Soil moisture plays a key role in hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. Accurate soil moisture measurements in remote ecosystems such as the Amazon are difficult and limited because of logistical constraints. Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture and require calibration to convert the TDR's dielectric permittivity measurement (K_a) to volumetric water content (θ_v). In this study, our objectives were to develop a field‐based calibration of TDR sensors in an old‐growth upland forest in the central Amazon, to evaluate the performance of the calibration, and then to apply the calibration to determine the dynamics of soil moisture content within a 14.2‐m‐deep vertical soil profile. Depth‐specific TDR calibration using local soils in a controlled laboratory setting yielded a novel K_a–θ_v third‐degree polynomial calibration. The sensors were later installed to their specific calibration depth in a 14.2‐m pit. The widely used K_a–θ_v relationship (Topp model) underestimated the site‐specific θ_v by 22–42%, indicating significant error in the model when applied to these well‐structured, clay‐rich tropical forest soils. The calibrated wet‐ and dry‐season θ_v data showed a variety of depth and temporal variations highlighting the importance of soil textural differentiation, root uptake depths, as well as event to seasonal precipitation effects. Data such as these are greatly needed for improving our understanding of ecohydrological processes within tropical forests and for improving models of these systems in the face of changing environmental conditions.

中文翻译：

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亚马逊中部热带森林土壤水分动力学的标定，测量和表征

土壤水分在陆地生态系统的水文，生物地球化学和能源预算中起着关键作用。由于后勤方面的限制，在亚马逊等偏远的生态系统中进行准确的土壤水分测量是困难且有限的。时域反射法（TDR）传感器广泛用于监测土壤湿度，需要进行校准才能将TDR的介电常数测量值（K _a）转换为体积水含量（_θv）。在这项研究中，我们的目标是在亚马逊河中部的一个古老的旱地森林中开发基于TDR传感器的现场标定，以评估标定的性能，然后将其应用于确定土壤湿度的动态变化。在14.2米深的垂直土壤剖面中的含量。在受控的实验室环境中使用局部土壤进行深度特定的TDR校准，得出了新颖的K _a –θ _v三次多项式校准。传感器随后安装在14.2米深的特定深度中。广泛使用的K _a –θ _v关系（Topp模型）低估了特定地点的_θv误差在22％至42％之间，表明将模型应用于结构良好的粘土丰富的热带森林土壤时，模型存在明显误差。校准后的湿季和干季_θv数据显示出各种深度和时间变化，突显了土壤质地差异，根吸收深度以及季节性降水影响的重要性。迫切需要这样的数据来增进我们对热带森林内生态水文过程的理解，并在面对不断变化的环境条件时改善这些系统的模型。