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Abstract Describing flow resistance from the properties of an underlying surface is a challenge in surface hydrology. Runoff models must specify a resistance formulation or “roughness scheme”—describing the functional relationship between flow resistance and flow depth/velocity—and its parameters. Uncertainty in runoff predictions derives from both the selected roughness scheme (e.g., Darcy Weisbach, Manning's, or laminar flow equations), and its parameterization with a roughness coefficient (e.g., Manning's ). Both choices are informed by model calibration to data, usually discharge, and, if available, velocity. In this study, a Saint Venant Equation‐based runoff model is calibrated to discharge and velocity data from 112 rainfall simulator experiments. The results are used to identify the optimal roughness scheme among four widely‐used options for each experiment, and to explore whether surface properties can be used to select the optimal roughness scheme and its coefficient. Among the tested roughness schemes, a transitional flow equation provided the best fit to the plurality of experiments. The most suitable roughness scheme for a given experiment was not related to measured surface properties. Regression models predicted the calibrated roughness coefficients with adjusted values between 0.48 and 0.54, depending on the roughness scheme used. Litter cover was the best predictor of the roughness coefficient, followed by soil cover and average canopy gap size. The results suggest that selection of an optimal roughness scheme based on surface properties alone remains difficult, but that once a scheme is selected, roughness coefficients can be estimated from surface properties. 

Abstract Observations show vulnerability segmentation between stems and leaves is highly variable within and between environments. While a number of species exhibit conventional vulnerability segmentation (stem leaf ), others exhibit no vulnerability segmentation and others reverse vulnerability segmentation (stem leaf ). We developed a hydraulic model to test hypotheses about vulnerability segmentation and how it interacts with other traits to impact plant conductance. We do this using a series of experiments across a broad parameter space and with a case study of two species with contrasting vulnerability segmentation patterns:Quercus douglasiiandPopulus trichocarpa. We found that while conventional vulnerability segmentation helps to preserve conductance in stem tissues, reverse vulnerability segmentation can better maintain conductance across the combined stem‐leaf hydraulic pathway, particularly when plants have more vulnerable s and have hydraulic segmentation with greater resistance in the leaves. These findings show that the impacts of vulnerability segmentation are dependent upon other plant traits, notably hydraulic segmentation, a finding that could assist in the interpretation of variable observations of vulnerability segmentation. Further study is needed to examine how vulnerability segmentation impacts transpiration rates and recovery from water stress. 

Abstract. Plant transpiration downregulation in the presence of soil water stress is a critical mechanism for predicting global water, carbon, and energy cycles. Currently, many terrestrial biosphere models (TBMs) represent this mechanism with an empirical correction function (β) of soil moisture – a convenient approach that can produce large prediction uncertainties. To reduce this uncertainty, TBMs have increasingly incorporated physically based plant hydraulic models (PHMs). However, PHMs introduce additional parameter uncertainty and computational demands. Therefore, understanding why and when PHM and β predictions diverge would usefully inform model selection within TBMs. Here, we use a minimalist PHM to demonstrate that coupling the effects of soil water stress and atmospheric moisture demand leads to a spectrum of transpiration responses controlled by soil–plant hydraulic transport (conductance). Within this transport-limitation spectrum, β emerges as an end-member scenario of PHMs with infinite conductance, completely decoupling the effects of soil water stress and atmospheric moisture demand on transpiration. As a result, PHM and β transpiration predictions diverge most for soil–plant systems with low hydraulic conductance (transport-limited) that experience high variation in atmospheric moisture demand and have moderate soil moisture supply for plants. We test these minimalist model results by using a land surface model at an AmeriFlux site. At this transport-limited site, a PHM downregulation scheme outperforms the β scheme due to its sensitivity to variations in atmospheric moisture demand. Based on this observation, we develop a new “dynamic β” that varies with atmospheric moisture demand – an approach that overcomes existing biases within β schemes and has potential to simplify existing PHM parameterization and implementation. 

Abstract In Mediterranean-type climates, asynchronicity between energy and water availability means that ecosystems rely heavily on the water-storing capacity of the subsurface to sustain plant water use over the summer dry season. The root-zone water storage capacity ( S m a x [L]) defines the maximum volume of water that can be stored in plant accessible locations in the subsurface, but is poorly characterized and difficult to measure at large scales. Here, we develop an ecohydrological modeling framework to describe how S m a x mediates root zone water storage ( S [L]), and thus dry season plant water use. The model reveals that where S m a x is high relative to mean annual rainfall, S is not fully replenished in all years, and root-zone water storage and therefore plant water use are sensitive to annual rainfall. Conversely, where S m a x is low, S is replenished in most years but can be depleted rapidly between storm events, increasing plant sensitivity to rainfall patterns at the end of the wet season. In contrast to both the high and low S m a x cases, landscapes with intermediate S m a x values are predicted to minimize variability in dry season evapotranspiration. These diverse plant behaviors enable a mapping between time variations in precipitation, evapotranspiration and S m a x , which makes it possible to estimate S m a x using remotely sensed vegetation data − that is, using plants as sensors. We test the model using observations of S m a x in soils and weathered bedrock at two sites in the Northern California Coast Ranges. Accurate model performance at these sites, which exhibit strongly contrasting weathering profiles, demonstrates the method is robust across diverse plant communities, and modes of storage and runoff generation.