditionally, these parameters have been derived based on limited field samples in urban forest management practice. In this study we used high-resolution Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data and multiple-spectral masking techniques to identify and map urban forest trees. Trees were identified based on their spectral character difference in AVIRIS data. The use of multiple-masking techniques shift the focus to the target land cover types only, thus reducing confounding noise during spectral analysis. The results were checked against ground reference data and by comparison to tree information in an existing geographical information system (GIS) database. At the tree type level, mapping was accomplished with 94% accuracy. At the tree species level, the average accuracy was 70% but this varied with both tree type and species. Of the four evergreen tree species, the average accuracy was 69%. For the 12 deciduous tree species, the average accuracy was 70%. The relatively low accuracy for several deciduous species was due to small tree size and overlapping among tree crowns at the 3.5 m spatial resolution of AVIRIS data. This urban forest tree species mapping method has the potential to increase data update intervals and accuracy while reducing costs compared to field sampling or other traditional methods.

Rainfall interception by tree crown and leaf litter: An interactive process

Urban forestry is an important green infrastructure strategy because healthy trees can intercept rainfall, reducing stormwater runoff and pollutant loading. Surface saturation storage capacity, defined as the thin film of water that must wet tree surfaces before flow begins, is the most important variable influencing rainfall interception processes. Surface storage capacity is known to vary widely among tree species, but it is little studied. This research measured surface storage capacities of 20 urban tree species in a rainfall simulator. The measurement system included a rainfall simulator, digital balance, digital camera, and computer. Eight samples were randomly collected from each tree species. Twelve rainfall intensities (3.5-139.5 mm h) were simulated. Leaf-on and leaf-off simulations were conducted for deciduous species. Stem and foliar surface areas were estimated using an image analysis method. Results indicated that surface storage capacities varied threefold among tree species, 0.59 mm for crape myrtle ( L.) and 1.81 mm for blue spruce ( Engelm.). The mean value across all species was 0.86 mm (0.11 mm SD). To illustrate application of the storage values, interception was simulated and compared across species for a 40-yr period with different rainfall intensities and durations. By quantifying the potential for different tree species to intercept rainfall under a variety of meteorological conditions, this study provides new knowledge that is fundamental to validating the cost-effectiveness of urban forestry as a green infrastructure strategy and designing functional plantings.

Municipal Forest Benefits and Costs in Five US Cities

A bioswale integrating an engineered soil and trees was installed in a parking lot to evaluate its ability to reduce storm runoff, pollutant loading, and support tree growth. The adjacent control and treatment sites each received runoff from eight parking spaces and were identical except that there was no bioswale for the control site. A tree was planted at both sites. Storm runoff, pollutant loading, and tree growth were measured. There were 50 storm events with a total precipitation of 563.8 mm during February 2007 and October 2008. The bioswale reduced runoff by 88.8% and total pollutant loading by 95.4%. The engineered soil provided a better aeration and drainage for tree growth than did the controls compacted urban soil. The superior performance of the bioswale demonstrated its potential use for large-scale application in parking lots and roadsides to reduce runoff and support tree growth.

Million trees Los Angeles canopy cover and benefit assessment

Tree health is a critical parameter for evaluating urban ecosystem health and sustainability. Traditionally, this parameter has been derived from field surveys. We used multispectral remote sensing data and GIS techniques to determine tree health at the University of California, Davis. The study area (363 ha) contained 8,962 trees of 215 species. Tree health conditions were mapped for each physiognomic type at two scales: pixel and whole tree. At the pixel scale, each tree pixel within the tree crown was classified as either healthy or unhealthy based on vegetation index values. At the whole tree scale, raster based statistical analysis was used to calculate tree health index which is the ratio of healthy pixels to entire tree pixels within the tree crown. The tree was classified as healthy if the index was greater than 70%. Accuracy was checked against a random sample of 1,186 trees. At the whole tree level, 86% of campus trees were classified as healthy with 88% mapping accuracy. At the pixel level, 86% of the campus tree cover was classified as healthy. This tree health evaluation approach allows managers to identify the location of unhealthy trees for further diagnosis and treatment. It can be used to track the spread of disease and monitor seasonal or annual changes in tree health. Also, it provides tree health information that is fundamental to modeling and analysis of the environmental, social, and economic services produced by urban forests.