525 Develop the Plant Hydrodynamics in the Noah-MP Land Surface Model

Tuesday, 14 January 2020
Hall B (Boston Convention and Exhibition Center)
Lingcheng Li, The Univ. of Texas, Austin, TX; and Z. L. Yang, A. M. Matheny, H. Zheng, S. C. Swenson, D. Lawrence, M. Barlage, and B. Yan

Plants with different hydraulic traits have different water use strategies. Isohydric plants such as maple trees exert tight stomata regulation to minimize transpiration. In contrast, anisohydric plants such as oak trees tend to tolerate low leaf water content and keep transpiration longer. These diverse plant hydraulics behaviors could have different effects on the carbon-water cycle and land-atmosphere interaction. However, plant hydrodynamics is currently excluded from most large-scale land surface/global earth system models. In this project, we develop a plant hydrodynamics module (an electric circuit analogy module, i.e., EC module) for the Noah-MP land surface model, a primary model employed in the NASA Land Information System, the next phase North American Land Data Assimilation System, and also the National Water Model. This newly developed model is evaluated using the observations (e.g., sap-flux, stem water storage, latent heat, soil moisture) from the University of Michigan Biological Station (UMBS). The evaluation is conducted at different scales (i.e., the stand level, species level, and tree level). This updated Noah-MP model with plant hydrodynamics showed better performance than the default Noah-MP model. We also compare the updated Noah-MP model with Community Land Model version 5 (CLM5), using a different EC type plant hydraulics module, and the finite difference ecosystem-scale tree crown hydrodynamics model (FETCH), using a porous media model with more detailed tree structure and microclimate considered. These three models, with different complexity, show compatible performance at UMBS. The augmented Noah-MP model can help better understand the role of plant hydrodynamics on the global climate system and water-carbon cycles, and also makes it possible to assimilate vegetation-centric remote sensing products for data assimilation systems.

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