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.

亚马逊流域是世界上最大的淡水系统，为当地人口、国家社会和整个人类提供重要的生态系统服务。今天，在巴西亚马逊河流域，30多座大型水电站大坝和大约170座较小的大坝正在建设中，这是政府长期计划的结果，这些计划旨在提高能源安全、经济增长、提高生活水平和工业化。尽管巴西的水电开发历史悠久，但在规划和决策中，水坝对河流、森林和社会系统的协同、累积和长期影响仍然被低估。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的方法来分析大坝的影响。 这种不确定性突出表明迫切需要进行协调研究。亚马逊水坝网络将汇集各种利益攸关方，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。 通过合作，该网络将整合和协调关于大坝建设和运营如何影响人民及其生计和环境的研究。 将通过讲习班、案例研究单元、双语网站、地图以及公布的数据和论文来分享知识、数据和技术。 亚马逊水坝网络的目标是促进跨学科和跨学科的研究协调，重点是通过在亚马逊和美国建设水电大坝来改变社会生态系统。地理重点最初包括亚马逊河流域的托坎廷斯、马德拉和辛古河流域以及美国的科罗拉多河流域。网络活动将侧重于四个关键的综合性主题：a）治理和社会行为者（一个贯穿各领域的总体主题，调节水坝系统的相互作用和结果）; B）流域水文学和地貌学; c）鱼类和渔业; d）土地使用/土地覆盖的变化。 该网络要处理的这些主题之间的重点交叉点包括：水文变化与森林生态系统之间的反馈;人类地理错位与土地使用变化;巴西水坝的监测和缓解办法;以及当地知识（渔民、土著人民、沿岸社区）对研究和管理因基础设施发展而改变的系统的相关性。该网络将通过在巴西和美国的研究地点举行的一系列活动，创建基于网络的学习和案例研究模块，以及对可获得的数据进行存档，使参与者和公众能够进行数据发现和综合、知识共享以及跨学科和社会学习。