NSF/EAR-BSF: Assessing controls on hydrologic connectivity, plant water availability and degradation risk in drylands with isotope tracers and Lagrangian modeling

由于缺乏与许多旱地生态系统相关的植被，降雨产生了大量的陆上径流，因此径流/径流过程对于向植被覆盖的地点提供径流变得重要，在那里水分可以渗透，导致可供植物使用的水资源的复杂模式。根据植被和地形模式等容易观察到的特征预测这些径流模式，将有助于评估景观对荒漠化的脆弱性。此外，它还将通过优化植被选择、山坡轮廓和种植设计，为恢复活动提供设计策略。这项研究旨在开发改进的技术，通过针对同位素示踪剂的实验行为测试径流径流过程的模型，以及通过开发新技术来提高模拟植被和地形表面的真实性，从而实现这种预测。这项研究将支持两名博士生(一名在以色列，一名在美国)，以及加州大学伯克利分校和内盖夫本古里安大学的研究地点之间的研究交流。该项目将与加州大学伯克利分校环境设计学院和以色列凯伦·卡耶梅斯犹太国家基金的合作伙伴一起，开展利用研究结果为土地恢复规划和指导方针提供信息的外联活动。该项目将根据植被的空间组织、山坡地形和风暴特征，推进对旱地易受径流过程破坏的预测。这种预测的目的是确定干预措施的高度优先事项，以防止退化，并为恢复设计提供信息，重点是改变植被分布和山坡形状。以前基于空间格局对植物水分有效性进行预测的尝试受到两个方面的限制：(I)用于预测的模型通常根据径流过程的间接度量进行测试，例如径流小区的净流量，而不是根据暴雨期间站点到站点的源汇输送的观测进行测试；因此，它们限制径流站点内的源-汇行为的能力是值得怀疑的。(2)用于将旱地表面特征与水再分配联系起来的模型实验依赖于理想化陆地表面的个别实现。因此，不确定性的特征很差，所得到的真实景观的度量标准的含义也不清楚。这项提议利用同位素示踪实验，揭示了以色列Lehavim长期生态研究遗址上一个特征良好的旱地山坡上植被的连通性和水分可用性的模式。这些模式将被用来检验径流-径流模型的预测，方法是采用欧拉-拉格朗日耦合建模技术，使这些模型能够预测拉格朗日输运。然后，用测试模型进行的模型实验将利用新兴的多点统计领域，以实现景观表面特征的真实实现，从而能够表征预测的水可获得性、退化风险或恢复质量的不确定性。这项研究将根据径流在山坡上被捕获的可能性，或通过从植被覆盖的斜坡出口流失的可能性，提供对荒漠化脆弱性的景观规模控制的见解。

Due to the lack of vegetation associated with many dryland ecosystems, rainfall generates significant overland flow so that runoff/runon processes become important for providing flows to vegetated sites where water can infiltrate, leading to complex patterns of water resources available to plants. Predicting these flow patterns from easily observable features such as vegetation and topographic patterns, would be instrumental in evaluating landscape vulnerability to desertification. In addition, it would provide design strategies for restoration activities by optimizing vegetation selection, hillslope contouring and planting design. This research aims to develop improved techniques to enable such predictions through models that test runon-runoff process against the experimental behavior of isotopic tracers and through the development of new techniques that improve the realism of modeled vegetation and topographic surfaces. The research will support two doctoral research students (one in Israel and one in the US), and research exchange between research sites at UC Berkeley and Ben Gurion University of the Negev. It will enable outreach activities that use the research findings to inform land restoration planning and guidelines, in conjunction with partners at the UC Berkeley College of Environmental Design and the Keren Kayemeth LeIsrael-Jewish National Fund in Israel.The project will advance predictions of dryland vulnerability to disruption of runon-runoff processes based on the spatial organization of vegetation, hillslope topography and storm characteristics. The aim of such predictions is to identify high priorities for intervention to prevent degradation, and to inform restoration designs, focusing on modification of the vegetation distribution and hillslope form. Previous modeling attempts to generalize the predictions of plant water availability based on spatial pattern were constrained in two ways: (i) The models used to make predictions are generally tested against indirect metrics of the runon-runoff process, such as net discharge from a runoff plot, rather than against observations of site-to-site source-sink transport of water during storm events; thus their ability to constrain the source-sink behavior within a runoff site is questionable. (ii) The model experiments used to connect dryland surface features to water redistribution rely on individual realizations of idealized land surfaces. Consequently, uncertainty is poorly characterized, and the meaning of the resulting metrics for real landscapes is unclear. This proposal exploits isotopic tracer experiments to reveal patterns of connectivity and water availability to vegetation on a well-characterized dryland hillslope at the Lehavim Long Term Ecological Research site in Israel. The patterns will be used to test the predictions of runoff-runon models by adapting these models to predict Lagrangian transport of water using Coupled Eulerian-Lagrangian Modeling techniques. Model experiments with the tested model will then draw on the emerging field of multi-point statistics to enable realistic realizations of landscape surface features, allowing uncertainty in predicted water availability, degradation risk or restoration quality to be characterized. The research will provide insights into landscape-scale controls on desertification vulnerability based on the likelihood that runoff is captured on a hillslope, or is lost via export from vegetated slopes.