Snow is a fundamental feature of the Rocky Mountains, with ecological implications for a majority of habitats and species. Climate change has the potential to alter this critical resource, with cascading effects on other systems and conservation targets. Expected changes include shifts in precipitation amounts, type, and timing and snow persistence (particularly affected by projections of more frequent rain-on-snow events), reductions in snow-water equivalent (SWE), and shifts in snowline elevation.

The North Central Climate Adaptation Science Center (NC CASC) has received input from both federal and state partners (JSC 2019) and from the Northwest CASC that improved snow models and snow projections are of high interest and value for species and ecosystem management. The fundamental functions of snow in creating and maintaining habitat conditions for numerous species highlight the importance of understanding changes to this landscape feature. Projections of snow distribution, depth, and persistence would provide critical data for assessments under the Endangered Species Act, as well as for federal, state and tribal climate-informed management planning. Yet such modelling is a highly computationally intensive activity with numerous methodological decision points. This necessitates input from multiple potential users to maximize the utility of the resulting model outputs.

There are substantial scale, algorithmic, and operationalization choices, and associated tradeoffs in terms of cost, uncertainty, and computational intensity, in how this modeling would be conducted for the entire Rocky Mountain region. Thus, it is imperative to engage directly with potential users to ensure the product and outputs are useful to multiple partners.

Proposed work includes a Collider, which would allow a face-to-face meeting of potential snow model end-users and the modeling team. This meeting would cap a series of virtual engagements to refine the relevant modeling design choices and tradeoffs, enabling an efficient consultation in person. We intend to convene up to 15 attendees, in addition to the three PIs. Collider attendees will predominantly be state, federal, and tribal land and resource managers from throughout the Rocky Mountain region that have expressed previous interest in detailed snow modelling. We envision a full-day meeting to be held in May 2020. The morning session would include lightning talk style presentations from managers regarding key snow-related management and data needs followed by presentations from the modelling team regarding critical trade-offs in snow modelling. Mid-day sessions would focus on clearly defining the modelling motivations and objectives and identifying specific stakeholder needs. The end of the day would comprise targeted discussions to identify the most useful deliverables given computational constraints. The collider will wrap-up by identifying specific next steps for conducting the snow modelling work.

This Collider will establish a working group for sustained engagement that will give natural resource managers in state, federal, and tribal agencies improved information on how snow, a major habitat component, will change over the coming decades. This will be done via direct co-development with individual users, and across multiple user groups, both to maximize the utility to individual user agencies and to minimize duplicative investment. The amount of geospatial and climate data needed to address this problem is large and one of the key outcomes will be defining and clarifying the proper scope of a solution to address these challenges. For example, the total area of the Level-III ecoregions is 763,795.11 km2, which would represent 12,220,722 and 3,055,180 model grid cells at 250 m and 500 m, respectively. While higher resolutions are preferred, the size of the input data, computational expense, and accuracy of meteorological forcing data at higher resolution all present unique challenges.

Project Team

Project Lead

Ben Livneh