Collaborative Research: North East Water Resources Network

合作研究：东北水资源网络

• 批准号: 1330406
• 负责人: Arthur Gold
• 金额: $ 200万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1330406&HistoricalAwards=false
• 关键词: Collaborative; Research; North; East; Water

IIA-1330446, University of Vermont, Judith Van Houten Linked to: IIA-1330238, University of Delaware, Daniel Leathers and IIA-1330406, University of Rhode Island, Jennifer Specker.Proposal Title: Collaborative Research: North East Water Resources NetworkThe North East Water Resources (NEWRnet) consortium is comprised of Delaware (DE), Rhode Island (RI), and Vermont (VT). This consortium will create an advanced sensor network in watersheds for gathering high-frequency, spatially-extensive water quality and quantity data and a network of lab and field-based experiments and agent-based models to investigate how to align sensor data and their visualization in a way that will be utilization by stakeholders. Intellectual MeritThe overarching goal of NEWRnet is to improve the environmental governance and market mechanisms that sustain and/or improve water resources by linking information from a network of high-frequency sensors to behavioral results from decision makers. NEWRnet will address and integrate two challenges in environmental science and management to: 1) Understand the impact of climate variability and extreme weather events on water quality and quantity across the north-south gradient from VT to DE; and 2) Provide valuable and rapid information to stakeholders (resource users, resource managers, and policy makers) to lead to more time efficient and more informed land and water use decision making. The research design includes a sensor network nested in watersheds in each of the three states, using the same sensors to gather and transmit high-frequency hydrological and geochemical data for sharing to address hypothesis-driven questions. Concurrently, NEWRnet will create a network across all three states for experimental economics and agent-based modeling. Value-added data displays from the sensors will be tested in lab and field-based experiments to examine how stakeholders react to frequent, multi-dimensional environmental information over large areas and longer time periods, which in turn will inform agent-based models that will simulate the decision making of information users (farmers, residents, businesses, town managers, governance bodies) on a broader scale. The modeling will involve these stakeholders and test scenarios for their use of these high-frequency water quality data. Workforce development and diversity programs will be integrated into the research.Broader ImpactsThe project has significant relevance to local communities as well as more broadly across the US. Researchers engage with diverse stakeholders (farmers, rural and urban residents, lake side residents and local to national level government agencies) and address water quality issues from a broad number of perspectives. Understanding how best to represent high-frequency data for use by stakeholders will be of broad interest beyond the three participating states. Appropriate displays of high-frequency data could allow policy makers and managers to accelerate their responses to low flow, drought and severe storm events, which are predicted to be more frequent and of higher intensity for our region, thereby reducing deleterious effects.

IIA-1330446，佛蒙特大学，朱迪思货车豪顿链接到：IIA-1330238，特拉华州大学，丹尼尔Leathers和IIA-1330406，罗德岛大学，詹妮弗Specker。提案标题：合作研究：东北水资源网络东北水资源（NEWRnet）联盟由特拉华州（DE），罗得岛（RI）和佛蒙特州（VT）组成。该联盟将在流域建立一个先进的传感器网络，用于收集高频，空间广泛的水质和水量数据，以及一个基于实验室和现场的实验和基于代理的模型网络，以研究如何以利益相关者利用的方式调整传感器数据及其可视化。NEWRnet的首要目标是通过将高频传感器网络的信息与决策者的行为结果联系起来，改善环境治理和维持和/或改善水资源的市场机制。NEWRnet将解决和整合环境科学和管理方面的两个挑战：1）了解气候变化和极端天气事件对从VT到DE的南北梯度水质和水量的影响;（2）为利益相关者提供有价值的快速信息（资源使用者、资源管理者和政策制定者），从而做出更省时、更知情的土地和水资源使用决策。研究设计包括一个传感器网络嵌套在流域在三个国家，使用相同的传感器收集和传输高频水文和地球化学数据共享，以解决假设驱动的问题。同时，NEWRnet将创建一个跨越所有三个州的网络，用于实验经济学和基于代理的建模。传感器的增值数据显示将在实验室和现场实验中进行测试，以研究利益相关者如何在大面积和更长时间内对频繁的多维环境信息做出反应，这反过来将为基于代理的模型提供信息，这些模型将在更大范围内模拟信息用户（农民，居民，企业，城镇管理者，治理机构）的决策。建模将涉及这些利益相关者和测试方案，以使用这些高频水质数据。劳动力发展和多样性计划将被纳入研究。更广泛的影响该项目具有显着的相关性，以当地社区以及更广泛的美国各地。研究人员与不同的利益相关者（农民，农村和城市居民，湖畔居民和地方到国家一级的政府机构），并从广泛的角度解决水质问题。了解如何最好地代表高频数据供利益相关者使用将是三个参与国以外的广泛兴趣。适当显示高频数据可以使决策者和管理人员加快对低流量、干旱和严重风暴事件的反应，预计这些事件在我们区域将更加频繁和强度更大，从而减少有害影响。