Mark Raleigh is working with researchers at the National Snow and Ice Data Center and colleagues at the Colorado Basin River Forecasting Center on the “dust-on-snow” problem. His research aims to understand the regional and historical context of dust deposition on Rocky Mountain snowpack and to assess how operational streamflow models can best account for the enhanced energy absorption that leads to rapid and earlier snowmelt in the spring. Specifically, Raleigh plans to use in-situ observations, models, and remote sensing to investigate (1) the synoptic conditions associated with dust deposition events, (2) the signature of dust-on-snow variability (space, time, concentration) within readily available historic hydrologic records (snowpack and streamflow), and (3) the array of modeling options that could be employed to account for enhanced snowmelt due to dust-on-snow in streamflow forecasting operations. He notes, “The dust-on-snow problem fascinates me because every drop of water in the Colorado Basin matters greatly, and in fact the basin is over-allocated. Dust-on-snow is actually making a bad situation even worse.” Recent research suggests that annual runoff in the basin has been reduced on the order of 5% because evapotranspiration has increased with premature snow cover loss from dust effects. Existing operational models do not directly account for the dust effects and recent work has shown a strong correlation between forecasting errors and annual dust loading. “There is great potential to work on the bridge between research and operations, and that is why I am thrilled to be joining CIRES,” he says. Raleigh is a native of Colorado and was previously a postdoctoral fellow at the National Center for Atmospheric Research in Boulder. He is thrilled to have the opportunity to continue working on snow hydrology in his home state.
- Webb, RW; Raleigh, MS; McGrath, D; Molotch, NP; Elder, K; Hiemstra, C; Brucker, L; Marshall, HP. (Oct 2020). Within-Stand Boundary Effects on Snow Water Equivalent Distribution in Forested Areas. WATER RESOURCES RESEARCH , 56(10). 10.1029/2019WR024905
- Rittger, K; Raleigh, MS; Dozier, J; Hill, AF; Lutz, JA; Painter, TH. (Jun 2020). Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping. WATER RESOURCES RESEARCH , 56(6). 10.1029/2019WR024914
- Smyth, EJ; Raleigh, MS; Small, EE. (May 2020). Improving SWE Estimation With Data Assimilation: The Influence of Snow Depth Observation Timing and Uncertainty. WATER RESOURCES RESEARCH , 56(5). 10.1029/2019WR026853