Woodson Lees Ferry WY flow forecast
Overview
This experimental streamflow forecast procedure was developed by David Woodson as a part of his PhD research at the University of Colorado Boulder. He now produces the forecast as a side project. The forecast uses a machine learning (random forest) model trained on Reclamation annual natural flow for the Colorado River at Lees Ferry, AZ for water years 1921-2023 as the predictand, and the following predictors:
- Pacific Decadal Oscillation (PDO) index, July-August-September average preceding the water year being forecast
- Atlantic Multidecadal Oscillation (AMO) index, July-August-September average preceding the water year being forecast
- Community Earth System Model - Large Ensemble (CESM-LE) forecasts of precipitation and minimum temperature, October through September average coinciding with the water year being forecast
For example, for the 2024 water year forecast (October 2023 - September 2024 total natural flow), PDO and AMO predictors are July 2023 through September 2023 monthly averages, and the CESM-LE precipitation and temperature predictors are October 2023 through September 2024 monthly averages. The random forest forecast is a 600-member ensemble.
WY 2024 Forecast
Figure 1 shows the streamflow forecast for Water Year 2024, in green, compared to the historical streamflows, 1921-2023, on which the model is calibrated (black line). The uncertainty in the forecasted 2024 streamflow (i.e., the distribution of the 600 model ensemble member) is depicted in two ways: The green boxplot shows the extent of the interquartile range (25th-75th percentiles) and the median or most-probable forecast (50th percentile), which is 10.8 maf. The green semi-violin density plot shows that the forecast has a bimodal distribution with many members clustered around 7 maf, indicating potential for a very dry year and a smaller second mode around 18 maf, indicating there is some potential for another wet year.
Figure 2 shows the 'variable importance' for each predictor over the 1921-2023 training period; each variable does add value (this is how the variables were selected), and AMO and PDO have the highest importance.