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Summary Trees partition biomass in response to resource limitation and physiological activity. It is presumed that these strategies evolved to optimize some measure of fitness. If the optimization criterion can be specified, then allometry can be modeled from first principles without prescribed parameterization.We present the Tree Hydraulics and Optimal Resource Partitioning (THORP) model, which optimizes allometry by estimating allocation fractions to organs as proportional to their ratio of marginal gain to marginal cost, where gain is net canopy photosynthesis rate, and costs are senescence rates. Root total biomass and profile shape are predicted simultaneously by a unified optimization. Optimal partitioning is solved by a numerically efficient analytical solution.THORP’s predictions agree with reported tree biomass partitioning in response to size, water limitations, elevated CO₂and pruning. Roots were sensitive to soil moisture profiles and grew down to the groundwater table when present. Groundwater buffered against water stress regardless of meteorology, stabilizing allometry and root profiles as deep as c. 30 m.Much of plant allometry can be explained by hydraulic considerations. However, nutrient limitations cannot be fully ignored. Rooting mass and profiles were synchronized with hydrological conditions and groundwater even at considerable depths, illustrating that the below ground shapes whole‐tree allometry. 

Abstract. Plant roots act as critical pathways of moisture from the subsurface to the atmosphere. Deep moisture uptake by plant roots can provide a seasonal buffer mechanism in regions with a well-defined dry season, such as the southern Amazon. Here, mature forests maintain transpiration (a critical source of atmospheric moisture in this part of the world) during drier months. Most existing state-of-the-art Earth system models do not have the necessary features to simulate subsurface-to-atmosphere moisture variations during dry-downs. These features include groundwater dynamics, a sufficiently deep soil column, dynamic root water uptake (RWU), and a fine model spatial resolution (<5 km). To address this, we present DynaRoot, a dynamic root water uptake scheme implemented in the Noah-Multiparameterization (Noah-MP) land surface model, a widely used model for studying kilometer-scale regional land surface processes. Our modifications include the implementation of DynaRoot, eight additional resolved soil layers reaching a depth of 20 mm, and soil properties that vary with depth. DynaRoot is computationally efficient and ideal for regional- or continental-scale climate simulations. We perform four 20-year uncoupled Noah-MP experiments for a region in the southern Amazon basin. Each experiment incrementally adds physical complexity. The experiments include the default Noah-MP with free drainage (FD), a case with an activated groundwater scheme that resolves water table variations (GW), a case with eight added soil layers and soil properties that vary with depth (SOIL), and a case with DynaRoot activated (ROOT). Our results show that DynaRoot allows mature forests in upland regions to avoid water stress during dry periods by taking up moisture from the deep vadose zone (where antecedent precipitation still drains downward). Conversely, RWU in valleys can access moisture from groundwater (while remaining constrained by the water table). Temporally, we capture a seasonal shift in RWU from shallower layers in wetter months to deeper soil layers in drier months, particularly over regions with dominant evergreen broadleaf (forest) vegetation. Compared to the control case, there is a domain-averaged increase in transpiration of about 29 % during dry months in the ROOT experiment. Critically, the ROOT experiment performs best in simulating the temporal evolution of dry-season transpiration using an observation-based ET (evapotranspiration) product as the reference. Future work will explore the effect of the DynaRoot uptake scheme on atmospheric variables in a coupled modeling framework. 

Abstract Increasing evidence suggests that tree growth is sink-limited by environmental and internal controls rather than by carbon availability. However, the mechanisms underlying sink-limitations are not fully understood and thus not represented in large-scale vegetation models. We develop a simple, analytically solved, mechanistic, turgor-driven growth model (TDGM) and a phloem transport model (PTM) to explore the mechanics of phloem transport and evaluate three hypotheses. First, phloem transport must be explicitly considered to accurately predict turgor distributions and thus growth. Second, turgor-limitations can explain growth-scaling with size (metabolic scaling). Third, turgor can explain realistic growth rates and increments. We show that mechanistic, sink-limited growth schemes based on plant turgor limitations are feasible for large-scale model implementations with minimal computational demands. Our PTM predicted nearly uniform sugar concentrations along the phloem transport path regardless of phloem conductance, stem water potential gradients and the strength of sink-demands contrary to our first hypothesis, suggesting that phloem transport is not limited generally by phloem transport capacity per se but rather by carbon demand for growth and respiration. These results enabled TDGM implementation without explicit coupling to the PTM, further simplifying computation. We test the TDGM by comparing predictions of whole-tree growth rate to well-established observations (site indices) and allometric theory. Our simple TDGM predicts realistic tree heights, growth rates and metabolic scaling over decadal to centurial timescales, suggesting that tree growth is generally sink and turgor limited. Like observed trees, our TDGM captures tree-size- and resource-based deviations from the classical ¾ power-law metabolic scaling for which turgor is responsible.