Water Supply Forecast Rodeo: Hindcast Evaluation

Water managers in the Western U.S. rely on accurate water supply forecasts to better operate facilities and mitigate drought. Help the Bureau of Reclamation improve seasonal water supply estimates in this probabilistic forecasting challenge! [Hindcast Evaluation Arena] #climate

$50,000 in prizes
jan 2024
99 joined

Report submission format

In addition to the code submission, you must submit a detailed model report outlining your solution methodology in order to be considered for Hindcast Stage Prizes. For details on the evaluation criteria, please refer to the Evaluation and Eligibility section in the Problem Description.

You can find the model report due date in the Hindcast Stage Key Dates section of the Home Page. Model reports can can be submitted by clicking on "Report submission" in the sidebar.

Report Format

Basic Requirements

  • Up to 8 pages of content, not including references
  • 8.5x11 inch paper size with minimum margin of 1 inch
  • Minimum font size of 11
  • Minimum single-line spacing
  • PDF file format
Submissions are required to be in English, but will not be judged based on English fluency. Judgment will be based on the content and ideas communicated.

Required Sections

  • Title

  • Abstract

    • Provide a concise summary of your solution, highlighting the key features and algorithm(s) underlying your solution, previous work that your solution builds on, and any novel or unique aspects of your solution.
  • Technical Approach

    • Clearly describe the technical details of the solution per the following subsections.

    • Algorithm and Architecture Selection

      • Describe the model(s) and architecture used in your solution and why you chose them. A block diagram is recommended.
      • What other models did you consider or experiment with? Why did you not choose them?
      • Does your solution incorporate any novel modifications to a "standard" use of the algorithm?
      • What simplifications or improvements could be made to run your model(s) more efficiently without sacrificing significant accuracy?
    • Data Sources and Feature Engineering

      • How did you approach the selection of feature/predictor data sources?
      • Which data sources does your solution use? Why?
      • What other data sources did you consider or experiment with? Why did you not choose them?
      • What preprocessing is done to the raw data to create the features you used?
      • Is there any physical explanation or intuition for the features you are using?
    • Uncertainty Quantification

      • What approach did you use to make predictions for the 0.10 and 0.90 quantiles?
    • Training and Evaluation Process

      • How did you train your model(s)?
      • What approach did you use to validate your model(s)?
  • Machine Specifications

    • List the hardware specifications and runtime needed to train your model(s).
  • References