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

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，冬季降水通常以雪的形式积累，在春季和夏季融化，为人类、生态系统和农业提供供水。随着气候变暖和融雪提前，这种自然储水量就会减少。由于水管理系统通常是根据历史观察到的变异性设计的，超出自然变异范围的变化可能会导致系统压力或故障。描述全球变暖何时以及在何种程度下水源流域水文条件发生显着变化是一个尚未充分探索的基础研究问题，但对水资源管理和政策制定也至关重要。通过与社区、研究人员和决策者网络密切合作，该项目将确保研究成果最好地为相关的气候变化适应和政策决策提供信息。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，并促进面向广大受众的以使用为中心的研究。自然气候变率和模型不确定性阻碍了在参考气候状态中出现气候变化信号时提供信息的能力。该项目在科罗拉多河上游流域的源头工作，将使用一种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程度，与水资源决策者合作确定相关指标和时间段支持适应决策，并完善检测变化并将其归因于变暖的机制。该项目框架包括具有新气候模型的大型集合、最先进的集合成员选择和动态降尺度、用于水文建模的创新的基于流程的模块化系统，以及使用统计学习。这种不确定性将通过模型链传播，以评估其对未来雪水资源可预测性和气候变化信号出现的影响。该项目将加强支撑水文气候研究的科学，同时使结果比产生单一确定性变化估计更具有可操作性，更适合风险评估。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。智力价值和更广泛的影响审查标准。