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Abstract Climatological analyses of moisture sources of precipitation have traditionally relied on reanalyses or models that parameterize convection. Convection‐permitting models (CPMs) are increasingly used in climate studies, as they better represent many precipitation processes than non‐CPMs. We found significant differences in precipitation moisture sources over the Amazon Basin using 1‐year CPM and non‐CPM WRF simulations with moisture tracers. Notably, the CPM estimates that about half of precipitation in the central Andes comes from the Amazon basin; a 20%–30% higher estimate than the non‐CPM. This suggests long‐term CPMs with tracers could improve climatological estimates. However, their high computational cost is prohibitive. To overcome this, we developed a revised 2L‐DRM model that replicates CPM‐with‐tracers estimates at a fraction of the cost, using only standard outputs. We applied this model to South America, analyzing precipitation moisture sources across 15 regions. 2L‐DRM can be used for other regions as continental‐scale CPM climatological simulations become available. 

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.