CNH-RCN: Amazon Dams Network: Advancing Integrative Research and Adaptive Management of Social-ecological Systems Transformed by Hydroelectric Dams

CNH-RCN：亚马逊水坝网络：推进水电大坝改造的社会生态系统的综合研究和适应性管理

• 批准号: 1617413
• 负责人: Bette Loiselle
• 金额: $ 49.97万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617413&HistoricalAwards=false
• 关键词: CNH; RCN; Amazon; Dams; Network

The Amazon basin is the largest freshwater system in the world, providing critical ecosystem services to local populations, national societies and humanity at large. Today in the Brazilian Amazon, construction of more than 30 large hydroelectric dams, and approximately 170 smaller dams on tributaries of the Amazon River are underway as a result of long-term governmental plans geared toward increased energy security, economic growth, improved living standards and industrialization. Despite the long history of hydropower development in Brazil, the synergistic, cumulative and long-term effects of dams on rivers, forests, and social systems are still undervalued in planning and decision-making. Uncertainty about the social-ecological effects of dam construction is due in large part to the existing piecemeal approach to analyzing the impacts of dams. This uncertainty highlights an urgent need for coordinated research. The Amazon Dams Network will bring together a diverse set of stakeholders, including fishermen, indigenous peoples, farmers, scientists, students, and decision-makers to address this need. Working together, the network will integrate and coordinate research on how dam construction and operations affect people, their livelihoods, and the environment. Sharing of knowledge, data and technology will occur through workshops, case-study modules, a bilingual website, maps, and published data and papers. The goal of the Amazon Dams Network is to advance inter- and trans-disciplinary research coordination, focusing on the transformation of social-ecological systems by hydroelectric dam construction in the Amazon and United States. The geographical focus initially includes the Tocantins, Madeira and Xingu River watersheds in the Amazon and the Colorado River watershed in the US. Network activities will focus on four critical integrative themes: a) governance and social actors (an overarching and cross-cutting theme that mediates interactions and outcomes in dammed systems); b) watershed hydrology and geomorphology; c) fish and fisheries; and d) land-use/land-cover change. Focal intersections among these themes to be addressed by the network include feedbacks between hydrologic change and forest ecosystems; human geographic dislocations and land-use change; monitoring and mitigation approaches for Brazilian dams; and the relevance of local knowledge (fishermen, indigenous peoples, riparian communities) for the study and management of systems transformed by infrastructure development. The network will enable data discovery and synthesis, sharing of knowledge, and inter- and transdisciplinary and social learning among participants and the public through a series of activities held at research sites in Brazil and the US, the creation of web-based learning and case studies modules, and the archiving of publically available data.

亚马逊盆地是世界上最大的淡水系统，为当地人口，民族社会和整个人类提供关键的生态系统服务。如今，在巴西亚马逊，由于长期的政府计划旨在提高能源安全，改善的生活水平，生活水平以及改善的生活水平，建设30多个大型水力水坝，以及大约170个较小的水坝在亚马逊河支流上工业化。尽管巴西的水力发电发展悠久，但在规划和决策制定中仍然低估了大坝对河流，森林和社会系统的协同，累积和长期影响。关于大坝建设的社会生态影响的不确定性在很大程度上是由于现有的零碎方法来分析大坝的影响。 这种不确定性凸显了对协调研究的迫切需求。亚马逊大坝网络将汇集各种利益相关者，包括渔民，土著人民，农民，科学家，学生和决策者，以满足这一需求。 该网络共同努力，将整合并协调大坝建设和运营如何影响人们，生计和环境的研究。 知识，数据和技术的共享将通过研讨会，案例研究模块，双语网站，地图以及已发布的数据和论文进行。 亚马逊水坝网络的目标是促进跨学科研究协调，重点是亚马逊和美国的水电大坝建设对社会生态系统的转变。最初的地理重点包括亚马逊的Tocantins，Madeira和Xingu River流域以及美国科罗拉多河流域。网络活动将重点关注四个关键的综合主题：a）治理和社会参与者（一个总体和跨切割主题，介导了堵塞系统中的互动和结果）； b）流域水文学和地貌； c）鱼类和渔业； d）土地使用/土地覆盖。 网络将解决的这些主题之间的焦点交集包括水文变化与森林生态系统之间的反馈；人类的地理位错和土地利用变化；巴西大坝的监测和缓解方法；以及当地知识（渔民，土著人民，河岸社区）与基础设施发展转变的系统的相关性。该网络将通过在巴西和美国的一系列活动中启用参与者和公众之间的数据发现和综合，知识共享以及跨学科和社会学习，创建基于Web的学习和案例研究模块以及公开可用数据的归档。