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Plant surface water storage greatly affects rainfall interception in water-limited environments. The storage characteristics of 55 common herbaceous species and their relationships with plant morphology, biomass-related traits, and leaf wettability were examined using artificial wetting method in semiarid Loess Plateau. Results indicated that plant mass storage ranged from 0.12-1.26 g g −1 , and Glycyrrhiza uralensis and Leymus secalinus had the highest and lowest values, respectively. Leaf storage ratio ranged from 40.2-93.2%, with the highest value in G. uralensis and the lowest in Chenopodium album. Fifty-two species had higher storage capacities in leaves than that in stems. Gramineous and leguminous species had relatively lower mass storage and leaf storage ratio than compositae and rosaceae. Plant and leaf mass storage were negatively correlated with leaf adaxial/abaxial contact angles, and stem mass storage was negatively correlated with plant height. Storage capacities were closely related to morphological and biomass-related traits, and leaf area was a better predictor of plant and leaf storage capacities, and stem fresh weight was a better predictor at the stem level. Path analysis revealed that leaf area and adaxial contact angle were two independent variables directly affecting plant and leaf storage capacities. Their ratio (i.e., wettability index) had higher correlations with storage capacities than other single trait and multiple regression models of these traits. Our results implied that high proportions of gramineous and leguminous species in grassland community would favour reducing interception loss, and wettability index can be an effective indicator for evaluating rainfall interception and vegetation hydrological benefits. KEYWORDSleaf area, leaf contact angle, leaf storage ratio, species family, surface water storage, wettability index

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The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. In this study, we extend the optimal scale method to the multi-optimal-scale method, adaptively selecting multiple optimal scales for each point in the tree point cloud to increase the distinctiveness of extracted geometric features. Compared with the optimal scale method, our method achieves higher separation accuracy. Compared with the multi-scale method, our method achieves more stable separation accuracy with a limited number of optimal scales. The running time of our method is greatly reduced when the optimization strategy is applied.