Desertification risks of dryland ecosystems inferred from the dynamics of coherent spatial vegetation patterning

从相干空间植被格局的动态推断旱地生态系统的荒漠化风险

• 批准号: 1013339
• 负责人: Gabriel Katul
• 金额: $ 30.12万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1013339&HistoricalAwards=false
• 关键词: Desertification; risks; dryland; ecosystems; inferred

This project will investigate the relationship between spatiotemporal variation of vegetation patterns and desertification probabilities in systems exhibiting patterned vegetation for current and projected future climates. Three hypotheses frame the scientific scope of the project: H1: In isolation, a) increased CO2 concentrations shift patterns towards a homogeneous vegetated condition; b) increased vapor pressure deficit shift patterns towards desertified conditions. H2: A length-scale threshold exists below which spatial variability (e.g. in soil patterns) acts as additive noise to vegetation patterns. Above this threshold, the properties of the vegetation pattern depend on both the dynamics of the pattern forming processes, and the wavelength of the spatial variation. H3: Temporal stochasticity in rainfall will blur sharp transitions to desertification predicted by mean-field theory into a "basin" of desertification probabilities. Three tasks are proposed to address these three hypotheses: Task 1: Model Development and Forward Modeling, intended to extend existing models and evaluate them at four data-rich study locations in Africa, the USA and Australia, along with assessing the implications of spatiotemporal variability on pattern morphology and desertification risk; Task 2: Remote Sensing Imagery Acquisition, Processing and Analysis, intended to support the modeling efforts in Tasks 1 and 3, and to assess the drivers of spatial variation in pattern morphology; Task 3: Inverse Modeling, incorporating the development of parameter estimation techniques, their application to synthetic pattern time series and ultimately to observations from case study sites. As a proof of concept, the investigators will initially develop an inverse modeling methodology using a phenomenological model (c.f. Lefever and Lejeune) with a reduced parameter space. They will then proceed to a full mechanistic inverse model that will draw on a detailed meta-analysis from the literature on stomatal and biochemical responses of plants under soil moisture stress to strongly constrain the physiological parameters. The proposed image analysis task (Task 2 above, initially focused on spectral techniques) will be extended to evaluate the suitability of a range of pattern identification techniques (including methods that rely on dimension reduction such as POD and wavelet thresholding) to discriminate different features of vegetation patterning over multiple spatial scales. The kernel-based approaches proposed here are non-Fickian and permit heavy-tailed seed dispersal kernels. Time delays can be explored by allowing the seed source function to become time dependent. The researchers have conducted extensive simulations using simplified routing schemes to identify key processes and sensitivities (which relate largely to surface roughness as a means of sustaining vegetation patterning). Extensive literature searches indicate a number of suitable datasets examining flow velocities, runoff coefficients and runoff scaling in patchy landscapes. We will draw on these datasets for model validation.

该项目将研究在当前和预测的未来气候下展示植被模式的系统中植被模式的时空变化与荒漠化概率之间的关系。 三个假设构成了该项目的科学范围： H1：单独来看，a）二氧化碳浓度的增加使植被条件趋于均匀； b) 增加的蒸气压赤字使模式向荒漠化条件转变。 H2：存在一个长度尺度阈值，低于该阈值，空间变异（例如土壤格局）将成为植被格局的附加噪声。超过这个阈值，植被格局的特性取决于格局形成过程的动态和空间变化的波长。 H3：降雨的时间随机性将使平均场理论预测的荒漠化急剧转变模糊为荒漠化概率的“盆地”。 提出了三项任务来解决这三个假设： 任务 1：模型开发和正向建模，旨在扩展现有模型并在非洲、美国和澳大利亚的四个数据丰富的研究地点对其进行评估，同时评估时空变异性的影响关于格局形态和荒漠化风险；任务2：遥感图像采集、处理和分析，旨在支持任务1和3中的建模工作，并评估模式形态空间变化的驱动因素；任务 3：逆向建模，结合参数估计技术的发展及其在合成模式时间序列中的应用，并最终应用于案例研究站点的观察。 作为概念证明，研究人员将首先使用具有缩小参数空间的现象学模型（参见 Lefever 和 Lejeune）开发逆向建模方法。 然后，他们将继续建立一个完整的机械逆模型，该模型将利用有关植物在土壤水分胁迫下的气孔和生化反应的文献的详细荟萃分析，以强烈限制生理参数。 所提出的图像分析任务（上面的任务 2，最初侧重于光谱技术）将扩展到评估一系列模式识别技术（包括依赖于降维的方法，如 POD 和小波阈值处理）来区分不同特征的适用性。多个空间尺度上的植被图案。 这里提出的基于内核的方法是非菲克式的，并且允许重尾种子传播内核。 可以通过允许种子源函数变得与时间相关来探索时间延迟。 研究人员使用简化的路线方案进行了广泛的模拟，以确定关键过程和敏感性（这在很大程度上与表面粗糙度作为维持植被模式的手段有关）。 广泛的文献检索表明有许多合适的数据集用于检查斑块景观中的流速、径流系数和径流比例。 我们将利用这些数据集进行模型验证。