Science Area 1: Standard Award: Model-Data Fusion to Examine Multiscale Dynamical Controls on Snow Cover and Critical Zone Moisture Inputs

Mountainous watersheds of the Western US and throughout the world play a critically important role in storing and releasing water that downstream populations depend on for agriculture, energy production, and industrial uses. Despite the broad availability of satellite datasets, numerical weather prediction tools, and on-the-ground observations, there remain key knowledge and data gaps about the mechanisms and pathways through which mountain watersheds store, retain, and release water. These pathways also have profound consequences for the way in which biogeochemical and weathering products, as well as contaminants are transformed and transported through watershed systems. Among the key knowledge gaps are the ways in which year-to-year variability in precipitation phase (i.e., rain or snow), vegetation, and topography control the persistence of mountain snowpacks throughout the winter time and the delivery of water to the subsurface and downstream river networks. Key data gaps that must be filled to improve this mechanistic understanding include historical information in high spatial and temporal detail about the amount and phase of precipitation, surface meteorological conditions, and the amount of water stored as snow. This project will address these and other knowledge and data gaps through an approach that merges extant data from a constellation of long-term satellite observations and cutting-edge models of the coupled land-atmosphere system. In order to capitalize on the existing Department of Energy efforts to advance understanding of watershed-scale ecohydrology and biogeochemistry, our project will focus on a community watershed near Crested Butte, Colorado; the East River watershed. We will synthesize new datasets that reconstruct more than 20 years of hydrometeorological conditions at the land surface - creating hindcasts of precipitation, temperature, winds, solar radiation, humidity and other variables - at spatial resolutions of 1 km and temporal resolutions of 1 hr. We will also use existing satellite datasets to evaluate the accuracy with which these hindcasts capture the spatial extent and temporal variability of mountain snowpacks in the study area. Using these new benchmark datasets, we will test hypotheses regarding the control that elevation and hillslope aspect exert on the onset, extent, and rate of the partitioning of precipitation between rain and snow, and movement of water from snowpacks to the subsurface. We will also examine associations between measures of rain-snow partitioning and elevation on the distribution of different vegetation types. Important outcomes of this research will be advances in our understanding of how water is delivered to mountain landscapes as precipitation, particularly in the form of snow; the way that terrain exerts control the spatiotemporal evolution of mountain snowpacks; and the corresponding implications for delivery of water to the subsurface and, ultimately, the river system. This project will also create important datasets that will support ongoing research efforts at the East River watershed and promote advances in scientific insights that will lead to novel future lines of inquiry.