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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.
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Tree species explain only half of explained spatial variability in plant water sensitivity
Abstract Spatiotemporal patterns of plant water uptake, loss, and storage exert a first‐order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional‐scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto‐regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species‐specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.
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- PAR ID:
- 10523686
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 30
- Issue:
- 7
- ISSN:
- 1354-1013
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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