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

该项目将调查当前和预测未来气候的植被模式系统中植被模式的时空变化与荒漠化概率之间的关系。三个假设框定了该项目的科学范围：h1：孤立地，a)增加的二氧化碳浓度使模式向均匀的植被条件转变；b)增加的水汽压差向沙化条件转变模式。H2：存在一个长度-尺度阈值，低于该阈值的空间变异性(例如，在土壤模式中)将作为植被模式的附加噪声。在这一阈值以上，植被格局的性质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模糊平均场理论预测的荒漠化向荒漠化概率的“盆地”的急剧过渡。为处理这三个假设，提出了三项任务：任务1：模型开发和正演模型，旨在扩展现有模型，并在非洲、美国和澳大利亚四个数据丰富的研究地点对其进行评价，同时评估时空变异性对格局形态和荒漠化风险的影响；任务2：遥感图像获取、处理和分析，旨在支持任务1和任务3中的建模工作，并评估格局形态空间变异的驱动因素；任务3：逆向建模，纳入参数估计技术的开发，将其应用于合成格局时间序列，最终用于案例研究地点的观察。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)具有更小的参数空间。然后，他们将继续建立一个完整的机械逆模型，该模型将从有关植物在土壤水分胁迫下的气孔和生化反应的文献中进行详细的荟萃分析，以强烈限制生理参数。拟议的图像分析任务(上文任务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)在多个空间尺度上区分植被模式的不同特征的适用性。这里提出的基于核的方法是非Fickian的，并且允许重尾种子扩散核。可以通过允许种子源函数变得依赖于时间来探索时间延迟。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(这在很大程度上与作为维持植被模式的手段的表面粗糙度有关)。广泛的文献搜索表明，有一些合适的数据集检验了斑块地貌中的流速、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。

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.