Spatial and Temporal Assessment of Vulnerability and Resilience in Semi-Arid Landscapes

半干旱景观脆弱性和恢复力的时空评估

• 批准号: 2597525
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597525
• 关键词: Spatial; Temporal; Assessment; Vulnerability; Resilience

Food security, particularly in dryer more climate sensitive areas of the world, is becoming increasingly unstable. Greater understanding of human and biological elements of agricultural and environmental systems, particularly in dryland areas, could help mitigate negative impacts of climate change on food security whilst simultaneously addressing issues of environmental degradation. However, biological systems, agricultural systems and the interplay between the two are complex and non-linear and, at present, available methods are not able to fully and simultaneously capture all dynamic and static elements of these integrated systems (Inwood et al., 2018). The aim of this PhD is to provide the means for landscape scale simultaneous assessment of agricultural and environmental systems, and their interplay, through development of a novel methodology and its application to open-source geo-spatial data. The objectives are three-fold: 1) Assess and adapt methodology for understanding the multivariate spatial-temporal interplay between elements of a complex system; 2) Apply the method to real-world data within two active study-sites and assess its effectiveness; 3) Perform 'what-if?' scenarios to identify leavers of change and 'tipping points' within the system. The geographical focus of this research are two study sites, one in Taita-Taveta hills, south-east Kenya, and the second in Yebelo, southern Ethiopia. Methodological efficacy will be established through mathematical proofs as well as testing in a real world environment via ground-truthing in each of the study sites. Ground-truthing will be informed by site visits and over 1000 RHoMIS household surveys collected and analysed for each study site. 'What-if?' scenarios includes changes in environmental factors such as changes in meteorological activity, increases/decreases in wildlife, changes in forest cover etc, as well as human, such as population change and policy interventions. Simultaneous multivariate spatial-temporal systems analysis is an unsolved problem (Chen et al., 2020). Multiple existing models are available within the latent variable, neural network and Bayesian hierarchical fields. A detailed literature review will be undertaken to identify the most likely path to success.

粮食安全，尤其是在烘干机中更加气候敏感地区的粮食安全，越来越不稳定。对农业和环境系统的人类和生物学元素，尤其是在旱地地区的更多了解，可以帮助减轻气候变化对粮食安全的负面影响，同时解决环境退化问题。但是，生物系统，农业系统以及两者之间的相互作用是复杂的和非线性的，目前，可用的方法无法完全且同时捕获这些集成系统的所有动态和静态元素（Inwood等，2018）。该博士的目的是通过开发新方法及其对开源地理空间数据的应用，同时评估农业和环境系统及其相互作用的景观量表的手段。这些目标是三个方面：1）评估和调整方法，以理解复杂系统元素之间的多元时空相互作用； 2）将方法应用于两个主动研究站点中的现实数据，并评估其有效性； 3）执行“假设什么？”识别系统中变化和“尖端”的外生者的方案。这项研究的地理重点是两个研究站点，一个在肯尼亚东南部的塔塔·塔维塔山，第二个位于埃塞俄比亚南部的耶贝洛。方法学的疗效将通过数学证明以及在现实世界中通过在每个研究地点进行地面进行测试来确定。将通过现场访问和1000多个Rhomis家庭调查收集和分析每个研究地点的地面探索。 '如果什么？'场景包括环境因素的变化，例如气象活动变化，野生动植物的增加/减少，森林覆盖范围的变化等以及人类，例如人口变化和政策干预措施。同时多元时空系统分析是一个未解决的问题（Chen等，2020）。在潜在变量，神经网络和贝叶斯分层字段中可用多种现有模型。将进行详细的文献综述，以确定最可能的成功途径。