The goal of this challenge is to estimate snow water equivalent (SWE) at a high spatiotemporal resolution over the Western U.S. using near real-time data sources. Prizes will be awarded based on the accuracy of model predictions and write-ups explaining the solutions as described below.
Seasonal mountain snowpack is a critical water resource throughout the Western U.S. Snowpack acts as a natural reservoir by storing precipitation throughout the winter months and releasing it as snowmelt when temperatures rise during the spring and summer. This meltwater becomes runoff and serves as a primary freshwater source for major streams, rivers and reservoirs. As a result, snowpack accumulation on high-elevation mountains significantly influences streamflow as well as water storage and allocation for millions of people.
Snow water equivalent (SWE) is the most commonly used measurement in water forecasts because it combines information on snow depth and density. SWE refers to the amount of liquid water contained in a snowpack, or the depth of water that would result if a column of snow was completely melted. Water resource managers use measurements and estimates of SWE to support a variety of water management decisions, including managing reservoir storage levels, setting water allocations, and planning for extreme weather events.
Over the past several decades, ground-based instruments including snow course and SNOwpack TELemetry (SNOTEL) stations have been used to monitor snowpacks. While ground measures can provide accurate SWE estimates, ground stations tend to be spatially limited and are not easily installed at high elevations. Recently, high resolution satellite imagery has strengthened snow monitoring systems by providing data in otherwise inaccessible areas at frequent time intervals.
Given the diverse landscape in the Western U.S. and shifting climate, new and improved methods are needed to accurately measure SWE at a high spatiotemporal resolution to inform water management decisions.
Getting better SWE estimates for mountain watersheds and headwater catchments will help to improve runoff and water supply forecasts, which in turn will help reservoir operators manage limited water supplies. Improved SWE information will also help water managers respond to extreme weather events such as floods and droughts.
This competition will include two tracks. For more information on each track, see the Problem Description.
TRACK 1: Prediction Competition is the core machine learning competition, where participants train models to estimate SWE at 1km resolution across 11 states in the Western U.S.
Stage 1: Model Development (Dec 7 – Feb 15) – YOU ARE HERE
Historical ground truth is made available along with input data sources for model training. This period is an opportunity to build your data pipelines and test modeling approaches. A public leaderboard will be made available to provide feedback, but prizes will not be awarded during this stage.
Stage 2: Model Evaluation (Jan 11 – Jul 1)
Stage 2a: Submission Testing (Jan 11 – Feb 15)
Package everything needed to perform inference on new data each week. This is an opportunity to make any final improvements to your model and ensure it works with approved data sources to generate predictions for real-time evaluation. Submit your code and frozen model weights to be eligible for Stage 2b.
Stage 2b: Real-time Evaluation (Feb 15 – Jul 1)
After the ❄ model freeze ❄, run your model on a weekly basis to generate and submit near real-time predictions throughout the winter and spring season. Predictions will be evaluated against ground truth labels as they become available and prizes will be awarded based on final private leaderboard rankings.
TRACK 2: Model Report Competition (entries due by Mar 15) is a model analysis competition. Everyone who successfully submits a model for real-time evaluation can also submit a report that discusses their solution methodology and explains its performance on historical data.