Innovative Analysis of Large River Topography and Dynamics (Ref:4243)

大河流地形和动力学的创新分析（参考：4243）

• 批准号: 2698584
• 金额: --
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2698584
• 关键词: Innovative; Analysis; Large; River; Topography

Fluvial system sustainability is vital for a half billion riparian residents of global river floodplains and deltas, and it requires knowledge and understanding of the land surface changes through time in response to anthropogenic and climatic forcing. Germane to such understanding are high quality topographic data enabling the quantification of surface morphology and change, which are essential for understanding responses of lowland sedimentary systems to these forcings. However, global topographic data are of insufficient quality and resolution, creating major challenges for managing lowland river-floodplain complexes in large alluvial rivers. This scarcity of topographic data is especially applicable to low-gradient tropical rivers where 1) accurate topographic survey data are scarce, 2) floodplains have significant vegetation precluding satellite observation of surfaces, and 3) previous global data (eg., SRTM) are technically limited in terms of accuracy and resolution for geomorphic applications. The TanDEM-X high-resolution SAR mission (directory.eoportal.org/web/eoportal/satellite-missions/t/tandem-x) offers a way forwards, especially when paired with remote sensing data and machine learning statistical methods.Our exploratory work with the high-resolution (~12m) TDX product in Amazonia and SE Asia has identified means to remove trees using a combination of tomographic analysis, remote sensing, and machine learning using GPUs - producing bare earth DEMs for fluvial systems that can be verified for locations where we have field survey and Lidar data from prior and current NERC, NSF, and NASA-funded projects. The PhD student would focus on the refinement and application of these novel data and techniques towards quantifying the topography and evolution of project rivers - the lower Mekong River and large portions of the Amazon, major river systems that can be used to calibrate and test these approaches in coordination with ongoing NERC-funded research. Following this verification and refinement against available data, the student would work with the supervisorial team to identify and pursue a range of intriguing scientific questions for additional research locations, thus providing the PhD candidate flexibility and intellectual independence to realize their own interests and pursue new opportunities for transformative research. Project Aims and MethodsAfter refining the analytical methods for study areas with suitable field data, the PhD research would focus on quantifying the floodplain topography and gradients that drive flow, sediment transport, and river evolution for research sites, providing novel scientific insight into the functioning of fluvial systems. To further evaluate observed relationships, the student would work closely with the supervisorial team to design the reminder of their PhD research. For example, potential causal mechanisms identified for study rivers could be tested using models for fluvial morphodynamics (both standard and Exeter research models). Measurements and predictions could be verified against extensive Exeter field datasets available for the study systems and elsewhere, with possibilities for fieldwork. The ultimate aim of this PhD will be to produce generic findings about the functioning and complex evolution of large river systems that are suitable for publication in prominent journals, preparing the student for further career success.

河流系统的可持续性对于全球河流洪泛平原和三角洲的5亿沿岸居民至关重要，它需要了解和理解随着时间的推移对人为和气候强迫的陆地表面变化。Germane这样的理解是高质量的地形数据，使量化的表面形态和变化，这是必不可少的了解这些强迫的低地沉积系统的响应。然而，全球地形数据的质量和分辨率不足，给管理大型冲积河流中的低地河流-洪泛区复合体带来了重大挑战。这种地形数据的稀缺性特别适用于低梯度热带河流，其中1）准确的地形测量数据稀缺，2）洪泛区有大量植被，无法对表面进行卫星观测，3）以前的全球数据（例如，SRTM）在技术上受限于地貌应用的精度和分辨率。TanDEM-X高分辨率SAR使命（directory.eoportal.org/web/eoportal/satellite-missions/t/tandem-x）提供了一种前进的方式，特别是当与遥感数据和机器学习统计方法配对时。我们在亚马逊和东南亚使用高分辨率（~ 12米）TDX产品的探索性工作已经确定了使用层析分析，遥感，和使用GPU的机器学习-为河流系统生成裸露地球DEM，可以在我们拥有来自先前和当前NERC，NSF和NASA资助项目的现场调查和激光雷达数据的位置进行验证。博士生将专注于这些新数据和技术的改进和应用，以量化项目河流的地形和演变-湄公河下游和亚马逊的大部分地区，主要河流系统可用于校准和测试这些方法，并与正在进行的NERC资助的研究相协调。在对现有数据进行验证和改进之后，学生将与监督团队合作，为其他研究地点确定和追求一系列有趣的科学问题，从而为博士候选人提供灵活性和智力独立性，以实现自己的兴趣并寻求变革性研究的新机会。项目目标和方法在完善了具有合适现场数据的研究区域的分析方法后，博士研究将侧重于量化洪泛区地形和梯度，这些地形和梯度驱动研究地点的流量，泥沙输运和河流演变，为河流系统的功能提供新的科学见解。为了进一步评估观察到的关系，学生将与导师团队密切合作，设计他们的博士研究的提醒。例如，可以使用河流形态动力学模型（标准和埃克塞特研究模型）来测试研究河流确定的潜在因果机制。测量和预测可以验证对广泛的埃克塞特现场数据集可用于研究系统和其他地方，与现场工作的可能性。该博士学位的最终目标是产生关于大型河流系统的功能和复杂演变的通用发现，这些发现适合在著名期刊上发表，为学生进一步的职业成功做好准备。