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，特别是在世界上对气候较为敏感的干旱地区，正变得越来越不稳定。更好地了解农业和环境系统中的人和生物因素，特别是旱地地区的人和生物因素，有助于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。然而，生物系统、农业系统以及两者之间的相互作用是复杂且非线性的，并且目前，可用的方法不能完全且同时捕获这些集成系统的所有动态和静态元素（Inwood等人，2018年）。该博士学位的目的是通过开发一种新的方法及其在开源地理空间数据中的应用，为农业和环境系统及其相互作用的景观规模同时评估提供手段。目标有三个方面：1）评估和调整方法，以了解复杂系统的元素之间的多变量时空相互作用; 2）将该方法应用于两个活跃的研究站点内的真实世界数据，并评估其有效性; 3）执行“如果？“方案，以确定系统内的变化和”临界点“的离开者。本研究的地理重点是两个研究地点，一个在肯尼亚东南部的Taita-Taveta丘陵，另一个在埃塞俄比亚南部的Yebelo。将通过数学证明以及在真实的世界环境中通过每个研究中心的地面实况进行测试来确定方法学有效性。实地考察将通过实地考察和为每个研究地点收集和分析的1000多份RHoMIS家庭调查来了解情况。“万一呢？“情景包括环境因素的变化，如气象活动的变化、野生动物的增加/减少、森林覆盖的变化等，以及人类因素的变化，如人口变化和政策干预。同时多变量时空系统分析是一个未解决的问题（Chen等人，2020年）。在潜在变量、神经网络和贝叶斯分层字段中有多个现有模型可用。将进行详细的文献综述，以确定最有可能的成功途径。