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

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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.