At present, no current snow water supply forecasting model can account for the impacts of burned area on snow accumulation, ablation, and runoff generation - this major GAP will only be exacerbated by…

This Cooperative Agreement award from the Bureau of Reclamation under the Snow Water Supply Forecasting Program (CFDA 15.078) will address key knowledge gaps in understanding the impacts of wildfire on snow accumulation, ablation, and runoff generation. The $876,296.41 project, with a period of performance from January 4, 2024 to September 30, 2026, will leverage a network of automated stations and field studies initiated following the 2020 Cameron Peak and East Troublesome Fires in Colorado. Key emerging technologies such as 4-component net radiometers, snow temperature sensors, unmanned aerial vehicle-based LiDAR, and ground penetrating radar will be used to collect detailed snow observations. These field data will be integrated with satellite remote sensing methods to refine the physics-based ISNOBAL snow model and improve streamflow forecasting, particularly in headwater systems affected by wildfire. As part of this effort, Boise State University will provide snow modeling expertise and support for mentoring graduate students at Colorado State University, the primary award recipient.

At present, no current snow water supply forecasting model can account for the impacts of burned area on snow accumulation, ablation, and runoff generation - this major GAP will only be exacerbated by projected wildfire increases in future decades. This project will address key knowledge gaps by leveraging and expanding a network of automated stations and field studies that were initiated following the 2020 cameron peak and east troublesome fires in north-central colorado. The project will apply key emerging technologies, including 4-component net radiometers, snow temperature sensors to estimate cold content, unmanned aerial vehicle-based light detection and ranging, and ground penetrating radar to measure snow depth and density. The project will use this suite of field observations and findings, in conjunction with satellite-based remote sensing methods, to refine parameterizations in a physically-based snow model, isnobal automated water supply model. The project will evaluate the extent to which wildfire impacts streamflow in these headwater systems and examine how these impacts can be incorporated into streamflow forecasts. The project will have multiple nested scales - point and plot scale observations, as well as remote sensing methods that include multiple fire-impacted basins in the western US, including the upper colorado river basin.