Collaborative Research: EAR-Climate: Estimating the Emergence of the Anthropogenic Warming Signal in Snow Water Resource Metrics

合作研究：EAR-气候：估计雪水资源指标中人为变暖信号的出现

• 批准号: 2218736
• 负责人: Keith Musselman
• 金额: $ 30万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218736&HistoricalAwards=false
• 关键词: Collaborative; Research; EAR; Climate; Estimating; Collaborative; Research; EAR; Climate; Estimating

In mountainous regions, winter precipitation typically accumulates as snow that melts in the spring and summer providing water supply for people, ecosystems, and agriculture. As the climate warms and snowmelt occurs earlier, this natural water storage is diminished. Since water management systems are often designed based on historically observed variability, change beyond the range of natural variability can result in system stress or failure. The characterization of when and at what global warming level significant change occurs in hydrologic conditions of headwater basins is an under-explored basic research question that is also vital to water resource management and policy making. By working closely with networks of communities, researchers, and decision-makers, the project will ensure that research outcomes best inform relevant climate change adaptation and policy decisions. The project will support career development for one postdoc and rural campus undergraduate students, communicate the climate sensitivity of snow water resources to decision-makers, and promote use-focused research that reaches broad audiences. Natural climate variability and model uncertainties impede the ability to inform when climate change signals emerge from a reference climate state. Working in the headwaters of the Upper Colorado River Basin, this project will use a novel model framework to estimate the emergence and magnitude of the anthropogenic warming signal in snow water resources metrics, work with water resource decision-makers to identify relevant metrics and time periods to support adaptation decisions, and refine mechanisms to detect change and attribute it to warming. The project framework includes a large ensemble with a new climate model, state-of-the-art ensemble member selection and dynamical downscaling, an innovative process-based modular system for hydrological modeling, and the separation of hydrologic outcomes into dynamic and thermodynamic drivers using statistical learning. The uncertainty will be propagated through a model chain to assess its impact on the predictability of future snow water resources and the emergence of climate change signals. The project will strengthen the science that underpins hydroclimatic research while making results more actionable and better suited for risk assessments than if a single deterministic estimate of change was produced.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.

在山区，冬季降水通常会积聚，因为春季和夏季融化的雪为人们，生态系统和农业提供了供水。随着气候温暖和融雪的发生，这种天然水的存储减少了。由于水管理系统通常是基于历史上观察到的可变性而设计的，因此自然变异范围的变化可能导致系统应力或故障。何时和在全球变暖水平的何时及以上的表征在源头水域的水文条件下发生了重大变化，这是一个爆发的基础研究问题，对水资源管理和政策制定也至关重要。通过与社区，研究人员和决策者的网络紧密合作，该项目将确保研究结果为相关的气候变化适应和政策决策提供最佳信息。该项目将支持一名博士后和农村校园本科生的职业发展，向决策者传达雪水资源的气候敏感性，并促进吸引广泛观众的注重使用的研究。自然气候变异性和模型不确定性阻碍了何时气候变化信号从参考气候状态出现的能力。该项目将在科罗拉多河上游流域的源头工作，将使用一个新颖的模型框架来估计雪水资源指标中人为变暖信号的出现和幅度，与水资源决策者一起工作，以确定相关的指标和时间段来确定支持适应决策，并提高适应机制以检测变化和归因于变化和变暖。该项目框架包括一个具有新的气候模型，最新的集合成员选择和动态降低的大型合奏，一种基于创新的过程的基于创新的过程模块化系统，用于水文建模以及使用统计学习将水文结果分为动态和热力学驱动程序。不确定性将通过模型链传播，以评估其对未来雪水资源的可预测性和气候变化信号的影响。该项目将加强基础氢化气候研究的科学，同时使成果更具起作用和更适合风险评估，而不是对变更的单个确定性估计。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。