The advent of high-resolution historical climate datasets paired with seasonal climate forecasts provide the potential to improve monitoring and planning for climate sensitive resources. However, the sheer size and complexity of these datasets limits their ability to be used by a wide range of stakeholders. We present a web-based application that overcomes these barriers called Climate Engine. Climate Engine utilizes 1) Google Earth Engine's parallel cloud computing platform to process large amounts of data and to enable users to interactively visualize different climate and remote sensing metrics at multiple time scales and in near real time and 2) University of Idaho web services to disseminate downscaled seasonal climate forecasts for the next 7 months from the North American Multi-model Ensemble project. The web services we developed allow the download of CSV files of point location data and the visualization of the forecast data with a) time series comparisons to historical averages and percentiles for point locations and b) spatial maps of the forecast projections for each month from the different models in the ensemble. By improving access to this data and to real-time visualizations, we aim to improve decision-making regarding restoration/rehabilitation activities.
Oral presentation and poster titles, abstracts, and authors from the Society for Range Management (SRM) Annual Meetings and Tradeshows, from 2013 forward.