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Bayesian inference of tree water relations using a Soil-Tree-Atmosphere Continuum model

机译:使用土壤 - 大气的树木与树木氛围的贝叶斯推动连续体模型

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To better understand root-soil water interactions, a mature white fir (Abies concolor) and the surrounding root zone were continuously monitored (sap flow, canopy stem water potential, soil moisture, and temperature), to characterize tree hydrodynamics. We present a hydrodynamic flow model, simulating unsaturated flow in the soil and tree with stress functions controlling spatially distributed root water uptake and canopy transpiration. Using the van Genuchten functions, we parameterize the effective retention and unsaturated hydraulic conductivity functions of the tree sapwood and soil, soil and canopy stress functions, and radial root zone distribution. To parameterize the in-situ tree water relationships, we combine a numerical model with observational data in an optimization framework, minimizing residuals between simulated and measured observational data of soil and tree canopy. Using the MCMC method, the HYDRUS model is run in an iterative process that adjusts parameters until residuals are minimized. Using these optimized parameters, the HYDRUS model simulates diurnal tree water potential and sap flow as a function of tree height, in addition to spatially distributed changes in soil water storage and soil water potential.
机译:为了更好地了解根土水合作用,连续监测成熟的白色FIR(Apies Condolor)和周围根区域(SAP流动,冠层杆水电电位,土壤水分和温度),以表征树流体动力学。我们介绍了一种流体动力学模型,模拟土壤和树中的不饱和流动,应力函数控制空间分布的根水摄取和冠层蒸腾。采用Van Genuchten功能,我们参数化树脂和土壤,土壤和冠层应力功能和径向根区分布的有效保留和不饱和液压导电功能。为了参数化原位树水关系,我们将具有在优化框架中的观测数据中的数值模型结合在一起,最大限度地减少了土壤和树木冠层的模拟和测量的观测数据之间的残差。使用MCMC方法,Hydrus模型在调整参数的迭代过程中运行,直到残留物被最小化。使用这些优化的参数,除了土壤储水和土壤水位潜力的空间分布变化之外,水模型以及树高度的函数,还模拟了昼夜树水电位和SAP流。

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