CNH2-S: Testing the strength of coupling among climate, natural, and human systems using big data

CNH2-S：利用大数据测试气候、自然和人类系统之间的耦合强度

• 批准号: 2009833
• 负责人: Virginia Iglesias
• 金额: $ 59.87万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2009833&HistoricalAwards=false
• 关键词: CNH2; Testing; strength; coupling; among

Climate variations cause economic losses every year, but adaptations built into resource and supply systems, such as agriculture and food, energy, water resources, and manufacturing supply chains, buffer these impacts. Although management strategies and adaptation have been examined in many case studies, systematic analysis that can add up to fundamental insight has been limited. To fill this gap, this project undertakes a big-data, hypothesis-driven, assessment of the sensitivity of a variety of socio-environmental systems to historical fluctuations in climate across the continental United States. The analytical challenge is to do this empirically, with evidence from diverse sources, across multiple sectors, at high resolution, and at the national scale. By contrasting systems under different management intensities experiencing similar climate conditions, this research expects to constrain changes in system sensitivity due to management. The project thus bridges theoretical approaches from the biophysical, social, and information sciences in ways that can be used to evaluate the effects of climate variability and change, the effectiveness of adaptations in the U.S. economy, and strategies for sustainability under changing conditions. Streamflow, vegetation, and wildfire dynamics are interconnected through water and energy limits and demands which are, in turn, regulated by climate. Management goals for water resources, agricultural productivity, and hazard mitigation ultimately aim to minimize the impact of disturbances on water availability, food production, and safety. Adaptation strategies typically focus on minimizing sensitivity to climate fluctuations, albeit in ways constrained by cost, technology, and competing needs across systems. This project will leverage the climatological diversity and long history of management and adaptation in the contiguous United States to conduct a big-data, hypothesis-driven analysis for how the strength of coupling between climate and socio-environmental systems varies as a function of management intensity. The conceptual framework for analyzing and interpreting the effects of management is based on complex-systems theory, whereby impact can be viewed as an emergent property. The degree of coupling between climate and systems under contrasting styles and intensities of management can be characterized as the sensitivity of system indicators (streamflow, reservoir levels, vegetation productivity, wildfire, crop yields, and economic activity) to changes in the statistical properties of the climate forcing (mean, variance, entropy, and determinism), and quantify it with dose-response functions. Results will be used to identify natural experiments where comparisons between heavily- managed systems adjacent to those that are managed minimally can constrain how management and adaptation affect system sensitivity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

气候变化每年都会造成经济损失，但资源和供应系统（如农业和粮食、能源、水资源和制造业供应链）中的适应措施可以缓冲这些影响。虽然在许多案例研究中对管理战略和适应性进行了研究，但能够形成基本见解的系统分析却很有限。为了填补这一空白，该项目进行了一项大数据，假设驱动，评估各种社会环境系统对美国大陆气候历史波动的敏感性。分析的挑战是凭经验做到这一点，证据来自不同来源，跨多个部门，高分辨率，并在全国范围内。通过对比不同管理强度下经历相似气候条件的系统，本研究期望限制由于管理引起的系统敏感性的变化。因此，该项目将生物物理学，社会学和信息科学的理论方法连接起来，可用于评估气候变异性和变化的影响，美国经济适应的有效性，以及在不断变化的条件下的可持续发展战略。 水流、植被和野火动态通过水和能源的限制和需求相互关联，而这些限制和需求又受到气候的调节。水资源、农业生产力和减灾的管理目标最终旨在最大限度地减少干扰对水资源可用性、粮食生产和安全的影响。适应战略通常侧重于最大限度地降低对气候波动的敏感性，尽管其方式受到成本、技术和各系统之间相互竞争的需求的限制。该项目将利用美国本土的气候多样性和长期的管理和适应历史，对气候和社会环境系统之间的耦合强度如何随管理强度而变化进行大数据、假设驱动的分析。分析和解释管理效果的概念框架是基于复杂系统理论，其中影响可以被视为一个新兴的属性。气候和系统之间的耦合程度的对比风格和强度的管理可以表征为系统指标（径流，水库水位，植被生产力，野火，作物产量和经济活动）的敏感性变化的统计特性的气候强迫（平均值，方差，熵，和确定性），并量化它与剂量响应函数。结果将被用来识别自然实验，在这些实验中，高度管理的系统与管理最低的系统之间的比较可以限制管理和适应如何影响系统灵敏度。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。