PyForecast continued development – expanding PyForecast’s reach and capabilities

Project ID: 22072
Principal Investigator: Jonathan Rocha
Research Topic: Water Supply Forecasting
Funded Fiscal Years: 2022, 2023 and 2024
Keywords: None

Research Question

"Can statistical runoff volume forecasts be improved by using new data products such as remote sensed data and other datasets not currently used in the forecasting process?"

Statistical volume forecasts are a critical aspect of reservoir operations within Reclamation. Currently, Reclamation offices across the C-PN and MB regions use a variety of methods and models to produce volumetric forecasts. Many of these forecasts are not evaluated for accuracy or uncertainty, nor have many been reviewed for statistical rigor. The lack of standardization and forecast validation has likely resulted in less-than-ideal reservoir operations in the mountain west.

This project seeks to: (a) continue the evaluation of the capabilities of the PyForecast software, (b) develop additional functionality towards addressing more use-cases for the software, and (c) determine how remote sensing products developed in other S&T projects can improve forecast skill. The software will be evaluated by generating hindcasts in C-PN, MB, and other stakeholder river basins, as well as conducting real-time forecasting during the 2022, 2023, and 2024 seasons. Developers from the C-PN and MB regions will continue to develop the software, train users, gather user input and suggestions, and add additional features and compatibility. New datasets from the recent Snow Water Supply Forecast Program will be incorporated into the software and the resulting forecasts will be compared to traditional snow products from the NRCS.

Need and Benefit

The Water Supply and Streamflow Forecasting section of the FY22 SSIP aims to "develop and improve solutions and tools to forecast and monitor water supplies". As a tool designed to consolidate the entire water supply forecasting workflow, PyForecast encapsulates the necessary techniques, technologies, and methods that are required for the timely generation of water supply forecasts. This proposal specifically aims to improve on the existing PyForecast software towards these goals.

Contributing Partners

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Research Products

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Last Updated: 6/22/20