Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720-1710, USA.
Tree Physiol. 2012 Dec;32(12):1458-70. doi: 10.1093/treephys/tps100. Epub 2012 Nov 6.
The heat pulse method is widely used to measure water flux through plants; it works by using the speed at which a heat pulse is propagated through the system to infer the velocity of water through a porous medium. No systematic, non-destructive calibration procedure exists to determine the site-specific parameters necessary for calculating sap velocity, e.g., wood thermal diffusivity and probe spacing. Such parameter calibration is crucial to obtain the correct transpiration flux density from the sap flow measurements at the plant scale and subsequently to upscale tree-level water fluxes to canopy and landscape scales. The purpose of this study is to present a statistical framework for sampling and simultaneously estimating the tree's thermal diffusivity and probe spacing from in situ heat response curves collected by the implanted probes of a heat ratio measurement device. Conditioned on the time traces of wood temperature following a heat pulse, the parameters are inferred using a Bayesian inversion technique, based on the Markov chain Monte Carlo sampling method. The primary advantage of the proposed methodology is that it does not require knowledge of probe spacing or any further intrusive sampling of sapwood. The Bayesian framework also enables direct quantification of uncertainty in estimated sap flow velocity. Experiments using synthetic data show that repeated tests using the same apparatus are essential for obtaining reliable and accurate solutions. When applied to field conditions, these tests can be obtained in different seasons and can be automated using the existing data logging system. Empirical factors are introduced to account for the influence of non-ideal probe geometry on the estimation of heat pulse velocity, and are estimated in this study as well. The proposed methodology may be tested for its applicability to realistic field conditions, with an ultimate goal of calibrating heat ratio sap flow systems in practical applications.
热脉冲法被广泛用于测量植物中的水流;它通过使用热脉冲在系统中传播的速度来推断水在多孔介质中的速度。目前还没有系统的、非破坏性的校准程序来确定计算液流速度所需的特定于站点的参数,例如木材热扩散率和探头间距。这种参数校准对于从植物尺度上的液流测量中获得正确的蒸腾通量密度以及随后将树木尺度上的水流通量上推到冠层和景观尺度至关重要。本研究的目的是提出一种统计框架,用于从热比测量装置的植入探头收集的原位热响应曲线中同时采样和估计树木的热扩散率和探头间距。基于热脉冲后木材温度的时间轨迹,参数通过基于马尔可夫链蒙特卡罗采样方法的贝叶斯反演技术进行推断。所提出的方法的主要优点是它不需要探针间距的知识或任何进一步的木质部侵入性采样。贝叶斯框架还能够直接量化估计液流速度的不确定性。使用合成数据的实验表明,使用相同仪器进行重复测试对于获得可靠和准确的解决方案至关重要。当应用于野外条件时,可以在不同的季节进行这些测试,并可以使用现有的数据记录系统实现自动化。引入经验因素来解释非理想探头几何形状对热脉冲速度估计的影响,并且在本研究中也进行了估计。可以测试所提出的方法在实际野外条件下的适用性,最终目标是在实际应用中校准热比液流系统。