A Big Root Approximation of Site‐Scale Vegetation Water Uptake. (25th December 2019) - Bouda, Martin
Abstract: Land surface model (LSM) predictions of soil moisture and transpiration under water‐limited conditions suffer from biases due to a lack of mechanistic process description of vegetation water uptake. Here, I derive a "big root" approach from the porous pipe equation for root water uptake and compare its predictions of soil moistures during the 2010 summer drought at the Wind River Crane site to two previously used Ohm's law analog plant hydraulic models. Due to a fuller representation of pressure gradients and flows within a complex root system architecture, the new formulation achieves somewhat improved fit and significantly lower bias compared to the Ohm's law analog models. A key advantage of the improved physical representation is the increased robustness of fits and predictions, making it less liable to overfitting. This new mechanistic model advances our understanding of vegetation water limitation at site scale with potential to improve LSM predictions of soil moisture, temperature and surface heat, water, and carbon fluxes. Plain Language Summary: This study introduces a new conceptualization of water uptake by plants, suitably simple for use in Earth System Models. Previously used descriptions of this process are known to cause prediction biases with respect to land surface water dynamics and temperatures in model simulations used by the International Panel on Climate Change. This can partly be attributed to an oversimplification of pressure gradients in the soil‐root system, which the new conceptualization describes more accurately. While this study only shows the benefits of the new idealization at a single site, its eventual application at greater scales should lead to better predictions of the interactions between vegetation, water, and a changing climate. Key Points: A new, physically based, upscaled model of root water uptake is derived from the porous pipe equation The new model reduces prediction bias as compared to Ohm's law analogs in a site‐scale case study Its physical basis yields more robust predictions without great increases in computational cost