Citizen science for landslide risk reduction and disaster resilience building in mountain regions

山区山体滑坡风险减少和抗灾能力建设的公民科学

• 批准号: NE/P000452/1
• 负责人: Wouter Buytaert
• 金额: $ 175.36万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FP000452%2F1
• 关键词: Citizen; science; landslide; risk; reduction; Citizen; science; landslide; risk; reduction

Mountains are hotspot of natural disasters, in particular those related to landslides. At the same time, scientific understanding about the natural processes that cause these disasters is lagging behind, because of the complexity of the physical environment and the difficulties facing data collection. The impact of these disasters on society is very high, especially because mountain regions often host less developed infrastructure and vulnerable populations. As a result, there is an urgent need to improve our understanding about how natural disasters in mountain regions occur, how they can be mitigated, and how people at risk can be made more resilient.This proposal will leverage recent technological and conceptual breakthroughs in environmental data collection, processing and communication to leapfrog resilience building in data-scarce and poor mountain communities in South Asia. In particular, we identify three convergent evolutions that hold great promise. First, technological developments in sensor networks and data management allow for participatory and grass-roots data collection and citizen science. Second, web- and cloud based ICT makes it possible to build more powerful analysis and prediction systems, assimilating heterogeneous data sources and tracking uncertainties. Lastly, this enables a more tailored and targeted flow of information for knowledge co-creation and decision-making. These evolutions are part of a trend towards more bottom-up and participatory approaches to the generation of scientific evidence that supports decision making on environmental processes, which is often referred to as "citizen science". We believe that a citizen science approach is particularly promising in remote mountain environments, because improving resilience and humanitarian response in these regions are inherently polycentric activities: a wide range of actors is involved in generating relevant information and scientific evidence, in decision-making and policy building, and in implementing actions both during a hazard and before and after. It is therefore paramount to strengthen the flow of information between these centres of activity, to make best use of existing knowledge, to identify the major knowledge gaps, and to allocate resources to eliminate these gaps.We will use the Karnali basin in Western Nepal as a pilot study. The Karnali basin is a remote and understudied basin that suffers from a complex interplay of natural hazards, including hydrologically-induced landslides and cascading hazards such as flooding. Over the last years, these hazards have caused serious damage to local infrastructure (e.g., roads, irrigation canals, houses, bridges) and affected livelihoods (e.g., 34760 families in the August 2014 floods).Using cost-effective sensor technologies, we will implement grass-roots monitoring of precipitation, river flow, soil moisture, and geomorphology. We will use those data to analyse meteorological extremes, and their impact on spatiotemporal patterns of landslide risk. By merging these data will other data sources such as satellite imagery, we aim to generate landslide risk maps at unprecedented resolution.At the same time, our participatory citizen science approach will enable us to design and implement a framework for bottom-up and polycentric community disaster resilience, based upon knowledge co-generation and sharing.Lastly, we will build upon the existing community-based flood early warning system implemented by our partner Practical Action Nepal, to create a comprehensive multi-hazard early warning system and knowledge exchange platform. For this, we will leverage recent developments in open-standards based, decentralized data processing and knowledge dissemination, such as mobile phones and web-interfaces.

山是自然灾害的热点，尤其是与滑坡有关的山脉。同时，由于物理环境的复杂性和数据收集的困难，对导致这些灾难的自然过程的科学理解落后于落后。这些灾难对社会的影响很高，尤其是因为山区通常拥有较少发达的基础设施和脆弱的人群。结果，迫切需要提高我们对山区自然灾害的发生，如何缓解山区的自然灾害以及如何使处于危险中的人们更具韧性。该提案将利用在环境数据收集中的最新技术和概念突破性的突破性，处理和沟通，以超越South Assia中的数据级别和贫困的山区社区，以超越型号的恢复性。特别是，我们确定了三种融合的发展，它们具有巨大的希望。首先，传感器网络和数据管理中的技术发展允许参与性和基层数据收集以及公民科学。其次，基于Web和云的ICT使得可以建立更强大的分析和预测系统，吸收异质数据源并跟踪不确定性。最后，这使知识共同创造和决策更量身定制和针对性的信息流。这些演变是促进更多自下而上和参与方法的趋势的一部分，以产生科学证据，以支持对环境过程的决策，这通常被称为“公民科学”。我们认为，在偏远的山区环境中，公民科学方法尤其有希望，因为这些地区的韧性和人道主义反应是固有的。因此，至关重要的是，在这些活动中心之间加强信息流，最佳利用现有知识，确定主要的知识差距，并分配资源来消除这些差距。我们将在尼泊尔西部使用Karnali盆地作为试点研究。卡纳利盆地是一个遥远而研究的盆地，遭受了复杂的自然危害相互作用，包括水文引起的滑坡和级联危险，例如洪水。在过去的几年中，这些危害对当地基础设施（例如道路，灌溉管，房屋，桥梁）和受影响的生计造成了严重损害（例如，2014年8月的34760个家庭。使用成本效益的传感器技术，我们将实施基层的降水，河流流量，水流，杂货，和Geom的水分，并进行了基层技术。我们将使用这些数据来分析气象极端及其对滑坡风险时空模式的影响。通过合并这些数据将将其他数据源（例如卫星图像）汇合，我们旨在在前所未有的解决方案下生成滑坡风险图。在同一时间，我们的参与性公民科学方法将使我们能够基于自下而上和多年社区的框架来设计和实施一个基于知识共享和共享的社区实施的框架，我们将建立较早的诉讼，我们将实施较早的诉讼。全面的多危险预警系统和知识交换平台。为此，我们将利用基于开放标准的，分散的数据处理和知识传播的最新发展，例如手机和Web-Interfaces。