SAI: Stormwater Resilience in Urban Areas

加强美国基础设施（SAI）是美国国家科学基金会的一个项目，旨在促进以人为本的基础研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门的创新，发展经济，创造就业机会，使公共部门提供的服务更有效率，加强社区，促进机会平等，保护自然环境，加强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI侧重于人类推理和决策、治理以及社会和文化过程的知识如何使有效的基础设施的建设和维护能够改善生活和社会，并以技术和工程的进步为基础。随着时间的推移，气候变化预计会增加强降雨事件的频率，造成更多的洪水和水质下降。在美国的许多城市地区，历史上对雨水基础设施的投资不足，对不同社会经济地位的社区产生了不平等的影响。其结果是城市地区在水质、可靠性和基础设施方面的差距越来越大。需要新的政策和创新的雨水管理方法来为未来的城市洪水做好准备，并确保公平的水资源管理。该SAI项目借鉴了社会科学、水文建模、环境工程和景观建筑学方面的专业知识，提高了公众的认识，评估了雨水基础设施的差异，并确定了可行的政策方案。洪水预测和雨水管理需要水文模型来模拟在各种控制和土地管理情景下，水通过水文过程从降水进入溪流的运动。这是一个本质上由数据驱动的过程，依赖于降水、排放量、土地利用、水资源利用和整个流域显著不同的水文测量。收集此类数据的常用方法不足以了解局地尺度的洪水，特别是由降雨引起的城市洪水。该项目通过利用两种形式的公民科学来改善城市雨水基础设施管理，解决了这一挑战。一种是依靠众包数据收集来识别暴雨洪水事件。另一种方法是让市民亲自参与水质检测。芝加哥之所以被用作开发地点，是因为它容易受到洪水的影响，而且其社区的历史模式在社会经济地位上存在很大差异。为了培养下一代工程师、科学家和倡导者解决雨水管理中最突出的问题，我们制定了一个新的培训计划，为学生提供公共政策、水文和气候科学以及社会正义问题的背景知识。该奖项由社会、行为和经济（SBE）科学理事会和地球科学理事会支持。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.The changing climate is expected to increase the frequency of heavy rainfall events, causing more floods and decreasing water quality over time. Within many urban areas of the U.S. there has been historically poor investment in stormwater infrastructure with unequal impacts among communities that vary in socioeconomic status. The result is growing disparities in water quality, reliability, and infrastructure within urban areas. New policies and innovative stormwater management approaches are needed to prepare for future urban flooding and to ensure equitable water management. Drawing from expertise in social science, hydrological modeling, environmental engineering, and landscape architecture, this SAI project improves public understanding, assesses stormwater infrastructure disparities, and identifies viable policy options moving forward.Flood prediction and stormwater management requires hydrological models to simulate the movement of water from precipitation into streams through hydrological processes under various control and land management scenarios. This is an inherently data-driven process, relying on measurements of precipitation, discharge, land usage, water usage, and hydrography that vary significantly throughout a watershed. Common approaches to collecting such data are not sufficient for understanding local-scale flooding, especially urban flooding caused by rainfall. This project addresses the challenge by utilizing two forms of citizen science to improve urban stormwater infrastructure management. One relies on crowdsourced data collection for identifying stormwater flooding events. The other involves citizen-engaged hands-on water quality testing. Chicago is used as the development site because of its vulnerability to flooding and its historical pattern of communities that vary substantially in socioeconomic status. To prepare the next generation of engineers, scientists, and advocates to address the most salient issues in stormwater management, a novel training program is developed to provide students with a background in public policy, hydrologic and climate science, and social justice issues.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Geosciences.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.