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Recently, incentives have been provided in many countries, including Canada and Denmark, to produce biochar for construction usage. This is done because biochar is carbon negative and can help achieve the emission reduction goal of 2030. This technical note aims to analyze the efficiency of biochar in soils with varying grain size distribution for enhancing water retention capacity (WRC). The combinations of biochar content and grain size distributions corresponding to the maximum and minimum efficiency were explored. Artificial Neural Network (ANN) based model for predicting Soil Water Characteristic Curve (SWCC) as a function of soil suction and grain size distribution was developed. A new factor (the ratio of fine (silt + clay) and coarse (sand) content) was proposed for the interpretation of the efficiency of biochar in soils. The newly developed model is able to predict SWCC reasonably well. Biochar amendment is found to influence both dry and wet sides of soils with a clay content lower than threshold content (6–8%). Beyond threshold content, the influence of biochar appears to reduce. However, in the case of high sand content soils (90%), the NWC value on the drier side is generally higher as compared to soils with lower sand content. Based on sensitivity analysis, it was found that the ratio of fine to sand content is the most influential, while biochar content is the least influential.

The objective of this short communication is to explore spatial distribution of soil suction in the vicinity of an urban tree considering effects of termite infestation and also radiant energy interception by canopy. A site in an urban landscape containing a mix of species cover (grass cover in the vicinity of trees) was selected for monitoring. A field monitoring program was designed to monitor soil (soil suction, moisture), vegetation parameters (tree height, grass leaf area), and meteorological parameters during drying/wetting cycles. As expected, before termite infestation, suction magnitude and rate of change were highest near the tree stem and at shallower depths, reflecting the dominance of transpiration over evaporation. However, after termite infestation, soil suction diminished significantly near the tree stem and increased farther from the tree stem, a phenomenon accompanied by loss of canopy area, as captured by a color analysis technique (i.e., an image processing method). These findings reflected a reduction in stomatal conductance, itself an indicator of transpiration loss through the stomata—likely because reduced canopy shading effects caused increased evaporation. Soil suction at deeper depths seemed not to be significantly affected, generally due to canopy and termite infestation. In summary, the study sheds new light on issues of tree maintenance by showing how termite infestation can significantly affect the performance of green infrastructure.

Root depth and leaf area ratio are two important features of a plant and exhibit a coupled relation. Assessing their coupled effects on induced soil suction is essential for analyzing the performance of a green infrastructure, such as water storage/drainage in green roofs and stability of a vegetated slope. Previously soil moisture induced by vegetation was often presented deterministically without considering the overall effects of leaf and root characteristics in a probabilistic manner. The main objective of this study is to investigate the influence of coupled variations in root and leaf characteristics on vegetation-induced soil suction. In addition, the coupled effects were analyzed using statistical approach. Different combinations of the leaf area index and root depth of the same plant were assessed. Probabilistic analysis was then conducted by computing suction profiles in form of quantiles. It was found that the biggest variability in suction profiles occurs at around 0.6 times the root depth and the minimum occurred at near surface and at maximum root depth. This depth at 0.6 times root depth corresponds to the maximum root density. It implies that the probabilistic analysis becomes more and more important while assessing suction profiles near the maximum root density.