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

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水。随着气候变暖，融雪时间提前，这种天然储水量减少。由于水管理系统通常是根据历史上观察到的可变性来设计的，因此超出自然可变性范围的变化可能导致系统压力或故障。水源流域水文条件何时以及在何种全球变暖水平上发生显著变化，这是一个尚未得到充分探索的基础研究问题，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密切合作，该项目将确保研究成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，并促进面向广泛受众的以用途为重点的研究。自然气候变率和模式的不确定性阻碍了我们在参考气候状态出现气候变化信号时提供信息的能力。在上科罗拉多河流域的源头，该项目将使用一个新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强度，与水资源决策者合作，确定相关指标和时间段，以支持适应决策，并完善机制，以检测变化并将其归因于变暖。项目框架包括一个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，一个创新的基于过程的水文建模模块化系统，以及使用统计学习将水文结果分离为动态和热力学驱动因素。这种不确定性将通过一个模式链传播，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的确定性变化估计更具有可操作性，更适合于风险评估。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。

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.