Coastal Erosion Vulnerabilities, Monsoon Dynamics, and Human Adaptive Response

该项目将调查低海拔环境中的海岸侵蚀如何与季节性降水模式联系在一起，人类如何看待其对侵蚀风险的脆弱性，以及人类如何在侵蚀面前确保生计。 沿海地区的农村人口依靠土地资源支持农业，依靠海洋资源支持渔业活动。 海岸侵蚀有可能威胁到经济和地缘政治的稳定，因为丧失的土地资源具有永久性，剥夺了人口的陆地资源和海洋资源。 侵蚀事件发生的地点和时间的变化给人们带来不确定性，他们必须在没有关于风险和脆弱性的连贯的科学信息的情况下应对侵蚀威胁。该项目有助于更好地理解沿海环境变化、低地沿海人口、大气科学和经济发展之间的交叉点。 研究结果将为沿海进程以及人类脆弱性和复原力提供信息，还将在定量地理空间分析方面取得方法上的进展。 学生将接受STEM科学培训，项目要素将用于加强两所大学现有课程的模块。 项目成果将在期刊和其他渠道传播，提交给政府机构和非政府组织，并与当地利益攸关方分享。 该项目将在复原力连接网络注册，这是一个国际虚拟空间，供全球和地方领导人与复原力科学从业人员进行互动。该项目的研究地点是一条大河的三角洲，该三角洲是一个大流域中季风降水驱动的排水口，该流域的海岸线特别容易受到侵蚀。 该地区每年的侵蚀率很高，海岸线每年可能后退100米以上，造成生计中断，农业和渔业家庭流离失所。 该项目有三个目标。 第一个目标是在多个尺度上描述海岸线侵蚀和季风动态的时空模式。 了解年度尺度的侵蚀率对于确定脆弱性特征特别重要，因为家庭需要在较短的时间范围内进行预测和应对，而在较粗的十年尺度上无法解决这一问题。 第二个目标是评估季风降水和侵蚀动力学之间的预测关系，假设降雨的时空模式可以帮助预测侵蚀行为。 第三个目标评估社会脆弱性、风险认知以及资源禀赋和适应行为如何促进复原力。 将采用混合方法实现这些目标，该方法将利用地球观测数据进行地理空间分析、统计和机器学习预测建模以及在两个村庄收集的定量和定性基本社会数据相结合。 调查人员将制作一份大约50年的海岸线变化时间序列记录，与月降水量数据一起用于开发侵蚀风险的年度预测模型。 将进行实地研究，通过进行家庭调查、焦点小组和关键知情人访谈，从当地人口的风险认知、脆弱性和适应行为方面收集社会数据。 虽然该项目将侧重于孟加拉国恒河-布拉马普特拉河-梅克纳河系统的三角洲地区，但该研究将为处理包括美国在内的其他地区人口稠密的沿海地区的海岸侵蚀提供新的见解。

This project will investigate how coastal erosion in low elevation environments is linked to seasonal precipitation patterns, how humans perceive their vulnerability to erosion risk, and how humans secure livelihoods in the face of erosion. Rural populations in coastal zones are dependent on land resources to support agriculture and on marine resources to support fishery-based activities. Coastal erosion has potential to threaten economic and geopolitical stabilities due to the permanent nature of lost land resources that deprives population of land-based resources and access to marine resources. Variation in the location and timing of erosion events produces uncertainty for populations who must negotiate erosion threats without the benefit of consistent science-informed information regarding risk and vulnerability. This project contributes to a better understanding of the intersection of coastal environmental change, lowland coastal populations, atmospheric science, and economic development. Results will inform coastal processes, as well as human vulnerability and resilience, and will also make methodological advances in quantitative geospatial analysis. Students will be trained in STEM science and project elements will be used to enhance modules of existing courses at two universities. Project results will be disseminated in journals and other outlets, presented to government agencies and NGOs, and shared with local stakeholders. The project will become registered with the Resilience Connections Network, an international virtual space for the interaction between global and local leaders on resilience science and practitioners. The study site for this project is the delta of a large river that is the outlet for water discharge driven by monsoon precipitation in a large watershed with a shoreline particularly vulnerable to erosion. The region has high rates of annual erosion whereby shorelines may retreat over 100 meters per year causing livelihood disruption and displacement of farming and fishing households. The project has three objectives. The first objective characterizes time-space patterns of shoreline erosion and monsoon dynamics at multiple scales. Understanding erosion rates at annual scales is particularly important to characterize vulnerability because households need to forecast and respond over relatively short time horizons that are not resolved at coarser decadal scales. The second objective assesses predictive relationships between monsoon precipitation and erosion dynamics with the hypothesis that time-space patterns of rainfall can help predict erosion behavior. A third objective assesses social vulnerability, risk perception and how resource endowments and adaptive behavior may promote resilience. These objectives will be addressed using a mixed-methods approach that combines geospatial analysis using earth observation data, statistical and machine-learning predictive modelling, and quantitative and qualitative primary social data collected in two villages. Investigators will produce an approximately 50-year time series record of shoreline change that will used in concert with monthly precipitation data to develop an annual predictive model of erosion risk. Field research will be done to collect social data from local populations in terms of their risk perception, vulnerability, and adaption behaviors by conducting a household survey, focus groups, and key informant interviews. Although the project will focus on the delta region of the Ganges-Brahmaputra-Meghna river system in Bangladesh, the research will provide new insights for dealing with coastal erosion in densely populated coastal zones in other regions, including the United States.