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

本项目将调查在当前和预测的未来气候条件下，植被格局的时空变化与系统中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：孤立地说，a）二氧化碳浓度增加使模式向均匀植被条件转变; B）蒸汽压不足增加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，空间变异性（例如土壤格局）就成为植被格局的附加噪声。在这个阈值以上，植被格局的性质取决于格局形成过程的动力学和空间变化的波长。 H3：降雨量的时间随机性将使平均场理论预测的向荒漠化的急剧过渡变得模糊，成为荒漠化概率的“盆地”。 提出了三项任务来解决这三个假设：任务1：模型开发和正演建模，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，同时沿着评估时空变异对格局形态和荒漠化风险的影响;任务2：遥感图像采集、处理和分析，旨在支持任务1和任务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：逆建模，包括参数估计技术的发展，其应用于合成模式时间序列，并最终从案例研究网站的观察。 作为概念的证明，研究人员将首先使用现象学模型（c.f. Lefever和Lejeune）的一个简化的参数空间。 然后，他们将继续进行一个完整的机械逆模型，该模型将利用土壤水分胁迫下植物气孔和生化反应的文献中的详细荟萃分析，以强烈约束生理参数。 拟议的图像分析任务（任务2以上，最初侧重于光谱技术）将被扩展到评估的模式识别技术（包括方法，依赖于降维，如POD和小波阈值）的范围内的适用性，以区分不同的功能，植被格局在多个空间尺度。 这里提出的基于核的方法是非Fickian的，并允许重尾种子扩散核。 时间延迟可以通过允许种子源函数变得依赖于时间来探索。 研究人员使用简化的路由方案进行了广泛的模拟，以确定关键过程和敏感性（主要与表面粗糙度有关，作为维持植被格局的一种手段）。 广泛的文献检索表明，一些合适的数据集检查流速，径流系数和径流缩放在斑块景观。 我们将利用这些数据集进行模型验证。

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.