Hydraulic redistribution (HR) on the field scale: Novel approaches for monitoring and assessing the implications of HR for the resilience of forest ecosystems

面对全球变化，全球生态系统正面临严峻挑战。在欧洲，由于干旱的直接和间接影响，最近的干旱造成了毁灭性的森林枯死。水力再分配（HR）-通过植物根系在不同土壤部分之间的水被动移动-是一个可能对生态系统适应干旱的能力产生影响的过程。水力提升（HL）是最常见的HR形式，由于湿润深层土壤和干燥表层土壤之间的水势梯度，水在夜间被动地移动到干燥土壤区域，可能提供植物使用的重要水源。二十多年的人力资源研究表明，这是一个对生态系统具有全球重要性的过程。尽管这已被证明具有全球重要性，但在大于树尺度和证据支持的建模方法的空间尺度上，关于人力资源的一致数据集非常稀缺。因此，我们目前无法准确预测，在预测的气候变化下，人力资源是否会成为森林和其他生态系统恢复力的重要组成部分。在这个项目中，我们将首次收集空间分布的（田间规模，最多1公顷）和时间（次每日至每周）高分辨率HR数据集。使用此数据集，我们旨在调查HR在现场规模上的相关性，评估HR的异质性并确定谁是HR的受益者。为了实现这一目标，我们将建立一个监测方案，专门设计来识别，监测和量化HR。该设置结合的方法，在现场测量的水稳定同位素与特定的生态水文监测技术和同位素标记实验在阔叶混交林。随后，该数据集将用于参数化和校准同位素启用土壤-植被-大气传输（SVAT）模型MuSICA，并使用端元混合分析（EMMA）确定HR对田间尺度蒸腾作用的贡献。校准模型，然后将用于预测的重要性，人力资源的调查混合阔叶林生态系统的气候变化下的恢复力。该项目的结果将有助于了解人力资源的森林生态系统的作用，现在和预测的未来气候条件下。它将有助于评估干旱和极端条件下人力资源的潜在缓冲作用，这有助于为森林管理提供信息，以发展未来有复原力的森林生态系统。此外，拟议的方法框架有可能成为一个标准程序，评估人力资源在更大的空间尺度。

In the face of global change, ecosystems world-wide are experiencing severe challenges. In Europe, the recent drought has caused devastating forest dieback due to direct and indirect effects of drought. Hydraulic redistribution (HR) - the passive movement of water between different soil parts via plant root systems - is a process that can potentially have an impact on the capacity of ecosystems to adapt to droughts. Hydraulic lift (HL) is the most common form of HR, where due to water potential gradients between wetter deep soil and dry surface soil water passively moves to the dry soil region during the night, potentially providing an important water source to be used by plants. More than two decades of research on HR have demonstrated that it is a process with a global importance for ecosystems. Despite this proven global importance, consistent datasets on HR on spatial scales larger than the tree-scale and evidence-backed modeling approaches are extremely scarce. As a consequence, we are currently not able to predict accurately if HR will be an important component for the resilience of forests and other ecosystems under predicted climatic changes. In this project, we will – for the first time – collect a spatially distributed (field-scale, up to 1 ha) and temporally (sub-daily to weekly) high resolution dataset on HR. With this dataset we aim at investigating the relevance of HR on the field scale, assess the heterogeneity of HR and identify who are the beneficiaries of HR. In order to achieve this, we will establish a monitoring scheme specifically designed to identify, monitor and quantify HR. This setup combines methods based on the in situ measurement of water stable isotopes with specific ecohydrological monitoring techniques and isotopic labeling experiments in a mixed broad-leaf forest. The dataset will subsequently be used to parametrize and calibrate the isotope-enabled soil-vegetation-atmosphere transfer (SVAT) model MuSICA and determine the contribution of HR to transpiration on the field scale using end-member mixing analysis (EMMA). The calibrated model will then be used in order to predict the importance of HR for the resilience of the investigated mixed broad-leaf forest ecosystem under climatic changes. The results of this project will help to understand the role of HR for forest ecosystems now and under predicted future climatic conditions. It will contribute to assess the potential buffering effect of HR during droughts and extreme conditions, which can help to inform forest management in terms of developing resilient forest ecosystems of the future. Furthermore, the proposed methodological framework could potentially become a standard procedure for assessing HR on larger spatial scales.