A Web-Based Data Assimilation Framework for Improving Operational Decision Making
Project ID: 4628
Principal Investigator: Douglas Blatchford
Research Topic: Water Operation Models and Decision Support Systems
Funded Fiscal Years: 2014
Keywords: hydrologic forecasting, decision support, kalman filtering, uncertainty, visualization, flood control, floodplain management, dam safety
Streamflow forecasts sometime disagree with real-time conditions observed by Reclamation operators. Recent studies show that changes in climatic conditions have resulted in changes to temperature and precipitation patterns throughout the West . Anecdotal evidence suggests that differences between streamflow forecasts versus observation is increasing, such that existing forecast methodologies incorporate increasing variability and uncertainty.
This research seeks to identify and research web-based assimilation methodologies that improve streamflow forecasts, including, but not limited to (1) a Bias Aware Ensemble Kalman Filter (EnKF), (2) a Bayesian correction algorithm that combines historical climatological datasets with datasets used to create model based forecasts, (3) a statistical methodology such as quantile regression of forecast ensembles, and (4) an analog forecast of hydrologic conditions from existing streamflow time series data.
Given the web-based assimilation methodologies identified above, a research question may be framed as follows:
What web-based assimilation methodologies if implemented, would significantly improve streamflow forecast accuracy?
Need and Benefit
As Reclamation's Dam Safety program estimates risk and downstream consequences for High Hazard Dams throughout the West, dam operations, operations forecasts, and hydrology are all important aspects of this risk assessment. Although risk and loss of life estimates are focused mostly on dam failure, many Reclamation dams are frequently required to pass non-failure releases that exceed safe-channel capacities, potentially posing a threat to life and property.
Reclamation operators also need to address short term operations on an hourly and daily basis, adjusting operations in a real-time, dynamic environment. Increased hydrologic variability as a result of climate change increases the need for rapid optimal decision making, serving as a key driver for identifying what research tool will optimize the accuracy of streamflow forecasts.
A scoping document will be submitted for public viewing.
This information was last updated on April 16, 2014
Contact the Research and Development Office with questions or comments about this page