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

本项目将研究植被格局时空变化与沙漠化概率之间的关系，这些系统在当前和预测的未来气候下呈现出植被格局。三个假设构成了该项目的科学范围：H1：孤立地，a)二氧化碳浓度的增加使模式转向均匀的植被状态；B)向沙化条件增加的蒸汽压亏损转移模式。H2：存在一个长度尺度阈值，低于该阈值，空间变异性（如土壤格局）就会成为植被格局的附加噪声。在此阈值以上，植被格局的性质既取决于格局形成过程的动态，也取决于空间变化的波长。H3：降雨的时间随机性会模糊平均场理论预测的沙漠化的急剧转变，使之成为沙漠化概率的“盆地”。为解决这三个假设，提出了三个任务：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，并评估模式形态空间变化的驱动因素；任务3：逆建模，结合参数估计技术的发展，将其应用于合成模式时间序列，并最终应用于案例研究地点的观察。作为概念的证明，研究人员将首先开发一种使用减少参数空间的现象学模型（c.f. Lefever和Lejeune）的逆建模方法。然后，他们将继续建立一个完整的机制逆模型，该模型将利用对土壤水分胁迫下植物气孔和生化反应的文献进行详细的荟萃分析，以强烈约束生理参数。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POD和小波阈值等降维方法）在多个空间尺度上区分植被模式不同特征的适用性。这里提出的基于核的方法是非菲克式的，并且允许重尾种子传播核。通过允许种子源函数与时间相关，可以探索时间延迟。研究人员使用简化的路径方案进行了广泛的模拟，以确定关键过程和敏感性（主要与维持植被模式的表面粗糙度有关）。广泛的文献搜索表明，许多合适的数据集检查流速，径流系数和径流尺度在斑块景观。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.